Search results for: operational data

Authors: Rawan K. Al-Duaij, Salem A. Al-Salem

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Projects, the unique and temporary process of achieving a set of requirements have always been challenging; Planning the schedule and budget, managing the resources and risks are mostly driven by a similar past experience or the technical consultations of experts in the matter. With that complexity of Projects in Scope, Time, and execution environment, Adjustment Orders are tools to reflect changes to the original project parameters after Contract signature. Adjustment Orders are the official/legal amendments to the terms and conditions of a live Contract. Reasons for issuing Adjustment Orders arise from changes in Contract scope, technical requirement and specification resulting in scope addition, deletion, or alteration. It can be as well a combination of most of these parameters resulting in an increase or decrease in time and/or cost. Most business leaders (handling projects in the interest of the owner) refrain from using Adjustment Orders considering their main objectives of staying within budget and on schedule. Success in managing the changes results in uninterrupted execution and agreed project costs as well as schedule. Nevertheless, this is not always practically achievable. In this paper, a detailed study through utilizing Industrial Engineering & Systems Management tools such as Six Sigma, Data Analysis, and Quality Control were implemented on the organization’s five years records of the issued Adjustment Orders in order to investigate their prevalence, and time and cost impact. The analysis outcome revealed and helped to identify and categorize the predominant causations with the highest impacts, which were considered most in recommending the corrective measures to reach the objective of minimizing the Adjustment Orders impacts. Data analysis demonstrated no specific trend in the AO frequency in past five years; however, time impact is more than the cost impact. Although Adjustment Orders might never be avoidable; this analysis offers’ some insight to the procedural gaps, and where it is highly impacting the organization. Possible solutions are concluded such as improving project handling team’s coordination and communication, utilizing a blanket service contract, and modifying the projects gate system procedures to minimize the possibility of having similar struggles in future. Projects in the Oil and Gas sector are always evolving and demand a certain amount of flexibility to sustain the goals of the field. As it will be demonstrated, the uncertainty of project parameters, in adequate project definition, operational constraints and stringent procedures are main factors resulting in the need for Adjustment Orders and accordingly the recommendation will be to address that challenge.

Keywords: adjustment orders, data analysis, oil and gas sector, systems management

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Authors: Sašo Pečnik, Borut Žalik

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This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders.

Keywords: filtering, graphics, level-of-details, LiDAR, real-time visualization

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Authors: Mst Ilme Faridatul, Bo Wu

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Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%.

Keywords: land cover, mapping, multi-temporal, spectral indices

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Authors: Asif Zaheer Shaikh, Zhaoyong Zhang, Jaime Yong, Ume Laila Shah

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Islamic finance is growing with remarkable pace, especially Islamic banking, a major segment of Islamic finance, is expanding rapidly. This paper discusses the position of Islamic finance and Islamic banking, around the world in general and particularly in Pakistan. History of Islamic banking in Pakistan is protested, presently a significant growth is observed. However Islamic banking is confronting with number of challenges, which are refraining from sustainable growth of this industry in Pakistan. Growth level of Islamic banks should be steeper to contribute substantial share in country’s economy. It is important to formulate effective policies, at institutional and operational level to address these challenges through close collaboration of key stakeholders.

Keywords: Islamic finance, challenges, Islamic banking, Pakistan

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Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

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Authors: Saria Abed, Tahar Khir, Ammar Ben Brahim

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A thermo-exergy optimization of a gas turbine cycle with two different regenerator designs is established. A comparison was made between the performance of the two regenerators and their roles in improving the cycle efficiencies. The effect of operational parameters (the pressure ratio of the compressor, the ambient temperature, excess of air, geometric parameters of the regenerators, etc.) on thermal efficiencies, the exergy efficiencies, and irreversibilities were studied using thermal balances and quantitative exegetic equilibrium for each component and for the whole system. The results are given graphically by using the EES software, and an appropriate discussion and conclusion was made.

