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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Analysis of Effective IBR Actuators for Forced Oscillation Mitigation

Analysis of Effective IBR Actuators for Forced Oscillation M...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Lakshmi Thotakura, N Aga Zhao, Yi Liu, Yilu The University of Tennessee Department of Electrical and Computer Engineering KnoxvilleTN37996 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9798350352528

Forced Oscillations (FOs) occurring at frequencies close to a system's natural oscillation mode can induce resonance and widespread of high-amplitude oscillations. Different from mitigation approaches for natural oscillations, addressing FO mitigation requires special attention due to uncertainties of the FO source localization. Currently, accurate identification of FO source at the component level is time-consuming and unreliable. Supplementary FO damping control (FODC) through Inverter-Based Resources (IBR) is one state-of-the-art FO mitigation method. Selecting effective IBRs for FO mitigation is a critical step to guarantee the FO damping performance. This study explores how to select the most effective IBRs for damping FO resonant event with an inter-area natural oscillation. The study results in the l2l-bus mesh system show that the FODC through the IBRs near the FO source and close to the generators with high participation factor of natural oscillations can effectively mitigate FO events than other locations. © 2025 IEEE.

关键词： Damping

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A Biologically Inspired Program-Level Imitation Approach for Robots

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IEEE Access 2025年 13卷 89481-89501页

作者： Aliasghari, Pourya Ghafurian, Moojan Nehaniv, Chrystopher L. Dautenhahn, Kerstin University of Waterloo Department of Electrical and Computer Engineering Waterloo N2L 3G1 ON Canada University of Waterloo Department of Systems Design Engineering Waterloo N2L 3G1 ON Canada

Robots may learn new skills from humans to better assist us with everyday tasks. We propose a novel, biologically inspired imitation approach to enable robots to understand and perform complex actions using high-level programs that incorporate sequential regularities between sub-goals a robot can recognize and physically achieve. To learn a new task, human-provided demonstrations—obtained by a robot through different modalities such as kinesthetic teaching or behavioural observation—are processed by an algorithm to discover multiple possible arrangements of sub-goals that achieve the task goal. When performing the task, the robot first evaluates the available sequences in the program based on user-defined criteria, through mental simulation of the real task, to find the optimal sequence of actions. The selected sequence is then executed using the hierarchical structure of actions embedded in the program. We implemented the proposed learning architecture on an iCub humanoid robot and evaluated the effectiveness of the system in multiple scenarios. Our approach accommodates variations in human teaching styles and is expected to help robots perform tasks with greater flexibility and efficiency, opening the way to more adaptable and intelligent robots. © 2013 IEEE.

关键词： Learning from demonstration program-level imitation robot learning

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Distributed Estimation and Motion Control in Multi-Agent systems Under Multiple Attacks

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IEEE Transactions on Automation Science and engineering 2025年 22卷 12548-12559页

作者： Jenabzadeh, Ahmadreza Shu, Zhan Huang, Tingwen Zhu, Quanmin Shang, Yilun Cui, Yukang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Peng Cheng Laboratory Shenzhen518000 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China University of the West of England School of Engineering BristolBS16 1QY United Kingdom Northumbria University Faculty of Engineering and Environment Department of Computer and Information Sciences Newcastle upon TyneNE1 8ST United Kingdom

This paper addresses the problem of distributed estimation and motion control (DEMC) in multi-agent systems (MASs) with both linear and Lipschitz nonlinear dynamics. Unlike conventional DEMC methods designed for MASs under ideal conditions, this work investigates scenarios where all agents are vulnerable to various forms of attacks. The considered attacks comprise false-data injection (FDI) attacks and denial of service (DoS) attacks that affect the communication channels among agents to destabilize the MAS. Also, the unbounded actuator attacks which exist in practical environments to intentionally degrade the MAS performance is considered. To cope with these kinds of attacks, two novel resilient approaches are established aimed at estimating and following a mobile target under attacks. The proposed distributed attack-resilient control strategies are designed based on a dual-layer structure, guaranteeing effective DEMC with an ultimately bounded error. The results from two simulation examples are provided to validate the presented algorithms. © 2004-2012 IEEE.

