Search results for: analytic network process

Authors: Achim Kampker, Heiner Hans Heimes, Mathias Ordung, Jan-Philip Ganser

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In the past years, the automotive industry has changed significantly. Innovative mobility concepts have become more important, and electric vehicles see a chance of replacing vehicles with combustion engines in the long term. However, the coming years will be characterized by coexistence. In this context, there are two possible production scenarios: One the one hand, electric vehicles could be manufactured in bespoke assembly lines. Concerning the uncertainty regarding sales figures development, this alternative boasts a high investment risk. Therefore, an integrated assembly building upon existing structures also seems a feasible solution. This empirical study aims at validating hypotheses concerning theoretical and practical challenges of the integrated production in the final assembly. In order to take a test of approaches of the research by analyzing censored feedback of professionals, these hypotheses are validated in the framework of an expert study. For this purpose, hypotheses have been generated on the basis of a requirements analysis and a concept specification. Thereupon, a list of question has been implemented and deduced from the hypotheses to execute an online- and written-survey and interviews with professionals. The interpretation and evaluation of the findings includes an inter-component comparison for the electric drivetrain. Furthermore, key drivers for a sufficient integrated product and process design are presented.

Keywords: automotive industry, final assembly, integrated manufacturing, product and process development

Procedia PDF Downloads 325

Authors: R. Nagarani

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An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.

Keywords: community detection, complex network, genetic algorithm, package, refactoring

Procedia PDF Downloads 404

Authors: N. F. F. Ebecken, G. C. Pereira

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Ecological systems are exposed and are influenced by various natural and anthropogenic disturbances. They produce various effects and states seeking response symmetry to a state of global phase coherence or stability and balance of their food webs. This research project addresses the development of a computational methodology for modeling plankton food webs. The use of algorithms to establish connections, the generation of representative fuzzy multigraphs and application of technical analysis of complex networks provide a set of tools for defining, analyzing and evaluating community structure of coastal aquatic ecosystems, beyond the estimate of possible external impacts to the networks. Thus, this study aims to develop computational systems and data models to assess how these ecological networks are structurally and functionally organized, to analyze the types and degree of compartmentalization and synchronization between oscillatory and interconnected elements network and the influence of disturbances on the overall pattern of rhythmicity of the system.

Keywords: ecological networks, plankton food webs, fuzzy multigraphs, dynamic of networks

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Authors: Devineni Vijay Bhaskar, Yendluri Raja

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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.

Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve

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Authors: Poonam Magu, Kumud Khanna, Premavathy Seetharaman

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The two important resources of any worker performing any type of work at any workplace are time and energy. These are important inputs of the worker and need to be utilised in the best possible manner. The kitchen is an important workplace where the homemaker performs many essential activities. Its layout should be so designed that optimum use of her resources can be achieved.Ideally, the shape of the kitchen, as determined by the physical space enclosed by the four walls, can be square, rectangular or irregular. But it is the shape of the arrangement of counter that one normally refers to while talking of the layout of the kitchen. The arrangement can be along a single wall, along two opposite walls, L shape, U shape or even island. A study was conducted in 50 kitchens belonging to middle income group families. These were DDA built kitchens located in North, South, East and West Delhi.The study was conducted in three phases. In the first phase, 510 non working homemakers were interviewed. The data related to personal characteristics of the homemakers was collected. Additional information was also collected regarding the kitchens-the size, shape , etc. The homemakers were also questioned about various aspects related to meal preparation-people performing the task, number of items cooked, areas used for meal preparation , etc. In the second phase, a suitable technique was designed for conducting time and motion study in the kitchen while the meal was being prepared. This technique was called Path Process Chart. The final phase was carried out in 50 kitchens. The criterion for selection was that all items for a meal should be cooked at the same time. All the meals were cooked by the homemakers in their own kitchens. The meal preparation was studied using the Path Process Chart technique. The data collected was analysed and conclusions drawn. It was found that of all the shapes, it was the kitchen with L shape arrangement in which, on an average a homemaker spent minimum time on meal preparation and also travelled the minimum distance. Thus, the average distance travelled in a L shaped layout was 131.1 mts as compared to 181.2 mts in an U shaped layout. Similarly, 48 minutes was the average time spent on meal preparation in L shaped layout as compared to 53 minutes in U shaped layout. Thus, the L shaped layout was more time and energy saving layout as compared to U shaped.