Keywords: exergy efficiency, gas turbine, heat transfer, irreversibility, optimization, regenerator, thermal efficiency

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Authors: Tsepiso Mofolo, Luna Bergh

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The Lesotho construction industry fails to embrace environmental practices, which has then lead to excessive consumption of resources, land degradation, air and water pollution, loss of habitats, and high energy usage. The industry is highly inefficient, and this undermines its capability to yield the optimum contribution to social, economic and environmental developments. Sustainable construction is, therefore, imperative to ensure the cultivation of benefits from all these intrinsic themes of sustainable development. The development of a sustainable construction industry requires a holistic approach that takes into consideration the interaction between Lean Construction principles, socio-economic and environmental policies, technological advancement and the principles of construction or project management. Sustainable construction is a cutting-edge phenomenon, forming a component of a subjectively defined concept called sustainable development. Sustainable development can be defined in terms of attitudes and judgments to assist in ensuring long-term environmental, social and economic growth in society. The key concept of sustainable construction is Lean Construction. Lean Construction emanates from the principles of the Toyota Production System (TPS), namely the application and adaptation of the fundamental concepts and principles that focus on waste reduction, the increase in value to the customer, and continuous improvement. The focus is on the reduction of socio-economic waste, and protestation of environmental degradation by reducing carbon dioxide emission footprint. Lean principles require a fundamental change in the behaviour and attitudes of the parties involved in order to overcome barriers to cooperation. Prevalent barriers to adoption of Lean Construction in Lesotho are mainly structural - such as unavailability of financing, corruption, operational inefficiency or wastage, lack of skills and training and inefficient construction legislation and political interferences. The consequential effects of these problems trigger down to quality, cost and time of the project - which then result in an escalation of operational costs due to the cost of rework or material wastage. Factor and correlation analysis of these barriers indicate that they are highly correlated, which then poses a detrimental potential to the country’s welfare, environment and construction safety. It is, therefore, critical for Lesotho’s construction industry to develop a robust governance through bureaucracy reforms and stringent law enforcement.

Keywords: construction industry, sustainable development, sustainable construction industry, lean construction, barriers to sustainable construction

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Authors: Jae Won Shin

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We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

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Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Benny Sand, Yotam Lurie, Shlomo Mark

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Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions.

Keywords: MIoT, data privacy, stakeholders, home healthcare, information privacy, AI

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Merve Yünlü, Nihat Kandemir, Aylin Ersoy

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Temperature sensors are used in home appliances not only to monitor the basic functions of the machine but also to minimize energy consumption and ensure safe operation. In parallel with the development of smart home applications and IoT algorithms, these sensors produce important data such as the frequency of use of the machine, user preferences, and the compilation of critical data in terms of diagnostic processes for fault detection throughout an appliance's operational lifespan. Commercially available thin-film resistive temperature sensors have a well-established manufacturing procedure that allows them to operate over a wide temperature range. However, these sensors are over-designed for white goods applications. The operating temperature range of these sensors is between -70°C and 850°C, while the temperature range requirement in home appliance applications is between 23°C and 500°C. To ensure the operation of commercial sensors in this wide temperature range, usually, a platinum coating of approximately 1-micron thickness is applied to the wafer. However, the use of platinum in coating and the high coating thickness extends the sensor production process time and therefore increases sensor costs. In this study, an attempt was made to develop a low-cost temperature sensor design and production method that meets the technical requirements of white goods applications. For this purpose, a custom design was made, and design parameters (length, width, trim points, and thin film deposition thickness) were optimized by using statistical methods to achieve the desired resistivity value. To develop thin film resistive temperature sensors, one side polished sapphire wafer was used. To enhance adhesion and insulation 100 nm silicon dioxide was coated by inductively coupled plasma chemical vapor deposition technique. The lithography process was performed by a direct laser writer. The lift-off process was performed after the e-beam evaporation of 10 nm titanium and 280 nm platinum layers. Standard four-point probe sheet resistance measurements were done at room temperature. The annealing process was performed. Resistivity measurements were done with a probe station before and after annealing at 600°C by using a rapid thermal processing machine. Temperature dependence between 25-300 °C was also tested. As a result of this study, a temperature sensor has been developed that has a lower coating thickness than commercial sensors but can produce reliable data in the white goods application temperature range. A relatively simplified but optimized production method has also been developed to produce this sensor.

