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25th International Conference on Interactive Collaborative Learning, ICL 2022

作者： Zabasta, Anatolijs Peuteman, Joan Kunicina, Nadezda Kazymyr, Volodymyr Hnatov, Andrii Sistuk, Volodymyr Bisenieks, Martins Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga1048 Latvia Faculty of Engineering Technology Department of Electrical Engineering KU Leuven Bruges8200 Belgium Information and Computer Systems Department Chernihiv National Technological University Chernihiv14035 Ukraine Vehicle Electronics Department Kharkiv National Automobile and Highway University Kharkiv61002 Ukraine Kryvyi Ryh National University Kryvyi Rih50027 Ukraine

ISBN: (纸本)9783031261893

The ERASMUS + project "Development of practically-oriented student-centered education in the field of modeling of Cyber-Physical systems" (CybPhys) focuses on curricula modernizations in close cooperation with potential employers. Since the CybPhys project pursues to develop practically-oriented student-centered curricula, we involved industry representatives in the creation of new courses from the very beginning of the project. In the paper, we describe the curriculum development stages and obtain feedback from the main stakeholders. As part of the practically-oriented education, European partners in Riga Technical University, KU Leuven, and the University of Cyprus should arrange three schools for Ukrainian students. In this research, we focus on implementing practically-oriented and student-centered training methods at the school for Ukrainian students arranged at the RTU in January 2022. We describe novel methods and approaches applied in the students’ school, such as transversal skills, Design Thinking, involvement of industry representatives in the teacher team, etc. The survey of Ukrainian students and teachers revealed the presence or absence of correlations between students’ general perception of the school and teaching/ learning methods, e.g. developments of creative thinking, practical application of theory, etc. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Quantum Renewable Scenario Generation

Quantum Renewable Scenario Generation

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Tang, Zefan Zhang, Peng Zhou, Yifan Brookhaven National Laboratory Interdisciplinary Science Department UptonNY11973 United States Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

This paper underpins the potential of quantum generative adversarial networks (QGANs) for renewable scenario generation in power grids. A single QGAN with either amplitude or angle encoding is hard to construct. To bridge the gaps, this paper devises a Multi-QGAN framework utilizing multiple QGANs. A correlation-based Multi-QGAN (CMulti-QGAN) approach is further established to improve the Multi-QGAN performance. Data from real solar systems in Connecticut are collected for numerical studies. Results demonstrate the effectiveness and robustness of Multi-QGAN and CMulti-QGAN, and also validate the superiority of CMulti-QGAN over Multi-QGAN. © 2022 IEEE.

关键词： Generative adversarial networks

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tatsukichi Shibuya Nakamasa Inoue Rei Kawakami Ikuro Sato Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in fine-tuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO

关键词： Training Speech coding Memory management Signal processing algorithms Signal processing Classification algorithms Servers Speech processing Optimization Image classification

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A Study of a Microwave Resonant Cavity Structure for Hydrocarbon Recovery from Waste

A Study of a Microwave Resonant Cavity Structure for Hydroca...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Evaggelos Sidopoulos Dimitrios A. Arnaoutoglou George C. Trichopoulos Ioannis Dermitzakis John N. Sahalos George A. Kyriacou Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Industrial Area of Alexandroupolis Green Oil S.A. Greece Department of Physics Radiocommunications Laboratory Aristotle University of Thessaloniki Greece

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

The aim of this article is to design a microwave heating device using resonant cavities operating at 915 MHz, which will replace the usual heat treatment devices in hydrocarbon waste refinery facilities. By analyzing the interaction of the waste with electromagnetic fields, selective heating can be achieved, providing the scope for energy savings compared to classical heating methods (e.g. hydrocarbon combustion). Electromagnetic eigen-analysis will be performed to estimate the resonance eigenvectors for the specific cavity, followed by electromagnetic analysis with a power supply. Subsequently, the heat transfer problem is solved by considering the electromagnetic energy absorbed by the material as the source when the waste material flows inside quartz tubes. The resulting temperature distribution determined that the desired heating had been achieved.

