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Control Conference (ANZCC), Australian and New Zealand

作者： Salman Ijaz Ahmed Nasr Mirza Tariq Hamayun Key Laboratory of More Electric Aircraft Zhejiang Province and members of Control Systems laboratory University of Nottingham Ningbo China Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore Campus Pakistan

This paper proposed a passive fault tolerant control (FTC) approach for the class of linear parameter-varying systems to deal with faults and failures in the actuator channel. The method provided in this study combines the robustness feature of integral sliding mode control (ISMC) and control allocation (CA) obtained through singular value decomposition of the input matrix. The purpose is to effectively handle faults and failures along the entire operating spectrum, which involves various scheduling parameters. This technique is effective because the same baseline controller, initially developed for a nominal system, can deal with a wide range of faults and failures without acquiring fault information. The performance of ISM-FTC is tested on a longitudinal model of aircraft system. Numerical simulations indicate no noticeable difference in tracking performance between nominal and faulty conditions.

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A discussion regarding the application of the hertz contact theory on biological samples in afm nanoindentation experiments

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Micro and Nanosystems 2021年第1期13卷 42-48页

作者： Kontomaris, Stylianos Vasileios Stylianou, A. Nikita, K.S. Malamou, A. Mobile Radio Communications Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Faculty of Architecture Engineering & Built Environment Athens Metropolitan College Athens Greece Cancer Biophysics Laboratory Department of Mechanical and Manufacturing Engineering Faculty of Engineering University of Cyprus Nicosia Cyprus Radar Systems and Remote Sensing Lab of School Electrical & Computer Engineering National Technical University of Athens Athens Greece

Atomic Force Microscopy (AFM) Nanoindentation procedure regarding biological samples poses significant challenges with respect to the accuracy of the provided results. These challenges are related to the inhomogeneity of biological samples, various uncertainties in experimental methods and certain approximations regarding the theoretical analysis. The most commonly used theoretical model for data processing at the linear elastic regime regarding biological samples is the Hertz model. This paper focuses on the investigation of the resulting errors of the basic equation of the Hertz theory that depends on the ratio, indentation depth/indenter’s radius regarding the Young’s modulus calculation. Several examples in the literature that do not take into account the value of the ratio indentation depth/indenter’s radius are reported and the related errors are presented and discussed. In addition, an extended new equation is derived which takes into account the influence of the aforementioned ratio on the calculation of the Young’s modulus and can be easily used for calculations. Moreover, a rational explanation, regarding the extended differences of the Young’s modulus calculations using the same experimental results when these are processed using the Hertz model and the Oliver & Pharr analysis (which is the general model that applies for any axisymmetric indenter) is provided. In conclusion, the derived equation is further combined with equations which take into account the shape of the sample in order to provide a complete and reliable theoretical tool which can be generally applied in order to reduce the errors produced by the current methodology. © 2021 Bentham Science Publishers.

关键词： Nanoindentation

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Exploring Equilibrium Strategies in Network Games with Generative AI

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IEEE Network 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore College of Computing and Data Science NTU Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Accurate Parameter Estimation for PMSMs using electrical and Mechanical Models Considering Temperature Effect and Inverter Distortion

Accurate Parameter Estimation for PMSMs using Electrical and...

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2022 Chinese Automation Congress, CAC 2022

作者： Tong, Zhe Meng, Bo Ding, Beichen Feng, Guodong Lai, Chunyan Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Sun Yat-sen University School of Intelligent Systems Engineering Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China Concordia University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9781665465335

This paper presents a decoupled parameter estimation method for PMSM considering temperature rise and inverter distortion. Both electrical and mechanical models are explored to derive the estimation model to achieve better parameter estimation accuracy. Specifically, the proposed approach firstly performs the initial estimation of the PM flux, winding resistance and friction coefficient by considering the temperature effect, and then derives the decoupled model for inductance estimation using both the electrical and mechanical measurements. Compared with other approaches, our decoupled approach employs the mechanical model and considers the temperature effect, which can make the parameter identification more accurate. The experiment is performed on a test PMSM to verify the estimation performance of the proposed approach. © 2022 IEEE.

