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Integrated encoder-decoder-based wide and deep convolution neural networks strategy for electricity theft arbitration

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Journal of engineering and Applied Science 2024年第1期71卷 94页

作者： Kumawat, Manoj Onaolapo, Adeniyi Sharma, Gulshan Barutcu, Ibrahim Cagri Adefarati, Temitope Bansal, Ramesh Department of Electrical Engineering National Institute of Technology Delhi Delhi India Department of Electrical Engineering Technology University of Johannesburg Johannesburg2006 South Africa Department of Electricity and Energy Çölemerik V.H.S. Hakkari University Hakkari30000 Turkey Department of Electrical and Electronics Engineering Federal University Oye Ekiti Oye Ekiti Nigeria Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electric Electronic and Computer Engineering University of Pretoria Pretoria South Africa

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments. © The Author(s) 2024.

关键词： Smart power grids

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Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach 38

Protected Test-Time Adaptation via Online Entropy Matching: ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bar, Yarin Shaer, Shalev Romano, Yaniv Department of Computer Science Technion-Israel Institute of Technology Israel Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Israel

We present a novel approach for test-time adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlabeled samples. Second, we devise an online adaptation mechanism that utilizes the evidence of distribution shifts captured by the detection tool to dynamically update the classifier's parameters. The resulting adaptation process drives the distribution of test entropy values obtained from the self-trained classifier to match those of the source domain, building invariance to distribution shifts. This approach departs from the conventional self-training method, which focuses on minimizing the classifier's entropy. Our approach combines concepts in betting martingales and online learning to form a detection tool capable of quickly reacting to distribution shifts. We then reveal a tight relation between our adaptation scheme and optimal transport, which forms the basis of our novel self-supervised loss. Experimental results demonstrate that our approach improves test-time accuracy under distribution shifts while maintaining accuracy and calibration in their absence, outperforming leading entropy minimization methods across various scenarios. © 2024 Neural information processing systems foundation. All rights reserved.

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Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

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Multimedia Tools and Applications 2025年第5期84卷 2209-2260页

作者： Karthiga, R. Narasimhan, K. Thanikaiselvan, V. Hemalatha, M. Amirtharajan, Rengarajan Department of ECE SRM Institute of Science and Technology Ramapuram Chennai600089 India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613 401 India School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Breast cancer is a significant health concern;early detection and treatment are critical to improving patient outcomes. Artificial Intelligence has the potential to assist healthcare professionals in the diagnosis and treatment of breast cancer, and clinical decision support systems using AI algorithms can help to improve the accuracy and speed of breast cancer detection and diagnosis. By analysing large amounts of data from various imaging modalities, AI algorithms can help identify breast cancer early, increasing the chances of successful treatment. This survey aims to briefly overview the most prevalent breast cancer types, their staging, and the methods and modalities for their diagnosis to address these problems. Our investigation showed that, when compared to ML, DL techniques achieved the highest accuracy, with an approximate 2% improvement in accuracy. This review article mainly focuses on four commonly used imaging modalities: Mammogram, Thermogram, Ultrasound imaging and histopathology images. This survey critically analyzes breast cancer detection approaches and modalities, as well as a cost-to-accuracy comparison. Explainable AI (XAI) techniques and their application for breast cancer diagnosis are critically examined, as well as their advantages and limitations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment

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IEEE Transactions on Power Systems 2024年第1期39卷 391-401页

作者： Wu, Jianghua Luh, Peter B. Chen, Yonghong Yan, Bing Bragin, Mikhail A. University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Miso CarmelIN46032 United States Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States University of California Department of Electrical and Computer Engineering RiversideCA92507 United States

Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming. Machine learning (ML) is a promising alternative. However, directly learning good solutions is difficult in view of the combinatorial nature of UC. This paper synergistically integrates ML within our recent decomposition and coordination method of Surrogate Lagrangian Relaxation to learn 'good enough' subproblem solutions of deterministic UC. Compared to original UC, a subproblem is much easier to learn. Nevertheless, predicting good-enough subproblem solutions is still challenging because of the 'jumps' of binary decisions and many types of constraints. To overcome these issues, subproblem dimensionality is reduced via aggregating multipliers. Multiplier distributions are novelly specified based on 'jumps' for effective learning. Loss functions are innovatively designed to improve prediction qualities. Ordinal Optimization and branch-and-cut are used as backups for unfamiliar cases. Furthermore, online self-learning is seamlessly integrated with offline learning to exploit solutions from daily operations. Results on the IEEE 118-bus system and the Polish 2383-bus system demonstrate that continual learning keeps on improving the subproblem-solving process with near-optimality of the overall solutions maintained. Our method opens a new direction to solve complicated UC. © 1969-2012 IEEE.

