Search results for: Network planning
2577 Prediction of Air-Water Two-Phase Frictional Pressure Drop Using Artificial Neural Network
Authors: H. B. Mehta, Vipul M. Patel, Jyotirmay Banerjee
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The present paper discusses the prediction of gas-liquid two-phase frictional pressure drop in a 2.12 mm horizontal circular minichannel using Artificial Neural Network (ANN). The experimental results are obtained with air as gas phase and water as liquid phase. The superficial gas velocity is kept in the range of 0.0236 m/s to 0.4722 m/s while the values of 0.0944 m/s, 0.1416 m/s and 0.1889 m/s are considered for superficial liquid velocity. The experimental results are predicted using different Artificial Neural Network (ANN) models. Networks used for prediction are radial basis, generalised regression, linear layer, cascade forward back propagation, feed forward back propagation, feed forward distributed time delay, layer recurrent, and Elman back propagation. Transfer functions used for networks are Linear (PURELIN), Logistic sigmoid (LOGSIG), tangent sigmoid (TANSIG) and Gaussian RBF. Combination of networks and transfer functions give different possible neural network models. These models are compared for Mean Absolute Relative Deviation (MARD) and Mean Relative Deviation (MRD) to identify the best predictive model of ANN.
Keywords: Minichannel, Two-Phase Flow, Frictional Pressure Drop, ANN, MARD, MRD.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14042576 Role of GIS in Distribution Power Systems
Authors: N. Rezaee, M Nayeripour, A. Roosta, T. Niknam
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With the prevalence of computer and development of information technology, Geographic Information Systems (GIS) have long used for a variety of applications in electrical engineering. GIS are designed to support the analysis, management, manipulation and mapping of spatial data. This paper presents several usages of GIS in power utilities such as automated route selection for the construction of new power lines which uses a dynamic programming model for route optimization, load forecasting and optimizing planning of substation-s location and capacity with comprehensive algorithm which involves an accurate small-area electric load forecasting procedure and simulates the different cost functions of substations.
Keywords: Geographic information systems (GIS), optimallocation and capacity, power distribution planning, route selection, spatial load forecasting.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 55032575 Research on Online Consumption of College Students in China with Stimulate-Organism-Reaction Driven Model
Authors: Wei Lu
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With the development of information technology in China, network consumption is becoming more and more popular. As a special group, college students have a high degree of education and distinct opinions and personalities. In the future, the key groups of network consumption have gradually become the focus groups of network consumption. Studying college students’ online consumption behavior has important theoretical significance and practical value. Based on the Stimulus-Organism-Response (SOR) driving model and the structural equation model, this paper establishes the influencing factors model of College students’ online consumption behavior, evaluates and amends the model by using SPSS and AMOS software, analyses and determines the positive factors of marketing college students’ consumption, and provides an effective basis for guiding and promoting college student consumption.
Keywords: College students, online consumption, stimulus-organism-response driving model, structural equation model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5922574 Improvement of the Reliability of the Industrial Electric Networks
Authors: M. Bouguerra, I. Habi
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The continuity in the electric supply of the electric installations is becoming one of the main requirements of the electric supply network (generation, transmission, and distribution of the electric energy). The achievement of this requirement depends from one side on the structure of the electric network and on the other side on the avaibility of the reserve source provided to maintain the supply in case of failure of the principal one. The avaibility of supply does not only depends on the reliability parameters of the both sources (principal and reserve) but it also depends on the reliability of the circuit breaker which plays the role of interlocking the reserve source in case of failure of the principal one. In addition, the principal source being under operation, its control can be ideal and sure, however, for the reserve source being in stop, a preventive maintenances which proceed on time intervals (periodicity) and for well defined lengths of time are envisaged, so that this source will always available in case of the principal source failure. The choice of the periodicity of preventive maintenance of the source of reserve influences directly the reliability of the electric feeder system In this work and on the basis of the semi- markovian's processes, the influence of the time of interlocking the reserve source upon the reliability of an industrial electric network is studied and is given the optimal time of interlocking the reserve source in case of failure the principal one, also the influence of the periodicity of the preventive maintenance of the source of reserve is studied and is given the optimal periodicity.Keywords: Semi-Markovians processes, reliability, optimization, industrial electric network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12702573 Using Social Network Analysis for Cyber Threat Intelligence
Authors: Vasileios Anastopoulos
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Cyber threat intelligence assists organisations in understanding the threats they face and helps them make educated decisions on preparing their defences. Sharing of threat intelligence and threat information is increasingly leveraged by organisations and enterprises, and various software solutions are already available, with the open-source malware information sharing platform (MISP) being a popular one. In this work, a methodology for the production of cyber threat intelligence using the threat information stored in MISP is proposed. The methodology leverages the discipline of social network analysis and the diamond model, a model used for intrusion analysis, to produce cyber threat intelligence. The workings of the proposed methodology are demonstrated with a case study on a production MISP instance of a real organisation. The paper concludes with a discussion on the proposed methodology and possible directions for further research.
