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Distortion Evaluation of EMP Sensors Using Associated-Hermite Functions

computers, Materials & Continua 2023年第1期74卷 1093-1105页

作者： Rupo Ma Siping Gao College of Command and Control Engineering Army Engineering UniversityNanjing210007China Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Department of Electrical and Computer Engineering National University of Singapore119260Singapore

Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and *** is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole ***,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement *** fidelity is usually employed to evaluate the distortion of an ***,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP *** this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual *** system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite *** of a sensor *** is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the *** of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.

关键词： Electromagnetic pulse associated-hermite functions system transfer matrix distortion rate

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Social NSTransformers: Low-Quality Pedestrian Trajectory Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5575-5588页

作者： Jiang, Zihan Ma, Yiqun Shi, Bingyu Lu, Xin Xing, Jian Goncalves, Nuno Jin, Bo Suzhou215000 China Northeast Forestry University College of Computer and Control Engineering Harbin150000 China University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal

This article introduces a novel model for low-quality pedestrian trajectory prediction, the social nonstationary transformers (NSTransformers), that merges the strengths of NSTransformers and spatiotemporal graph transformer (STAR). The model can capture social interaction cues among pedestrians and integrate features across spatial and temporal dimensions to enhance the precision and resilience of trajectory predictions. We also propose an enhanced loss function that combines diversity loss with logarithmic root mean squared error (log-RMSE) to guarantee the reasonableness and diversity of the generated trajectories. This design adapts well to complex pedestrian interaction scenarios, thereby improving the reliability and accuracy of trajectory prediction. Furthermore, we integrate a generative adversarial network (GAN) to model the randomness inherent in pedestrian trajectories. Compared to the conventional standard Gaussian distribution, our GAN approach better simulates the intricate distribution found in pedestrian trajectories, enhancing the trajectory prediction's diversity and robustness. Experimental results reveal that our model outperforms several state-of-the-art methods. This research opens the avenue for future exploration in low-quality pedestrian trajectory prediction. © 2024 IEEE.

关键词： Stars

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Physical Beam Sharing for Communications With Multiple Low Earth Orbit Satellites

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IEEE Transactions on Signal Processing 2024年 72卷 2783-2798页

作者： He, Yan-Yin Tsai, Shang-Ho Poor, H. Vincent Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Physical (analog) beamforming is expected to become an important technique in Low Earth Orbit (LEO) satellite transmission in upcoming 6G communications. To build dense networks via LEO satellites and decrease deployment expenses, the corresponding satellites should have minimal hardware, low computational complexity, and limited power consumption. Additionally, issues such as different propagation delays, Doppler effects, long feedback delays, and serious attenuation caused by bad weather should also be addressed in LEO satellite communications. This work proposes a novel physical beam sharing scheme for multiple-satellite systems that enables a satellite to simultaneously serve multiple users using only one Radio-Frequency (RF) chain, consequently leading to a significant reduction in hardware complexity and power consumption. In this case, all users can achieve full multiplexing gain. Furthermore, we propose two ways of optimizing for this novel scheme. One is maximizing the throughput of all users, and the other is minimizing the LEO satellite system's power consumption. For the problems, we propose both solutions using instantaneous Channel State Information (iCSI) and long-term statistical Channel State Information (sCSI). Simulation results demonstrated that the proposals significantly outperform conventional schemes in terms of throughput and power consumption, and the performance of the proposed sCSI solutions is close to that of iCSI ones. © 1991-2012 IEEE.

关键词： Electric power utilization

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Microscopy-Guided 3-D Reconstruction of Nanodendrites in Biosensors

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IEEE Sensors Journal 2023年第9期23卷 9025-9032页

作者： Fan, Sue-Yuan Huang, Yi-Pin Khuntia, Sucharita Chang, Jen-Wen Liou, Ci-Ruei Zhang, Bing Tai, Li-Chia Institute of Electrical and Control Engineering Department of Electrical and Computer Engineering Hsinchu30010 Taiwan Lam Research FremontCA94538 United States

Nanodendritic structures have gained increasing popularity in electrochemical sensors. However, it is still rare to generate a 3-D model in a short period of time to understand the structure-function relationship of the sensors. Here, we report the construction of a 3-D model for nanodendritic, metallic structures frequently grown on top of bioelectronics. This is achieved by merging two sources of 2-D dendritic information, which includes top-view images from scanning electron microscopy (SEM) and side-view visualization from Monte Carlo simulations. The microscopy images provide the boundary conditions to tune the Monte Carlo simulations to construct the 3-D dendritic morphology. We validated the 3-D model by comparing the dendritic area densities predicted via this model with those computed from microscopy images. In addition, tuning the simulation parameters in the 3-D model can be used to find the optimized dendritic density, which is an essential indicator for sensitivity enhancement. The success of this model provides a means to understand the sensitivity limits of bioelectronics through dendritic growth without the need for time-consuming sensor fabrication and testing. Furthermore, our SEM-guided Monte Carlo technique provides a dendritic model with a significant resemblance to experimental images. It possesses the potential for applications in 3-D morphological investigations for future biosensor design. © 2001-2012 IEEE.

