Search results for: automated driving

Authors: B. Godefroy, D. Beladjine, K. Beddiar

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In France, construction BIM actors commonly evoke the BIM gains for exploitation by integrating of the life cycle of a building. The standardization of level 7 of development would achieve this stage of the digital model. The householders include local public authorities, social landlords, public institutions (health and education), enterprises, facilities management companies. They have a dual role: owner and manager of their housing complex. In a context of financial constraint, the BIM of exploitation aims to control costs, make long-term investment choices, renew the portfolio and enable environmental standards to be met. It assumes a knowledge of the existing buildings, marked by its size and complexity. The information sought must be synthetic and structured, it concerns, in general, a real estate complex. We conducted a study with professionals about their concerns and ways to use it to see how householders could benefit from this development. To obtain results, we had in mind the recurring interrogation of the project management, on the needs of the operators, we tested the following stages: 1) Inculcate a minimal culture of BIM with multidisciplinary teams of the operator then by business, 2) Learn by BIM tools, the adaptation of their trade in operations, 3) Understand the place and creation of a graphic and technical database management system, determine the components of its library so their needs, 4) Identify the cross-functional interventions of its managers by business (operations, technical, information system, purchasing and legal aspects), 5) Set an internal protocol and define the BIM impact in their digital strategy. In addition, continuity of management by the integration of construction models in the operation phase raises the question of interoperability in the control of the production of IFC files in the operator’s proprietary format and the export and import processes, a solution rivaled by the traditional method of vectorization of paper plans. Companies that digitize housing complex and those in FM produce a file IFC, directly, according to their needs without recourse to the model of construction, they produce models business for the exploitation. They standardize components, equipment that are useful for coding. We observed the consequences resulting from the use of the BIM in the property industry and, made the following observations: a) The value of data prevail over the graphics, 3D is little used b) The owner must, through his organization, promote the feedback of technical management information during the design phase c) The operator's reflection on outsourcing concerns the acquisition of its information system and these services, observing the risks and costs related to their internal or external developments. This study allows us to highlight: i) The need for an internal organization of operators prior to a response to the construction management ii) The evolution towards automated methods for creating models dedicated to the exploitation, a specialization would be required iii) A review of the communication of the project management, management continuity not articulating around his building model, it must take into account the environment of the operator and reflect on its scope of action.

Keywords: information system, interoperability, models for exploitation, property industry

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Authors: Ripon Sarkar, Kabita Chaterjee, Ananya Barui

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Smoking is one of the leading causes of oral cancer. Cigarette smoke affects various cellular parameters and alters molecular metabolism of cells. Epithelial cells losses their cytoskeleton structure, membrane integrity, cellular polarity that subsequently initiates the process of epithelial cells to mesenchymal transition due to long exposure of cigarette smoking. It changes the normal cellular metabolic activity which induces oxidative stress and enhances the reactive oxygen spices (ROS) formation. Excessive ROS and associated oxidative stress are considered to be a driving force in alteration in cellular phenotypes, polarity distribution and mitochondrial metabolism. Noninvasive assessment of such parameters plays essential role in development of routine screening system for early diagnosis of oral cancer. Electrical cell-substrate impedance sensing (ECIS) is one of such method applied for detection of cellular membrane impedance which can be correlated to cell membrane integrity. Present study intends to explore the alteration in cellular impedance along with the expression of cellular polarity molecules and cytoskeleton distributions in oral epithelial cells of habitual smokers and to correlate the outcome to that of clinically diagnosed oral leukoplakia and oral squamous cell carcinoma patients. Total 80 subjects were categorized into four study groups: nonsmoker (NS), cigarette smoker (CS), oral leukoplakia (OLPK) and oral squamous cell carcinoma (OSCC). Cytoskeleton distribution was analyzed by staining of actin filament and generation of ROS was measured using assay kit using standard protocol. Cell impedance was measured through ECIS method at different frequencies. Expression of E-cadherin and protease-activated receptor (PAR) proteins were observed through immune-fluorescence method. Distribution of actin filament is well organized in NS group however; distribution pattern was grossly varied in CS, OLPK and OSCC. Generation of ROS was low in NS which subsequently increased towards OSCC. Expressions of E-cadherin and change in cellular electrical impedance in different study groups indicated the hallmark of cancer progression from NS to OSCC. Expressions of E-cadherin, PAR protein, and cell impedance were decreased from NS to CS and farther OSCC. Generally, the oral epithelial cells exhibit apico-basal polarity however with cancer progression these cells lose their characteristic polarity distribution. In this study expression of polarity molecule and ECIS observation indicates such altered pattern of polarity among smoker group. Overall the present study monitored the alterations in intracellular ROS generation and cell metabolic function, membrane integrity in oral epithelial cells in cigarette smokers. Present study thus has clinical significance, and it may help in developing a noninvasive technique for early diagnosis of oral cancer amongst susceptible individuals.

Keywords: cigarette smoking, early oral cancer detection, electric cell-substrate impedance sensing, noninvasive screening

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Authors: Saqib Aziz, Brad Alexander, Christoph Gengnagel, Stefan Weinzierl

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This paper explores a generative design, simulation, and optimization workflow for the integration of acoustical diffuser and/or absorber geometry with embedded coupled Helmholtz-resonators for full-scale 3D printed building components. Large-scale additive manufacturing in conjunction with algorithmic CAD design tools enables a vast amount of control when creating geometry. This is advantageous regarding the increasing demands of comfort standards for indoor spaces and the use of more resourceful and sustainable construction methods and materials. The presented methodology highlights these new technological advancements and offers a multimodal and integrative design solution with the potential for an immediate application in the AEC-Industry. In principle, the methodology can be applied to a wide range of structural elements that can be manufactured by additive manufacturing processes. The current paper focuses on a case study of an application for a biaxial load-bearing beam grillage made of reinforced concrete, which allows for a variety of applications through the combination of additive prefabricated semi-finished parts and in-situ concrete supplementation. The semi-prefabricated parts or formwork bodies form the basic framework of the supporting structure and at the same time have acoustic absorption and diffusion properties that are precisely acoustically programmed for the space underneath the structure. To this end, a hybrid validation strategy is being explored using a digital and cross-platform simulation environment, verified with physical prototyping. The iterative workflow starts with the generation of a parametric design model for the acoustical geometry using the algorithmic visual scripting editor Grasshopper3D inside the building information modeling (BIM) software Revit. Various geometric attributes (i.e., bottleneck and cavity dimensions) of the resonator are parameterized and fed to a numerical optimization algorithm which can modify the geometry with the goal of increasing absorption at resonance and increasing the bandwidth of the effective absorption range. Using Rhino.Inside and LiveLink for Revit, the generative model was imported directly into the Multiphysics simulation environment COMSOL. The geometry was further modified and prepared for simulation in a semi-automated process. The incident and scattered pressure fields were simulated from which the surface normal absorption coefficients were calculated. This reciprocal process was repeated to further optimize the geometric parameters. Subsequently the numerical models were compared to a set of 3D concrete printed physical twin models, which were tested in a .25 m x .25 m impedance tube. The empirical results served to improve the starting parameter settings of the initial numerical model. The geometry resulting from the numerical optimization was finally returned to grasshopper for further implementation in an interdisciplinary study.