Keywords: Cyber threat intelligence, diamond model, malware information sharing platform, social network analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5122572 Soft Computing Based Cluster Head Selection in Wireless Sensor Network Using Bacterial Foraging Optimization Algorithm
Authors: A. Rajagopal, S. Somasundaram, B. Sowmya, T. Suguna
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Wireless Sensor Networks (WSNs) enable new applications and need non-conventional paradigms for the protocol because of energy and bandwidth constraints, In WSN, sensor node’s life is a critical parameter. Research on life extension is based on Low-Energy Adaptive Clustering Hierarchy (LEACH) scheme, which rotates Cluster Head (CH) among sensor nodes to distribute energy consumption over all network nodes. CH selection in WSN affects network energy efficiency greatly. This study proposes an improved CH selection for efficient data aggregation in sensor networks. This new algorithm is based on Bacterial Foraging Optimization (BFO) incorporated in LEACH.Keywords: Bacterial Foraging Optimization (BFO), Cluster Head (CH), Data-aggregation protocols, Low-Energy Adaptive Clustering Hierarchy (LEACH).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 34782571 A Microscopic Simulation Model for Earthmoving Operations
Authors: Jiali Fu
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Earthmoving operations are a major part of many construction projects. Because of the complexity and fast-changing environment of such operations, the planning and estimating are crucial on both planning and operational levels. This paper presents the framework ofa microscopic discrete-event simulation system for modeling earthmoving operations and conducting productivity estimations on an operational level.A prototype has been developed to demonstrate the applicability of the proposed framework, and this simulation system is presented via a case study based on an actual earthmoving project. The case study shows that the proposed simulation model is capable of evaluating alternative operating strategies and resource utilization at a very detailed level.Keywords: Earthmoving operation, microscopic simulation, discrete-event simulation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19852570 An Exploration of Brand Storytelling in a Video Sharing Social Network
Authors: Charmaine du Plessis
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The brand storytelling themes and emotional appeals of three major global brands were analysed by means of visual rhetoric in a digital environment focusing on the ethos communication technique. A well-known framework of five basic brand personality dimensions was used to delineate the analysis. Brand storytelling as a branding technique is becoming increasingly popular, especially since all brands can tell a story to connect and engage with consumers on an emotional level. Social media have changed the way in which brand stories are shared with online consumers, while social video networking sites in particular create an opportunity to share brand stories with a much greater target audience through electronic word of mouth (eWOM). The findings not only confirm three dimensions in the traditional brand personality framework, but can also serve as a heuristic tool for other researchers analyzing brand storytelling in a social video sharing network environment.
Keywords: Communication technique, visual rhetoric, social video sharing network, brand storytelling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22032569 Annotations of Gene Pathways Images in Biomedical Publications Using Siamese Network
Authors: Micheal Olaolu Arowolo, Muhammad Azam, Fei He, Mihail Popescu, Dong Xu
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As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Manually annotating pathway diagrams is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy.