Keywords: thin film resistive sensor, temperature sensor, household appliance, sustainability, energy efficiency

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Authors: Sophie Baudel, Aline Duplaa, Jean Muller, Stephan Lauriol, Yann Bernard

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Argos is the main satellite telemetry system used by the wildlife research community, since its creation in 1978, for animal tracking and scientific data collection all around the world, to analyze and understand animal migrations and behavior. The marine mammals' biology is one of the major disciplines which had benefited from Argos telemetry, and conversely, marine mammals biologists’ community has contributed a lot to the growth and development of Argos use cases. The Argos constellation with 6 satellites in orbit in 2017 (Argos 2 payload on NOAA 15, NOAA 18, Argos 3 payload on NOAA 19, SARAL, METOP A and METOP B) is being extended in the following years with Argos 3 payload on METOP C (launch in October 2018), and Argos 4 payloads on Oceansat 3 (launch in 2019), CDARS in December 2021 (to be confirmed), METOP SG B1 in December 2022, and METOP-SG-B2 in 2029. Argos 4 will allow more frequency bands (600 kHz for Argos4NG, instead of 110 kHz for Argos 3), new modulation dedicated to animal (sea turtle) tracking allowing very low transmission power transmitters (50 to 100mW), with very low data rates (124 bps), enhancement of high data rates (1200-4800 bps), and downlink performance, at the whole contribution to enhance the system capacity (50,000 active beacons per month instead of 20,000 today). In parallel of this ‘institutional Argos’ constellation, in the context of a miniaturization trend in the spatial industry in order to reduce the costs and multiply the satellites to serve more and more societal needs, the French Space Agency CNES, which designs the Argos payloads, is innovating and launching the Argos ANGELS project (Argos NEO Generic Economic Light Satellites). ANGELS will lead to a nanosatellite prototype with an Argos NEO instrument (30 cm x 30 cm x 20cm) that will be launched in 2019. In the meantime, the design of the renewal of the Argos constellation, called Argos For Next Generations (Argos4NG), is on track and will be operational in 2022. Based on Argos 4 and benefitting of the feedback from ANGELS project, this constellation will allow revisiting time of fewer than 20 minutes in average between two satellite passes, and will also bring more frequency bands to improve the overall capacity of the system. The presentation will then be an overview of the Argos system, present and future and new capacities coming with it. On top of that, use cases of two Argos hardware modules will be presented: the goniometer pathfinder allowing recovering Argos beacons at sea or on the ground in a 100 km radius horizon-free circle around the beacon location and the new Argos 4 chipset called ‘Artic’, already available and tested by several manufacturers.

Keywords: Argos satellite telemetry, marine protected areas, oceanography, maritime services

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Authors: Johannes Atug, Stefan Braunreuther, Gunther Reinhart

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Volatile markets, as well as increasing global competition in manufacturing, lead to a high demand of flexible and agile production systems. These advanced production systems in turn conduct to high capital expenditure along with high investment risks. Developments in production regarding digitalization and cyber-physical systems result to a merger of informational- and operational technology. The approach of this paper is to benefit from this merger and present a framework of a production network with scalable production capacity and low capital expenditure by adaptation of the IT concept 'everything as a service' into the production environment.

Keywords: digital manufacturing system, everything as a service, reconfigurable production, value network

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Authors: F. Amin, S. Feizi

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Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.