关键词： Electromagnetic heating Waste materials Temperature distribution Heat treatment Power demand Prototypes Hydrocarbons

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Predicting Coronary Heart Disease Mortality Using Machine Learning Algorithms

Predicting Coronary Heart Disease Mortality Using Machine Le...

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Artificial Intelligence x Multimedia (AIxMM), Conference on

作者： Soha Parto Ali Akbar Safavi Shiva Naghsh Mahsa Keikha Amir Sharafkhaneh School of Electrical and Computer Engineering Shiraz University Shiraz Iran Department of Mechatronic Systems Engineering Simon Fraser University Burnaby Canada Department of Medicine Baylor College of Medicine Houston TX USA

ISBN: (数字)9798350390971

ISBN: (纸本)9798350390988

This study aimed to predict Coronary Heart Disease (CHD) mortality over a 10 to 15-year period using Machine Learning (ML) techniques. We studied a rather large variety of features associated to CHD mortality from the Sleep Heart Health Study (SHHS) dataset. Using the most important features identified through mutual information, several ML algorithms, including LR, SVM, KNN, ETC, and RF, were trained. A 10-fold cross-validation was employed to ensure accuracy. As a result, the KNN algorithm (k = 18) outperformed the others, achieving an accuracy of 76.05%, an AUC of 79.41%, a sensitivity of 81.08%, a specificity of 71.28%, and a precision of 73.48%. Our proposed approach reasonably well predicts CHD mortality many years in advance, aiming to identify critical factors for CHD mortality prediction, monitor patients, and provide early warnings of the need for prevention and treatment through medical intervention.

关键词： Heart Training Machine learning algorithms Accuracy Sensitivity Mortality Nearest neighbor methods Prediction algorithms Mutual information Diseases

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An efficient data aggregation scheme with local differential privacy in smart grid

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Digital Communications and Networks 2022年第3期8卷 333-342页

作者： Na Gai Kaiping Xue Bin Zhu Jiayu Yang Jianqing Liu Debiao He School of Cyber Science and Technology University of Science and Technology of ChinaHefeiAnhui230027China Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefeiAnhui230027China Department of Electrical and Computer Engineering University of Alabama in HuntsvilleHuntsvilleAL35899USA Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhanBeihu430072China

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

关键词： Local differential privacy Data aggregation Smart grid Privacy preserving

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A New Look for Outage and Ergodic Improvements in Coordinated IoT Paradigms with Hybrid Multiple Access

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IEEE Transactions on Vehicular Technology 2025年

作者： Vu, Thai-Hoc Le, Anh-Tu Huynh-The, Thien da Costa, Daniel Benevides Kim, Sunghwan Kyonggi University School of Electronic Engineering Korea Republic of VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science 17. Listopadu 2172/15 Ostrava708 00 Czech Republic Ho Chi Minh City University of Technology and Education Department of Computer and Communications Engineering Viet Nam Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia

As spectrum utilization becomes increasingly scarce due to the exponential growth of intelligent connected devices in the Internet of Things (IoT), developing efficient communication protocols with simultaneous improvements in energy and spectrum usage is critical in response to the demands of cost-effective IoT devices. In this work, we propose a hybrid orthogonal and non-orthogonal multiple-access protocol in coordinated direct uplink/downlink and relay transmission for low-cost IoT paradigms, where a source node engages in direct transmission with proximate IoT users and relies on a relay node for distant connections. This protocol offers a successive interference cancellation–free approach and does not require any channel disparity for power allocation. Exact and asymptotic analytical expressions for the outage probability (OP) and ergodic capacity (EC) are derived. Numerical results demonstrate that the proposed protocol significantly improves OP–by 8 folds for the respective proximate users while maintaining the same OP for distant users over classical coordinated non-orthogonal multiple access–and delivers superior EC performance for proximate IoT users at low to moderate signal-to-noise ratios (SNRs) and distant users at most SNR. Moreover, critical tasks for minimizing the maximal OP and maximizing the ergodic sum capacity have been undertaken to achieve outage fairness and enhance the overall system's capacity. These non-convex problems are efficiently addressed by applying the analytical asymptotic-inspired alternative optimization. © 1967-2012 IEEE.

