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3rd IEEE International Conference on electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Bera, Bidisha Basu, Sarmistha National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Global Institute of Science and Technology Department of Computer Science and Technology Haldia India Calcutta Institute of Engineering and Management Department of Physics Kolkata India

ISBN: (纸本)9798350390728

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a Machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Spectral Estimation of Weak Signals with Strong Interference via Modulo Sampling 59

Spectral Estimation of Weak Signals with Strong Interference...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Ahmed, Shoaib Zeng, Cengcang Li, Hongbin Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

ISBN: (纸本)9798331513269

This paper introduces a framework for spectral analysis of weak signals in the presence of strong interference that can potentially exceed the dynamic range of the analog-to-digital converter (ADC) employed for data acquisition. The framework combines modulo sampling to avoid ADC saturation with interference cancellation (IC) and adaptive spectral estimators such as amplitude and phase estimation of a sinusoid (APES) and Capon for high-resolution spectral analysis. Numerical simulations validate the proposed approach's ability to accurately recover weak signals' spectrum with significantly lower mean-squared error (MSE) in amplitude estimation compared to conventional methods in the considered scenarios. Notably, it is shown that APES and Capon are able to effectively reject the out-of-range interference and reveal weak spectral content, without requiring the interference cancellation step. © 2025 IEEE.

关键词： Mean square error

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Efficient Classification of Rice Varieties Using Optimized Convolutional Neural Networks 8

Efficient Classification of Rice Varieties Using Optimized C...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Pathak, Abhishek Kumar Singh, Ankit Kumar Bhatia, Vimal Singh, Puneet Indian Institute of Technology Department of Electrical Engineering Indore India Motihari College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331531539

Rice type classification is a crucial task in agri-cultural automation, aimed at improving quality control and ensuring market standards. This study presents a deep learning-based approach using a optimized Convolutional Neural Network (CNN) model implemented with PyTorch to classify five rice varieties: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. Our proposed model diverges from conventional methods that rely heavily on complex machine learning algorithms or pretrained CNN architectures like MobileNetV2, VGG16, EfficientNetB0 etc., which require a large number of parameters and layers, re-sulting in increased computational complexity. Through proposed approach, we achieved a remarkable 99.81% validation accuracy and a minimal validation loss of 0.01 % with a significantly reduced model size 30.76 MB. The simplicity of the proposed CNN architecture not only enhances computational efficiency but also maintains high accuracy, making it a viable solution for rice type classification in practical applications. This study demonstrates that a fine-tuned CNN model can outperform more complex models, providing an efficient and effective alternative for agricultural classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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RIS-Aided MIMO Downlink Transmission for Ultradense LEO Satellite-Terrestrial Networks

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IEEE Internet of Things Journal 2025年第11期12卷 15304-15318页

作者： Zhang, Xin Qin, Xiaohan Zhang, Zitian Cai, Lin X. Zhou, Haibo Zhuang, Weihua Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou310018 China Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Ultradense low-Earth orbit (LEO) satellite-terrestrial network (ULSN) has evolved as a new paradigm to provide ubiquitous and high-capacity communications in next generation wireless networks. However, the direct LEO satellite broadband connectivity faces significant challenges in urban environments due to the masking effect, which limits the reliability and availability of communication links in ULSNs. To address this, reconfigurable intelligent surface (RIS) is emerging as a promising solution in ULSNs. In this article, we investigate RIS-aided downlink data transmission in urban environments of multiusers in ULSNs. We set up a mixed-integer programming (MIP) model for maximizing the sum rate of terrestrial users in ULSNs. To solve the complex MIP problem, we propose a two-phase joint optimization algorithm with a deep learning phase and an alternative optimization (AO) phase. In the deep learning phase, a deep neural network (DNN) algorithm is employed to obtain the optimal user association matrix based on the positions of terrestrial users and LEO satellites. Then in the AO phase, successive convex approximation is utilized to transform the nonconvex subproblems of beamforming and RIS phase design into convex formulations and iteratively solve them. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms. © 2014 IEEE.

关键词： Integer programming

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Distributed Global Nash Equilibrium of Interactive Adversarial Graphical Games

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Journal of Systems Science & Complexity 2025年第2期38卷 613-632页

作者： ZHANG Yizhong LIAN Bosen LEWIS Frank L. Department of Electrical and Computer Engineering Auburn University UTA Research Institute University of Texas at Arlington

This article formulates interactive adversarial differential graphical games for synchronization control of multiagent systems(MASs) subject to adversarial inputs interacting with the systems through topology communications. Local control and interactive adversarial inputs affect each agent's local synchronization error via local networks. The distributed global Nash equilibrium(NE) solutions are guaranteed in the games by solving the optimal control input of each agent and the worst-case adversarial input based solely on local states and communications. The asymptotic stability of the local synchronization error dynamics and the NE are guaranteed. Furthermore, the authors devise a data-driven online reinforcement learning(RL) algorithm that only computes the distributed Nash control online using system trajectory data, eliminating the need for explicit system dynamics. A simulation-based example validates the game and algorithm.

