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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Berjis, Zaid Ameen, Siddeeq Y. Al-Jammas, Mohammed H. Electrical and Computer Engineering Department College of Engineering University of Duhok Duhok Iraq Energy Engineering Department College of Technical Engineering Duhok Polytechnic University Duhok Iraq Computer and Information Engineering Department College of Electronics Ninevah University Mosul Iraq

ISBN: (纸本)9798331533557

The Internet of Things (IoT) has changed many industries by enabling smart devices to transmit data, operate autonomously, and interact in real-time. Among its most prominent applications are in healthcare and industrial sectors, where IoT is classified into two classes: massive Machine-Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). While mMTC devices prioritize low energy consumption and can handle large-scale deployments with minimal data rates, URLLC devices are focused on mission-critical applications requiring ultra-low latency and high reliability. This paper examines the challenges and opportunities of IoT implementation in healthcare and industrial sectors, focusing on interoperability, data security, and scalability issues. The objective is to provide actionable solutions for the effective integration of IoT technologies. The investigation shows that in healthcare, mMTC is utilized for non-critical monitoring, while URLLC supports critical tasks like remote surgery. In industrial settings, mMTC improves processes like supply chain management, while URLLC enables automation and real-time control of machinery. Despite these opportunities, challenges such as data privacy, security, interoperability, and reliability remain. Addressing these issues is crucial for unlocking the full potential of IoT in healthcare and industrial sectors. © 2024 IEEE.

关键词： Data privacy

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Experimental Validation of Dynamics for Air Cushion Vehicles

Experimental Validation of Dynamics for Air Cushion Vehicles

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Sakri, Shivani Dim, Matthew Brizzolara, Stefano Virginia Polytechnic Institute and State University Department of Aerospace and Ocean Engineering BlacksburgVA United States Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9798331540081

The research study conducts experimental tests to validate the theoretical mathematical model of hovercraft dynamics. Static and dynamic tests have been conducted to understand vehicle response and cushion dynamics. The study allows us to use experimental data to calibrate the theoretical model to understand the vehicle and fluid physics better. © 2024 IEEE.

关键词： Air cushion vehicles

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Control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Parasitic Capacitance Analysis of Integrated Transformers with Parallel Windings

Parasitic Capacitance Analysis of Integrated Transformers wi...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yuan, Tianlong Jin, Feng Li, Qiang Virginia Polytechnic and State University Center for Power Electronics Systems Bradley Department of Electrical and Computer Engineering Blacksburg United States

ISBN: (纸本)9798350376067

The electric transportation market has experienced rapid growth in recent years, driven by increasing environmental concerns. Resonant converters play a crucial role in this sector due to their ability to provide isolation and regulation functions with high efficiency. A resonant inductance embedded transformer is a critical component of the LLC resonant converter, significantly influencing the power density of the entire system, particularly in high-power applications. To achieve higher power density, the operating frequency often exceeds hundreds of kilohertz. At such high frequencies, parasitic capacitance cannot be ignored, as it may cause electromagnetic interference (EMI) issues. In high-power applications, integrated transformers with parallel windings are commonly used, adding complexity to the system. Measuring the parasitic capacitance of these transformers becomes increasingly challenging. This paper presents various measurement methods for an integrated transformer with parallel windings in a 30 kW LLC resonant converter. The results obtained from different measurement techniques were consistent with each other and with simulation results, validating the accuracy of the methods employed. © 2024 IEEE.

关键词： Resonant converters

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Analysis and Comparison of Integrated Planar Transformers for CLLC Resonant Converters

Analysis and Comparison of Integrated Planar Transformers fo...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yuan, Tianlong Jin, Feng Yu, Gibum Li, Qiang Virginia Polytechnic and State University Center for Power Electronics Systems Bradley Department of Electrical and Computer Engineering Blacksburg United States

ISBN: (纸本)9798350376067

The advancement of electric vehicles and green energy technologies necessitates on-board chargers (OBCs) with higher power density and reduced costs. Integrated planar magnetics emerge as a highly competitive option due to their superior power density and ease of manufacturing compared to traditional magnetic designs. However, detailed flux analysis, particularly the distribution on the plate, remains essential for evaluating various transformer structures. This paper presents a comprehensive comparison of different core structures for integrated transformers, validated through an 11-kW CLLC resonant converter prototype with a 98.4% peak efficiency and 420 W/inch3 power density. © 2024 IEEE.

关键词： Resonant converters

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Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF 41

Principled Penalty-based Methods for Bilevel Reinforcement L...

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41st International Conference on Machine Learning, ICML 2024

作者： Shen, Han Yang, Zhuoran Chen, Tianyi Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States Department of Statistics and Data Science Yale University United States

Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised learning setting, where static objective functions with benign structures are considered. But bilevel problems such as incentive design, inverse reinforcement learning (RL), and RL from human feedback (RLHF) are often modeled as dynamic objective functions that go beyond the simple static objective structures, which pose significant challenges of using existing bilevel solutions. To tackle this new class of bilevel problems, we introduce the first principled algorithmic framework for solving bilevel RL problems through the lens of penalty formulation. We provide theoretical studies of the problem landscape and its penalty-based (policy) gradient algorithms. We demonstrate the effectiveness of our algorithms via simulations in the Stackelberg game and RLHF. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Explaining Machine Learning Based Speed Anomaly Detection System Using eXplainable Artificial Intelligence 1

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13th International Conference on Information Systems and Advanced Technologies, ICISAT 2023