Keywords: Biological pathway, gene identification, object detection, Siamese network, ResNet.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2462568 Biosignal Measurement using Personal Area Network based on Human Body Communication
Authors: Yong-Gyu Lee, Jin-Hee Park, Gilwon Yoon
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In this study, we introduced a communication system where human body was used as medium through which data were transferred. Multiple biosignal sensing units were attached to a subject and wireless personal area network was formed. Data of the sensing units were shared among them. We used wideband pulse communication that was simple, low-power consuming and high data rated. Each unit functioned as independent communication device or node. A method of channel search and communication among the modes was developed. A protocol of carrier sense multiple access/collision detect was implemented in order to avoid data collision or interferences. Biosignal sensing units should be located at different locations due to the nature of biosignal origin. Our research provided a flexibility of collecting data without using electrical wires. More non-constrained measurement was accomplished which was more suitable for u-Health monitoring.Keywords: Human body communication, wideband pulse communication, personal area network, biosignal.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21762567 The SEMONT Monitoring and Risk Assessment of Environmental EMF Pollution
Authors: Dragan Kljajic, Nikola Djuric, Karolina Kasas-Lazetic, Danka Antic
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Wireless communications have been expanded very fast in recent decades. This technology relies on an extensive network of base stations and antennas, using radio frequency signals to transmit information. Devices that use wireless communication, while offering various services, basically act as sources of non-ionizing electromagnetic fields (EMF). Such devices are permanently present in human vicinity and almost constantly radiate, causing EMF pollution of the environment. This fact has initiated development of modern systems for observation of the EMF pollution, as well as for risk assessment. This paper presents the Serbian electromagnetic field monitoring network – SEMONT, designed for automated, remote and continuous broadband monitoring of EMF in the environment. Measurement results of the SEMONT monitoring at one of the test locations, within the main campus of the University of Novi Sad, are presented and discussed, along with corresponding exposure assessment of the general population, regarding the Serbian legislation.
Keywords: EMF monitoring, exposure assessment, sensor nodes, wireless network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22552566 Robust Stability in Multivariable Neural Network Control using Harmonic Analysis
Authors: J. Fernandez de Canete, S. Gonzalez-Perez, P. del Saz-Orozco, I. Garcia-Moral
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Robust stability and performance are the two most basic features of feedback control systems. The harmonic balance analysis technique enables to analyze the stability of limit cycles arising from a neural network control based system operating over nonlinear plants. In this work a robust stability analysis based on the harmonic balance is presented and applied to a neural based control of a non-linear binary distillation column with unstructured uncertainty. We develop ways to describe uncertainty in the form of neglected nonlinear dynamics and high harmonics for the plant and controller respectively. Finally, conclusions about the performance of the neural control system are discussed using the Nyquist stability margin together with the structured singular values of the uncertainty as a robustness measure.Keywords: Robust stability, neural network control, unstructured uncertainty, singular values, distillation column.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16272565 On The Analysis of a Compound Neural Network for Detecting Atrio Ventricular Heart Block (AVB) in an ECG Signal
Authors: Salama Meghriche, Amer Draa, Mohammed Boulemden
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Heart failure is the most common reason of death nowadays, but if the medical help is given directly, the patient-s life may be saved in many cases. Numerous heart diseases can be detected by means of analyzing electrocardiograms (ECG). Artificial Neural Networks (ANN) are computer-based expert systems that have proved to be useful in pattern recognition tasks. ANN can be used in different phases of the decision-making process, from classification to diagnostic procedures. This work concentrates on a review followed by a novel method. The purpose of the review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in ECG signals. The developed method is based on a compound neural network (CNN), to classify ECGs as normal or carrying an AtrioVentricular heart Block (AVB). This method uses three different feed forward multilayer neural networks. A single output unit encodes the probability of AVB occurrences. A value between 0 and 0.1 is the desired output for a normal ECG; a value between 0.1 and 1 would infer an occurrence of an AVB. The results show that this compound network has a good performance in detecting AVBs, with a sensitivity of 90.7% and a specificity of 86.05%. The accuracy value is 87.9%.Keywords: Artificial neural networks, Electrocardiogram(ECG), Feed forward multilayer neural network, Medical diagnosis, Pattern recognitionm, Signal processing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24712564 Data Mining on the Router Logs for Statistical Application Classification
Authors: M. Rahmati, S.M. Mirzababaei
