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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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Possibilities of Adopting Electric Vehicles in the Agritourism Development Context 18

Possibilities of Adopting Electric Vehicles in the Agritouri...

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Samuil, Ionela Stancioiu, Loredana Ionica, Andreea Cristina Leba, Monica University of Petrosani Department of Management and Industrial Engineering Petrosani Romania University of Petrosani Engineering and Management Doctoral School Petrosani Romania University of Petrosani Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9789893347928

In a society that is looking for ways of sustainable development, the use of electric vehicles (EVs) can be an applicable solution in different fields of activity, including the tourism industry. The current research analyzes the possibility of using Buggy EVs in a predominantly agritourism region. In this sense, with the help of STELLA Architect, dynamic modeling software, a simulation is run to determine the potential behavior of tourists who use different types of off-road vehicles and their willingness to become Buggy EV users. Based on the data obtained, an algorithm is developed to size the fleet of Buggy EVs necessary for carrying out the activity, taking into account the most important characteristics of the existing Buggy EV, namely range, charging time and price. The results obtained from the study can be used as a starting point for improving the tourist experience, by integrating the technology we are so connected to. © 2023 ITMA.

关键词： computer software

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A network-based bioinformatic analysis for identifying potential repurposable drugs for obesity avoiding hepatic steatosis side-effect

A network-based bioinformatic analysis for identifying poten...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Brunetti, Martina Fiscon, Giulia Costanzo, Alessia Di Arca, Marcello Paci, Paola Sapienza University of Rome Department of Experimental Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science "a. Ruberti" National Research Council of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Sapienza University of Rome Policlinico Umberto I Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Obesity is a complex multifactorial disorder characterized by the excess accumulation of body fat that impairs human health due to the risk of developing other diseases, including cardiovascular and hepatic diseases, hypertension, diabetes, hyperlipidemia. Its spread has been progressively accelerating, resulting in an unprecedented epidemic with no significant signs of slowing down any time soon. Drug therapy via the proposal repurposing solutions can represent an actionable treatment strategy, even if the emergence of drug-induced adverse effects can affect the treatment of this pathology. In this study, we propose a network-based analysis to identify a list of drug candidates predicted to be repurposable for obesity that are unlikely to produce specific adverse side-effect, such as hepatic steatosis. © 2024 IEEE.

关键词： Nutrition

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Two-level deep reinforcement learning approach for energy management of integrated energy system considering deep peak shaving for combined heat and power and coordinated scheduling of electric vehicles

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Applied Thermal engineering 2025年 275卷

作者： Zhang, Pengchao Yang, Tingting Yan, Huiru Zhu, Yansong Hu, Yong Zeng, Deliang Liu, Jizhen School of Control and Computer Engineering North China Electric Power University Beijing102206 China

The development and utilization of renewable energy have effectively reduced dependence on limited fossil fuel resources and greenhouse gas emissions. However, the accompanying uncertainty of renewable energy generation presents substantial challenges to the stability of the power system. Existing energy management algorithms of Integrated Energy Systems (IES) often rely on precise system models and load forecasting, which frequently fail to adapt to the high level of uncertainty of realistic scenarios, thereby limiting scheduling flexibility and efficiency. To address these issues, a two-stage scheduling strategy for energy management based on two-level Deep Reinforcement Learning (DRL), referred to as TLPPO, is proposed. The results indicate that the TLPPO effectively enhances the peak-shaving capabilities of the IES, with a 10.24% and 16.16% increase in the self-balancing rate compared to other multi-agent algorithms. Additionally, the two-stage dispatch strategy enhances the peak-shaving capabilities of the IES, allowing it to locally absorb a high proportion of renewable energy, reaching up to 82.63% of the total load. This strategy also significantly reduces the duration of deep peak-shaving for coal-fired power plant units. Validation under typical scenarios demonstrates that it completely eliminates operations below 40%, limits the 40%–50% operating range to just 3 h, and ensures the economic and safe operation of the power plant. These research findings serve as a reference for the management of IES with high renewable energy penetration. © 2025 Elsevier Ltd

关键词： Deep reinforcement learning

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Estimation of D-Q Inductances and Initial Rotor Position Considering Voltage Distortions in High-Speed Motors for Air Compressors of Fuel-Cell Systems

Estimation of D-Q Inductances and Initial Rotor Position Con...

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2023 IEEE International Symposium on Sensorless control for Electrical Drives, SLED 2023

作者： Lee, Min Seong Lee, Sang Gyu Kang, Sung Ho Yoon, Young Doo Hanyang University Department of Automotive Engineering Automotive-Computer Convergence Korea Republic of Hanyang University Department of Vehicle Control Engineering Korea Republic of Hanyang University Department of Automotive Engineering Korea Republic of

ISBN: (纸本)9798350335422

It is difficult to use a position sensor for high-speed motors because of mechanical limitations such as vibration and heat. When the initial position of the motor is unknown, there is a risk of initial start failure or temporary reverse rotation. Reverse rotation would cause slight damage to an airfoil bearing in high-speed applications, degrading its durability. Therefore, an initial position estimation algorithm is required for high-speed motor drives. Additionally, to implement current controllers and sensorless control algorithms, it is necessary to know the electrical parameters. This study proposes a method for estimating the dq-axis inductances and initial position. The inductances are estimated considering the voltage distortion caused by the inverter nonlinearity;thus, the inductance values are identified with less variation regardless of the injected voltage levels. Moreover, because the inductances can be estimated even at a low voltage, there is low risk of rotor movement during the estimation process. Simulation and experimental results for a 120-krpm high-speed motor for fuel-cell systems verified the effectiveness of the proposed method. © 2023 IEEE.