Keywords: kitchen layout, meal preparation, path process chart technique, workplace

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Authors: Kemal Polat

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In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

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Authors: Anna Strąkowska, Marian Zaborski

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The studies were focused on POSS application with methylvinylsilicone rubber (MVQ). The obtained results indicate that they can be successfully incorporated into silica-filled rubbers as modifying agents since they enhance cross-link density and improve most properties of the resulting network. It is also worth noting that the incorporation of POSS molecules resulted in stabilizing effect against adverse changes induced by the climatic, ozone or UV ageing of the rubbers. Furthermore, we obtained interesting results of rubbers surface modification using POSS functionalised with halogen groups (Cl, F, and Br). As the results, surface energy of the elastomeric composites and their hydrophobicity increased, barrier properties improved and thermal stability increased as well. Additionally, the studies with silicone rubber and POSS containing acidic and alkaline groups revealed composites with self-healing properties. The observed effects strictly depend on a kind and quantity of functional groups present in angles of POSS cages.

Keywords: elastomeric composites, POSS, properties modyfication, silicone rubber

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Authors: Abigail Qian Zhou

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In today's China, the well-educated middle class, with stable jobs and above-average income, are the driving force behind its Internet society. Through the analysis of data from the 2015 Chinese General Social Survey and 50 interviewees, this study investigates the current situation of this group’s specific internet usage. The findings of this study demonstrate that daily life among the members of this socioeconomic group is closely tied to the Internet. For Chinese middle class, the Internet is used to socialize and entertain self and others. It is also used to search for and share information as well as to build their identities. The empirical results of this study will provide a reference, supported by factual data, for enterprises seeking to target the Chinese middle class through online marketing efforts.

Keywords: middle class, Internet use, network behaviour, online marketing, China

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Authors: Harpal Singh, Sakshi Batra

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The widespread and easy accesses to multimedia contents and possibility to make numerous copies without loss of significant fidelity have roused the requirement of digital rights management. Thus this problem can be effectively solved by Digital watermarking technology. This is a concept of embedding some sort of data or special pattern (watermark) in the multimedia content; this information will later prove ownership in case of a dispute, trace the marked document’s dissemination, identify a misappropriating person or simply inform user about the rights-holder. The primary motive of digital watermarking is to embed the data imperceptibly and robustly in the host information. Extensive counts of watermarking techniques have been developed to embed copyright marks or data in digital images, video, audio and other multimedia objects. With the development of digital video-based innovations, copyright dilemma for the multimedia industry increases. Video watermarking had been proposed in recent years to serve the issue of illicit copying and allocation of videos. It is the process of embedding copyright information in video bit streams. Practically video watermarking schemes have to address some serious challenges as compared to image watermarking schemes like real-time requirements in the video broadcasting, large volume of inherently redundant data between frames, the unbalance between the motion and motionless regions etc. and they are particularly vulnerable to attacks, for example, frame swapping, statistical analysis, rotation, noise, median and crop attacks. In this paper, an effective, robust and imperceptible video watermarking algorithm is proposed based on hybridization of powerful mathematical transforms; Fractional Fourier Transform (FrFT), Discrete Wavelet transforms (DWT) and Singular Value Decomposition (SVD) using redundant wavelet. This scheme utilizes various transforms for embedding watermarks on different layers by using Hybrid systems. For this purpose, the video frames are portioned into layers (RGB) and the watermark is being embedded in two forms in the video frames using SVD portioning of the watermark, and DWT sub-band decomposition of host video, to facilitate copyright safeguard as well as reliability. The FrFT orders are used as the encryption key that allows the watermarking method to be more robust against various attacks. The fidelity of the scheme is enhanced by introducing key generation and wavelet based key embedding watermarking scheme. Thus, for watermark embedding and extraction, same key is required. Therefore the key must be shared between the owner and the verifier via some safe network. This paper demonstrates the performance by considering different qualitative metrics namely Peak Signal to Noise ratio, Structure similarity index and correlation values and also apply some attacks to prove the robustness. The Experimental results are presented to demonstrate that the proposed scheme can withstand a variety of video processing attacks as well as imperceptibility.