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With the advance of information technology in the new era the applications of Internet to access data resources has steadily increased and huge amount of data have become accessible in various forms. Obviously, the network providers and agencies, look after to prevent electronic attacks that may be harmful or may be related to terrorist applications. Thus, these have facilitated the authorities to under take a variety of methods to protect the special regions from harmful data. One of the most important approaches is to use firewall in the network facilities. The main objectives of firewalls are to stop the transfer of suspicious packets in several ways. However because of its blind packet stopping, high process power requirements and expensive prices some of the providers are reluctant to use the firewall. In this paper we proposed a method to find a discriminate function to distinguish between usual packets and harmful ones by the statistical processing on the network router logs. By discriminating these data, an administrator may take an approach action against the user. This method is very fast and can be used simply in adjacent with the Internet routers.Keywords: Data Mining, Firewall, Optimization, Packetclassification, Statistical Pattern Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16542563 Beyond Possibilities: Re- Reading Republican Ankara
Authors: Zelal Çinar
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This paper aims to expose the effects of the ideological program of Turkish Republic on city planning, through the first plan of Ankara. As the new capital, Ankara was planned to be the ‘showcase’ of modern Turkey. It was to represent all new ideologies and the country’s cultural similarities with the west. At the same time it was to underline the national identity and independence of Turkish republic. To this end, a new plan for the capital was designed by German city planner Carl Christopher Lörcher. Diametrically opposed with the existing fabric of the city, this plan was built on the basis of papers and plans, on ideological aims. On the contrary, this paper argues that the city is a machine of possibilities, rather than a clear, materialized system.
Keywords: Architecture, ideology, modernization, urban planning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18572562 Parallel Computation of Data Summation for Multiple Problem Spaces on Partitioned Optical Passive Stars Network
Authors: Khin Thida Latt, Mineo Kaneko, Yoichi Shinoda
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In Partitioned Optical Passive Stars POPS network,nodes and couplers become free after slot to slot in some computation.It is necessary to efficiently utilize free couplers and nodes to be cost effective. Improving parallelism, we present the fast data summation algorithm for multiple problem spaces on P OP S(g, g) with smaller number of nodes for the case of d =n = g. For the case of d >n > g, we simulate the calculation of large number of data items dedicated to larger system with many nodes on smaller system with smaller number of nodes. The algorithm is faster than the best know algorithm and using smaller number of nodes and groups make the system low cost and practical.Keywords: Partitioned optical passive stars network, parallelcomputing, optical computing, data sum
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11782561 Single Spectrum End Point Predict of BOF with SVM
Authors: Ling-fei Xu, Qi Zhao, Yan-ru Chen, Mu-chun Zhou, Meng Zhang, Shi-xue Xu
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SVM ( Support Vector Machine ) is a new method in the artificial neural network ( ANN ). In the steel making, how to use computer to predict the end point of BOF accuracy is a great problem. A lot of method and theory have been claimed, but most of the results is not satisfied. Now the hot topic in the BOF end point predicting is to use optical way the predict the end point in the BOF. And we found that there exist some regular in the characteristic curve of the flame from the mouse of pudding. And we can use SVM to predict end point of the BOF, just single spectrum intensity should be required as the input parameter. Moreover, its compatibility for the input space is better than the BP network.
Keywords: SVM, predict, BOF, single spectrum intensity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13592560 SNC Based Network Layer Design for Underwater Wireless Communication Used in Coral Farms
Authors: T. T. Manikandan, Rajeev Sukumaran
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For maintaining the biodiversity of many ecosystems the existence of coral reefs play a vital role. But due to many factors such as pollution and coral mining, coral reefs are dying day by day. One way to protect the coral reefs is to farm them in a carefully monitored underwater environment and restore it in place of dead corals. For successful farming of corals in coral farms, different parameters of the water in the farming area need to be monitored and maintained at optimal level. Sensing underwater parameters using wireless sensor nodes is an effective way for precise and continuous monitoring in a highly dynamic environment like oceans. Here the sensed information is of varying importance and it needs to be provided with desired Quality of Service(QoS) guarantees in delivering the information to offshore monitoring centers. The main interest of this research is Stochastic Network Calculus (SNC) based modeling of network layer design for underwater wireless sensor communication. The model proposed in this research enforces differentiation of service in underwater wireless sensor communication with the help of buffer sizing and link scheduling. The delay and backlog bounds for such differentiated services are analytically derived using stochastic network calculus.