Keywords: big data, next generation networks, network transformation, strategy

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Authors: Dustin Schöder

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The potential benefits of the utilization of highly automated and autonomous trucks in logistics operations are the subject of interest to the entire logistics industry. The benefits of the use of these new technologies were scientifically investigated and implemented in roadmaps. So far, reliable data and experiences from real life use cases are still limited. A German research consortium of both academics and industry developed a highly automated (SAE level 4) vehicle for yard operations at railports and container depots. After development and testing, a several month field test at the DUSS Terminal in Ulm-Dornstadt (Germany) and the nearby DB Intermodal Services Container Depot in Ulm-Dornstadt was conducted. The truck was piloted in a shuttle service between both sites. In a holistic automation approach, the vehicle was integrated into a digital communication platform so that the truck could move autonomously without a driver and his manual interactions with a wide variety of stakeholders. The main goal is to investigate the effects of highly automated trucks in the key processes of container loading, unloading and container relocation on holistic railport yard operation. The field test data were used to investigate changes in process efficiency of key processes of railport and container yard operations. Moreover, effects on the capacity utilization and potentials for smothering peak workloads were analyzed. The results state that process efficiency in the piloted use case was significantly higher. The reason for that could be found in the digitalized data exchange and automated dispatch. However, the field test has shown that the effect is greatly varying depending on the ratio of highly automated and manual trucks in the yard as well as on the congestion level in the loading area. Furthermore, the data confirmed that under the right conditions, the capacity utilization of highly automated trucks could be increased. In regard to the potential for smothering peak workloads, no significant findings could be made based on the limited requirements and regulations of railway operation in Germany. In addition, an empirical survey among railport managers, operational supervisors, innovation managers and strategists (n=15) within the logistics industry in Germany was conducted. The goal was to identify key characteristics of future railports and terminals as well as requirements that railports will have to meet in the future. Furthermore, the railport processes where automation and autonomization make the greatest impact, as well as hurdles and challenges in the introduction of new technologies, have been surveyed. Hence, further potential use cases of highly automated and autonomous applications could be identified, and expectations have been mapped. As a result, a highly detailed and practice-based roadmap towards a ‘terminal 4.0’ was developed.

Keywords: highly automated driving, autonomous driving, SAE level 4, railport operations, container depot, intermodal logistics, potentials of autonomization

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Authors: George Kyriakopoulos, Photini Pazartzis, Anthi Koskina, Crystalie Bourcha

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The need for securing safe access to and return from outer space, as well as ensuring the viability of outer space operations, maintains vivid the debate over the promotion of organization of space traffic through a Space Traffic Management System (STM). The proliferation of outer space activities in recent years as well as the dynamic emergence of the private sector has gradually resulted in a diverse universe of actors operating in outer space. The said developments created an increased adverse impact on outer space sustainability as the case of the growing number of space debris clearly demonstrates. The above landscape sustains considerable threats to outer space environment and its operators that need to be addressed by a combination of scientific-technological measures and regulatory interventions. In this context, recourse to recent technological advancements and, in particular, to Artificial Intelligence (AI) and machine learning systems, could achieve exponential results in promoting space traffic management with respect to collision avoidance as well as launch and re-entry procedures/phases. New technologies can support the prospects of a successful space traffic management system at an international scale by enabling, inter alia, timely, accurate and analytical processing of large data sets and rapid decision-making, more precise space debris identification and tracking and overall minimization of collision risks and reduction of operational costs. What is more, a significant part of space activities (i.e. launch and/or re-entry phase) takes place in airspace rather than in outer space, hence the overall discussion also involves the highly developed, both technically and legally, international (and national) Air Traffic Management System (ATM). Nonetheless, from a regulatory perspective, the use of AI for the purposes of space traffic management puts forward implications that merit particular attention. Key issues in this regard include the delimitation of AI-based activities as space activities, the designation of the applicable legal regime (international space or air law, national law), the assessment of the nature and extent of international legal obligations regarding space traffic coordination, as well as the appropriate liability regime applicable to AI-based technologies when operating for space traffic coordination, taking into particular consideration the dense regulatory developments at EU level. In addition, the prospects of institutionalizing international cooperation and promoting an international governance system, together with the challenges of establishment of a comprehensive international STM regime are revisited in the light of intervention of AI technologies. This paper aims at examining regulatory implications advanced by the use of AI technology in the context of space traffic management operations and its key correlating concepts (SSA, space debris mitigation) drawing in particular on international and regional considerations in the field of STM (e.g. UNCOPUOS, International Academy of Astronautics, European Space Agency, among other actors), the promising advancements of the EU approach to AI regulation and, last but not least, national approaches regarding the use of AI in the context of space traffic management, in toto. Acknowledgment: The present work was co-funded by the European Union and Greek national funds through the Operational Program "Human Resources Development, Education and Lifelong Learning " (NSRF 2014-2020), under the call "Supporting Researchers with an Emphasis on Young Researchers – Cycle B" (MIS: 5048145).