Keywords: acoustical design, additive manufacturing, computational design, multimodal optimization

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Authors: Natalia C. E. S. Nascimento, Mercia L. Oliveira, Fernando R. A. Lima, Leonardo T. C. do Nascimento, Marina B. Silveira, Brigida G. A. Schirmer, Andrea V. Ferreira, Carlos Malamut, Juliana B. da Silva

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Tissue hypoxia is a common characteristic of solid tumors leading to decreased sensitivity to radiotherapy and chemotherapy. In the clinical context, tumor hypoxia assessment employing the positron emission tomography (PET) tracer ¹⁸F-fluoromisonidazole ([¹⁸F]FMISO) is helpful for physicians for planning and therapy adjusting. The aim of this work was to implement the synthesis of 18F-FMISO in a TRACERlab® MXFDG module and also to establish the quality control procedure. [¹⁸F]FMISO was synthesized at Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN/Brazil) using an automated synthesizer (TRACERlab® MXFDG, GE) adapted for the production of [¹⁸F]FMISO. The FMISO chemical standard was purchased from ABX. 18O- enriched water was acquired from Center of Molecular Research. Reagent kits containing eluent solution, acetonitrile, ethanol, 2.0 M HCl solution, buffer solution, water for injections and [¹⁸F]FMISO precursor (dissolved in 2 ml acetonitrile) were purchased from ABX. The [¹⁸F]FMISO samples were purified by Solid Phase Extraction method. The quality requirements of [¹⁸F]FMISO are established in the European Pharmacopeia. According to that reference, quality control of [¹⁸F]FMISO should include appearance, pH, radionuclidic identity and purity, radiochemical identity and purity, chemical purity, residual solvents, bacterial endotoxins, and sterility. The duration of the synthesis process was 53 min, with radiochemical yield of (37.00 ± 0.01) % and the specific activity was more than 70 GBq/µmol. The syntheses were reproducible and showed satisfactory results. In relation to the quality control analysis, the samples were clear and colorless at pH 6.0. The spectrum emission, measured by using a High-Purity Germanium Detector (HPGe), presented a single peak at 511 keV and the half-life, determined by the decay method in an activimeter, was (111.0 ± 0.5) min, indicating no presence of radioactive contaminants, besides the desirable radionuclide (¹⁸F). The samples showed concentration of tetrabutylammonium (TBA) < 50μg/mL, assessed by visual comparison to TBA standard applied in the same thin layer chromatographic plate. Radiochemical purity was determined by high performance liquid chromatography (HPLC) and the results were 100%. Regarding the residual solvents tested, ethanol and acetonitrile presented concentration lower than 10% and 0.04%, respectively. Healthy female mice were injected via lateral tail vein with [¹⁸F]FMISO, microPET imaging studies (15 min) were performed after 2 h post injection (p.i), and the biodistribution was analyzed in five-time points (30, 60, 90, 120 and 180 min) after injection. Subsequently, organs/tissues were assayed for radioactivity with a gamma counter. All parameters of quality control test were in agreement to quality criteria confirming that [¹⁸F]FMISO was suitable for use in non-clinical and clinical trials, following the legal requirements for the production of new radiopharmaceuticals in Brazil.

Keywords: automatic radiosynthesis, hypoxic tumors, pharmacopeia, positron emitters, quality requirements

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Authors: Rachid Belhachemi, Pierre Rostan, Alexandra Rostan

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This paper develops a discrete-time option pricing model for index options. The model consists of two key ingredients. First, daily stock return innovations are driven by a continuous hidden threshold mixed skew-normal (HTSN) distribution which generates conditional non-normality that is needed to fit daily index return. The most important feature of the HTSN is the inclusion of a latent state variable with a continuum of states, unlike the traditional mixture distributions where the state variable is discrete with little number of states. The HTSN distribution belongs to the class of univariate probability distributions where parameters of the distribution capture the dependence between the variable of interest and the continuous latent state variable (the regime). The distribution has an interpretation in terms of a mixture distribution with time-varying mixing probabilities. It has been shown empirically that this distribution outperforms its main competitor, the mixed normal (MN) distribution, in terms of capturing the stylized facts known for stock returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence. Second, heteroscedasticity in the model is captured by a threeexogenous-factor GARCH model (GARCHX), where the factors are taken from the principal components analysis of various world indices and presents an application to option pricing. The factors of the GARCHX model are extracted from a matrix of world indices applying principal component analysis (PCA). The empirically determined factors are uncorrelated and represent truly different common components driving the returns. Both factors and the eight parameters inherent to the HTSN distribution aim at capturing the impact of the state of the economy on price levels since distribution parameters have economic interpretations in terms of conditional volatilities and correlations of the returns with the hidden continuous state. The PCA identifies statistically independent factors affecting the random evolution of a given pool of assets -in our paper a pool of international stock indices- and sorting them by order of relative importance. The PCA computes a historical cross asset covariance matrix and identifies principal components representing independent factors. In our paper, factors are used to calibrate the HTSN-GARCHX model and are ultimately responsible for the nature of the distribution of random variables being generated. We benchmark our model to the MN-GARCHX model following the same PCA methodology and the standard Black-Scholes model. We show that our model outperforms the benchmark in terms of RMSE in dollar losses for put and call options, which in turn outperforms the analytical Black-Scholes by capturing the stylized facts known for index returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence.

Keywords: continuous hidden threshold, factor models, GARCHX models, option pricing, risk-premium

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Authors: Liou Shyhnan, Chia Han Yang

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The study aims to investigate the promotion of innovation in the development of cultural and creative industries (CCI) and apply research on culture and creativity to this promotion. Using the research perspectives of culture and creativity as the starting points, this study has examined the challenges, trends, and opportunities that have emerged from the development of the CCI until the present. It is found that a definite context of cause and effect exist between them, and that a homologous theoretical basis can be used to understand and interpret them. Based on the characteristics of the aforementioned challenges and trends, this study has compiled two main theoretical systems for conducting research on culture and creativity: (i) reciprocal process between creativity and culture, and (ii) a mechanism for innovation involving multicultural convergence. Both theoretical systems were then used as the foundation to arrive at possible research propositions relating to the two developmental systems. This was respectively done through identification of the theoretical context through a literature review, and interviews and observations of actual case studies within Taiwan’s CCI. In so doing, the critical factors that can address the aforementioned challenges and trends were discovered. Our results indicated that, for reciprocal process between creativity and culture, we recognize that culture serves as creative resources in cultural and creative industries. According to shared consensus, culture provides symbolic meanings and emotional attachment for products and experiences offered by CCI. Besides, different cultures vary in their effects on creativity processes and standards, thus engendering distinctive preferences for and evaluations of the creative expressions and experiences of CCIs. In addition, we identify that creativity serves as the engine for driving the continuation and rebirth of cultures. Accounting for the core of culture, the employment of technology, design, and business facilitates the transformation and innovation mechanism for promoting culture continuity. In addition, with cultural centered, the digital technology, design thinking, and business model are critical constitutes of the innovation mechanism to promote the cultural continuity. Regarding cultural preservation and regeneration of local spaces and folk customs, we argue that the preservation and regeneration of local spaces and cultural cultures must embody the interactive experiences of present-day life. And cultural space and folk custom would regenerate with interact and experience in modern life. Regarding innovation mechanism for multicultural convergence, we propose that innovative stakeholders from different disciplines (e.g., creators, designers, engineers, and marketers) in CCIs rely on the establishment of a cocreation mechanism to promote interdisciplinary interaction. Furthermore, CCI development needs to develop a cocreation mechanism for enhancing the interdisciplinary collaboration among CCI innovation stakeholders. We further argue multicultural mixing would enhance innovation in developing CCI, and assuming an open and mutually enlightening attitude to enrich one another’s cultures in the multicultural exchanges under globalization will create diversity in homogenous CCIs. Finally, for promoting innovation in developing cultural and creative industries, we further propose a model for joint knowledge creation that can be established for enhancing the mutual reinforcement of theoretical and practical research on culture and creativity.