Keywords: Underwater Coral Farms, SNC, differentiated service, delay bound, backlog bound.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3662559 A Subjective Scheduler Based on Backpropagation Neural Network for Formulating a Real-life Scheduling Situation
Authors: K. G. Anilkumar, T. Tanprasert
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This paper presents a subjective job scheduler based on a 3-layer Backpropagation Neural Network (BPNN) and a greedy alignment procedure in order formulates a real-life situation. The BPNN estimates critical values of jobs based on the given subjective criteria. The scheduler is formulated in such a way that, at each time period, the most critical job is selected from the job queue and is transferred into a single machine before the next periodic job arrives. If the selected job is one of the oldest jobs in the queue and its deadline is less than that of the arrival time of the current job, then there is an update of the deadline of the job is assigned in order to prevent the critical job from its elimination. The proposed satisfiability criteria indicates that the satisfaction of the scheduler with respect to performance of the BPNN, validity of the jobs and the feasibility of the scheduler.Keywords: Backpropagation algorithm, Critical value, Greedy alignment procedure, Neural network, Subjective criteria, Satisfiability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14852558 Coverage and Connectivity Problem in Sensor Networks
Authors: Meenakshi Bansal, Iqbal Singh, Parvinder S. Sandhu
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In over deployed sensor networks, one approach to Conserve energy is to keep only a small subset of sensors active at Any instant. For the coverage problems, the monitoring area in a set of points that require sensing, called demand points, and consider that the node coverage area is a circle of range R, where R is the sensing range, If the Distance between a demand point and a sensor node is less than R, the node is able to cover this point. We consider a wireless sensor network consisting of a set of sensors deployed randomly. A point in the monitored area is covered if it is within the sensing range of a sensor. In some applications, when the network is sufficiently dense, area coverage can be approximated by guaranteeing point coverage. In this case, all the points of wireless devices could be used to represent the whole area, and the working sensors are supposed to cover all the sensors. We also introduce Hybrid Algorithm and challenges related to coverage in sensor networks.Keywords: Wireless sensor networks, network coverage, Energy conservation, Hybrid Algorithms.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17182557 An Evaluation of Neural Network Efficacies for Image Recognition on Edge-AI Computer Vision Platform
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Image recognition enables machine-like robotics to understand a scene and plays an important role in computer vision applications. Computer vision platforms as physical infrastructure, supporting Neural Networks for image recognition, are deterministic to leverage the performance of different Neural Networks. In this paper, three different computer vision platforms – edge AI (Jetson Nano, with 4GB), a standalone laptop (with RTX 3000s, using CUDA), and a web-based device (Google Colab, using GPU) are investigated. In the case study, four prominent neural network architectures (including AlexNet, VGG16, GoogleNet, and ResNet (34/50)), are deployed. By using public ImageNets (Cifar-10), our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.