关键词： Multi agent systems

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Relational Maneuvering Guidance Strategy for Leader-Follower Flexible Formation in Three Dimensions

Relational Maneuvering Guidance Strategy for Leader-Follower...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Ranjan, Praveen Kumar Sinha, Abhinav Cao, Yongcan Unmanned Systems Lab Department of Electrical and Computer Engineering The University of Texas at San Antonio San AntonioTX78249 United States Lab Department of Aerospace Engineering and Engineering Mechanics University of Cincinnati CincinnatiOH45221 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In this paper, we propose a new three-dimensional formation scheme for a leader-follower unmanned aerial vehicle (UAV) system, where the follower maintains formation by converging to a set of positions relative to the leader rather than a single point. Particularly, the follower maintains a fixed distance from the leader and constrains its bearing angle (which is the angle between its velocity vector and its line-of-sight (LOS) with respect to the leader) within some predefined intervals. This allows the follower to converge to a larger set, thereby enabling time-varying formation geometry and different orientations between the leader and the follower. This provides the follower flexibility to maneuver independently of the leader while maintaining formation. We model leader-follower UAVs as non-holonomic agents where their linear and angular speeds are chosen as control inputs to design closed-loop guidance laws under formation constraints. The proposed flexible-geometry formation scheme allows the follower to anticipate the leader’s movement and emulates the pilot’s behavior as in real air combat, tight formation, etc. The designed guidance laws are robust to the leader’s maneuver changes since the follower UAV’s control law does not need information on the leader’s angular speed control and only uses relative measurements. This makes the design lucrative even when global measurements are unavailable or if the leader UAV is non-cooperative. Finally, we present simulations for various target maneuvers to highlight the merits of the proposed flexible formation control laws. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Neuromorphic Wireless Split Computing with Multi-Level Spikes

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 502-516页

作者： Wu, Dengyu Chen, Jiechen Rajendran, Bipin Vincent Poor, H. Simeone, Osvaldo LondonWC2R 2LS United Kingdom Laboratory CIIPS Department of Engineering LondonWC2R 2LS United Kingdom Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Inspired by biological processes, neuromorphic computing leverages spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have shown that embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. To scale neuromorphic computing to larger workloads, split computing - where an SNN is partitioned across two devices - is a promising solution. In such architectures, the device hosting the initial layers must transmit information about the spikes generated by its output neurons to the second device. This establishes a trade-off between the benefits of multi-level spikes, which carry additional payload information, and the communication resources required for transmitting extra bits between devices. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs. We propose digital and analog modulation schemes for an orthogonal frequency division multiplexing (OFDM) radio interface to enable efficient communication. Simulation and experimental results using software-defined radios reveal performance improvements achieved by multi-level SNN models and provide insights into the optimal payload size as a function of the connection quality between the transmitter and receiver. © 2023 CCBY.

关键词： Digital storage

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A Novel Approach for Enhancing Joint Moment Prediction in Complex Gait Patterns Using Pretrained Weights in Time-Series Models

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IEEE Sensors Journal 2025年第10期25卷 17987-18000页

作者： Xiong, Baoping Lou, Jie Guo, Yinghui Gan, Zhenhua Zeng, Nianyin Xu, Yong Fujian University of Technology School of Computer Science and Mathematics Fuzhou350116 China Xiamen University Department of Instrumental and Electrical Engineering Xiamen361005 China Xiamen Ocean Vocational College Xiamen Key Laboratory of Intelligent Fishery Fujian Xiamen361100 China

