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Intelligent integrated sensing and communication:a survey

Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Demand side management with wireless channel impact in IoT-enabled smart grid system

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Digital Communications and Networks 2025年第2期11卷 493-504页

作者： Md.Farhad Hossain Kumudu S.Munasinghe Nishant Jagannath Khandakar Tanvir Ahmed Md.Nabid Hasan Ibrahim Elgendi Abbas Jamalipour Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology School of Information Technology and Systems University of Canberra School of Electrical and Information Engineering The University of Sydney

Demand Side Management(DSM) is a vital issue in smart grids, given the time-varying user demand for electricity and power generation cost over a day. On the other hand, wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid. The design of any DSM system using a wireless network must consider the wireless link impairments, which is missing in existing literature. In this paper, we propose a DSM system using a Real-Time Pricing(RTP) mechanism and a wireless Neighborhood Area Network(NAN) with data transfer uncertainty. A Zigbee-based Internet of Things(IoT) model is considered for the communication infrastructure of the NAN. A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link. The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users, decision-makers, and energy providers. A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices. Simulation results indicate that the proposed system benefits users and energy providers. Furthermore, experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN, which can substantially impact the performance of the proposed DSM system. Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price, user welfare, and provider welfare.

关键词： Smart grid Real time pricing Demand side management Wireless communications Zigbee

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A novel MODWT–local pattern transformation feature fusion approach for high-impedance fault detection in medium voltage power distribution networks

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Neural Computing and Applications 2025年 1-15页

作者： Varghese, P. Rini Subathra, M.S.P. Peter, Geno Stonier, Albert Alexander Kuppusamy, Ramya Teekaraman, Yuvaraja Department of Electrical and Electronics Engineering School of Engineering and Technology Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Department of Robotics Engineering School of Engineering and Technology Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India CRISD School of Engineering and Technology University of Technology Sarawak Sarawak Malaysia School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Bengaluru562106 India Kuwait Jahra Kuwait

Due to the arcing nature, the high-impedance fault (HIF), which typically occurs in medium voltage (MV) distribution networks, poses a risk to equipment, personnel, and livestock. Early fault detection can save lives and prevent equipment destruction. Since the fault current in a power system is contained within the normal current range, identifying the occurrence of HIF is difficult. Using maximal overlap discrete wavelet transform (MODWT) and a combination of MODWT–local pattern transformations (local binary pattern, local gradient pattern, local neighbor gradient pattern, and local neighbor descriptive pattern), the paper analyses the current signals from radial and mesh distribution networks and features extracted during HIF and non-HIF (line-to-ground (LG), double line (LL), double line-to-ground (LLG), and triple line-to-ground (LLLG) fault conditions). For the first time in power distribution networks for fault analysis, the suggested algorithm performs the HIF detection in the MV distribution system. To determine the best feature sets from the extracted features, the Kruskal–Wallis test was performed. Bidirectional long short-term memory (Bi-LSTM) was used for the chosen feature sets to classify the data as HIF or non-HIF. MODWT–LGP fusion achieves the highest accuracy for both networks among the four algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Discrete wavelet transforms

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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Dynamic Analysis of Uniformity and Difference for Grid-following and Grid-forming Voltage Source Converters Using Phasor and Topological Homology Methods

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 3-14页

作者： Haiyu Zhao Hongyu Zhou Wei Yao Qihang Zong Jinyu Wen State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China

Grid-following voltage source converter(GFLVSC)and grid-forming voltage source converter(GFM-VSC)have different dynamic characteristics for active power-frequency and reactive power-voltage supports of the power *** paper aims to clarify and recognize the difference between gridfollowing(GFL)and grid-forming(GFM)frequency-voltage support more intuitively and ***,the phasor model considering circuit constraints is established based on the port circuit equations of the *** is revealed that the voltage and active power linearly correspond to the horizontal and vertical axes in the phasor space referenced to the grid voltage ***,based on topological homology,GFL and GFM controls are transformed and mapped into different *** topological similarity of the characteristic curves for GFL and GFM controls is the essential cause of their *** on the above model,it is indicated that GFL-VSC and GFM-VSC possess uniformity with regard to active power response,type of coupling,and phasor *** differ in synchronization,power coupling mechanisms,dynamics,and active power-voltage operation domain in the quasi-steady *** studies are undertaken on GFL-VSC and GFM-VSC integrated into a four-machine two-area *** results verify that the dynamic uniformity and difference of GFL-VSC and GFM-VSC are intuitively and comprehensively revealed.