Keywords: discrete wavelet transform, robustness, video watermarking, watermark

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Authors: Andrey Norov

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In the recent years, Samoilov Research Institute for Mineral Fertilizers JSC “NIUIF”, the oldest (established in September 1919) industry-oriented institute in Russia, has developed a range of sustainable, environment-friendly, zero-waste technologies that ensure minimal consumption of materials and energy resources and fully consistent with the principles of Green Chemistry that include: - Ecofriendly energy and resource saving technology of sulfuric acid from sulfur according to DC-DA scheme (double conversion - double absorption); - Improved zero-waste technology of wet phosphoric acid (WPA) by dihydrate-hemihydrate process applicable to various types of phosphate raw materials; - Flexible, efficient, zero-waste, universal technology of NP / NPS / NPK / NPKS fertilizers with maximum heat recovery from chemical processes; - Novel, zero-waste, no-analogue technology of granular PK / PKS / NPKS fertilizers with controlled dissolution rate and nutrient supply into the soil, which allows to process a number of wastes and by-products; - Innovative resource-saving joint processing of wastes from the production of phosphogypsum and fluorosilicic acid (FSA) into ammonium sulfate with simultaneous neutralization of fluoride compounds with no lime used. - New fertilizer technology of increased environmental and agrochemical efficiency (currently under development). All listed green technologies are patented with Russian and Eurasian patents. The development of ecofriendly, safe, green technologies is ongoing in JSC “NIUIF”.

Keywords: NPKS fertilizers, FSA, sulfuric acid, WPA

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Authors: Dimitrios J. Dimitriou, Maria F. Sartzetaki

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Transport infrastructure assets are key components of the national asset portfolio. The decision to invest in a new infrastructure in transports could take from a few years to some decades. This is mainly because of the need to reserve and spent many capitals, the long payback period, the number of the stakeholders involved in decision process and –many times- the investment and business risks are high. Therefore, the decision assessment framework is an essential challenge linked with the key decision factors meet the stakeholder expectations highlighting project trade-offs, financial risks, business uncertainties and market limitations. This paper examines the decision process for new transport infrastructure projects in cross border regions, where a wide range of stakeholders with different expectation is involved. According to a consequences analysis systemic approach, the relationship of transport infrastructure development, economic system development and stakeholder expectation is analyzed. Adopting the on system of system methodological approach, the decision making framework, variables, inputs and outputs are defined, highlighting the key shareholder’s role and expectations. The application provides the methodology outputs presenting the proposed decision framework for a strategic railway project in north Greece deals with the upgrade of the existing railway corridor connecting Greece, Turkey and Bulgaria.

Keywords: decision making, system of system, cross-border, infrastructure project

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Authors: Victoria H. Hamilton

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Charles Bonnet Syndrome (CBS) is when a person experiences pseudohallucinations that fill in visual information from any type of sight loss. CBS arises from an epiphenomenal process, with the physical actions of sight resulting in the mental formations of images. These pseudohallucinations—referred to as visions by the CBS community—manifest in a wide range of forms, from complex scenes to simple geometric shapes. To share these unique visual experiences, a remote co-creation website was created where CBS participants communicated their lived experiences. This created a reflexive process, and we worked to produce true representations of these interesting and little-known phenomena. Digital reconstruction of the visions is utilised as it echoes the vivid, experiential movie-like nature of what is being perceived. This paper critically analyses co-creation as a method for making digital assets. The implications of the participants' vision impairments and the application of ethical safeguards are examined in this context. Important to note, this research is of a medical syndrome for a non-medical, practice-based design. CBS research to date is primarily conducted by the ophthalmic, neurological, and psychiatric fields and approached with the primary concerns of these specialties. This research contributes a distinct approach incorporating practice-based digital design, autoethnography, and phenomenology. Autoethnography and phenomenology combine as a foundation, with the first bringing understanding and insights, balanced by the second philosophical, bigger picture, and established approach. With further refining, it is anticipated that the research may be applied to other conditions. Conditions where articulating internal experiences proves challenging and the use of digital methods could aid communication. Both the research and CBS communities will benefit from the insights regarding the relationship between cognitive perceptions and the vision process. This research combines the digital visualising of visions with interest in the link between metaphor, embodied cognition, and image. The argument for a link between CBS visions and metaphor may appear evident due to the cross-category mapping of images that is necessary for comprehension. They both are— CBS visions and metaphors—the experience of picturing images, often with lateral connections and imaginative associations.