Keywords: culture and creativity, innovation, cultural and creative industries, cultural mixing

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Authors: R. Yuan, S. Sivasankaran, N. Dutta, K. Ebrahimi

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This paper investigates the software capability and computer-aided engineering (CAE) method of modelling transient heat transfer process occurred in the vehicle underhood region during vehicle thermal soak phase. The heat retention from the soak period will be beneficial to the cold start with reduced friction loss for the second 14°C worldwide harmonized light-duty vehicle test procedure (WLTP) cycle, therefore provides benefits on both CO₂ emission reduction and fuel economy. When vehicle undergoes soak stage, the airflow and the associated convective heat transfer around and inside the engine bay is driven by the buoyancy effect. This effect along with thermal radiation and conduction are the key factors to the thermal simulation of the engine bay to obtain the accurate fluids and metal temperature cool-down trajectories and to predict the temperatures at the end of the soak period. Method development has been investigated in this study on a light-duty passenger vehicle using coupled aerodynamic-heat transfer thermal transient modelling method for the full vehicle under 9 hours of thermal soak. The 3D underhood flow dynamics were solved inherently transient by the Lattice-Boltzmann Method (LBM) method using the PowerFlow software. This was further coupled with heat transfer modelling using the PowerTHERM software provided by Exa Corporation. The particle-based LBM method was capable of accurately handling extremely complicated transient flow behavior on complex surface geometries. The detailed thermal modelling, including heat conduction, radiation, and buoyancy-driven heat convection, were integrated solved by PowerTHERM. The 9 hours cool-down period was simulated and compared with the vehicle testing data of the key fluid (coolant, oil) and metal temperatures. The developed CAE method was able to predict the cool-down behaviour of the key fluids and components in agreement with the experimental data and also visualised the air leakage paths and thermal retention around the engine bay. The cool-down trajectories of the key components obtained for the 9 hours thermal soak period provide vital information and a basis for the further development of reduced-order modelling studies in future work. This allows a fast-running model to be developed and be further imbedded with the holistic study of vehicle energy modelling and thermal management. It is also found that the buoyancy effect plays an important part at the first stage of the 9 hours soak and the flow development during this stage is vital to accurately predict the heat transfer coefficients for the heat retention modelling. The developed method has demonstrated the software integration for simulating buoyancy-driven heat transfer in a vehicle underhood region during thermal soak with satisfying accuracy and efficient computing time. The CAE method developed will allow integration of the design of engine encapsulations for improving fuel consumption and reducing CO₂ emissions in a timely and robust manner, aiding the development of low-carbon transport technologies.

Keywords: ATCT/WLTC driving cycle, buoyancy-driven heat transfer, CAE method, heat retention, underhood modeling, vehicle thermal soak

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Authors: Feyi Olorunshola

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Over the last few years, the senior travel market has been noted for its potential in the wider tourism industry. The tourism sector includes the hotel and hospitality, travel, transportation, and several other subdivisions to make it economically viable. In particular, the hotel attracts a substantial part of the expenditure in tourism activities as when people plan to travel, suitable accommodation for relaxation, dining, entertainment and so on is paramount to their decision-making. The global retail value of the hotel as of 2018 was significant for tourism. But, despite indications of the hotel to the tourism industry at large, very few empirical studies are available to establish how this sector can leverage on the senior demographic to achieve competitive advantage. Predominantly, studies on the mature market have focused on destination tourism, with a limited investigation on the hotel which makes a significant contribution to tourism. Also, several scholarly studies have demonstrated the importance of the senior travel market to the hotel, yet there is very little empirical research in the field which has explored the driving factors that will become the accepted new normal for this niche segment post-pandemic. Giving that the hotel already operates in a highly saturated business environment, and on top of this pre-existing challenge, the ongoing global health outbreak has further put the sector in a vulnerable position. Therefore, the hotel especially the full-service luxury category must evolve rapidly for it to survive in the current business environment. The hotel can no longer rely on corporate travelers to generate higher revenue since the unprecedented wake of the pandemic in 2020 many organizations have invented a different approach of conducting their businesses online, therefore, the hotel needs to anticipate a significant drop in business travellers. However, the rooms and the rest of the facilities must be occupied to keep their business operating. The way forward for the hotel lies in the leisure sector, but the question now is to focus on the potential demographics of travelers, in this case, the seniors who have been repeatedly recognized as the lucrative market because of increase discretionary income, availability of time and the global population trends. To achieve the study objectives, a mixed-method approach will be utilized drawing on both qualitative (netnography) and quantitative (survey) methods, cognitive and decision-making theories (means-end chain) and competitive theories to identify the salient drivers explaining senior hotel choice and its influence on their decision-making. The target population are repeated seniors’ age 65 years and over who are UK resident, and from the top tourist market to the UK (USA, Germany, and France). Structural equation modelling will be employed to analyze the datasets. The theoretical implication is the development of new concepts using a robust research design, and as well as advancing existing framework to hotel study. Practically, it will provide the hotel management with the latest information to design a competitive marketing strategy and activities to target the mature market post-pandemic and over a long period.

Keywords: competitive advantage, covid-19, full-service hotel, five-star, luxury hotels

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Rosemary R. Carlton, Roxane Caron

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Almost three decades ago, bell hooks (1994) identified the classroom as “the most radical space of possibility in the academy”. A feminist scholar, educator, and activist, hooks urged educators to transgress the boundaries of what might be customary or considered acceptable in teaching, thus encouraging the pursuit of new ways of knowing and different strategies for sharing knowledge. This paper reflects upon a particular response to hooks’ still relevant call for transgression in teaching. Specifically, this paper reports on the design, implementation, and preliminary analysis of a social work course integrating active pedagogy and transnational exchange to encourage students’ critical thinking and autonomous learning in their development as social workers in a global context. The bachelor’s level course, Pratiques spécifiques: Projet international, was developed collaboratively across three francophone institutions of higher learning in Belgium, Canada, and France: the Haute École de Namur-Liège-Luxembourg (Hénallux); the Université de Montréal; and, the Institut d’enseignement supérieur et professionnel, l’IRTS Paris Île-de-France. The driving aims of the course are to promote autonomous learning and critical thinking through a lens of transnational understandings of social problems -competencies indispensable to students’ development as social workers. The course is offered to two paired cohorts, one addressing the subject of “migrations” (Canada/France) and the other the subject of “sexual exploitation” (Canada/Belgium). Through the adaptation of a critical pedagogy of problem-based learning, students are called upon to actively engage in acquiring and applying knowledge to respond to “real life” social issues relating to migration or sexual exploitation. At the conclusion of the course, each cohort of students is brought together for a week-long intensive period of transnational exchange either at the Université de Montréal in Canada or at Hénallux in Belgium. Extending the bounds of the classroom across international borders allows students novel opportunities to deepen and expand their understandings of issues relating to predefined social issues and to critically examine associated social work practices. The paper opens with a presentation of the social work course. Specifically, the authors will outline their adaptation of a pedagogy of problem-based learning integrating transnational exchange in the design and implementation of the course. Returning to hooks’ notion of transgression in teaching, the paper offers a preliminary analysis of how and with what effect the course provides opportunities to transgress hierarchical student-teacher relationships; transgress conventional modes of learning to explore diverse sources of knowledge and transgress the walls of the university to engage with and learn from local and global partners. The paper concludes with a consideration of the potential influence of such transgressions in teaching for students’ development of critical thinking in their practice of social work in global context.

