Search results for: active distribution network (ADN)

Authors: Sema Kayapinar, Ismail Karaoglan, Turan Paksoy, Hadi Gokcen

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Green Supply Chain Management is gaining growing interest among researchers and supply chain management. The concept of Green Supply Chain Management is to integrate environmental thinking into the Supply Chain Management. It is the systematic concept emphasis on environmental problems such as reduction of greenhouse gas emissions, energy efficiency, recycling end of life products, generation of solid and hazardous waste. This study is to present a green supply chain network model integrated Internet of Things applications. Internet of Things provides to get precise and accurate information of end-of-life product with sensors and systems devices. The forward direction consists of suppliers, plants, distributions centres and sales and collect centres while, the reverse flow includes the sales and collects centres, disassembled centre, recycling and disposal centre. The sales and collection centre sells the new products are transhipped from factory via distribution centre and also receive the end-of life product according their value level. We describe green logistics activities by presenting specific examples including “recycling of the returned products and “reduction of CO2 gas emissions”. The different transportation choices are illustrated between echelons according to their CO2 gas emissions. This problem is formulated as a mixed integer linear programming model to solve the green supply chain problems which are emerged from the environmental awareness and responsibilities. This model is solved by using Gams package program. Numerical examples are suggested to illustrate the efficiency of the proposed model.

Keywords: green supply chain optimization, internet of things, greenhouse gas emission, recycling

Procedia PDF Downloads 319

Authors: Xuan Zhao, Wen Jiang

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In this study, a web crawler was used to collect POI sample data from 38 districts and counties of Chongqing in 2022, and ArcGIS was combined to coordinate and projection conversion and realize data visualization. Nuclear density analysis and spatial correlation analysis were used to explore the spatial distribution characteristics of elderly care facilities in Chongqing, and K mean cluster analysis was carried out with GeoDa to study the spatial concentration degree of elderly care resources in 38 districts and counties. Finally, the driving force of spatial differentiation of elderly care facilities in various districts and counties of Chongqing is studied by using the method of geographic detector. The results show that: (1) in terms of spatial distribution structure, the distribution of elderly care facilities in Chongqing is unbalanced, showing a distribution pattern of ‘large dispersion and small agglomeration’ and the asymmetric pattern of ‘west dense and east sparse, north dense and south sparse’ is prominent. (2) In terms of the spatial matching between elderly care resources and the elderly population, there is a weak coordination between the input of elderly care resources and the distribution of the elderly population at the county level in Chongqing. (3) The analysis of the results of the geographical detector shows that the single factor influence is mainly the number of elderly population, public financial revenue and district and county GDP. The high single factor influence is mainly caused by the elderly population, public financial income, and district and county GDP. The influence of each influence factor on the spatial distribution of elderly care facilities is not simply superimposed but has a nonlinear enhancement effect or double factor enhancement. It is necessary to strengthen the synergistic effect of two factors and promote the synergistic effect of multiple factors.

Keywords: aging, elderly care facilities, spatial differentiation, geographical detector, driving force analysis, Mountain city

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Authors: Radha Tomar, Satish K. Jain, Manish Panchal, P. S. Rathore

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In the presented work, inset fed microstrip patch antenna (IFMPA) based two port MIMO Antenna system has been proposed, which is suitable for wireless local area network (WLAN) applications. IFMPA has been designed, optimized for 2.4 GHz and applied for MIMO formation. The optimized parameters of the proposed IFMPA have been used for fabrication of antenna and two port MIMO in a laboratory. Fabrication of the designed MIMO antenna has been done and tested experimentally for performance parameters like Envelope Correlation Coefficient (ECC), Mean Effective Gain (MEG), Directive Gain (DG), Channel Capacity Loss (CCL), Multiplexing Efficiency (ME) etc and results are compared with simulated parameters extracted with simulated S parameters to validate the results. The simulated and experimentally measured plots and numerical values of these MIMO performance parameters resembles very much with each other. This shows the success of MIMO antenna design methodology.