Keywords: artificial intelligence, space traffic management, space situational awareness, space debris

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Authors: Apkar Salatian

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To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: design pattern, filtering, compression, architectural design

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Mario M. Figueroa de la Cruz, Claudia I. Solorzano, Raul Acosta, Ignacio Funes

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This technological development project seeks to create a tool that allows companies, in need of implementing a Data Center, intelligently determining factors for allocating resources support cooling and power supply (UPS) in its conception. The results should show clearly the speed, robustness and reliability of a system designed for deployment in environments where they must manage and protect large volumes of data.

Keywords: telecommunications, data center, fuzzy logic, expert systems

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Authors: Glenn Auld Knierim

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Automated robotic winding of high-temperature superconductors (HTS) addresses precision, efficiency, and reliability critical to the commercialization of products. Today’s HTS materials are mature and commercially promising but require manufacturing attention. In particular to the exaggerated rectangular cross-section (very thin by very wide), winding precision is critical to address the stress that can crack the fragile ceramic superconductor (SC) layer and destroy the SC properties. Damage potential is highest during peak operations, where winding stress magnifies operational stress. Another challenge is operational parameters such as magnetic field alignment affecting design performance. Winding process performance, including precision, capability for geometric complexity, and efficient repeatability, are required for commercial production of current HTS. Due to winding limitations, current HTS magnets focus on simple pancake configurations. HTS motors, generators, MRI/NMR, fusion, and other projects are awaiting robotic wound solenoid, planar, and spherical magnet configurations. As with conventional power cables, full transposition winding is required for long length alternating current (AC) and pulsed power cables. Robotic production is required for transposition, periodic swapping of cable conductors, and placing into precise positions, which allows power utility required minimized reactance. A full transposition SC cable, in theory, has no transmission length limits for AC and variable transient operation due to no resistance (a problem with conventional cables), negligible reactance (a problem for helical wound HTS cables), and no long length manufacturing issues (a problem with both stamped and twisted stacked HTS cables). The Infinity Physics team is solving manufacturing problems by developing automated manufacturing to produce the first-ever reliable and utility-grade commercial SC cables and magnets. Robotic winding machines combine mechanical and process design, specialized sense and observer, and state-of-the-art optimization and control sequencing to carefully manipulate individual fragile SCs, especially HTS, to shape previously unattainable, complex geometries with electrical geometry equivalent to commercially available conventional conductor devices.

Keywords: automated winding manufacturing, high temperature superconductor, magnet, power cable

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Authors: Amy Gooden, Meshandren Naidoo

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Genetic research is not confined to a particular jurisdiction. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear.

Keywords: cross-border, data, genetic testing, law, regulation, research, sharing, South Africa

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Authors: Wipada Chaiwchan

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This research aims to study behaviors in using social networks of Corporate personnel of Suan Sunandha Rajabhat University. The sample used in the study were two groups: 1) Academic Officer 70 persons and 2) Operation Officer 143 persons were used in this study. The tools in this research consisted of questionnaire which the data were analyzed by using percentage, average (X) and Standard deviation (S.D.) and Independent Sample T-Test to test the difference between the mean values obtained from two independent samples, and One-way anova to analysis of variance, and Multiple comparisons to test that the average pair of different methods by Fisher’s Least Significant Different (LSD). The study result found that the most of corporate personnel have purpose in using social network to information awareness aspect was knowledge and online conference with social media. By using the average more than 3 hours per day in everyday. Using time in working in one day and there are computers connected to the Internet at home, by using the communication in the operational processes. Behaviors using social networks in relation to gender, age, job title, department, and type of personnel. Hypothesis testing, and analysis of variance for the effects of this analysis is divided into three aspects: The use of online social networks, the attitude of the users and the security analysis has found that Corporate Personnel of Suan Sunandha Rajabhat University. Overall and specifically at the high level, and considering each item found all at a high level. By sorting of the social network (X=3.22), The attitude of the users (X= 3.06) and the security (X= 3.11). The overall behaviors using of each side (X=3.11).