Keywords: active learning, critical pedagogy, social work intervention, transnational learning

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Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

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Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Panagiotis Gkekas, Christos Sougles, Dionysios Kehagias, Dimitrios Tzovaras

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This paper presents the architecture of the “Intelligent cooperative integrated system for road safety and road infrastructure maintenance towards 2020” (ODOS2020) advanced infrastructure, which implements a number of cooperative ITS applications based on Internet of Things and Infrastructure-to-Vehicle (V2I) technologies with the purpose to enhance the active road safety level of vehicles through the provision of a fully automated V2I environment. The primary objective of the ODOS2020 project is to contribute to increased road safety but also to the optimization of time for maintenance of road infrastructure. The integrated technological solution presented in this paper addresses all types of vehicles and requires minimum vehicle equipment. Thus, the ODOS2020 comprises a low-cost solution, which is one of its main benefits. The system architecture includes an integrated notification system to transmit personalized information on road, traffic, and environmental conditions, in order for the drivers to receive real-time and reliable alerts concerning upcoming critical situations. The latter include potential dangers on the road, such as obstacles or road works ahead, extreme environmental conditions, etc., but also informative messages, such as information on upcoming tolls and their charging policies. At the core of the system architecture lies an integrated sensorial network embedded in special road infrastructures (strips) that constantly collect and transmit wirelessly information about passing vehicles’ identification, type, speed, moving direction and other traffic information in combination with environmental conditions and road wear monitoring and predictive maintenance data. Data collected from sensors is transmitted by roadside infrastructure, which supports a variety of communication technologies such as ITS-G5 (IEEE-802.11p) wireless network and Internet connectivity through cellular networks (3G, LTE). All information could be forwarded to both vehicles and Traffic Management Centers (TMC) operators, either directly through the ITS-G5 network, or to smart devices with Internet connectivity, through cloud-based services. Therefore, through its functionality, the system could send personalized notifications/information/warnings and recommendations for upcoming events to both road users and TMC operators. In the course of the ODOS2020 project pilot operation has been conducted to allow drivers of both C-ITS equipped and non-equipped vehicles to experience the provided added value services. For non-equipped vehicles, the provided information is transmitted to a smartphone application. Finally, the ODOS2020 system and infrastructure is appropriate for installation on both urban, rural, and highway environments. The paper presents the various parts of the system architecture and concludes by outlining the various challenges that had to be overcome during its design, development, and deployment in a real operational environment. Acknowledgments: Work presented in this paper was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation (call RESEARCH–CREATE–INNOVATE) under contract no. Τ1EDK-03081 (project ODOS2020).

Keywords: infrastructure to vehicle, intelligent transportation systems, internet of things, road safety

Procedia PDF Downloads 86

Authors: Inigo Sanz-Pena, Shanika Arachchi, Dilani Dhammika, Sanjaya Mallikarachchi, Jeewantha S. Bandula, Alison H. McGregor, Nicolas Newell

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The use of orthotic braces for adolescent idiopathic scoliosis (AIS) patients is the most common non-surgical treatment to prevent deformity progression. The traditional method to create an orthotic brace involves casting the patient’s torso to obtain a representative geometry, which is then rectified by an orthotist to the desired geometry of the brace. Recent improvements in 3D scanning technologies, rectification software, CNC, and additive manufacturing processes have given the possibility to compliment, or in some cases, replace manual methods with digital approaches. However, the rectification process remains dependent on the orthotist’s skills. Therefore, the rectification process needs to be carefully characterized to ensure that braces designed through a digital workflow are as efficient as those created using a manual process. The aim of this study is to compare 3D scans of patients with AIS against 3D scans of both pre- and post-rectified casts that have been manually shaped by an orthotist. Six AIS patients were recruited from the Ragama Rehabilitation Clinic, Colombo, Sri Lanka. All patients were between 10 and 15 years old, were skeletally immature (Risser grade 0-3), and had Cobb angles between 20-45°. Seven spherical markers were placed at key anatomical locations on each patient’s torso and on the pre- and post-rectified molds so that distances could be reliably measured. 3D scans were obtained of 1) the patient’s torso and pelvis, 2) the patient’s pre-rectification plaster mold, and 3) the patient’s post-rectification plaster mold using a Structure Sensor Mark II 3D scanner (Occipital Inc., USA). 3D stick body models were created for each scan to represent the distances between anatomical landmarks. The 3D stick models were used to analyze the changes in position and orientation of the anatomical landmarks between scans using Blender open-source software. 3D Surface deviation maps represented volume differences between the scans using CloudCompare open-source software. The 3D stick body models showed changes in the position and orientation of thorax anatomical landmarks between the patient and the post-rectification scans for all patients. Anatomical landmark position and volume differences were seen between 3D scans of the patient’s torsos and the pre-rectified molds. Between the pre- and post-rectified molds, material removal was consistently seen on the anterior side of the thorax and the lateral areas below the ribcage. Volume differences were seen in areas where the orthotist planned to place pressure pads (usually at the trochanter on the side to which the lumbar curve was tilted (trochanter pad), at the lumbar apical vertebra (lumbar pad), on the rib connected to the apical vertebrae at the mid-axillary line (thoracic pad), and on the ribs corresponding to the upper thoracic vertebra (axillary extension pad)). The rectification process requires the skill and experience of an orthotist; however, this study demonstrates that the brace shape, location, and volume of material removed from the pre-rectification mold can be characterized and quantified. Results from this study can be fed into software that can accelerate the brace design process and make steps towards the automated digital rectification process.

Keywords: additive manufacturing, orthotics, scoliosis brace design, sculpting software, spinal deformity

Procedia PDF Downloads 122

Authors: Robert Pocklington

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Most current language courses expect students to be ‘vocational’, sacrificing their free time in order to learn. However, pupils with a full-time job, or bringing up children, hardly have a spare moment. Others just need the language as a tool or a qualification, as if it were book-keeping or a driving license. Then there are children in unstructured families whose stressful life makes private study almost impossible. And the countless parents whose evenings and weekends have become a nightmare, trying to get the children to do their homework. There are many arguments against homework being a necessity (rather than an optional extra for more ambitious or dedicated students), making a clear case for teaching methods which facilitate full learning of the key content within the classroom. A methodology which could be described as Programmed Mastery Learning has been used at Fluency Language Academy (Spain) since 1992, to teach English to over 4000 pupils yearly, with a staff of around 100 teachers, barely requiring homework. The course is structured according to the tenets of Programmed Learning: small manageable teaching steps, immediate feedback, and constant successful activity. For the Mastery component (not stopping until everyone has learned), the memorisation and practice are entrusted to flashcard-based drilling in the classroom, leading all students to progress together and develop a permanently growing knowledge base. Vocabulary and expressions are memorised using flashcards as stimuli, obliging the brain to constantly recover words from the long-term memory and converting them into reflex knowledge, before they are deployed in sentence building. The use of grammar rules is practised with ‘cue’ flashcards: the brain refers consciously to the grammar rule each time it produces a phrase until it comes easily. This automation of lexicon and correct grammar use greatly facilitates all other language and conversational activities. The full B2 course consists of 48 units each of which takes a class an average of 17,5 hours to complete, allowing the vast majority of students to reach B2 level in 840 class hours, which is corroborated by an 85% pass-rate in the Cambridge University B2 exam (First Certificate). In the past, studying for qualifications was just one of many different options open to young people. Nowadays, youngsters need to stay at school and obtain qualifications in order to get any kind of job. There are many students in our classes who have little intrinsic interest in what they are studying; they just need the certificate. In these circumstances and with increasing government pressure to minimise failure, teachers can no longer think ‘If they don’t study, and fail, its their problem’. It is now becoming the teacher’s problem. Teachers are ever more in need of methods which make their pupils successful learners; this means assuring learning in the classroom. Furthermore, homework is arguably the main divider between successful middle-class schoolchildren and failing working-class children who drop out: if everything important is learned at school, the latter will have a much better chance, favouring inclusiveness in the language classroom.