关键词： Renewable energy grid-following converter grid-forming converter phasor method topological homology

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Application Of Lightweight Facial Expression Recognition Based On Deep Convolutional Asymmetric Residual Attention Network Model In Education Management

Journal of Applied Science and Engineering

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Journal of Applied Science and engineering 2025年第11期28卷 2227-2237页

作者： Kong, Po School of Electronics and Electrical Engineering Zhengzhou University of Science and Technology Zhengzhou450064 China

In view of the low accuracy of facial expression recognition in natural state, which is easily affected by noise and other factors, this paper proposes a lightweight facial expression recognition method based on deep convolutional asymmetric residual attention network model. In this paper, Ghost module and depth-separable convolution are introduced to replace 1×1 convolution and 3×3 convolution in Bottleneck, which retains more original feature information and improve the feature extraction ability of backbone branches. A parallel deep convolutional residual structure is designed in the shallow network to enhance the model’s ability to represent the local details of facial expressions and integrate them with the global features. A spatial grouping enhanced attention mechanism is established in the deep network to improve the stability of expression feature distribution and enhance the ability of the model to discriminate subtle changes in expression. In order to avoid over-fitting of the model, the output structure of the backbone network is improved without greatly increasing the computational complexity. The Mish activation function is used to replace the ReLU activation function in Bottleneck, which improves the accuracy of expression recognition. The expression recognition accuracy of the proposed method on the published seven-classification dataset RAF-DB, AffectNet-7 and eight-classification dataset AffectNet-8 reaches 88.44%, 63.20% and 60.23%, respectively. The experimental results show that the proposed method can improve the expression recognition accuracy while reducing network parameters. It is proved that the method is effective and has a certain application prospect. © The Author(’s).

关键词： Convolution

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The optimal control theory——a scientific approach to fundamentally solving control problems

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Science China(Information Sciences) 2025年第1期68卷 389-391页

作者： Huanshui ZHANG School of Electrical and Automation Engineering Shandong University of Science and Technology School of Control Science and Engineering Shandong University

The maximum principle has bridged mathematical optimization to optimal control,ushering in significant developments and refinements in optimal control theory,notably during the 1960s with the advent of linear quadratic (LQ)control and linear quadratic estimation (LQE).This progression propelled optimal control theory into further advancements,encompassing stochastic control,robust/H-infinity control,model predictive control (MPC),networked control,and reinforcement learning *** control,established upon a rigorous mathematical foundation,extends static optimization theory to dynamic systems,exhibiting scientific essence,unity,and ***,since its inception,optimal control theory has served as an indispensable core role across all control-related domains,including communication-constrained control in networked systems,consensus control,cooperative control,and reinforcement learning control.

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Enhancing Grid-Connected Hybrid Renewable Energy System with Super Lift Luo Converter for Improved Power Management and Reduced THD

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 129-143页

作者： Ganga Bhavani, Chaladi S. Bhanu Prasad, N. Ravi Kishore, D. Department of Electrical and Electronics Engineering GIET University Odisha Gunupur India Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology Rajahmundry India