Keywords: multiple-input and multiple-output, wireless local area network, vector network analyzer, envelope correlation coefficient

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Authors: Jung Lee

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The last decade has witnessed more people than ever before using social media and broadening their social circles. Social media users connect not only with their friends but also with professional acquaintances, primarily coworkers, and clients; personal and professional social circles are mixed within the same social media platform. Considering the positive aspect of social media in facilitating communication and mutual understanding between individuals, we infer that social media interactions with co-workers could indeed benefit one’s professional life. However, given privacy issues, sharing all personal details with one’s co-workers is not necessarily the best practice. Should one connect with coworkers via social media? Will social media connections with coworkers eventually benefit one’s long-term career? Will the benefit differ across cultures? To answer, this study examines how social media can contribute to organizational communication by tracing the foundation of user motivation based on social capital theory, leader-member exchange (LMX) theory and expectancy theory of motivation. Although social media was originally designed for personal communication, users have shown intentions to extend social media use for professional communication, especially when the proper incentive is expected. To articulate the user motivation and the mechanism of the incentive expectation scheme, this study applies those three theories and identify six antecedents and three moderators of social media use motivation including social network flaunt, shared interest, perceived social inclusion. It also hypothesizes that the moderating effects of those constructs would significantly differ based on the relationship hierarchy among the workers. To validate, this study conducted a survey of 329 active social media users with acceptable levels of job experiences. The analysis result confirms the specific roles of the three moderators in social media adoption for organizational communication. The present study contributes to the literature by developing a theoretical modeling of ambivalent employee perceptions about establishing social media connections with co-workers. This framework shows not only how both positive and negative expectations of social media connections with co-workers are formed based on expectancy theory of motivation, but also how such expectations lead to behavioral intentions using career success model. It also enhances understanding of how various relationships among employees can be influenced through social media use and such usage can potentially affect both performance and careers. Finally, it shows how cultural factors induced by social media use can influence relations among the coworkers.

Keywords: the social network, workplace, social capital, motivation

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Anna Ouskova Leonteva, Michel Risser, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

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This paper proposes an efficient optimization tool for an active magnetic regenerator (AMR) model, operating in two modes: magnetic refrigeration system (MRS) and thermo-magnetic generator (TMG). The aim of this optimizer is to improve the design of the AMR by applying a multi-physics multi-scales numerical model as a core of evaluation functions to achieve industrial requirements for refrigeration and energy conservation systems. Based on the multi-objective non-dominated sorting genetic algorithm 3 (NSGA3), it maximizes four different objectives: efficiency and power density for MRS and TMG. The main contribution of this work is in the simultaneously application of a CPU-parallel NSGA3 version to the AMR model in both modes for studying impact of control and design parameters on the performance. The parametric study of the optimization results are presented. The main conclusion is that the common (for TMG and MRS modes) optimal parameters can be found by the proposed tool.

Keywords: ecological refrigeration systems, active magnetic regenerator, thermo-magnetic generator, multi-objective evolutionary optimization, industrial optimization problem, real-world application

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Authors: Badar Muneer, Waseem Shabir, Faisal Karim Shaikh

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Substrate Integrated waveguide based 6-element array of Planar Inverted F antenna (PIFA) has been presented and analyzed parametrically in this paper. The antenna is fed with coupled transverse slots on a plate laminated waveguide cavity to ensure wide bandwidth and simplicity of feeding network. The two-layer structure has one layer dedicated for feeding network and the top layer dedicated for radiating elements. It has been demonstrated that the presented feeding technique for feeding such class of array antennas can be far simple in structure and miniaturized in size when it comes to designing large phased array antenna systems. A good return loss and standing wave ratio of 2:1 has been achieved while maintaining properties of typical PIFA.

Keywords: feeding network, laminated waveguide, PIFA, transverse slots

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Authors: Li Hui, Riyadh Hindi

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Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.

Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network

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Authors: Navneet Kaur, S. D. Tiwari