Keywords: Charles Bonnet Syndrome, digital design, visual hallucinations, visual perception

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Vladimir Simovic, Matija Varga, Predrag Oreski

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XHTML and XBRL are the standard languages for creating a database for the purpose of displaying financial statements on web applications. Today, XBRL is one of the most popular languages for business reporting. A large number of countries in the world recognize the role of XBRL language for financial reporting and the benefits that the reporting format provides in the collection, analysis, preparation, publication and the exchange of data (information) which is the positive side of this language. Here we present all advantages and opportunities that a company may have by using the XBRL format for business reporting. Also, this paper presents XBRL and other languages that are used for creating the database, such XML, XHTML, etc. The role of the AJAX complex model and technology will be explained in detail, and during the exchange of financial data between the web client and web server. Here will be mentioned basic layers of the network for data exchange via the web.

Keywords: XHTML, XBRL, XML, JavaScript, AJAX technology, data exchange

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Authors: Waddah S. Abdullah, Khaled F. Al-Omari

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Electrokinetic remediation is one of the most influential and effective methods to decontaminate contaminated soils. Electroosmosis and electromigration are the processes of electrochemical extraction of contaminants from soils. The driving force that causes removing contaminants from soils (electroosmosis process or electromigration process) is voltage gradient. Therefore, the electric field distribution throughout the soil domain is extremely important to investigate and to determine the factors that help to establish a uniform electric field distribution in order to make the clean-up process work properly and efficiently. In this study, small-sized passive electrodes (made of graphite) were placed at predetermined locations within the soil specimen, and the voltage drop between these passive electrodes was measured in order to observe the electrical distribution throughout the tested soil specimens. The electrokinetic test was conducted on two types of soils; a sandy soil and a clayey soil. The electrical distribution throughout the soil domain was conducted with different tests properties; and the electrical field distribution was observed in three-dimensional pattern in order to establish the electrical distribution within the soil domain. The effects of density, applied voltages, and degree of saturation on the electrical distribution within the remediated soil were investigated. The distribution of the moisture content, concentration of the sodium ions, and the concentration of the calcium ions were determined and established in three-dimensional scheme. The study has shown that the electrical conductivity within soil domain depends on the moisture content and concentration of electrolytes present in the pore fluid. The distribution of the electrical field in the saturated soil was found not be affected by its density. The study has also shown that high voltage gradient leads to non-uniform electric field distribution within the electroremediated soil. Very importantly, it was found that even when the electric field distribution is uniform globally (i.e. between the passive electrodes), local non-uniformity could be established within the remediated soil mass. Cracks or air gaps formed due to temperature rise (because of electric flow in low conductivity regions) promotes electrical tortuosity. Thus, fracturing or cracking formed in the remediated soil mass causes disconnection of electric current and hence, no removal of contaminant occur within these areas.

Keywords: contaminant removal, electrical tortuousity, electromigration, electroosmosis, voltage distribution

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: Farej Ahmed Emhmmed

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Fracture of duplex stainless steels (DSS) was investigated in air and in 3.5 wt % NaCl solution. Tow sets of fatigued specimens were heat treated at 475ºC for different times and pulled to failure either in air or after kept in 3.5% NaCl with polarization of -900 mV/ SCE. Fracture took place in general by ferrite cleavage and austenite ductile fracture in transgranular mode. Specimens measured stiffness (Ms) was affected by the aging time, with higher values measured for specimens aged for longer times. Microstructural features played a role in "blocking" the crack propagation process leading to lower the CTOD values specially for specimens aged for short times. Unbroken ligaments/ austenite were observed at the crack wake. These features may exerted a bridging stress, blocking effect, at the crack tip giving resistance to the crack propagation process i.e the crack mouth opening was reduced. Higher stress intensity factor Kıc values were observed with increased amounts of crack growth suggesting longer zone of unbroken ligaments in the crack wake. The bridging zone was typically several mm in length. Attempt to model the bridge stress was suggested to understand the role of ligaments/unbroken austenite in increasing the fracture toughness factor.