Keywords: flashcard drilling, fluency method, mastery learning, programmed learning, teaching English as a foreign language

Procedia PDF Downloads 85

Authors: Tesfaye Dagne, Dagmawit Solomon, Firmaye Bogale, Yosef Gebreyohannes, Samson Mideksa, Mamuye Hadis, Desalegn Ararso, Ermias Woldie, Tsegaye Getachew, Sabit Ababor, Zelalem Kebede

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Globally, road traffic accident (RTA) is causing millions of deaths and injuries every year. It is one of the leading causes of death among people of all age groups and the problem is worse among young reproductive age group. Moreover the problem is increasing with an increasing number of vehicles. The majority of the problem happen in low and middle income countries (LMIC), even if the number of vehicles in these countries is low compared to their population. So, the objective of this paper is to summarize the best available evidence on interventions that can reduce road traffic accidents in low and middle income countries (LMIC). Method: A rapid evidence synthesis approach adapted from the SURE Rapid Response Service was applied to search, appraise and summarize the best available evidence on effective intervention in reducing road traffic injury. To answer the question under review, we searched for relevant studies from databases including PubMed, the Cochrane Library, TRANSPORT, Health system evidence, Epistemonikos, and SUPPORT summary. The following key terms were used for searching: Road traffic accident, RTA, Injury, Reduc*, Prevent*, Minimiz*, “Low and middle-income country”, LMIC. We found 18 articles through a search of different databases mentioned above. After screening for the titles and abstracts of the articles, four of them which satisfy the inclusion criteria were included in the final review. Then we appraised and graded the methodological quality of systematic reviews that are deemed to be highly relevant using AMSTAR. Finding: The identified interventions to reduce road traffic accidents were legislation and enforcement, public awareness/education, speed control/ rumble strips, road improvement, mandatory motorcycle helmet, graduated driver license, street lighting. Legislation and Enforcement: Legislation focusing on mandatory motorcycle helmet usage, banning cellular phone usage when driving, seat belt laws, decreasing the legal blood alcohol content (BAC) level from 0.06 g/L to 0.02 g/L bring the best result where enforcement is there. Public Awareness/Education: focusing on seat belt use, child restraint use, educational training in health centers and schools/universities, and public awareness with media through the distribution of videos, posters/souvenirs, and pamphlets are effective in the short run. Speed Control: through traffic calming bumps, or speed bumps, rumbled strips are effective in reducing accidents and fatality. Mandatory Motorcycle Helmet: is associated with reduction in mortality. Graduated driver’s license (GDL): reduce road traffic injury by 19%. Street lighting: is a low-cost intervention which may reduce road traffic accidents.

Keywords: evidence synthesis, injury, rapid review, reducing, road traffic accident

Procedia PDF Downloads 136

Authors: O. Squillace, R. L. Thompson

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Migration of small molecules to, and across the surface of polymer matrices is a little-studied problem with important industrial applications. Tackifiers in adhesives, flavors in foods and binding agents in paints all present situations where the function of a product depends on the ability of small molecules to migrate through a polymer matrix to achieve the desired properties such as softness, dispersion of fillers, and to deliver an effect that is felt (or tasted) on a surface. It’s been shown that the chemical and molecular structure, surface free energies, phase behavior, close environment and compatibility of the system, influence the migrants’ motion. When differences in behavior, such as occurrence of segregation to the surface or not, are observed it is then of crucial importance to identify and get a better understanding of the driving forces involved in the process of molecular migration. In this aim, experience is meant to be allied with theory in order to deliver a validated theoretical and computational toolkit to describe and predict these phenomena. The systems that have been chosen for this study aim to address the effect of polarity mismatch between the migrants and the polymer matrix and that of a second migrant over the first one. As a non-polar resin polymer, polyvinyl acetate is used as the material to which more or less polar migrants (sorbitol, carvone, octanoic acid (OA), triacetin) are to be added. Through contact angle measurement a surface excess is seen for sorbitol (polar) mixed with PVAc as the surface energy is lowered compare to the one of pure PVAc. This effect is increased upon the addition of carvon or triacetin (non-polars). Surface micro-structures are also evidenced by atomic force microscopy (AFM). Ion beam analysis (Nuclear Reaction Analysis), supplemented by neutron reflectometry can accurately characterize the self-organization of surfactants, oligomers, aromatic molecules in polymer films in order to relate the macroscopic behavior to the length scales that are amenable to simulation. The nuclear reaction analysis (NRA) data for deuterated OA 20% shows the evidence of a surface excess which is enhanced after annealing. The addition of 10% triacetin, as a second migrant, results in the formation of an underlying layer enriched in triacetin below the surface excess of OA. The results show that molecules in polarity mismatch with the matrix tend to segregate to the surface, and this is favored by the addition of a second migrant of the same polarity than the matrix. As studies have been restricted to materials that are model supported films under static conditions in a first step, it is also wished to address the more challenging conditions of materials under controlled stress or strain. To achieve this, a simple rig and PDMS cell have been designed to stretch the material to a defined strain and to probe these mechanical effects by ion beam analysis and atomic force microscopy. This will make a significant step towards exploring the influence of extensional strain on surface segregation, flavor release in cross-linked rubbers.

Keywords: polymers, surface segregation, thin films, molecular migration

Procedia PDF Downloads 100

Authors: Alfredo Fortunato, Carmelofrancesco Origlia, Sara Laurita, Rossella Nicoletti

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In the post-pandemic recovery phase of tourism, the role of a Destination Management Organization (DMO) as a coordinated management system of all the elements that make up a destination (attractions, access, marketing, human resources, brand, pricing, etc.) is also becoming relevant for local territories. The objective of a DMO is to maximize the visitor's perception of value and quality while ensuring the competitiveness and sustainability of the destination, as well as the long-term preservation of its natural and cultural assets, and to catalyze benefits for the local economy and residents. In carrying out the multiple functions to which it is called, the DMO can leverage a collective intelligence that comes from the ability to pool information, explicit and tacit knowledge, and relationships of the various stakeholders: policymakers, public managers and officials, entrepreneurs in the tourism supply chain, researchers, data journalists, schools, associations and committees, citizens, etc. The DMO potentially has at its disposal large volumes of data and many of them at low cost, that need to be properly processed to produce value. Based on these assumptions, the paper presents a conceptual framework for building an information system to support the DMO in the intelligent management of a tourist destination tested in an area of southern Italy. The approach adopted is data-informed and consists of four phases: (1) formulation of the knowledge problem (analysis of policy documents and industry reports; focus groups and co-design with stakeholders; definition of information needs and key questions); (2) research and metadatation of relevant sources (reconnaissance of official sources, administrative archives and internal DMO sources); (3) gap analysis and identification of unconventional information sources (evaluation of traditional sources with respect to the level of consistency with information needs, the freshness of information and granularity of data; enrichment of the information base by identifying and studying web sources such as Wikipedia, Google Trends, Booking.com, Tripadvisor, websites of accommodation facilities and online newspapers); (4) definition of the set of indicators and construction of the information base (specific definition of indicators and procedures for data acquisition, transformation, and analysis). The framework derived consists of 6 thematic areas (accommodation supply, cultural heritage, flows, value, sustainability, and enabling factors), each of which is divided into three domains that gather a specific information need to be represented by a scheme of questions to be answered through the analysis of available indicators. The framework is characterized by a high degree of flexibility in the European context, given that it can be customized for each destination by adapting the part related to internal sources. Application to the case study led to the creation of a decision support system that allows: •integration of data from heterogeneous sources, including through the execution of automated web crawling procedures for data ingestion of social and web information; •reading and interpretation of data and metadata through guided navigation paths in the key of digital story-telling; •implementation of complex analysis capabilities through the use of data mining algorithms such as for the prediction of tourist flows.