关键词： Adversarial inputs differential graphical games Nash equilibrium reinforcement learning

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Security and Privacy Threats of O-RAN for Home Healthcare 6

Security and Privacy Threats of O-RAN for Home Healthcare

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6th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Tang, Bing-Han Liu, I-Hsien Chen, Hou-Hua Li, Jung-Shian Institute of Computer and Communication Engineering National Cheng Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9798350396133

In recent years, the rapid expansion of home healthcare has increased the demand for efficient medical services and the optimal utilization of medical resources. Thus, Open Radio Access Network (O-RAN) technology is used for such an innovation. As a revolutionary wireless communication architecture, O-RAN technology offers advantages, including openness, flexibility, and scalability, rendering it an ideal solution for home healthcare applications. However, the utilization of O-RAN and 5G base stations in home healthcare systems also presents information security concerns. One significant risk entails the potential for unauthorized base stations to intercept or manipulate data, posing a threat to the integrity and confidentiality of home healthcare information. Moreover, there exists the risk of individuals attempting to intercept messages to access sensitive medical data, thereby compromising patient privacy. Given these challenges, it is imperative to identify and address potential security vulnerabilities inherent in O-RAN technology, thereby ensuring the robust security of home healthcare systems. © 2024 IEEE.

关键词： Differential privacy

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Passive islanding detection in microgrids using artificial neural networks

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Applied Nanoscience (Switzerland) 2023年第4期13卷 2885-2900页

作者： Alyasiri, Ali Majeed Mohammed Kurnaz, Sefer Department of Electrical and Computer Engineering Institute of Science Altinbas University Istanbul Turkey

This research focuses on modeling and simulating voltage control of passive islanding detections with distributed generation. This research presents how reactive power generation and/or absorption can be utilized to partake voltage control in medium voltage distribution through multi-microgrids for passive islanding detection with rule-based mathematical model and artificial neural network (ANN). With the increased emphasis on renewable energy, modern power grids have become more reliant to smaller distributed generation units. Unlike traditional power grids that rely on larger centralized units, the detection of islanding events in these grids is more complex. However, it is still important to maintain the connection to the power grid to maintain high stability of the system and is also important to deenergize the grid when an islanding event happens, to protect the workers that may be working on the grid to clear the cause of the islanding. Islanding detection methods are categorized into passive, active and communication based. Active methods detect islanding by measuring the influence of noise they add to the grid, to predict the size of the grid, which reduces the quality of the power provided on that grid. Communication-based methods are expensive and highly reliable on the communications infrastructure, which limits their application. Unlike existing methods, the proposed method of ANN relies on the instantaneous current and voltage values to detect any islanding with an accuracy of 97.89% precisely. These values are directly fed to the ANN, i.e., without applying any feature extraction, so that, faster and more accurate decisions are made. The proposed method is expected to combine the high accuracy of ANNs with the faster change in instantaneous values, so that, faster and more accurate detection can be achieved. © 2022, King Abdulaziz City for Science and Technology.

关键词： Detection Distributed energy Distribution grid Islanding Microgrid Reactive power Voltage control

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Preprocessing Taste Data for Deep Neural Networks 14

Preprocessing Taste Data for Deep Neural Networks

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Lee, Hyunjong Jung, Han Hee Kwak, Jeongho Yea, Junwoo Choi, Jihwan P. Jang, Kyung-In Department of Electrical Engineering and Computer Science Daegu Korea Republic of Dgist Department of Robotics and Mechatronics Engineering Daegu Korea Republic of Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of Department of Aerospace Engineering Dajeon Korea Republic of

ISBN: (纸本)9798350313277

Analyzing wine using taste data is a promising field due to the explosive expansion of online commerce. However, because of the wide variety of wine types with different flavors and aromas, it is difficult for consumers to choose the wine that suits their taste, and also difficult for sellers to recommend appropriate wines to consumers. Therefore, it is necessary to numerically analyze and classify wine, and a deep learning algorithm which mimics the human brain is appropriate for analyzing the wine data [1]. In this paper, we introduce several studies of wine classification using deep learning architectures and propose preprocessing methods for applying the taste data of wine to deep learning networks. © 2023 IEEE.

关键词： data augmentation deep learning preprocessing taste classification taste sensor wine

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Threat to Rogue Base Station in Home Healthcare Ecosystem 6

Threat to Rogue Base Station in Home Healthcare Ecosystem

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6th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Chen, Hou-Hua Liu, I-Hsien Tang, Bing-Han Li, Jung-Shian Institute of Computer and Communication Engineering National Cheng Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9798350396133

With increasing aging populations and their chronic illnesses, the importance of the home healthcare ecosystem is growing to reduce the need for hospital visits by providing medical care at home. The integration of 5G technology holds potential in the home healthcare ecosystem as 5G offers low latency, high throughput, and massive machine-type communication, supporting various medical devices and sensors, fundamentally transforming patient care. It also allows for remote real-time monitoring of vital signs, facilitating access to healthcare services. However, concerns about information security become serious, particularly regarding the cybersecurity of 5G base stations in the ecosystem. Unauthorized access or tampering with these base stations disrupts medical devices, leads to data breaches, or even deploys rogue base stations for malicious purposes such as stealing user information. Therefore, we investigated the threat of rogue base stations and found an effective way to secure the home healthcare ecosystem. © 2024 IEEE.

关键词： 5G mobile communication systems

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