关键词： Inductance

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Differential gene correlation analysis to investigate evolutionary divergence between Cardamine hirsuta and Arabidopsis thaliana

Differential gene correlation analysis to investigate evolut...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： D'Annunzio, Federica Fiscon, Giulia Bertolotti, Gaia Dello Ioio, Raffaele Paci, Paola Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science 'A. Ruberti' National Research Council of Rome Department of Computer Control and Management Engineering 'A. Ruberti' Rome Italy Sapienza University of Rome Department of Biology and Biotechnology 'Charles Darwin' Rome Italy Department of Computer Control and Management Engineering 'A. Ruberti' Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Dissecting the intricate regulatory dynamics between genes stands as a critical step towards the development of precise predictive models within biological systems. A highly effective strategy in this pursuit involves an examination of variations in correlation among gene pairs across different conditions trough differential gene correlation analysis. We anticipated that the application of this method to phylogenetically close plant species could reveal powerful insights on how genes' relationships have a fundamental role in evolutionary dynamics. Building upon this principle, our investigation focused primarily on conducting a comprehensive differential correlation analysis of the roots' orthologous genes of two closely related plant species: Cardamine hirsuta and Arabidopsis thaliana. By leveraging their shared phylogenetic proximity, we were able to draw comparisons that shed light on how evolutionary pressures might have shaped their gene regulatory networks. By integrating differential expression analysis, differential correlation analysis and functional enrichment analysis, we offer a robust framework for future studies aiming to unravel the complexities of adaptation and evolution across a wide range of biological systems. © 2024 IEEE.

关键词： Plant diseases

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Task planning of Robotic Grasping for Deep-Container Environment Based on Dynamic Space Constraints RRTC Algorithm 20

Task planning of Robotic Grasping for Deep-Container Environ...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Xu, Yan Liu, Yaqiu Liu, Xun Zhang, Lin Li, Peibo Northeast Forestry University College of Computer and Control Engineering Harbin China Shanghai Jiao Tong University School of Mechanical Engineering Shanghai China Donghua University College of Mechanical Engineering Shanghai China

ISBN: (纸本)9798350320831

Multi-axis collaborative manipulators encounter challenges such as slow planning speed, low efficiency, and poor path quality while conducting grasping operations. This paper proposes the Dynamic Space Constraints RRTC motion planning algorithm that considers the grasping pose in the deep-container picking problem and introduces a novel method to optimize the collision-free grasping pose in compliance with environmental constraints. The paper also proposes a prior graph path generation algorithm based on the end-effector's sphere space constraint that compresses the body's search space to improve efficiency. Avoiding complicated non-convex constraint modeling required for obstacle avoidance can be achieved by modeling nonconvex sets with an obstacle-free state as the union of a finite number of convex regions. Based on the prior path, the algorithm generates a dynamic constraint manifold and uses a bidirectional search tree that employs a sliding constraint manifold for traction. The algorithm includes an adaptive step length mechanism dynamically based on the obstacle distance for higher efficiency and accuracy. Experimental results demonstrate that the Dynamic Space Constraints RRTC algorithm plans an optimized path that satisfies the constraint condition compared to traditional algorithms in terms of computation time and efficiency. © 2023 IEEE.

关键词： Motion planning

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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Distributed Event-Triggered Adaptive Cooperative Circumnavigation control of Networked Unmanned Aerial Vehicles 20

Distributed Event-Triggered Adaptive Cooperative Circumnavig...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Ghommam, Jawhar Fareh, Raouf Al Naimi, Ibrahim Sultan Qaboos University College of Engineering Department of Electrical and Computer Engineering Oman Laboratory of Control Energy Management ENIS Tunisia University of Sharjah Electrical Engineering Dept. United Arab Emirates

ISBN: (纸本)9798350332568

In this paper, the objective for a group of unmanned aerial vehicle agents (UAVs) to achieve three dimensional circumnavigation around a moving target which information is made available to all agents in the group. The cooperative circumnavigation is to drive the UAVs to orbit around the target according to a given elliptical desired spatial formation. Due to the thrust limitation needed to fly the drone, existing cyclic pursuit algorithms cannot be extended directly to achieve this objective. Thus the proposed algorithm is worked out to take into account this constraint in order to achieve such objective. The drones are subject to unknown external disturbance, also the masses of those agent drones are assumed to be unknown. Furthermore, the communication cost can be decreased and the Zeno behavior is shown to be excluded. The proposed controller guarantees the bounded control effort irrespective of the external disturbance and model uncertainties of the drone. Numerical simulations are conducted to illustrate the efficacy of the approach. © 2023 IEEE.

关键词： Drones

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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