Keywords: AlexNet, VGG, GoogleNet, ResNet, ImageNet, Cifar-10, Edge AI, Jetson Nano, CUDA, GPU.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2192556 Isotropic Stress Distribution in Cu/(001) Fe Two Sheets
Authors: A. Derardja, L. Baroura, M. Brioua
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The nanotechnology based on epitaxial systems includes single or arranged misfit dislocations. In general, whatever is the type of dislocation or the geometry of the array formed by the dislocations; it is important for experimental studies to know exactly the stress distribution for which there is no analytical expression [1, 2]. This work, using a numerical analysis, deals with relaxation of epitaxial layers having at their interface a periodic network of edge misfit dislocations. The stress distribution is estimated by using isotropic elasticity. The results show that the thickness of the two sheets is a crucial parameter in the stress distributions and then in the profile of the two sheets. A comparative study between the case of single dislocation and the case of parallel network shows that the layers relaxed better when the interface is covered by a parallel arrangement of misfit. Consequently, a single dislocation at the interface produces an important stress field which can be reduced by inserting a parallel network of dislocations with suitable periodicity.Keywords: Parallel array of misfit, interface, isotropic elasticity, single crystalline substrates, coherent interface
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15692555 Seamless Handover in Urban 5G-UAV Systems Using Entropy Weighted Method
Authors: Anirudh Sunil Warrier, Saba Al-Rubaye, Dimitrios Panagiotakopoulos, Gokhan Inalhan, Antonios Tsourdos
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The demand for increased data transfer rate and network traffic capacity has given rise to the concept of heterogeneous networks. Heterogeneous networks are wireless networks, consisting of devices using different underlying radio access technologies (RAT). For Unmanned Aerial Vehicles (UAVs) this enhanced data rate and network capacity are even more critical especially in their applications of medicine, delivery missions and military. In an urban heterogeneous network environment, the UAVs must be able switch seamlessly from one base station (BS) to another for maintaining a reliable link. Therefore, seamless handover in such urban environments has become a major challenge. In this paper, a scheme to achieve seamless handover is developed, an algorithm based on Received Signal Strength (RSS) criterion for network selection is used and Entropy Weighted Method (EWM) is implemented for decision making. Seamless handover using EWM decision-making is demonstrated successfully for a UAV moving across fifth generation (5G) and long-term evolution (LTE) networks via a simulation level analysis. Thus, a solution for UAV-5G communication, specifically the mobility challenge in heterogeneous networks is solved and this work could act as step forward in making UAV-5G architecture integration a possibility.
Keywords: Air to ground, A2G, fifth generation, 5G, handover, mobility, unmanned aerial vehicle, UAV, urban environments.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4302554 Formal Verification of a Multicast Protocol in Mobile Networks
Authors: M. Matash Borujerdi, S.M. Mirzababaei
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As computer network technology becomes increasingly complex, it becomes necessary to place greater requirements on the validity of developing standards and the resulting technology. Communication networks are based on large amounts of protocols. The validity of these protocols have to be proved either individually or in an integral fashion. One strategy for achieving this is to apply the growing field of formal methods. Formal methods research defines systems in high order logic so that automated reasoning can be applied for verification. In this research we represent and implement a formerly announced multicast protocol in Prolog language so that certain properties of the protocol can be verified. It is shown that by using this approach some minor faults in the protocol were found and repaired. Describing the protocol as facts and rules also have other benefits i.e. leads to a process-able knowledge. This knowledge can be transferred as ontology between systems in KQML format. Since the Prolog language can increase its knowledge base every time, this method can also be used to learn an intelligent network.Keywords: Formal methods, MobiCast, Mobile Network, Multicast.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13782553 In Search of a Suitable Neural Network Capable of Fast Monitoring of Congestion Level in Electric Power Systems
Authors: Pradyumna Kumar Sahoo, Prasanta Kumar Satpathy
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This paper aims at finding a suitable neural network for monitoring congestion level in electrical power systems. In this paper, the input data has been framed properly to meet the target objective through supervised learning mechanism by defining normal and abnormal operating conditions for the system under study. The congestion level, expressed as line congestion index (LCI), is evaluated for each operating condition and is presented to the NN along with the bus voltages to represent the input and target data. Once, the training goes successful, the NN learns how to deal with a set of newly presented data through validation and testing mechanism. The crux of the results presented in this paper rests on performance comparison of a multi-layered feed forward neural network with eleven types of back propagation techniques so as to evolve the best training criteria. The proposed methodology has been tested on the standard IEEE-14 bus test system with the support of MATLAB based NN toolbox. The results presented in this paper signify that the Levenberg-Marquardt backpropagation algorithm gives best training performance of all the eleven cases considered in this paper, thus validating the proposed methodology.
Keywords: Line congestion index, critical bus, contingency, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17862552 An Inverse Optimal Control Approach for the Nonlinear System Design Using ANN
Authors: M. P. Nanda Kumar, K. Dheeraj
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The design of a feedback controller, so as to minimize a given performance criterion, for a general non-linear dynamical system is difficult; if not impossible. But for a large class of non-linear dynamical systems, the open loop control that minimizes a performance criterion can be obtained using calculus of variations and Pontryagin’s minimum principle. In this paper, the open loop optimal trajectories, that minimizes a given performance measure, is used to train the neural network whose inputs are state variables of non-linear dynamical systems and the open loop optimal control as the desired output. This trained neural network is used as the feedback controller. In other words, attempts are made here to solve the “inverse optimal control problem” by using the state and control trajectories that are optimal in an open loop sense.