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Magnetic nanoparticles possess fascinating properties which make their behavior unique in comparison to corresponding bulk materials. Superparamagnetism is one such interesting phenomenon exhibited only by small particles of magnetic materials. In this state, the thermal energy of particles become more than their magnetic anisotropy energy, and so particle magnetic moment vectors fluctuate between states of minimum energy. This situation is similar to paramagnetism of non-interacting ions and termed as superparamagnetism. The magnetization of such systems has been described by Langevin function. But, the estimated fit parameters, in this case, are found to be unphysical. It is due to non-consideration of particle size distribution. In this work, analysis of magnetization data on NiO nanoparticles is presented considering the effect of particle size distribution. Nanoparticles of NiO of two different sizes are prepared by heating freshly synthesized Ni(OH)₂ at different temperatures. Room temperature X-ray diffraction patterns confirm the formation of single phase of NiO. The diffraction lines are seen to be quite broad indicating the nanocrystalline nature of the samples. The average crystallite size are estimated to be about 6 and 8 nm. The samples are also characterized by transmission electron microscope. Magnetization of both sample is measured as function of temperature and applied magnetic field. Zero field cooled and field cooled magnetization are measured as a function of temperature to determine the bifurcation temperature. The magnetization is also measured at several temperatures in superparamagnetic region. The data are fitted to an appropriate expression considering a distribution in particle size following a least square fit procedure. The computer codes are written in PYTHON. The presented analysis is found to be very useful for estimating the particle size distribution present in the samples. The estimated distributions are compared with those determined from transmission electron micrographs.

Keywords: anisotropy, magnetization, nanoparticles, superparamagnetism

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Authors: Baeza S. Roberto

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The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

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Authors: Bashir Abdu Muzakkari, Aisha Umar Sulaiman, Mohamed Afendee Muhamad, Sanah Abdullahi Muaz

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The field of the Internet of Things (IoT) is rapidly expanding and has the potential to completely change how we work, live, and interact with the world. The Internet of Things (IoT) is the term used to describe a network of networked physical objects, including machinery, vehicles, and buildings, which are equipped with electronics, software, sensors, and network connectivity. This review paper aims to provide a comprehensive overview of the current state of IoT, including its definition, key components, development history, and current applications. The paper will also discuss the challenges and opportunities presented by IoT, as well as its potential impact on various industries, such as healthcare, agriculture, and transportation. In addition, this paper will highlight the ethical and security concerns associated with IoT and the need for effective solutions to address these challenges. The paper concludes by highlighting the prospects of IoT and the directions for future research in this field.

Keywords: internet of things, IoT, sensors, network

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Authors: H. Hajimolahoseini, J. Hashemi, D. Redfearn

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Background: Catheter ablation is an effective therapy for symptomatic atrial fibrillation (AF). The intracardiac electrocardiogram (IEGM) collected during this procedure contains precious information that has not been explored to its full capacity. Novel processing techniques allow looking at these recordings from different perspectives which can lead to improved therapeutic approaches. In our previous study, we showed that variation in amplitude measured through Shannon Entropy could be used as an AF recurrence risk stratification factor in patients who received Ibutilide before the electrograms were recorded. The aim of this study is to further investigate the effect of Ibutilide on characteristics of the recorded signals from the left atrium (LA) of a patient with persistent AF before and after administration of the drug. Methods: The IEGMs collected from different intra-atrial sites of 12 patients were studied and compared before and after Ibutilide administration. First, the before and after Ibutilide IEGMs that were recorded within a Euclidian distance of 3 mm in LA were selected as pairs for comparison. For every selected pair of IEGMs, the Probability Distribution Function (PDF) of the amplitude in time domain and magnitude in frequency domain was estimated using the regression analysis. The PDF represents the relative likelihood of a variable falling within a specific range of values. Results: Our observations showed that in time domain, the PDF of amplitudes was fitted to a Gaussian distribution while in frequency domain, it was fitted to a Rayleigh distribution. Our observations also revealed that after Ibutilide administration, the IEGMs would have significantly narrower short-tailed PDFs both in time and frequency domains. Conclusion: This study shows that the PDFs of the IEGMs before and after administration of Ibutilide represents significantly different properties, both in time and frequency domains. Hence, by fitting the PDF of IEGMs in time domain to a Gaussian distribution or in frequency domain to a Rayleigh distribution, the effect of Ibutilide can easily be tracked using the statistics of their PDF (e.g., standard deviation) while this is difficult through the waveform of IEGMs itself.