Keywords: collective intelligence, data framework, destination management, smart tourism

Procedia PDF Downloads 97

Authors: Waleed Al-Nuaimy, Luke Anastassiou, Manjinder Kainth

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As the process of teaching has evolved with the advent of new technologies over the ages, so has the process of learning. Educators have perpetually found themselves on the lookout for new technology-enhanced methods of teaching in order to increase learning efficiency and decrease ever expanding workloads. Shortly after the invention of the internet, web-based learning started to pick up in the late 1990s and educators quickly found that the process of providing learning material and marking assignments could change thanks to the connectivity offered by the internet. With the creation of early web-based virtual learning environments (VLEs) such as SPIDER and Blackboard, it soon became apparent that VLEs resulted in higher reported computer self-efficacy among students, but at the cost of students being less satisfied with the learning process . It may be argued that the impersonal nature of VLEs, and their limited functionality may have been the leading factors contributing to this reported dissatisfaction. To this day, often faced with the prospects of assigning colossal engineering cohorts their homework and assessments, educators may frequently choose optimally curated assessment formats, such as multiple-choice quizzes and numerical answer input boxes, so that automated grading software embedded in the VLEs can save time and mark student submissions instantaneously. A crucial skill that is meant to be learnt during most science and engineering undergraduate degrees is gaining the confidence in using, solving and deriving mathematical equations. Equations underpin a significant portion of the topics taught in many STEM subjects, and it is in homework assignments and assessments that this understanding is tested. It is not hard to see that this can become challenging if the majority of assignment formats students are engaging with are multiple-choice questions, and educators end up with a reduced perspective of their students’ ability to manipulate equations. Artificial intelligence (AI) has in recent times been shown to be an important consideration for many technologies. In our paper, we explore the use of new AI based software designed to work in conjunction with current VLEs. Using our experience with the software, we discuss its potential to solve a selection of problems ranging from impersonality to the reduction of educator workloads by speeding up the marking process. We examine the software’s potential to increase learning efficiency through its features which claim to allow more customized and higher-quality feedback. We investigate the usability of features allowing students to input equation derivations in a range of different forms, and discuss relevant observations associated with these input methods. Furthermore, we make ethical considerations and discuss potential drawbacks to the software, including the extent to which optical character recognition (OCR) could play a part in the perpetuation of errors and create disagreements between student intent and their submitted assignment answers. It is the intention of the authors that this study will be useful as an example of the implementation of AI in a practical assessment scenario insofar as serving as a springboard for further considerations and studies that utilise AI in the setting and marking of science and engineering assignments.

Keywords: engineering education, assessment, artificial intelligence, optical character recognition (OCR)

Procedia PDF Downloads 100

Authors: Arjun Kumar Rath, Titus Dhanasingh

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Embedded product development requires software to test hardware functionality during development and finding issues during manufacturing in larger quantities. As the components are getting integrated, the devices are tested for their full functionality using advanced software tools. Benchmarking tools are used to measure and compare the performance of product features. At present, these tests are based on a variety of methods involving varying hardware and software platforms. Typically, these tests are custom built for every product and remain unusable for other variants. A majority of the tests goes undocumented, not updated, unusable when the product is released. To bridge this gap, a solution accelerator in the form of a framework can address these issues for running all these tests from one place, using an off-the-shelf tests library in a continuous integration environment. There are many open-source test frameworks or tools (fuego. LAVA, AutoTest, KernelCI, etc.) designed for testing embedded system devices, with each one having several unique good features, but one single tool and framework may not satisfy all of the testing needs for embedded systems, thus an extensible framework with the multitude of tools. Embedded product testing includes board bring-up testing, test during manufacturing, firmware testing, application testing, and assembly testing. Traditional test methods include developing test libraries and support components for every new hardware platform that belongs to the same domain with identical hardware architecture. This approach will have drawbacks like non-reusability where platform-specific libraries cannot be reused, need to maintain source infrastructure for individual hardware platforms, and most importantly, time is taken to re-develop test cases for new hardware platforms. These limitations create challenges like environment set up for testing, scalability, and maintenance. A desirable strategy is certainly one that is focused on maximizing reusability, continuous integration, and leveraging artifacts across the complete development cycle during phases of testing and across family of products. To get over the stated challenges with the conventional method and offers benefits of embedded testing, an embedded test framework (ETF), a solution accelerator, is designed, which can be deployed in embedded system-related products with minimal customizations and maintenance to accelerate the hardware testing. Embedded test framework supports testing different hardwares including microprocessor and microcontroller. It offers benefits such as (1) Time-to-Market: Accelerates board brings up time with prepacked test suites supporting all necessary peripherals which can speed up the design and development stage(board bring up, manufacturing and device driver) (2) Reusability-framework components isolated from the platform-specific HW initialization and configuration makes the adaptability of test cases across various platform quick and simple (3) Effective build and test infrastructure with multiple test interface options and preintegrated with FUEGO framework (4) Continuos integration - pre-integrated with Jenkins which enabled continuous testing and automated software update feature. Applying the embedded test framework accelerator throughout the design and development phase enables to development of the well-tested systems before functional verification and improves time to market to a large extent.

Keywords: board diagnostics software, embedded system, hardware testing, test frameworks

Procedia PDF Downloads 119

Authors: D. Madhushanka, Y. Liu, H. C. Fernando

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Fires are one of the foremost factors of land surface disturbance in diverse ecosystems, causing soil erosion and land-cover changes and atmospheric effects affecting people's lives and properties. Generally, the severity of the fire is calculated as the Normalized Burn Ratio (NBR) index. This is performed manually by comparing two images obtained afterward. Then by using the bitemporal difference of the preprocessed satellite images, the dNBR is calculated. The burnt area is then classified as either unburnt (dNBR<0.1) or burnt (dNBR>= 0.1). Furthermore, Wildfire Severity Assessment (WSA) classifies burnt areas and unburnt areas using classification levels proposed by USGS and comprises seven classes. This procedure generates a burn severity report for the area chosen by the user manually. This study is carried out with the objective of producing an automated tool for the above-mentioned process, namely the World Wildfire Severity Assessment Tool (WWSAT). It is implemented in Google Earth Engine (GEE), which is a free cloud-computing platform for satellite data processing, with several data catalogs at different resolutions (notably Landsat, Sentinel-2, and MODIS) and planetary-scale analysis capabilities. Sentinel-2 MSI is chosen to obtain regular processes related to burnt area severity mapping using a medium spatial resolution sensor (15m). This tool uses machine learning classification techniques to identify burnt areas using NBR and to classify their severity over the user-selected extent and period automatically. Cloud coverage is one of the biggest concerns when fire severity mapping is performed. In WWSAT based on GEE, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for both pre-fire and post-fire image compositing. The parallel processing capabilities and preloaded geospatial datasets of GEE facilitated the production of this tool. This tool consists of a Graphical User Interface (GUI) to make it user-friendly. The advantage of this tool is the ability to obtain burn area severity over a large extent and more extended temporal periods. Two case studies were carried out to demonstrate the performance of this tool. The Blue Mountain national park forest affected by the Australian fire season between 2019 and 2020 is used to describe the workflow of the WWSAT. This site detected more than 7809 km2, using Sentinel-2 data, giving an error below 6.5% when compared with the area detected on the field. Furthermore, 86.77% of the detected area was recognized as fully burnt out, of which high severity (17.29%), moderate-high severity (19.63%), moderate-low severity (22.35%), and low severity (27.51%). The Arapaho and Roosevelt National Forest Park, California, the USA, which is affected by the Cameron peak fire in 2020, is chosen for the second case study. It was found that around 983 km2 had burned out, of which high severity (2.73%), moderate-high severity (1.57%), moderate-low severity (1.18%), and low severity (5.45%). These spots also can be detected through the visual inspection made possible by cloud-free images generated by WWSAT. This tool is cost-effective in calculating the burnt area since satellite images are free and the cost of field surveys is avoided.