关键词： Scanning electron microscopy

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A two-stage progressive deep segmentation network for tumor detection in breast ultrasound images

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Multimedia Tools and Applications 2024年 1-23页

作者： Zaidkilani, Nadeem Abdel-Nasser, Mohamed Garcia, Miguel Angel Puig, Domenec Department of Computer Engineering and Mathematics University Rovira i Virgili Tarragona43007 Spain Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronic and Communications Technology Autonomous University of Madrid Madrid Spain

Segmenting tumorous regions in breast ultrasound images is a challenging problem due to several factors, including the relatively low contrast of the available images, the presence of speckle noise, and the considerable variations in breast mass sizes and shapes. Current methods are not precise enough and prone to misdetections. An efficient deep neural model is proposed for automatically segmenting tumorous regions in breast ultrasound images. The model is constituted by two consecutive encoder-decoder (autoencoder) networks. The first autoencoder extracts a preliminary binary mask from the given image. The second autoencoder refines that mask after concatenating it with the original image. The encoders within each autoencoder can be defined by applying any state-of-the-art network. In addition, cost-sensitive learning has been used in order to focalize training on the segmentation errors of the minority class (tumor). Semantic segmentation based on advanced deep learning methods is thus applied in order to enhance tumor segmentation in breast ultrasound images. The proposed model offers advanced capabilities for automated segmentation with the aim of helping physicians identify and diagnose tumors using state-of-the-art techniques. This model outperforms recent tumor segmentation methods in the experiments conducted on two public datasets of breast ultrasound images (UDIAT and BUSI). The largest improvement for both datasets was achieved by using CoAtNet as baseline model (Dice index equal to 84.49% and 78.94%, respectively). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Ultrasonic imaging

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User Input Search - Custom Motion Estimation Algorithm Optimized for UAVs 46

User Input Search - Custom Motion Estimation Algorithm Optim...

引用

46th ICT and Electronics Convention, MIPRO 2023

作者： Benjak, J. Hofman, D. Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Zagreb Croatia

ISBN: (纸本)9789532331042

Unmanned aerial vehicle technology is growing rapidly as it finds its application in the various industries, including military & defense, agriculture, logistics, transportation, healthcare, entertainment and many others. One of the fastest growing industries including drones is the entertainment industry. More specifically, First Person View (FPV) piloting has become a popular sport which attracts huge masses. In FPV systems, the pilot wears FPV goggles and controls the drone using a controller, and the drone transmits video data to the goggles in real time. Since drones usually fly very quickly, the video quality in terms of resolution, compression artefacts and end-to-end delay must be maintained. This paper explores the idea of optimizing the Motion Estimation (ME) algorithm of existing High Efficiency Video Coding (HEVC) algorithms by utilizing user input from the controller. For example, if the drone is directed to hover to the left, then it would make sense to search for the most similar blocks in the previous video frame only on the left side of the referent block. For the purpose of this research, a HEVC video coder was customized to receive additional input besides the video data - the user input from the controller, or in other words, drone movement directions. We call this ME algorithm User Input Search (UIS). Our UIS algorithm is compared with the standard ME algorithms and its efficiency is tested. © 2023 MIPRO Croatian Society.

关键词： Real time systems

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Bicheng Zhou, Conghao Liu, Haoyu He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering China University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350351255

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving Power Response of Single-controllable VSG-Based Active Distribution Network

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 102-114页

作者： Shah Fahad Arman Goudarzi Pierluigi Siano Ji Xiang Department of Electrical and Computer Engineering Missouri University of Science and TechnologyUSA University of Victoria Canada Department of Management&Innovation Systems University of SalernoItaly Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg2006South Africa National Laboratory of Industrial Control Technology College of Electrical EngineeringZhejiang UniversityHangzhou310058China

controlling an active distribution network(ADN)from a single PCC has been advantageous for improving the performance of coordinated Intermittent RESs(IRESs).Recent studies have proposed a constant PQ regulation approach at the PCC of ADNs using coordination of non-MPPT based ***,due to the intermittent nature of DGs coupled with PCC through uni-directional broadcast communication,the PCC becomes vulnerable to transient *** address this challenge,this study first presents a detailed mathematical model of an ADN from the perspective of PCC regulation to realize rigidness of PCC against ***,an H_(∞)controller is formulated and employed to achieve optimal performance against disturbances,consequently,ensuring the least oscillations during transients at ***,an eigenvalue analysis is presented to analyze convergence speed limitations of the newly derived system ***,simulation results show the proposed method offers superior performance as compared to the state-of-the-art methods.

关键词： Cooperative control of DGs hierarchical control system high penetration of intermittent RESs H_(∞)control optimal control in power systems single-controllable active distribution networks

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Architecture of AC-DC Energy Nodes in Supergrids: A Technical Foundation for Future Renewable Integration 5

Architecture of AC-DC Energy Nodes in Supergrids: A Technica...

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5th National and the 1st International Conference on Applied Research in electrical engineering, AREE 2025

作者： Mousavi, Mohammad Hossein Moradi, Hassan Rouzbehi, Kumars Razi University Department of Computer and Electrical Engineering Kermanshah Iran University of Seville Department of System Engineering and Automatic Control Seville Spain

ISBN: (纸本)9798350377170

The AC-DC Energy Nodes (ADENs) concept offers a transformative approach to modernizing power grids, particularly in the context of supergrids. By centralizing power flows from diverse renewable energy sources, such as offshore wind farms, ADENs enable efficient long-distance power transmission through HVDC technology. This design enhances the balance of supply and demand across regions and countries, significantly improving grid stability and resilience. However, the implementation of ADENs presents substantial technical, regulatory, and cybersecurity challenges. This study delves into these challenges while also addressing the geopolitical complexities of transnational connections, underscoring the critical importance of policy alignment for successful deployment. © 2025 IEEE.

关键词： Wind power integration

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