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Annual Conference of Industrial Electronics Society

作者： Atif Iqbal Haitham Abu-Rub Sk. Moin Ahmed Patricio Cortes Jose Rodriguez Electrical & Computer Engineering Program Texas A and M University Qatar Doha Qatar Electrical Engineering Department Aligarh Muslim University TAMUQ Aligarh India Departmento de Electronica Universidad Tècnica Federico Santa Maria Chile

The paper proposes finite state model based current control of a three-level neutral point clamped (NPC) five-phase voltage source inverter. The space vector model of a five-phase voltage source inverter (VSI) yield 243 space vectors, with 240 active and three zero vectors. The presented technique utilizes only 21 space vectors for the current control and hence the name finite state is given. Search is made by the algorithm to find the vector which minimizes the chosen cost function. The performance of the current control heavily depends upon the choice of the cost function, the number of vectors used and the sampling time. The developed technique is tested for RLE load using simulation approach. The proposed technique offers very good capacitor voltage balancing and offers high performance results in terms of current distortion. The technique is very useful for real time implementation due to a small number of vectors being used in contrary to space vector PWM where at least 43 vectors are required to obtain similar results. Simulation results are given in the this submission, while the experimental validation of the concept will be provided in the final paper.

关键词： Inverters Cost function Predictive models Aerospace electronics Current control Switches Pulse width modulation

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Mitigation of Torsional Oscillations Hazard in Cascaded Induction Motors Driving a Large Mechanical Load

Mitigation of Torsional Oscillations Hazard in Cascaded Indu...

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International Universities Power engineering Conference (UPEC)

作者： K. E. Amin R. M. Hamouda A. Abu Siada M. A. El-Dessouki Electrical Power & Machines Department Ain Shams University Cairo Egypt Electromechanics – Energy Program Heliopolis University Cairo Egypt Electrical & Computer Engineering Curtin University Curtin Australia

ISBN: (纸本)9781538629116

This paper presents a mitigation method for the torsional oscillations and stresses occurring in a cascaded set of 2 induction motors driving large mechanical load through elastic mechanical shafts. The idea of using cascaded induction motors connected through mechanical shaft sections can be used to drive a large mechanical load. The usage of such electromechanical system is popular in industrial applications like mining, oil and gas industry. However, a little scope of work was introduced to present a mitigation method for torsional stresses that may be appeared on the mechanical shaft sections while the system starts against full load condition. The analysis of transient performance for such electromechanical system is essential to ensure the operational reliability of the system parts. An effective starting technique by using variable frequency drives is introduced to mitigate the destructive effects of high torsional oscillations that has a bad effect on the mechanical shaft sections. The electromechanical system was implemented using Matlab/Simulink. The electrical & mechanical system mathematical models were developed in ABC reference frame. Results indicate that torsional stresses are minimized and suppressed while using variable frequency drives to start the induction motors instead of direct online starting.

关键词： Induction motors Shafts Stress Torque Variable speed drives Resonant frequency Electromechanical systems

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Reconfiguration of Power Distribution Systems in the Presence of Voltage-Dependent Loads

Reconfiguration of Power Distribution Systems in the Presenc...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Meisam Mahdavi Konrad Schmitt Stephen Bayne Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical and Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock TX USA

Load power can be changed by voltage fluctuation of network buses, in which reconfiguring the topology of distribution systems impacts the magnitude of bus voltages. Only a few papers have considered voltage-dependent loads in their reconfiguration models but introduced nonlinear formulations or highly approximated linear approaches. While nonlinear models can be solved by metaheuristic algorithms without guaranteeing the optimality of solutions or may be implemented by commercial nonlinear solvers, they require intensive and time-consuming computations. Moreover, linearized models are highly approximated by piecewise linear functions with many unknown parameters. Thus, this paper presents the effective design of the reconfiguration problems, including voltage dependency of loads, which can be solved using commercial linear solvers. The proposed models are precise enough to find accurate results for the reconfiguration problem and are adequately fast to converge to optimal solutions. The results show that the proposed solutions not only reduce active power losses significantly but increase the minimum voltage of the system effectively.

关键词： Voltage fluctuations Fluctuations Network topology Computational modeling Metaheuristics Power distribution Distribution networks

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Bulk power system restoration interdependency risk modeling

Bulk power system restoration interdependency risk modeling

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IEEE Canada Electric Power Conference, EPEC

作者： Nathan Ozog Eric Desjardins Juri Jatskevich British Columbia Transmission Corporation Asset Program Definition Group Vancouver BC Canada British Columbia Transmission Corporation Asset Program Management Group Vancouver BC Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

The interdependencies that exist within and between infrastructures can cause unexpected system properties to emerge when their components fail due to large disruptions. As witnessed following emergencies such as the North American 2003 Blackout, the complexities of these interdependencies make it very difficult to effectively recover infrastructure because of the challenges they create in prioritizing the most critical components. To study dependencies in electrical bulk power systems, an interdependency model of the British Columbia Transmission Corporation bulk power network and its communications system was developed, along with a post-blackout restoration procedure. Using these, simulations of a post-blackout recovery were carried out to study the level of risk that communications outages may pose to the electrical networkpsilas recovery. These simulations revealed a correlation between restoration time and the number of communication points lost. This research also demonstrated there is value in combining the results of such simulations with risk evaluation tools.