关键词： Signal to noise ratio

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Study of monolithically integrated 940 nm AlGaAs distributed Bragg reflectors on graded GaAsP/bulk Si substrates

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Optical Materials Express 2023年第4期13卷 1077-1091页

作者： Guo, Jia Zhao, Yunlong Feifel, Markus Cheng, Hao-Tien Yang, Yun-Cheng Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui Department of Materials Engineering The University of British Columbia VancouverBCV6 T 1Z4 Canada Fraunhofer Institute for Solar Energy Systems ISE Heidenhofstraße 2 Freiburg79110 Germany Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University Taiwan Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6 T 1Z4 Canada

We report the fabrication of monolithically integrated 940 nm AlGaAs distributed Bragg reflectors (DBRs) on graded GaAsP/Si substrates. Low-density surface bumps and cross-hatch patterns were observed on the DBR surfaces. Cross-sectional DBR layers are smooth and flat. The reflectance spectra of the GaAsP/Si DBRs have lower intensities than those of the GaAs DBRs and have double peaks. Transfer matrix method calculations, surface scratch and polishing tests were conducted, which suggest that the surface cross-hatch was the cause of the inferior DBR reflectance spectra. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

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Development of the Function Check Stability of Digital Media Receivers using Interference Mitigation Techniques

Development of the Function Check Stability of Digital Media...

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International electrical engineering Congress (iEECON)

作者： Pontakan Lhuangchon Chaiyong Soemphol Department of Electrical and Computer Engineering Faculty of Engineering at Mahasarakham University Mahasarakham Thailand Faculty of Engineering at Mahasarakham University Research Unit for Computational Electromagnetics and Optical Systems (CEMOS) Mahasarakham Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This study explores techniques to mitigate interference in assessing functions related to AM radio wave signals in digital media receivers. Signal transmission cable testing focused on BNC RG59 75-ohm cables with length of 300 mm. Six methods were employed: Method 1 used a Ferrite core, Method 2 used copper tape, Method 3 connected a grounding wire, Method 4 combined grounding wire connection with a Ferrite core, Method 5 combined grounding wire connection with copper tape, and Method 6 combined grounding wire connection with both a Ferrite Core and copper tape. The result has been shown that Method 6 yielded optimal results, with AM 1000 kHz Usable Sens, AM Interstation Noise L, and AM Interstation Noise R functions differing from reference values by −3.53 dBm, −5.05 dBm, and −5.26 dBm, respectively, emphasizing its efficacy in enhancing the assessed functions.

关键词： Ferrites Grounding Wires Noise Receivers Interference Media

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Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees

arXiv

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arXiv 2023年

作者： Queeney, James Ozcan, Erhan Can Paschalidis, Ioannis Ch. Cassandras, Christos G. Mitsubishi Electric Research Laboratories United States Division of Systems Engineering Boston University United States Department of Electrical and Computer Engineering

Robustness and safety are critical for the trustworthy deployment of deep reinforcement learning. Real-world decision making applications require algorithms that can guarantee robust performance and safety in the presence of general environment disturbances, while making limited assumptions on the data collection process during training. In order to accomplish this goal, we introduce a safe reinforcement learning framework that incorporates robustness through the use of an optimal transport cost uncertainty set. We provide an efficient implementation based on applying Optimal Transport Perturbations to construct worst-case virtual state transitions, which does not impact data collection during training and does not require detailed simulator access. In experiments on continuous control tasks with safety constraints, our approach demonstrates robust performance while significantly improving safety at deployment time compared to standard safe reinforcement learning. © 2023, CC BY.

关键词： Reinforcement learning

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