Keywords: atrial fibrillation, catheter ablation, probability distribution function, time-frequency characteristics

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Authors: C. Villamizar, M. Serrato

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In the pursuit of athletic performance, the role of physical training which is determined by a number of charges or taxes on physiological stress and musculoskeletal systems of the human body generated by the intensity and duration is fundamental. Given the physical demands of these activities both training and competitive must take into account the optimal relationship with a straining process recovery post favoring the process of overcompensation which aims to facilitate the return and rising energy potential and protein synthesis also of different tissues. Allowing muscle function returns to baseline or pre-exercise states. If this recovery process is not performed or is not allowed in a proper way, will result in an increased state of fatigue. Active recovery, is one of the strategies implemented in the sport for a return to pre-exercise physiological states. However, there are some adverse assumptions regarding the negative effects, as is the possibility of increasing the degradation of muscle glycogen and thus delaying the synthesis thereof. For them, it is necessary to investigate what would be the effects generated application made at different times after the effort. The aim of this study was to determine the effects of active recovery post effort made at three different times: immediately, at 12 and 24 hours on biochemical markers creatine kinase in youth soccer player’s categories. A randomized controlled trial with allocation to three groups was performed: A. active recovery immediately after the effort; B. active recovery performed at 12 hours after the effort; C. active recovery made at 24 hours after the effort. This study included 27 subjects belonging to a Colombian soccer team of the second division. Vital signs, weight, height, BMI, the percentage of muscle mass, fat mass percentage, personal medical history, and family were valued. The velocity, explosive force and Creatin Kinase (CK) in blood were tested before and after interventions. SAFT 90 protocol (Soccer Field specific Aerobic Test) was applied to participants for generating fatigue. CK samples were taken one hour before the application of the fatigue test, one hour after the fatigue protocol and 48 of the initial CK sample. Mean age was 18.5 ± 1.1 years old. Improvements in jumping and speed recovery the 3 groups (p < 0.05), but no statistically significant differences between groups was observed after recuperation. In all participants, there was a significant increment of CK when applied SAFT 90 in all the groups (median 103.1-111.1). The CK measurement after 48 hours reflects a recovery in all groups, however the group C, a decline below baseline levels of -55.5 (-96.3 /-20.4) which is a significant find. Other research has shown that CK does not return quickly to their baseline, but our study shows that active recovery favors the clearance of CK and also to perform recovery 24 hours after the effort generates higher clearance of this biomarker.

Keywords: active recuperation, creatine phosphokinase, post training, young soccer players

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Authors: Ilaisa Kacivakanadina, Samson Viulu, Brad Carte, Katy Soapi

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Two bioactive compounds namely Vulgamycin (also known as enterocin A) and 5-deoxyenterocin were purified from a marine bacterial strain 1903. Strain 1903 was isolated from marine sediments collected from the Solomon Islands. Morphological features of strain 1903 showed that it belongs to the genus Streptomyces. The two secondary metabolites were extracted using EtOAc and purified by chromatographic methods using EtOAc and hexane solvents. Mass spectrum and NMR data of pure compounds were used to elucidate the chemical structures. In this study, results showed that both compounds were strongly active against Wild Type Staphylococcus aureus (WTSA) (MIC < 1 µg/mL) and in Brine shrimp assays (BSA) (MIC < 1 µg/mL). 5-deoxyenterocin was also active against Rifamycin resistant Staphylococcus aureus (RRSA) (MIC, 250 µg/mL) while vulgamycin showed bioactivity against Methicillin resistant Staphylococcus aureus (MRSA) (MIC 250 µg/mL). To the best of our knowledge, this is the first study that showed the bio-activity of 5-deoxyenterocin. This is also the first time that Vulgamycin has been reported to be active in a BSA. There has not been any mechanism of action studies for these two compounds against pathogens. This warrants further studies on their mechanism of action against microbial pathogens.

Keywords: 5-deoxyenterocin, bioactivity, brine shrimp assay (BSA), vulgamycin

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Authors: Balasaheb Bansode, L. Ladusingh

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The public distribution system is an important component of food security. It is a massive welfare program undertaken by Government of India and implemented by state government since India being a federal state; for achieving multiple objectives like eliminating hunger, reduction in malnutrition and making food consumption affordable. This program reaches at the community level through the various agencies of the government. The paper focuses on the accessibility of PDS at household level and how the present policy framework results in exclusion and inclusion errors. It tries to explore the sanctioned food grain quantity received by differentiated ration cards according to income criterion at household level, and also it has highlighted on the type of corruption in food distribution that is generated by the PDS system. The data used is of secondary nature from NSSO 68 round conducted in 2012. Bivariate and multivariate techniques have been used to understand the working and consumption of food for this paper.