Keywords: stainless steels, fracture toughness, crack keying effect, ligaments

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Authors: Lior Davidovitch

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Traditional training based on static models and case studies is the backbone of most teaching and training programs of engineering education. However, project management learning is characterized by dynamics models that requires new and enhanced learning method. The results of empirical experiments evaluating the effectiveness and efficiency of using cognitive simulator as a new training technique are reported. The empirical findings are focused on the impact of keeping and reviewing learning history in a dynamic and interactive simulation environment of engineering education. The cognitive simulator for engineering project management learning had two learning history keeping modes: manual (student-controlled), automatic (simulator-controlled) and a version with no history keeping. A group of industrial engineering students performed four simulation-runs divided into three identical simple scenarios and one complicated scenario. The performances of participants running the simulation with the manual history mode were significantly better than users running the simulation with the automatic history mode. Moreover, the effects of using the undo enhanced further the learning process. The findings indicate an enhancement of engineering students’ learning and decision making when they use the record functionality of the history during their engineering training process. Furthermore, the cognitive simulator as educational innovation improves students learning and training. The practical implications of using simulators in the field of engineering education are discussed.

Keywords: cognitive simulator, decision making, engineering learning, project management

Procedia PDF Downloads 236

Authors: Nataša Todorović, Jovana Nikolov

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Despite the great interest in β⁻β⁻ decay, β⁺β⁺ decays are rarely investigated due to the low probability of detecting these processes with available low-level equipment. If β⁺β⁺, β⁺EC, or ECEC decay occurs in a thin sample of a material, the positrons will be stopped and annihilated inside the material, leading to the emission of two or four coincidence gamma photons energy of 511 keV. The paper presents the results of measurements of double beta decay of ⁶⁴Zn, ⁵⁰Cr, and ⁵⁴Fe isotopes. In the first experiment, 511-keV gamma rays originating from the annihilation of positrons in natural zinc were measured by a coincidence technique to obtain a non-zero value for the (0ν+2ν) half-life. In the second experiment, the result of measuring double beta decay of ⁵⁰Cr is presented, which suggests a result other than zero at 95% CL and gives the lowest limit for the half-life of this process. In the third experiment, neutrino-less ECEC decay of ⁵⁴Fe was examined. Under the decay theory, gamma rays are emitted whose energy does not coincide with the energies of gamma rays emitted by nuclei from known discrete excited states. Iron shield of an internal volume of 1 m³ and thickness of 25 cm served as a source for measuring the (0ν+2ν) process in ⁵⁴Fe, whose yield in natural iron is 5.4%. We obtain the lower limit for the half-life for ⁵⁴Fe: T(0ν, K, K)>4.4x10²⁰ yr, T(0ν, K, L)>4.1x10²⁰ yr, and T(0ν, L, L)>5.0x10²⁰ yr. For ⁵⁰Cr limit for the half-life is T(0ν+2ν)>1.3(6)x10¹⁸ yr, and for ⁶⁴Zn T(0ν+2ν, ECβ+)=1.1(0.9)x10⁹ years.

Keywords: neutrinoless double beta decay, half-life, ⁶⁴Zn, ⁵⁰Cr, and, ⁵⁴Fe

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Authors: Ambali Alade Odebowale, Andargachew Mekonnen Berhe, Haroldo T. Hattori, Andrey E. Miroshnichenko