Accurately predicting joint moments is crucial for assessing human motor function;however, obtaining such data directly from individuals remains challenging. While the gait data for common activities like walking (WAK) and running can be easily acquired through accelerometers, gyroscopes, and electromyography (EMG) sensors, the sensor data for less common or irregular gait patterns are relatively scarce. This scarcity limits the generalization ability of existing time-series models in predicting joint moments during complex gait patterns. To address this issue, this article proposes a novel method to improve the performance of time-series models in predicting joint moments during complex gait scenarios by utilizing pretrained weights. These pretrained weights are derived from the gait data collected from a large sample of healthy individuals, capturing key information that represents various muscle activity characteristics. The pretrained weights are then applied to time-series models, significantly enhancing their prediction accuracy in complex gait conditions. Experimental results show notable improvements in metrics, such as variance accounted for (VAF), root mean square error (RMSE), and the coefficient of determination ( R2 ), confirming that the pretrained weights improve the generalization capability of joint moment prediction models. © 2001-2012 IEEE.

关键词： Mean square error

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Protecting Your Attention during Distributed Graph Learning: Efficient Privacy-Preserving Federated Graph Attention Network

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1949-1964页

作者： Zhou, Jinhao Wu, Jun Ni, Jianbing Wang, Yuntao Pan, Yanghe Su, Zhou Waseda University Graduate School of Information Production and Systems Fukuoka808-0135 Japan Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features for improved graph representation learning while maintaining data decentralization. However, these mechanisms inherently process sensitive information, which is vulnerable to privacy threats like graph reconstruction and attribute inference. Additionally, their role in assigning varying and changing importance to nodes challenges traditional privacy methods to balance privacy and utility across varied node sensitivities effectively. Our study fills this gap by proposing an efficient privacy-preserving FGAT (PFGAT). We present an attention-based dynamic differential privacy (DP) approach via an improved multiplication triplet (IMT). Specifically, we first propose an IMT mechanism that leverages a reusable triplet generation method to efficiently and securely compute the attention mechanism. Second, we employ an attention-based privacy budget that dynamically adjusts privacy levels according to node data significance, optimizing the privacy-utility trade-off. Third, the proposed hybrid neighbor aggregation algorithm tailors DP mechanisms according to the unique characteristics of neighbor nodes, thereby mitigating the adverse impact of DP on graph attention network (GAT) utility. Extensive experiments on benchmarking datasets confirm that PFGAT maintains high efficiency and ensures robust privacy protection against potential threats. © IEEE.

关键词： Differential privacy

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Monocular vision navigation system for UAV autonomous mission: a real-time window-based obstacle avoidance approach

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Neural Computing and Applications 2025年 1-18页

作者： Javaid, Abdulrahman Alnaser, Mustafa Baroudi, Uthman Alfaraj, Amjad Research and Development Department Yokogawa Saudi Arabia Company Al Khobar Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Center for Intelligent Secure Systems King Fahd University of Petroleum Minerals Dhahran Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum Minerals Dhahran Saudi Arabia

The need for autonomous unmanned aerial vehicles (UAVs) is rapidly increasing for various industrial applications today. However, the realization of UAV autonomy requires a careful mix of Artificial Intelligence (AI) and computer vision (CV) techniques to be implemented to address end-user challenges and satisfy field requirements. This study aims to develop an intelligent system for UAV to achieve autonomy with standard low-cost hardware components. We have infused state-of-the-art AI and CV technologies into our developed system architecture to improve autonomous navigational capabilities of UAVs. The proposed architecture has been implemented and tested on the DJI Tello UAV equipped only with a monocular camera on-board and an inertial measurement unit (IMU). We focused on solving multiple practical technical problems that have arisen during this study due to the hardware limitations of the UAV. The study investigated methods for representing obstacles within an environmental map, which are crucial for effective avoidance and path planning, such as their edges, width, and shape, to ensure accurate detection and navigation. The experiments were carried out in an environment that included dynamic obstacles that were not considered in the map. The experimental results showed outstanding performance in terms of successful autonomous navigation for a wide range of light intensity. These results show that the proposed approach has a high potential to achieve autonomous UAV operation in the field with low hardware and energy requirements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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