关键词： Parameter estimation

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Validating a Chaos Based PRBG Under Different Chaotic Maps

Validating a Chaos Based PRBG Under Different Chaotic Maps

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Moysis, Lazaros Lawnik, Marcin Volos, Christos Baptista, Murilo S. Fragulis, George F. Goudos, Sotirios K. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece Aristotle University of Thessaloniki Laboratory of Nonlinear Systems - Circuits and Complexity Physics Department Thessaloniki Greece University of Western Macedonia Department of Mechanical Engineering Kozani Greece Silesian University of Technology Faculty of Applied Mathematics Department of Mathematics Applications and Methods for Artificial Intelligence Gliwice44-100 Poland Supa University of Aberdeen Institute for Complex Systems and Mathematical Biology Department of Physics AberdeenAB24 3UX United Kingdom Aristotle University of Thessaloniki ELEDIA@AUTH Physics Department Thessaloniki Greece

ISBN: (纸本)9798350318845

Chaos-based random bit generators are abundantly used in chaos-based encryption and security applications, as a fast, deterministic source of randomness, to perform actions like permutation and substitution. The chaotic map used as a source of randomness though may heavily affect the bit generator's statistical randomness, key space, and speed. Thus, any given bit generator may not be equally efficient for all the chaotic maps used as a seed. Motivated by this, the present work considers a bit generator that utilizes a common modulo-based hashing technique, and studies its performance under a plethora of different chaotic maps. For each map, the generator is evaluated with respect to randomness, key space, and operations per bit ratio. This analysis can serve as a guideline for random bit generator usage in future chaos-based encryption designs. © 2024 IEEE.

关键词： Cryptography

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GSLB: The Graph Structure Learning Benchmark 37

GSLB: The Graph Structure Learning Benchmark

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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

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Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics

Deep Learning-Based Estimation of Arterial Stiffness from PP...

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

作者： Kiana Pilevar Abrisham Khalil Alipour Bahram Tarvirdizadeh Mohammad Ghamari Department of Mechatronics Engineering School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Advanced Service Robots (ASR) Laboratory University of Tehran Tehran Iran Department of Electrical and Computer Engineering Kettering University Flint Michigan USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) – the gold standard in this field – is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R 2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics.

关键词： Accuracy Noise Estimation Predictive models Feature extraction Brachytherapy Data models Arteries Spectrogram Monitoring

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Bi-level Co-Optimization Architecture for Transmission and Distribution Voltage Control

Bi-level Co-Optimization Architecture for Transmission and D...

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IEEE General Meeting Power& Energy Society

作者： Mohammad Abujubbeh Kumarsinh Jhala Karthikeyan Balasubramaniam Department of Electrical Engineering and Computer Engineering graduate intern at Argonne National Laboratory Kansas State University Center for Energy Environmental and Economic Systems Analysis (CEEESA) Argonne National Laboratory

With the increased penetration levels of behind-the-meter distributed energy resources in the distribution system and large utility-scale resources in the transmission system, it is critical to model and control these resources in an efficient and scalable manner to maintain the grid voltage stability. Traditional methods of voltage control mechanisms address the issue of voltage control in either transmission or distribution systems in isolation, which is not optimal. Therefore, there is a need for a co-optimization approach for improved grid efficiency. This work presents a bi-level architecture for transmission and distribution system voltage co-optimization. The proposed architecture uses sequential updates between the transmission and distribution system for improved accuracy. The proposed approach has been verified on the IEEE 9-bus transmission system coupled with the IEEE 13 and IEEE 123 node test distribution systems. In addition to capturing the impact of systems on each other, results show that the proposed approach helps to eliminate voltage violations at both the transmission and distribution systems.

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Robust Design of IRS-Aided Multi-Group Multicast System With Imperfect CSI

Robust Design of IRS-Aided Multi-Group Multicast System With...

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作者： Jiang, Weiheng Xiong, Peiyun Nie, Jiangtian Ding, Zhiguo Pan, Cunhua Xiong, Zehui Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering Jurong West639798 Singapore Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates The University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Tampines487372 Singapore

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design. © 2002-2012 IEEE.

关键词： Beamforming

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