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Solar thermophotovoltaic systems (STPV) have emerged as a promising solution to overcome the Shockley-Queisser limit, a significant impediment in the direct conversion of solar radiation into electricity using conventional solar cells. The STPV system comprises essential components such as an optical concentrator, selective emitter, and a thermophotovoltaic (TPV) cell. The pivotal element in achieving high efficiency in an STPV system lies in the design of a spectrally selective emitter or absorber. Traditional methods for designing and optimizing selective emitters are often time-consuming and may not yield highly selective emitters, posing a challenge to the overall system performance. In recent years, the application of machine learning techniques in various scientific disciplines has demonstrated significant advantages. This paper proposes a novel nanostructure composed of four-layered materials (SiC/W/SiO2/W) to function as a selective emitter in the energy conversion process of an STPV system. Unlike conventional approaches widely adopted by researchers, this study employs a machine learning-based approach for the design and optimization of the selective emitter. Specifically, a random forest algorithm (RFA) is employed for the design of the selective emitter, while the optimization process is executed using genetic algorithms. This innovative methodology holds promise in addressing the challenges posed by traditional methods, offering a more efficient and streamlined approach to selective emitter design. The utilization of a machine learning approach brings several advantages to the design and optimization of a selective emitter within the STPV system. Machine learning algorithms, such as the random forest algorithm, have the capability to analyze complex datasets and identify intricate patterns that may not be apparent through traditional methods. This allows for a more comprehensive exploration of the design space, potentially leading to highly efficient emitter configurations. Moreover, the application of genetic algorithms in the optimization process enhances the adaptability and efficiency of the overall system. Genetic algorithms mimic the principles of natural selection, enabling the exploration of a diverse range of emitter configurations and facilitating the identification of optimal solutions. This not only accelerates the design and optimization process but also increases the likelihood of discovering configurations that exhibit superior performance compared to traditional methods. In conclusion, the integration of machine learning techniques in the design and optimization of a selective emitter for solar thermophotovoltaic systems represents a groundbreaking approach. This innovative methodology not only addresses the limitations of traditional methods but also holds the potential to significantly improve the overall performance of STPV systems, paving the way for enhanced solar energy conversion efficiency.

Keywords: emitter, genetic algorithm, radiation, random forest, thermophotovoltaic

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Authors: Aymen Sekhri, Emmanuel Kwabena Frimpong, Bolaji Mubarak Ayeyemi, Aleksi Hirvonen, Matias Hirvonen, Tedros Tesfay Andemichael

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Fixation identification, which involves isolating and identifying fixations and saccades in eye-tracking protocols, is an important aspect of eye-movement data processing that can have a big impact on higher-level analyses. However, fixation identification techniques are frequently discussed informally and rarely compared in any meaningful way. With two state-of-the-art algorithms, we will implement fixation detection and analysis in this work. The velocity threshold fixation algorithm is the first algorithm, and it identifies fixation based on a threshold value. For eye movement detection, the second approach is U'n' Eye, a deep neural network algorithm. The goal of this project is to analyze and visualize eye-tracking data from an eye gaze dataset that has been provided. The data was collected in a scenario in which individuals were shown photos and asked whether or not they recognized them. The results of the two-fixation detection approach are contrasted and visualized in this paper.

Keywords: human-computer interaction, eye-tracking, CNN, fixations, saccades

Procedia PDF Downloads 118

Authors: F. Saptouw

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This paper will examine the digitization process of the |Xam and !Kun notebooks, authored by Lucy Lloyd, Dorothea Bleek and Wilhelm Bleek, and their collaborators |a!kunta, ||kabbo, ≠kasin, Dia!kwain, !kweiten ta ||ken, |han≠kass'o, !nanni, Tamme, |uma, and Da during the 19th century. Detail will be provided about the status of the archive, the creation of the digital archive and selected research projects linked to the archive. The Digital Bleek and Lloyd project is an example of institutional collaboration by the University of Cape Town, University of South Africa, Iziko South African Museum, the National Library of South Africa and the Western Cape Provincial Archives and Records Service. The contemporary value of the archive will be discussed in relation to its current manifestation as a collection of archival and digital objects, each with its own set of properties and archival risk factors. This tension between the two ways to access the archive will be interrogated to shed light on the slippages between the digital object and the archival object. The primary argument is that the process of digitization generates an ontological shift in the status of the archival object. The secondary argument is an engagement with practices to curate the encounters with these ontologically shifted objects and how to relate to each as a contemporary viewer. In conclusion this paper will argue for regarding these archival objects according to the interpretive framework utilized to engage secular relics.

Keywords: archive, curatorship, digitization, museum practice

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Authors: Sallama Resen, Celal Öztürk

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Distance estimation between tow nodes has wide scope of surveillance and tracking applications. This paper suggests a Bluetooth Low Energy (BLE) technology as a media for transceiver and receiver signal in small indoor areas. As an example, BLE communication technologies used in child safety domains. Local network is designed to detect child position in indoor school area consisting Mobile Beacons (MB), Access Points (AP) and Smart Phones (SP) where MBs stuck in children’s shoes as wearable sensors. This paper presents a technique that can detect mobile beacons’ position and help finding children’s location within dynamic environment. By means of bluetooth beacons that are attached to child’s shoes, the distance between the MB and teachers SP is estimated with an accuracy of less than one meter. From the simulation results, it is shown that high accuracy of position coordinates are achieved for multi-mobile beacons in different environments.