作者： Idris, Hussaini Aliyu Ahmad, Aminu Diginsa, Muhammad Umar Aliyu, Buhari Muhammad, Adamu Yahaya, Abdulgaffar Abubakar Suleiman, Sabiu Usman Alexandria Egypt School of Computer Science and Engineering Central South University Hunan Changsha China Department of Computer Engineering Technology Bilyaminu Usman Polytechnic Hadejia Hadejia Nigeria Department of Computer Science Jigawa State Polytechnic Dutse Jigawa Nigeria Department of Electrical Electronics Engineering Jigawa State Polytechnic Dutse Jigawa Nigeria Department of Computer Science Mewar University Gangarar Chittorgarh Rajasthan India

ISBN: (数字)9783031605949

ISBN: (纸本)9783031605932

The goal of combating road accidents through the application of advanced communication and network technologies in transportation system called intelligent transportation system (ITS) has recorded a remarkable success. Nevertheless, the complex nature of the ITS makes cyberattacks a major concern even with cryptographic techniques employed for authentication of users, as they cannot prevent attacks from authenticated users (insider attackers). To solve this problem, several data-centric approaches for anomaly detection using machine learning (ML) are proposed by the research community. However, those approaches are limited in interpretability and explainability due to the complexity of some ML algorithms and black-box nature of neural networks which makes threat intelligence and analysis difficult. In this paper, we proposed an eXplainable artificial intelligence (XAI)-based speed anomaly detection system using trusted vehicles method with more than 99% accuracy, precision, recall and F1-Score where Shapley Additive exPlanation (SHAP) is further employed to explain the predictions made by the models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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computer Science Education Facing Unconventional Odds: Case Studies from the Arab World

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ACM Inroads 2024年第1期15卷 6-17页

作者： Aly, Sherif G. Echihabi, Karima Eldawlatly, Seif Shuaib, Khaled Tekli, Joe Department of Computer Science and Engineering The American University in Cairo Cairo Egypt Department of Computer Science Mohamed VI Polytechnic University Ben Guerir Morocco Department of Computer Science and Engineering The American University in Cairo AUC Avenue New Cairo Cairo11835 Egypt Ain Shams University Cairo Egypt College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

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Transferable Learning of GCN Sampling Graph Data Clusters from Different Power Systems 60

Transferable Learning of GCN Sampling Graph Data Clusters fr...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Wu, Tong Scaglione, Anna Arnold, Daniel Chen, Tianyi University of Central Florida The Department of Electrical and Computer Engi-neering OrlandoFL32816 United States Cornell University The Department of Electrical and Computer Engineering Cornell Tech New York CityNY10044 United States Lawrence Berkeley National Laboratory Rensselaer Polytechnic Institute The Department of Electrical TroyNY12180 United States Computer and Systems Engineering Rensselaer Polytechnic Institute The Department of Electrical TroyNY12180 United States

ISBN: (纸本)9798331541033

Contemporary neural network (NN) detectors for power systems face two primary challenges. First, each power system requires individual training of NN detectors to accommodate its unique configuration and base demands. Second, significant changes within the power system, such as the introduction of new substations or new generators, necessitate retraining. To overcome these issues, we introduce a novel architecture, the Nodal Graph Convolutional Neural Network (NGCN), which utilizes graph convolutions at each bus and its neighborhoods. This approach allows the training process to encompass multiple power systems and include all buses, thereby enhancing the transferability of the method across different power systems. The NGCN is particularly effective for detection tasks, such as cyber-attacks on smart inverters and false data injection attacks. Our tests demonstrate that the NGCN significantly improves performance over traditional NNs, boosting detection accuracy from approximately 85% to around 97% for the aforementioned task. Furthermore, the transferable NGCN, which is trained by samples from multiple power systems, performs considerably better in evaluations than the NGCN trained on a single power system. © 2024 IEEE.

关键词： Convolutional neural networks

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On Contrastive Learning for Domain Generalization 23

On Contrastive Learning for Domain Generalization

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Nguyen, Thuan Brown, D. Richard East Tennessee State University Department of Engineering Engineering Technology and Surveying TN United States Worcester Polytechnic Institute Department of Electrical and-Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350374889

Given the training data and labels from several seen domains, Domain Generalization (DG) aims to learn models that generalize well on unlabeled data from unseen domains. Due to the distribution of data and/or labels may vary between domains, current DG methods are mainly based on the theme of domain-invariant representation learning which decomposes the learning process into two steps: (1) finding a representation function from the input space to the representation/feature space to learn the so-called domain-invariant features, i.e., the features that are statistically stable between domains, and (2) designing a classifier on top of these domain-invariant features which is mutually optimal for all domains. Recent works on DG show that Contrastive Learning (CL) which was originally proposed to learn a representation function such that similar samples are pulled closer while dissimilar samples are pushed far away in the representation/feature space, appears as a promising solution for domain-invariant features learning. In this paper, we first establish the fundamental theory to justify why CL might be a potential approach for DG by showing that CL, under particular settings, allows a mechanism to minimize a loss function that enforces an optimal classifier for all training domains. Based on these foundations, we revisit the recent works that apply CL for DG to indicate their limitations and suggest a modification by combining the CL method with current DG methods for better out-of-domain generalization. Our numerical results point out that our proposed algorithm can achieve state-of-the-art performance on multiple DG benchmark datasets. © 2024 IEEE.

关键词： Contrastive Learning

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