Keywords: burnt area, burnt severity, fires, google earth engine (GEE), sentinel-2

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

Procedia PDF Downloads 112

Authors: Grace E. Santella, Shawn G. Gallaher, Joseph P. Smith

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In order to determine the importance of small-system riverine heat flux on regional landfast sea ice breakup, our study explores the annual spring freshet of the Sagavanirktok River from 2014-2019. Seasonal heat cycling ultimately serves as the driving mechanism behind the freshet; however, as an emerging area of study, the extent to which inland thermodynamics influence coastal tundra geomorphology and connected landfast sea ice has not been extensively investigated in relation to small-scale Arctic river systems. The Sagavanirktok River is a small-to-midsized river system that flows south-to-north on the Alaskan North Slope from the Brooks mountain range to the Beaufort Sea at Prudhoe Bay. Seasonal warming in the spring rapidly melts snow and ice in a northwards progression from the Brooks Range and transitional tundra highlands towards the coast and when coupled with seasonal precipitation, results in a pulsed freshet that propagates through the Sagavanirktok River. The concentrated presence of newly exposed vegetation in the transitional tundra region due to spring melting results in higher absorption of solar radiation due to a lower albedo relative to snow-covered tundra and/or landfast sea ice. This results in spring flood runoff that advances over impermeable early-season permafrost soils with elevated temperatures relative to landfast sea ice and sub-ice flow. We examine the extent to which interannual temporal variability influences the onset and magnitude of river discharge by analyzing field measurements from the United States Geological Survey (USGS) river and meteorological observation sites. Rapid influx of heat to the Arctic Ocean via riverine systems results in a noticeable decay of landfast sea ice independent of ice breakup seaward of the shear zone. Utilizing MODIS imagery from NASA’s Terra satellite, interannual variability of river discharge is visualized, allowing for optical validation that the discharge flow is interacting with landfast sea ice. Thermal erosion experienced by sediment fast ice at the arrival of warm overflow preconditions the ice regime for rapid thawing. We investigate the extent to which interannual heat flux from the Sagavanirktok River’s freshet significantly influences the onset of local landfast sea ice breakup. The early-season warming of atmospheric temperatures is evidenced by the presence of storms which introduce liquid, rather than frozen, precipitation into the system. The resultant decreased albedo of the transitional tundra supports the positive relationship between early-season precipitation events, inland thermodynamic cycling, and degradation of landfast sea ice. Early removal of landfast sea ice increases coastal erosion in these regions and has implications for coastline geomorphology which stress industrial, ecological, and humanitarian infrastructure.

Keywords: Albedo, freshet, landfast sea ice, riverine heat flux, seasonal heat cycling

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Authors: Dipankar Saha, J. P. Singh, C. B. Pandey

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Indian Thar desert being the seventh largest in the world is the main hot sand desert occupies nearly 385,000km2 and about 9% of the area of the country harbours several species likely the flora of 682 species (63 introduced species) belonging to 352 genera and 87 families. The degree of endemism of plant species in the Thar desert is 6.4 percent, which is relatively higher than the degree of endemism in the Sahara desert which is very significant for the conservationist to envisage. The advent and development of computer technology for digitization and data base management coupled with the rapidly increasing importance of biodiversity conservation resulted in the invention of biodiversity informatics as discipline of basic sciences with multiple applications. Aichi Target 19 as an outcome of Convention of Biological Diversity (CBD) specifically mandates the development of an advanced and shared biodiversity knowledge base. Information on species distributions in space is the crux of effective management of biodiversity in the rapidly changing world. The efficiency of biodiversity management is being increased rapidly by various stakeholders like researchers, policymakers, and funding agencies with the knowledge and application of biodiversity informatics. Herbarium specimens being a vital repository for biodiversity conservation especially in climate change scenario the digitization process usually aims to improve access and to preserve delicate specimens and in doing so creating large sets of images as a part of the existing repository as arid plant information facility for long-term future usage. As the leaf characters are important for describing taxa and distinguishing between them and they can be measured from herbarium specimens as well. As a part of this activity, laminar characterization (leaves being the most important characters in assessing climate change impact) initially resulted in classification of more than thousands collections belonging to ten families like Acanthaceae, Aizoaceae, Amaranthaceae, Asclepiadaceae, Anacardeaceae, Apocynaceae, Asteraceae, Aristolochiaceae, Berseraceae and Bignoniaceae etc. Taxonomic diversity indices has also been worked out being one of the important domain of biodiversity informatics approaches. The digitization process also encompasses workflows which incorporate automated systems to enable us to expand and speed up the digitisation process. The digitisation workflows used to be on a modular system which has the potential to be scaled up. As they are being developed with a geo-referencing tool and additional quality control elements and finally placing specimen images and data into a fully searchable, web-accessible database. Our effort in this paper is to elucidate the role of BIs, present effort of database development of the existing botanical collection of institute repository. This effort is expected to be considered as a part of various global initiatives having an effective biodiversity information facility. This will enable access to plant biodiversity data that are fit-for-use by scientists and decision makers working on biodiversity conservation and sustainable development in the region and iso-climatic situation of the world.

Keywords: biodiversity informatics, climate change, digitization, herbarium, laminar characters, web accessible interface

Procedia PDF Downloads 203

Authors: Etienne Herrick

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As an economically and ecologically feasible method for farmers to introduce greater diversity into their crop rotations, cover cropping presents a valuable opportunity for improving the sustainability of food production. Planted in-between cash crop growing seasons, cover crops serve to enhance agroecosystem functioning, rather than being destined for sale or consumption. In fact, cover crops may hold the capacity to deliver multiple ecosystem functions or services simultaneously (multifunctionality). Building upon this line of research will not only benefit society at present, but also support its continued survival through its potential for restoring depleted soils and reducing the need for energy-intensive and harmful external inputs like fertilizers and pesticides. This study utilizes a trait-based approach to explore the influence of inter- and intra-specific interactions in summer cover crop mixtures and monocultures on functional trait expression and ecosystem services. Functional traits that enhance ecosystem services related to agricultural production include height, specific leaf area (SLA), root, shoot ratio, leaf C and N concentrations, and flowering phenology. Ecosystem services include biomass production, weed suppression, reduced N leaching, N recycling, and support of pollinators. Employing a trait-based approach may allow for the elucidation of mechanistic links between plant structure and resulting ecosystem service delivery. While relationships between some functional traits and the delivery of particular ecosystem services may be readily apparent through existing ecological knowledge (e.g. height positively correlating with weed suppression), this study will begin to quantify those relationships so as to gain further understanding of whether and how measurable variation in functional trait expression across cover crop mixtures and monocultures can serve as a reliable predictor of variation in the types and abundances of ecosystem services delivered. Six cover crop species, including legume, grass, and broadleaf functional types, were selected for growth in six mixtures and their component monocultures based upon the principle of trait complementarity. The tricultures (three-way mixtures) are comprised of a legume, grass, and broadleaf species, and include cowpea/sudex/buckwheat, sunnhemp/sudex/buckwheat, and chickling vetch/oat/buckwheat combinations; the dicultures contain the same legume and grass combinations as above, without the buckwheat broadleaf. By combining species with expectedly complimentary traits (for example, legumes are N suppliers and grasses are N acquirers, creating a nutrient cycling loop) the cover crop mixtures may elicit a broader range of ecosystem services than that provided by a monoculture, though trade-offs could exist. Collecting functional trait data will enable the investigation of the types of interactions driving these ecosystem service outcomes. It also allows for generalizability across a broader range of species than just those selected for this study, which may aid in informing further research efforts exploring species and ecosystem functioning, as well as on-farm management decisions.