Keywords: bluetooth low energy, child safety, mobile beacons, received signal strength

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Authors: Meet Patel, Darshan Patel, Nilay Shah

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Excess growth of wind-based renewable energy sources is required to identify the optimal location and damping capacity of doubly fed induction-generator-based (DFIG) wind farms while it penetrates into the transmission network. In this analysis, various ratings of DFIG wind farms are penetrated into the Single Machine Infinite Bus (SMIB ) at a different distance of the transmission line. On the basis of detailed examinations, a prime position is evaluated to maximize the stability of overall systems. A damping controller is designed at an optimum location to mitigate the small oscillations. The proposed model was validated using eigenvalue analysis, calculation of the participation factor, and time-domain simulation.

Keywords: DFIG, small signal stability, eigenvalues, time domain simulation

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Authors: Chowduru Ramachandra Sharma, Shatunjay Rawat

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A false positive state is when the IDS/IPS identifies an activity as an attack, but the activity is acceptable behavior in the system. False positives in a Network Intrusion Detection System ( NIDS ) is an issue because they desensitize the administrator. It wastes computational power and valuable resources when rules are not tuned properly, which is the main issue with anomaly NIDS. Furthermore, most false positives reduction techniques are not performed during the real-time of attempted intrusions; instead, they have applied afterward on collected traffic data and generate alerts. Of course, false positives detection in ‘offline mode’ is tremendously valuable. Nevertheless, there is room for improvement here; automated techniques still need to reduce False Positives in real-time. This paper uses the Snort signature detection model to redirect the alerted attacks to Honeypots and verify attacks.

Keywords: honeypot, TPOT, snort, NIDS, honeybird, iptables, netfilter, redirection, attack detection, docker, snare, tanner

Procedia PDF Downloads 140

Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

Procedia PDF Downloads 218

Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

Procedia PDF Downloads 511

Authors: Corentin Jankowiak, Aggelos Zacharopoulos, Caterina Brandoni

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In a context of increasing stress put on the electricity network by the decarbonization of many sectors, energy storage is likely to be the key mitigating element, by acting as a buffer between production and demand. In particular, the highest potential for storage is when connected closer to the loads. Yet, low voltage storage struggles to penetrate the market at a large scale due to the novelty and complexity of the solution, and the competitive advantage of fossil fuel-based technologies regarding regulations. Strong and reliable numerical simulations are required to show the benefits of storage located near loads and promote its development. The present study was restrained from excluding aggregated control of storage: it is assumed that the storage units operate independently to one another without exchanging information – as is currently mostly the case. A computationally light battery model is presented in detail and validated by direct comparison with a domestic battery operating in real conditions. This model is then used to develop Peak-Shaving (PS) control strategies as it is the decentralized service from which beneficial impacts are most likely to emerge. The aggregation of flatter, peak- shaved consumption profiles is likely to lead to flatter and arbitraged profile at higher voltage layers. Furthermore, voltage fluctuations can be expected to decrease if spikes of individual consumption are reduced. The crucial part to achieve PS lies in the charging pattern: peaks depend on the switching on and off of appliances in the dwelling by the occupants and are therefore impossible to predict accurately. A performant PS strategy must, therefore, include a smart charge recovery algorithm that can ensure enough energy is present in the battery in case it is needed without generating new peaks by charging the unit. Three categories of PS algorithms are introduced in detail. First, using a constant threshold or power rate for charge recovery, followed by algorithms using the State Of Charge (SOC) as a decision variable. Finally, using a load forecast – of which the impact of the accuracy is discussed – to generate PS. A performance metrics was defined in order to quantitatively evaluate their operating regarding peak reduction, total energy consumption, and self-consumption of domestic photovoltaic generation. The algorithms were tested on load profiles with a 1-minute granularity over a 1-year period, and their performance was assessed regarding these metrics. The results show that constant charging threshold or power are far from optimal: a certain value is not likely to fit the variability of a residential profile. As could be expected, forecast-based algorithms show the highest performance. However, these depend on the accuracy of the forecast. On the other hand, SOC based algorithms also present satisfying performance, making them a strong alternative when the reliable forecast is not available.