关键词： Power system restoration Power system modeling Power system reliability Power system simulation Robustness Risk analysis Power system analysis computing Communication cables Poles and towers Communication system control

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Comparison Analysis of Intrusion Detection using Deep Learning in IoT Networks

Comparison Analysis of Intrusion Detection using Deep Learni...

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International Conference of Information and Communication Technology (ICoICT)

作者： Syariful Ikhwan Purwanto Purwanto Adian Fatchur Rochim Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

This study focuses on network security for Internet of Things (IoT) devices, which are particularly vulnerable to attack due to limited security measures. To protect IoT networks from attackers, the research implemented intrusion detection on the networks. The study compared several Deep Learning algorithms, including DNN, CNN, LSTM, and AE, to identify the most effective algorithm for solving network security problems using intrusion detection. The research used the UNSW-NB15 dataset for testing and employed binary classification for evaluation. The results showed that the DNN algorithm achieved an accuracy value of 99.76% and a loss value of 0.006%, outperforming the other algorithms. This study highlights the importance of implementing intrusion detection in protecting IoT devices and networks and demonstrates the efficacy of the DNN algorithm in detecting and preventing network security breaches.

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

作者： Kurniawan, Rahmad Iskandar, Iwan Lestari, Fitra Harpizon, Habibi Al Rasyid Husti, Ilyas Faculty of Mathematics and Natural Sciences Universitas Riau Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Informatics Engineering Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798350313635

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia's top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube's features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments. © 2023 IEEE.

关键词： Sentiment analysis

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RECENT ADVANCES IN DEEP LEARNING APPLIED TO SKIN CANCER DETECTION

arXiv

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arXiv 2019年

作者： Pacheco, Andre G.C. Krohling, Renato R. Graduate Program in Computer Science PPGI Federal University of Espírito Santo UFES Av. Fernando Ferrari 514 Vitória-ES Brazil Production Engineering Department Graduate Program in Computer Science PPGI Federal University of Espírito Santo UFES Av. Fernando Ferrari 514 Vitória-ES Brazil

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to automated skin cancer detection have become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take. Copyright © 2019, The Authors. All rights reserved.

关键词： Dermatology

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Early detection of epilepsy seizures based on a weightless neural network

Early detection of epilepsy seizures based on a weightless n...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kleber de Aguiar Felipe M. G. França Valmir C. Barbosa César A. D. Teixeira Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil Centre for Informatics and Systems (CISUC) University of Coimbra Coimbra Portugal

This work introduces a new methodology for the early detection of epileptic seizure based on the WiSARD weightless neural network model and a new approach in terms of preprocessing the electroencephalogram (EEG) data. WiSARD has, among other advantages, the capacity of perform the training phase in a very fast way. This speed in training is due to the fact that WiSARD's neurons work like Random Access Memories (RAM) addressed by input patterns. Promising results were obtained in the anticipation of seizure onsets in four representative patients from the European Database on Epilepsy (EPILEPSIAE). The proposed seizure early detection WNN architecture was explored by varying the detection anticipation (δ) in the 2 to 30 seconds interval, and by adopting 2 and 3 seconds as the width of the Sliding Observation Window (SOW) input. While in the most challenging patient (A) one obtained accuracies from 99.57% (δ=2s; SOW=3s) to 72.56% (δ=30s; SOW=2s), patient D seizures could be detected in the 99.77% (δ=2s; SOW=2s) to 99.93% (δ=30s; SOW=3s) accuracy interval.

关键词： Electroencephalography Accuracy Training Databases Biological neural networks Epilepsy Feature extraction

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Colloidal Surfaces with Boundaries, Apex Boojums, and Nested Elastic Self-Assembly of Nematic Colloids

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Physical Review Letters 2016年第27期117卷 277801-277801页

作者： Sungoh Park Qingkun Liu Ivan I. Smalyukh Department of Physics University of Colorado Boulder Colorado 80309 USA Department of Electrical Computer and Energy Engineering Soft Materials Research Center and Materials Science and Engineering Program University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute University of Colorado and National Renewable Energy Laboratory Boulder Colorado 80309 USA

Self-assembly of colloidal particles is poised to become a powerful composite material fabrication technique, but remains challenged by a limited control over the ensuing structures. We develop a new breed of nematic colloids that are physical analogs of a mathematical surface with boundary, interacting with the molecular alignment field without inducing defects when flat. However, made from a thin nanofoil, they can be shaped to prompt formation of self-compensating defects that drive preprogramed elastic interactions mediated by the nematic host. To show this, we wrap the nanofoil on all triangular side faces of a pyramid, except its square base. The ensuing pyramidal cones induce point defects with fractional hedgehog charges of opposite signs, spontaneously align with respect to the far-field director to form elastic dipoles and nested assemblies with tunable spacing. Nanofoils shaped into octahedrons interact as elastic quadrupoles. Our findings may drive realization of low-symmetry colloidal phases.

关键词： Defects Point defects Self-assembly Colloids Liquid crystals Nematic liquid crystals

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Devi Fitrianah Kristien Margi Suryaningrum Noviyanti Tri Maretta Sagala Vina Ayumi Siew Mooi Lim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Engineering Program Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Computer Science Department Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665473286

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.

关键词： Training Biological system modeling Sociology Ecosystems Fish Statistics Monitoring

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