Keywords: calories intake, entitle food quantity, poverty aliviation through PDS, target error

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Authors: Matej Komanec, Jan Bohata, Stanislav Zvanovec, Tomas Nemecek, Jan Broucek, Josef Beran

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With the exponential growth of social networks, video streaming and increasing demands on data rates, the number of newly built data centers rises proportionately. The data centers, however, have to adjust to the rapidly increased amount of data that has to be processed. For this purpose, multi-mode (MM) fiber based infrastructures are often employed. It stems from the fact, the connections in data centers are typically realized within a short distance, and the application of MM fibers and components considerably reduces costs. On the other hand, the usage of MM components brings specific requirements for installation service conditions. Moreover, it has to be taken into account that MM fiber components have a higher production tolerance for parameters like core and cladding diameters, eccentricity, etc. Due to the high demands for the reliability of data center components, the determination of properly excited optical field inside the MM fiber core belongs to the key parameters while designing such an MM optical system architecture. Appropriately excited mode field of the MM fiber provides optimal power budget in connections, leads to the decrease of insertion losses (IL) and achieves effective modal bandwidth (EMB). The main parameter, in this case, is the encircled flux (EF), which should be properly defined for variable optical sources and consequent different mode-field distribution. In this paper, we present detailed investigation and measurements of the mode field distribution for short MM links purposed in particular for data centers with the emphasis on reliability and safety. These measurements are essential for large MM network design. The various scenarios, containing different fibers and connectors, were tested in terms of IL and mode-field distribution to reveal potential challenges. Furthermore, we focused on estimation of particular defects and errors, which can realistically occur like eccentricity, connector shifting or dust, were simulated and measured, and their dependence to EF statistics and functionality of data center infrastructure was evaluated. The experimental tests were performed at two wavelengths, commonly used in MM networks, of 850 nm and 1310 nm to verify EF statistics. Finally, we provide recommendations for data center systems and networks, using OM3 and OM4 MM fiber connections.

Keywords: optical fiber, multi-mode, data centers, encircled flux

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Authors: Serge Provost, Yishan Zang

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Copulas are currently utilized in finance, reliability theory, machine learning, signal processing, geodesy, hydrology and biostatistics, among several other fields of scientific investigation. It follows from Sklar's theorem that the joint distribution function of a multidimensional random vector can be expressed in terms of its associated copula and marginals. Since marginal distributions can easily be determined by making use of a variety of techniques, we address the problem of securing the distribution of the copula. This will be done by using several approaches. For example, we will obtain bivariate least-squares approximations of the empirical copulas, modify the kernel density estimation technique and propose a criterion for selecting appropriate bandwidths, differentiate linearized empirical copulas, secure Bernstein polynomial approximations of suitable degrees, and apply a corollary to Sklar's result. Illustrative examples involving actual observations will be presented. The proposed methodologies will as well be applied to a sample generated from a known copula distribution in order to validate their effectiveness.

Keywords: copulas, Bernstein polynomial approximation, least-squares polynomial approximation, kernel density estimation, density approximation

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Authors: Yuchao Hua, Lingai Luo

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Thermal energy storage is of critical importance for the highly-efficient utilization of renewable energy sources. Over the past decades, single-tank thermocline technology has attracted much attention owing to its high cost-effectiveness. In the present work, we investigate the influence of the filler porosity’s non-uniform distribution on the thermal performance of the packed-bed sensible heat thermocline storage tanks on the basis of the analytical model obtained by the Laplace transform. It is found that when the total amount of filler materials (i.e., the integration of porosity) is fixed, the different porosity distributions can result in the significantly-different behaviors of outlet temperature and thus the varied charging and discharging efficiencies. Our results indicate that a non-uniform distribution of the fillers with the proper design can improve the heat storage performance without changing the total amount of the filling materials.