Keywords: decentralised control, domestic integrated batteries, electricity network performance, peak-shaving algorithm

Procedia PDF Downloads 104

Authors: Natalia Hordynska, Magdalena Szechynska-Hebda, Miroslaw Sobczak, Elzbieta Rozanska, Joanna Troczynska, Zofia Banaszak, Maria Wedzony

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Albinism is a common problem encountered in wheat and triticale breeding programs, which require in vitro culture steps e.g. generation of doubled haploids via androgenesis process. Genetic factor is a major determinant of albinism, however, environmental conditions such as temperature and media composition influence the frequency of albino plant formation. Cold incubation of wheat and triticale spikes induced a switch from gametophytic to sporophytic development. Further, androgenic structures formed from anthers of the genotypes susceptible to androgenesis or treated with cold stress, had a pool of structurally primitive plastids, with small starch granules or swollen thylakoids. High temperature was a factor inducing andro-genesis of wheat and triticale, but at the same time, it was a factor favoring the formation of albino plants. In genotypes susceptible to albinism or after heat stress conditions, cells formed from anthers were vacuolated, and plastids were eliminated. Partial or complete loss of chlorophyll pigments and incomplete differentiation of chloroplast membranes result in formation of tissues or whole plant unable to perform photosynthesis. Indeed, susceptibility to the andro-genesis process was associated with an increase of total concentration of photosynthetic pigments in anthers, spikes and regenerated plants. The proper balance of the synthesis of various pigments, was the starting point for their proper incorporation into photosynthetic membranes. In contrast, genotypes resistant to the androgenesis process and those treated with heat, contained 100 times lower content of photosynthetic pigments. In particular, the synthesis of violaxanthin, zeaxanthin, lutein and chlorophyll b was limited. Furthermore, deregulation of starch and lipids synthesis, which led to the formation of very complex starch granules and an increased number of oleosomes, respectively, correlated with the reduction of the efficiency of androgenesis. The content of other sugars varied depending on the genotype and the type of stress. The highest content of various sugars was found for genotypes susceptible to andro-genesis, and highly reduced for genotypes resistant to androgenesis. The most important sugars seem to be glucose and fructose. They are involved in sugar sensing and signaling pathways, which affect the expression of various genes and regulate plant development. Sucrose, on the other hand, seems to have minor effect at each stage of the androgenesis. The sugar metabolism was related to metabolic activity of microspores. The genotypes susceptible to androgenesis process had much faster mitochondrium- and chloroplast-dependent energy conversion and higher heat production by tissues. Thus, the effectiveness of metabolic processes, their balance and the flexibility under the stress was a factor determining the direction of microspore development, and in the later stages of the androgenesis process, a factor supporting the induction of androgenic structures, chloroplast formation and the regeneration of green plants. The work was financed by Ministry of Agriculture and Rural Development within Program: ‘Biological Progress in Plant Production’, project no HOR.hn.802.15.2018.

Keywords: androgenesis, chloroplast, metabolism, temperature stress

Procedia PDF Downloads 250

Authors: Carlette Groome

Abstract:

This paper examined the role of drama in the lives of four women. The researcher concluded that drama can be an avenue of healing and could be an effective means of social work intervention in the communities as well as female empowerment. The participants in the study were able to, through the dramatic process; re-write their life’s scripts by resolving paradoxes and conflicts related to the themes unearthed. The research conducted examined the role of drama in the lives of four women living in volatile communities in Jamaica, who were each exposed to violence in one, or multiple, forms. The women were trained by Sistren Theatre Collective in the use of drama for education (edutainment), and were actresses in Sistren's street theatre drama group. Using their own personal and collective experiences, they used drama to raise social consciousness at the community level, about violence and other issues affecting women. The study employed a narrative case study approach and was grounded in a constructivist paradigm. This paradigm was coupled with a basic interpretive qualitative method and the concept of the reflective practitioner provided the foundation for the analysis. Through individual conversations with the women, themes of abuse, resilience, self- esteem, and empowerment arose sharply. The women explored drama and understood it to be instrumental in healing different aspects of their lives. Also, through the dramatic process; they were able to re-write their life’s scripts by resolving paradoxes and conflicts related to the themes unearthed.

Keywords: women, drama, healing, community

Procedia PDF Downloads 329