Keywords: social network, behaviors, social media, computer information systems

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Authors: Baris Can Yalcin

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Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor.

Keywords: design, mechatronics, motion sensor, data acquisition

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Authors: Ateeth Shetty, K. S. Jayappa, Ratheesh Ramakrishnan, A. S. Rajawat

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Suspended Sediment Concentration (SSC) was estimated for the period of four months (November, 2013 to February 2014) using Oceansat-2 (Ocean Colour Monitor) satellite images to understand the coastal dynamics and regional sediment transport, especially distribution and budgeting in coastal waters. The coastal zone undergoes continuous changes due to natural processes and anthropogenic activities. The importance of the coastal zone, with respect to safety, ecology, economy and recreation, demands a management strategy in which each of these aspects is taken into account. Monitoring and understanding the sediment dynamics and suspended sediment transport is an important issue for coastal engineering related activities. A study of the transport mechanism of suspended sediments in the near shore environment is essential not only to safeguard marine installations or navigational channels, but also for the coastal structure design, environmental protection and disaster reduction. Such studies also help in assessment of pollutants and other biological activities in the region. An accurate description of the sediment transport, caused by waves and tidal or wave-induced currents, is of great importance in predicting coastal morphological changes. Satellite-derived SSC data have been found to be useful for Indian coasts because of their high spatial (360 m), spectral and temporal resolutions. The present paper outlines the applications of state‐of‐the‐art operational Indian Remote Sensing satellite, Oceansat-2 to study the dynamics of sediment transport.

Keywords: suspended sediment concentration, ocean colour monitor, sediment transport, case – II waters

Procedia PDF Downloads 247

Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

Abstract:

Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

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Authors: Hsin-Yi Huang, Ming-Sheng Liu, Jiun-Yan Shiau

Abstract:

Planning the order picking lists of warehouses to achieve when the costs associated with logistics on the operational performance is a significant challenge. In e-commerce era, this task is especially important productive processes are high. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A Zone2 picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.

Keywords: order picking, warehouse, clustering, unsupervised learning

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Authors: Ashish Dhamaniya, Satish Chandra

Abstract:

Speed and traffic volume data are collected on different sections of four lane and six lane roads in three metropolitan cities in India. Speed data are analyzed to fit the statistical distribution to individual vehicle speed data and all vehicles speed data. It is noted that speed data of individual vehicle generally follows a normal distribution but speed data of all vehicle combined at a section of urban road may or may not follow the normal distribution depending upon the composition of traffic stream. A new term Speed Spread Ratio (SSR) is introduced in this paper which is the ratio of difference in 85^th and 50^th percentile speed to the difference in 50^th and 15^th percentile speed. If SSR is unity then speed data are truly normally distributed. It is noted that on six lane urban roads, speed data follow a normal distribution only when SSR is in the range of 0.86 – 1.11. The range of SSR is validated on four lane roads also.

Keywords: normal distribution, percentile speed, speed spread ratio, traffic volume

Procedia PDF Downloads 414

Authors: Ming Wei

Abstract:

Over the past two decades, Location Based Service (LBS) data have been increasingly applied to urban and transportation studies due to their comprehensiveness and consistency. However, compared to other LBS data including mobile phone data, GPS and social networking platforms, smart card data collected from public transport users have arguably yet to be fully exploited in urban systems analysis. By using five weekdays of passenger travel transaction data taken from go card – Southeast Queensland’s transit smart card – this paper analyses the spatiotemporal distribution of passenger movement with regard to the land use patterns in Brisbane. Work and residential places for public transport commuters were identified after extracting journeys-to-work patterns. Our results show that the locations of the workplaces identified from the go card data and residential suburbs are largely consistent with those that were marked in the land use map. However, the intensity for some residential locations in terms of population or commuter densities do not match well between the map and those derived from the go card data. This indicates that the misalignment between residential areas and workplaces to a certain extent, shedding light on how enhancements to service management and infrastructure expansion might be undertaken.

Keywords: big data, smart card data, travel pattern, land use

Procedia PDF Downloads 282