Keywords: energy storage, heat thermocline storage tank, packed bed, transient thermal analysis

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from Mc Rae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-world, resilience to damage

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Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

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Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear modeling, NNARX

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Authors: Behrooz Daneshmand

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Software Defined Networking (SDN) is designed to meet the future needs of 5G mobile networks. The SDN architecture offers a new solution that involves separating the control plane from the data plane, which is usually paired together. Network functions traditionally performed on specific hardware can now be abstracted and virtualized on any device, and a centralized software-based administration approach is based on a central controller, facilitating the development of modern applications and services. These plan standards clear the way for a more adaptable, speedier, and more energetic network beneath computer program control compared with a conventional network. We accept SDN gives modern inquire about openings to security, and it can significantly affect network security research in numerous diverse ways. Subsequently, the SDN architecture engages systems to effectively screen activity and analyze threats to facilitate security approach modification and security benefit insertion. The segregation of the data planes and control and, be that as it may, opens security challenges, such as man-in-the-middle attacks (MIMA), denial of service (DoS) attacks, and immersion attacks. In this paper, we analyze security threats to each layer of SDN - application layer - southbound interfaces/northbound interfaces - controller layer and data layer. From a security point of see, the components that make up the SDN architecture have a few vulnerabilities, which may be abused by aggressors to perform noxious activities and hence influence the network and its administrations. Software-defined network assaults are shockingly a reality these days. In a nutshell, this paper highlights architectural weaknesses and develops attack vectors at each layer, which leads to conclusions about further progress in identifying the consequences of attacks and proposing mitigation strategies.

Keywords: software-defined networking, security, SDN, 5G/IMT-2020

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Authors: Saeed Khandan Siar, Stefan Tenbohlen, Christian Breuer, Raphael Lebreton

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The goal of this article is to investigate the detailed temperature distribution and the fluid flow of an oil cooled winding of a power transformer by means of computational fluid dynamics (CFD). The experimental setup consists of three passes of a zig-zag cooled disc type winding, in which losses are modeled by heating cartridges in each winding segment. A precise temperature sensor measures the temperature of each turn. The laboratory setup allows the exact control of the boundary conditions, e.g. the oil flow rate and the inlet temperature. Furthermore, a simulation model is solved using the open source computational fluid dynamics solver OpenFOAM and validated with the experimental results. The model utilizes the laminar and turbulent flow for the different mass flow rate of the oil. The good agreement of the simulation results with experimental measurements validates the model.

Keywords: CFD, conjugated heat transfer, power transformers, temperature distribution

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Authors: Jett Black, Jordyn Godsey, Gaby G. Dagher, Steve Cutchin

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Despite the growing interest in distributed ledger technology, many data visualizations of blockchain are limited to monotonous tabular displays or overly abstract graphical representations that fail to adequately educate individuals on blockchain components and their functionalities. To address these limitations, it is imperative to develop data visualizations that offer not only comprehensive insights into these domains but education as well. This research focuses on providing a conceptual understanding of the consensus process that underlies blockchain technology. This is accomplished through the implementation of a dynamic network visualization and an interactive educational tool called BlueVision. Further, a controlled user study is conducted to measure the effectiveness and usability of BlueVision. The findings demonstrate that the tool represents significant advancements in the field of blockchain visualization, effectively catering to the educational needs of both novice and proficient users.

Keywords: blockchain, visualization, consensus, distributed network

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Authors: Diego Aguirre, Olac Fuentes

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In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques.

Keywords: deep learning, image classification, supervised learning, weight initialization

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Authors: Omojokun Gabriel Aju

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Communication network is a process of exchanging data between two or more devices via some forms of transmission medium using communication protocols. The data could be in form of text, images, audio, video or numbers which can be grouped into FTP, Email, HTTP, VOIP or Video applications. The effectiveness of such data exchange will be proved if they are accurately delivered within specified time. While some senders will not really mind when the data is actually received by the receiving device, inasmuch as it is acknowledged to have been received by the receiver. The time a data takes to get to a receiver could be very important to another sender, as any delay could cause serious problem or even in some cases rendered the data useless. The validity or invalidity of a data after delay will therefore definitely depend on the type of data (information). It is therefore imperative for the network device (such as router) to be able to differentiate among the packets which are time sensitive and those that are not, when they are passing through the same network. So, here is where the queuing disciplines comes to play, to handle network resources when such network is designed to service widely varying types of traffics and manage the available resources according to the configured policies. Therefore, as part of the resources allocation mechanisms, a router within the network must implement some queuing discipline that governs how packets (data) are buffered while waiting to be transmitted. The implementation of the queuing discipline will regulate how the packets are buffered while waiting to be transmitted. In achieving this, various queuing disciplines are being used to control the transmission of these packets, by determining which of the packets get the highest priority, less priority and which packets are dropped. The queuing discipline will therefore control the packets latency by determining how long a packet can wait to be transmitted or dropped. The common queuing disciplines are first-in-first-out queuing, Priority queuing and Weighted-fair queuing (FIFO, PQ and WFQ). This paper critically evaluates and analyse through the use of Optimized Network Evaluation Tool (OPNET) Modeller, Version 14.5 the effects of three queuing disciplines (FIFO, PQ and WFQ) on the performance of 5 different applications (FTP, HTTP, E-Mail, Voice and Video) within specified parameters using packets sent, packets received and transmission delay as performance metrics. The paper finally suggests some ways in which networks can be designed to provide better transmission performance while using these queuing disciplines.