In recent decades, use of renewable energy sources (RES) has become increasingly popular due to its reliable and clean source of electricity. Utilizing RES like solar and wind helps in ensuring energy sustainably. Furthermore, it is admirable to employ renewable resources popularly in order to generate energy that is both affordable and dependable. Solar and wind energy sources are intermittent, and their energy output can vary due to weather conditions. Combining them helps to mitigate the variability in power generation. In the proposed work in combination of solar and wind turbine source is implemented for increased system reliability with minimized cost. The proposed approach uses Super Lift Luo Converter to boost up the voltage generated from PV system. The employed Proportional Integral (PI) controller regulates the converter operation and aids in the generation of a stable voltage at the DC link. A similar process ensues in the Doubly Fed Induction Generator based Wind Energy Conversion System, which produces AC voltage that is converted into DC by a PWM rectifier to supply the DC Link with input power. In case of excess generation of energy from the hybrid systems, a battery is utilized for storing energy. For the conversion of DC electricity to AC, a three-phase voltage source inverter is supplied the DC link voltage. Grid synchronization is accomplished utilizing a PI controller, while the LC filter decreases harmonics. Grid synchronization is accomplished with help of a PI controller, and harmonics are reduced by an LC filter. The performance of suggested concept is appraised using the experimental setup and the corresponding results are verified utilizing MATLAB simulation. The proposed system operates at the efficiency of 96%, with reduced Total Harmonic Distortion (THD) of 0.93%. © The Institution of Engineers (India) 2024.

关键词： Pulse width modulation

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Fault classification and identification through machine learning approaches for a solar PV – battery based water pumping system

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Multimedia Tools and Applications 2025年第14期84卷 13843-13864页

作者： Sowmmiya, U. Abinaya, K. Jhunjhunwala, Rishik Satpathy, Prateek Arul, R. Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur India Department of Electrical and Electronics Engineering Vellore Institute of Technology Chennai India

The world progresses towards enabling renewable sources into the mainstream supply of energy and it is imperative to develop systems that can handle new challenges and disturbances. This paper aims at machine learning model-based fault identification and classification of an islanded Solar PV – battery integrated system feeding a water pumping system. The system is powered through solar PV and battery (discharge mode) which are controlled through Semi Pilot Cell based Fractional Open Circuit Voltage (SFOCV) method and conventional voltage control method respectively. The water pumping system is simulated using MATLAB which is interfaced with RT-LAB software for extracting the real-time data from the OPAL – RT Controller (OP4510) through Software-in-Loop (SiL) approach. All possible faults associated with a stand-alone solar PV-battery system are aggregated to execute the machine learning model. This data is pre-processed and the distribution plots are observed to describe the statistical nature of the data. The processed data is then used to train a Logistic Regression (LR) Model and Artificial Neural Network (ANN) Model to identify whether there is a fault or non-fault condition (Binary Classification) and then classify which fault case has occurred (Multi-class Classification). The models are trained and validated in the controller. The resulting comparative parameters such as accuracy and kappa values indicate that ANN model proves to be a better solution for fault identification and classification. Also, the cost functions (error) seem to be less in ANN model and the comparative results are discussed in this work. The proposed intelligent, fault identification and classification model being implemented with real-time data ensures reliability and immediate indication to the stakeholders on the status of the fault in the system. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： MATLAB

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Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7

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High Voltage 2025年第1期10卷 69-77页

作者： Yong Yang Shuai Yang Chuan Li Yunxuan Wang Xiaoqian Pi Yuxin Lu Ruohan Wu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and ElectronicsHuazhong University of Science and TechnologyWuhanChina State Grid Hunan Electric Power Company ChangshaChina

Efficient and accurate insulator defect detection is essential for maintaining the safe and stable operation of transmission ***,the detection effectiveness is adversely impacted by complex and changeable environmental backgrounds,particularly under extreme weather that elevates accident ***,this research proposes a high-precision intelligent strategy based on the synthetic weather algorithm and improved YOLOv7 for detecting insulator defects under extreme *** proposed meth-odology involves augmenting the dataset with synthetic rain,snow,and fog algorithm ***,the original dataset undergoes augmentation through affine and colour transformations to improve model's generalisation performance under complex power inspection *** achieve higher recognition accuracy in severe weather,an improved YOLOv7 algorithm for insulator defect detection is proposed,integrating focal loss with SIoU loss function and incorporating an optimised decoupled head *** results indicate that the synthetic weather algorithm processing significantly improves the insulator defect detection accuracy under extreme weather,increasing the mean average precision by 2.4%.Furthermore,the authors’improved YOLOv7 model achieves 91.8%for the mean average precision,outperforming the benchmark model by 2.3%.With a detection speed of 46.5 frames per second,the model meets the requirement of real-time detection of insulators and their defects during power inspection.

关键词： weather extreme algorithm

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