Keywords: Inverse Optimal Control, Radial basis function neural network, Controller Design.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22862551 Classification of Initial Stripe Height Patterns using Radial Basis Function Neural Network for Proportional Gain Prediction
Authors: Prasit Wonglersak, Prakarnkiat Youngkong, Ittipon Cheowanish
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This paper aims to improve a fine lapping process of hard disk drive (HDD) lapping machines by removing materials from each slider together with controlling the strip height (SH) variation to minimum value. The standard deviation is the key parameter to evaluate the strip height variation, hence it is minimized. In this paper, a design of experiment (DOE) with factorial analysis by twoway analysis of variance (ANOVA) is adopted to obtain a statistically information. The statistics results reveal that initial stripe height patterns affect the final SH variation. Therefore, initial SH classification using a radial basis function neural network is implemented to achieve the proportional gain prediction.Keywords: Stripe height variation, Two-way analysis ofvariance (ANOVA), Radial basis function neural network, Proportional gain prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16462550 An MADM Framework toward Hierarchical Production Planning in Hybrid MTS/MTO Environments
Authors: H. Rafiei, M. Rabbani
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This paper proposes a new decision making structure to determine the appropriate product delivery strategy for different products in a manufacturing system among make-to-stock, make-toorder, and hybrid strategy. Given product delivery strategies for all products in the manufacturing system, the position of the Order Penetrating Point (OPP) can be located regarding the delivery strategies among which location of OPP in hybrid strategy is a cumbersome task. In this regard, we employ analytic network process, because there are varieties of interrelated driving factors involved in choosing the right location. Moreover, the proposed structure is augmented with fuzzy sets theory in order to cope with the uncertainty of judgments. Finally, applicability of the proposed structure is proven in practice through a real industrial case company. The numerical results demonstrate the efficiency of the proposed decision making structure in order partitioning and OPP location.Keywords: Hybrid make-to-stock/make-to-order, Multi-attribute decision making, Order partitioning, Order penetration point.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22212549 Low-Noise Amplifier Design for Improvement of Communication Range for Wake-up Receiver Based Wireless Sensor Network Application
Authors: Ilef Ketata, Mohamed Khalil Baazaoui, Robert Fromm, Ahmad Fakhfakh, Faouzi Derbel
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The integration of wireless communication, e.g. in realor quasi-real-time applications, is related to many challenges such as energy consumption, communication range, latency, quality of service, and reliability. The improvement of wireless sensor network performance starts by enhancing the capabilities of each sensor node. While consuming less energy, wake-up receiver (WuRx) nodes have an impact on reducing latency. The solution for sensitivity improvements of sensor nodes, and WuRx in particular, with an energy consumption expense is low-noise amplifier (LNAs) blocks placed in the RF Antenna. This paper presents a comparative study for improving communication range and decreasing the energy consumption of WuRx nodes.
Keywords: Wireless sensor network, wake-up receiver, duty-cycled, low-noise amplifier, envelope detector, range study.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2062548 Improvement of Synchronous Machine Dynamic Characteristics via Neural Network Based Controllers
Authors: S. A. Gawish, F. A. Khalifa, R. M. Mostafa
Abstract:
This paper presents Simulation and experimental study aimed at investigating the effectiveness of an adaptive artificial neural network stabilizer on enhancing the damping torque of a synchronous generator. For this purpose, a power system comprising a synchronous generator feeding a large power system through a short tie line is considered. The proposed adaptive neuro-control system consists of two multi-layered feed forward neural networks, which work as a plant model identifier and a controller. It generates supplementary control signals to be utilized by conventional controllers. The details of the interfacing circuits, sensors and transducers, which have been designed and built for use in tests, are presented. The synchronous generator is tested to investigate the effect of tuning a Power System Stabilizer (PSS) on its dynamic stability. The obtained simulation and experimental results verify the basic theoretical concepts.Keywords: Adaptive artificial neural network, power system stabilizer, synchronous generator.
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