Keywords: applications, first-in-first-out queuing (FIFO), optimised network evaluation tool (OPNET), packets, priority queuing (PQ), queuing discipline, weighted-fair queuing (WFQ)

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

Abstract:

Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

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Authors: Mehmet Hakan Karaata

Abstract:

In this paper, we first coin a new graph theocratic problem with numerous applications. Second, we provide two algorithms for the problem. The first solution is using a brute-force techniques, whereas the second solution is based on an initial identification of the cycles in the given graph. We then provide a correctness proof of the algorithm. The applications of the problem include graph analysis, graph drawing and network structuring.

Keywords: algorithm, cycle, graph algorithm, graph theory, network structuring

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Authors: Erzen Muharemi, Emmanuel De Jaeger, Jos Knockaert

Abstract:

The work presented here investigates the modeling of power electronics converters in terms of their harmonic production. Specifically, it addresses high-frequency emissions in the range of 2-150 kHz, referred to as supraharmonics. This paper models a conventional converter, namely the boost converter used as an active power factor corrector (APFC). Furthermore, the modeling is extended to the case of the interleaved boost converter, which offers advantages such as halving the emissions. Finally, a comparison between the theoretical, numerical, and experimental results will be provided.

Keywords: APFC, boost converter, converter modeling, double fourier series, supraharmonics

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

Abstract:

The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: John K. Avor, Choong-Koo Chang

Abstract:

The transfer of electrical loads at power generation stations from Standby Auxiliary Transformer (SAT) to Unit Auxiliary Transformer (UAT) and vice versa is through a fast bus transfer scheme. Fast bus transfer is a time-critical application where the transfer process depends on various parameters, thus transfer schemes apply advance algorithms to ensure power supply reliability and continuity. In a nuclear power generation station, supply continuity is essential, especially for critical class 1E electrical loads. Bus transfers must, therefore, be executed accurately within 4 to 10 cycles in order to achieve safety system requirements. However, the main problem is that there are instances where transfer schemes scrambled due to inaccurate interpretation of key parameters; and consequently, have failed to transfer several critical loads from UAT to the SAT during main generator trip event. Although several techniques have been adopted to develop robust transfer schemes, a combination of Artificial Neural Network and Fuzzy Systems (Neuro-Fuzzy) has not been extensively used. In this paper, we apply the concept of Neuro-Fuzzy to determine plant operating mode and dynamic prediction of the appropriate bus transfer algorithm to be selected based on the first cycle of voltage information. The performance of Sequential Fast Transfer and Residual Bus Transfer schemes was evaluated through simulation and integration of the Neuro-Fuzzy system. The objective for adopting Neuro-Fuzzy approach in the bus transfer scheme is to utilize the signal validation capabilities of artificial neural network, specifically the back-propagation algorithm which is very accurate in learning completely new systems. This research presents a combined effect of artificial neural network and fuzzy systems to accurately interpret key bus transfer parameters such as magnitude of the residual voltage, decay time, and the associated phase angle of the residual voltage in order to determine the possibility of high speed bus transfer for a particular bus and the corresponding transfer algorithm. This demonstrates potential for general applicability to improve reliability of the auxiliary power distribution system. The performance of the scheme is implemented on APR1400 nuclear power plant auxiliary system.

Keywords: auxiliary power system, bus transfer scheme, fuzzy logic, neural networks, reliability

Procedia PDF Downloads 155