Keywords: agroecology, cover crops, functional traits, multifunctionality, trait complementarity

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Bijit Kalita, K. V. N. Surendra

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The pulley works under both compressive loading due to contacting belt in tension and central torque due to cause rotation. In a power transmission system, the belt pulley assemblies offer a contact problem in the form of two mating cylindrical parts. In this work, we modeled a pulley as a heavy two-dimensional circular disk. Stress analysis due to contact loading in the pulley mechanism is performed. Finite element analysis (FEA) is conducted for a pulley to investigate the stresses experienced on its inner and outer periphery. In most of the heavy-duty applications, most frequently used mechanisms to transmit power in applications such as automotive engines, industrial machines, etc. is Belt Drive. Usually, very heavy circular disks are used as pulleys. A pulley could be entitled as a drum and may have a groove between two flanges around the circumference. A rope, belt, cable or chain can be the driving element of a pulley system that runs over the pulley inside the groove. A pulley is experienced by normal and shear tractions on its contact region in the process of motion transmission. The region may be belt-pulley contact surface or pulley-shaft contact surface. In 1895, Hertz solved the elastic contact problem for point contact and line contact of an ideal smooth object. Afterward, this hypothesis is generally utilized for computing the actual contact zone. Detailed stress analysis in such contact region of such pulleys is quite necessary to prevent early failure. In this paper, the results of the finite element analyses carried out on the compressed disk of a belt pulley arrangement using fracture mechanics concepts are shown. Based on the literature on contact stress problem induced in the wide field of applications, generated stress distribution on the shaft-pulley and belt-pulley interfaces due to the application of high-tension and torque was evaluated in this study using FEA concepts. Finally, the results obtained from ANSYS (APDL) were compared with the Hertzian contact theory. The study is mainly focused on the fatigue life estimation of a rotating part as a component of an engine assembly using the most famous Paris equation. Digital Image Correlation (DIC) analyses have been performed using the open-source software. From the displacement computed using the images acquired at a minimum and maximum force, displacement field amplitude is computed. From these fields, the crack path is defined and stress intensity factors and crack tip position are extracted. A non-linear least-squares projection is used for the purpose of the estimation of fatigue crack growth. Further study will be extended for the various application of rotating machinery such as rotating flywheel disk, jet engine, compressor disk, roller disk cutter etc., where Stress Intensity Factor (SIF) calculation plays a significant role on the accuracy and reliability of a safe design. Additionally, this study will be progressed to predict crack propagation in the pulley using maximum tangential stress (MTS) criteria for mixed mode fracture.

Keywords: crack-tip deformations, contact stress, stress concentration, stress intensity factor

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Authors: Ishita Singh

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Social-dimension of Climate Change is no longer peripheral to Science, Technology and Innovation (STI). Indeed, STI is being mobilized to address small farmers’ vulnerability and adaptation to Climate Change. The experiences from the cold desert of Leh (Ladakh) in North-West Himalaya illustrate the potential of STI to address the challenges of Climate Change and the needs of small farmers through the use of Artificial Glacier Techniques. Small farmers have a unique technique of water harvesting to augment irrigation, called “Artificial Glaciers” - an intricate network of water channels and dams along the upper slope of a valley that are located closer to villages and at lower altitudes than natural glaciers. It starts to melt much earlier and supplements additional irrigation to small farmers’ improving their livelihoods. Therefore, the issue of vulnerability, adaptive capacity and adaptation strategy needs to be analyzed in a local context and the communities as well as regions where people live. Leh (Ladakh) in North-West Himalaya provides a Case Study for exploring the ways in which adaptation to Climate Change is taking place at a community scale using Artificial Glacier Technology. With the above backdrop, an attempt has been made to analyze the rural poor households' vulnerability and adaptation practices to Climate Change using this technology, thereby drawing lessons on vulnerability-livelihood interactions in the cold desert of Leh (Ladakh) in North-West Himalaya, India. The study is based on primary data and information collected from 675 households confined to 27 villages of Leh (Ladakh) in North-West Himalaya, India. It reveals that 61.18% of the population is driving livelihoods from agriculture and allied activities. With increased irrigation potential due to the use of Artificial Glaciers, food security has been assured to 77.56% of households and health vulnerability has been reduced in 31% of households. Seasonal migration as a livelihood diversification mechanism has declined in nearly two-thirds of households, thereby improving livelihood strategies. Use of tactical adaptations by small farmers in response to persistent droughts, such as selling livestock, expanding agriculture lands, and use of relief cash and foods, have declined to 20.44%, 24.74% and 63% of households. However, these measures are unsustainable on a long-term basis. The role of policymakers and societal stakeholders becomes important in this context. To address livelihood challenges, the role of technology is critical in a multidisciplinary approach involving multilateral collaboration among different stakeholders. The presence of social entrepreneurs and new actors on the adaptation scene is necessary to bring forth adaptation measures. Better linkage between Science and Technology policies, together with other policies, should be encouraged. Better health care, access to safe drinking water, better sanitary conditions, and improved standards of education and infrastructure are effective measures to enhance a community’s adaptive capacity. However, social transfers for supporting climate adaptive capacity require significant amounts of additional investment. Developing institutional mechanisms for specific adaptation interventions can be one of the most effective ways of implementing a plan to enhance adaptation and build resilience.

Keywords: climate change, adaptation, livelihood, stakeholders

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Authors: James Brown, Stella Kortekaas, Ian Finnie, George Zhang, Christine Devine, Neil Healy

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The use of GIS as an analysis tool for engineering decision making is now best practice in the offshore industry. GIS enables multidisciplinary data integration, analysis and visualisation which allows the presentation of large and intricate datasets in a simple map-interface accessible to all project stakeholders. Presenting integrated geoscience and geotechnical data in GIS enables decision makers to be well-informed. This paper is a successful case study of how GIS spatial analysis techniques were applied to help select the most favourable pipeline route. Routing a pipeline through any natural environment has numerous obstacles, whether they be topographical, geological, engineering or financial. Where the pipeline is subjected to external hydrostatic water pressure and is carrying pressurised hydrocarbons, the requirement to safely route the pipeline through hazardous terrain becomes absolutely paramount. This study illustrates how the application of modern, GIS-based pipeline routing techniques enabled the identification of a single most-favourable pipeline route crossing of a challenging seabed terrain. Conventional approaches to pipeline route determination focus on manual avoidance of primary constraints whilst endeavouring to minimise route length. Such an approach is qualitative, subjective and is liable to bias towards the discipline and expertise that is involved in the routing process. For very short routes traversing benign seabed topography in shallow water this approach may be sufficient, but for deepwater geohazardous sites, the need for an automated, multi-criteria, and quantitative approach is essential. This study combined multiple routing constraints using modern least-cost-routing algorithms deployed in GIS, hitherto unachievable with conventional approaches. The least-cost-routing procedure begins with the assignment of geocost across the study area. Geocost is defined as a numerical penalty score representing hazard posed by each routing constraint (e.g. slope angle, rugosity, vulnerability to debris flows) to the pipeline. All geocosted routing constraints are combined to generate a composite geocost map that is used to compute the least geocost route between two defined terminals. The analyses were applied to select the most favourable pipeline route for a potential gas development in deep water. The study area is geologically complex with a series of incised, potentially active, canyons carved into a steep escarpment, with evidence of extensive debris flows. A similar debris flow in the future could cause significant damage to a poorly-placed pipeline. Protruding inter-canyon spurs offer lower-gradient options for ascending an escarpment but the vulnerability of periodic failure of these spurs is not well understood. Close collaboration between geoscientists, pipeline engineers, geotechnical engineers and of course the gas export pipeline operator guided the analyses and assignment of geocosts. Shorter route length, less severe slope angles, and geohazard avoidance were the primary drivers in identifying the most favourable route.

Keywords: geocost, geohazard, pipeline route determination, pipeline route optimisation, spatial analysis

Procedia PDF Downloads 367

Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

Procedia PDF Downloads 103