关键词： Integer programming

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Comparing Centralized and Decentralized Storage in Microgrids: Implications for Consumer Behavior 28

Comparing Centralized and Decentralized Storage in Microgrid...

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28th International electrical Power Distribution Conference, EPDC 2024

作者： Dousti, Hazhir Hagh, Mehrdad Tarafdar Jirdehi, Mehdi Ahmadi Kermanshah University of Technology Faculty of Electrical Engineering Department of Electrical Engineering Kermanshah Iran University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798350385502

Due to the increase in demand for electricity, the lack of fossil fuels, and the use of renewable energy sources, the use of energy storage systems becomes necessary. The use of storage systems in different parts of microgrids has countless advantages. These benefits include improving the overall efficiency of the system, increasing the stability of the network, the possibility of optimal control strategies at both the national and regional levels and the effective identification of vulnerabilities in the system. This paper focuses on the issue of coordinated and optimal energy management in a microgrid, especially when dealing with centralized and decentralized storage. This study simultaneously examines the impact of the presence of storage systems in both centralized and decentralized modes, as well as the creation of incentive policies to reduce demand. It examines the interaction between these two factors and their effects on cost reduction and overall network voltage deviation. The work has been done on a standard IEEE network with 69 buses using MATLAB software. It has been shown that in the presence of decentralized energy storage, network performance and consumer consumption are better, and network losses and voltage deviations are also reduced. © 2024 IEEE.

关键词： Renewable energy

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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

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National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko Department of Mechanical Engineering Seoul National University IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Department of Chemical Engineering Stanford University Department of Mechanical Engineering Ajou University George W.Woodruff School of Mechanical Engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

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Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained engineering Optimization

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Journal of Bionic engineering 2024年第1期21卷 374-408页

作者： Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali Mirjalili Laith Abualigah Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahromFrasIran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehranIran Department of Electrical and Computer Engineering University ofCyprusNicosiaCyprus Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD 4006Australia University Research and Innovation Center Obuda University1034 BudapestHungary Department of Electrical and Computer Engineering Lebanese American UniversityByblos 13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.

关键词： Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA)

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Optimization of Residential Net Load using Demand Response and Battery Storage 3

Optimization of Residential Net Load using Demand Response a...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Bracho, Jorge Ilia, Neofytos Makrides, George Tseng, Alex R. Herodotou, Panagiotis Georgiou, George E. PV Technology Laboratory Department of Electrical and Computer Engineering Cyprus for Intelligent Nicosia Cyprus Princeton University Electrical and Computer Engineering NJ United States

ISBN: (纸本)9798350375923

Demand side management (DSM) and solar-plus-storage systems are enabling technologies that facilitate consumers to increase self-consumption (SC), while reducing electricity costs through efficient and intelligent electricity usage. Even though many demand response (DR) strategies have been presented for the residential sector, a major challenge remains the joint optimal utilization and synergies of DR and the battery systems flexibility scheduling. This paper explores how the optimization of DR and battery energy storage systems (BESS) scheduling can notably improve the net load profile of residential users. The obtained results indicate that the optimized DR model and battery storage effectively support load shifting and peak energy utilization. Specifically, the analysis suggests that the combination of DR and an 8kWh battery storage system increased the SC from 55.04% to 95.84% and the SS from 37.50% to 66.88%, compared to a baseline scenario (without battery storage and DR actions). Ultimately, the proposed optimization achieved a higher degree of energy autonomy for the yearly profile of a median household by minimizing the sum of absolute values of the net load with load shifting and BESS charging/discharging. Finally, the optimization model can be used by aggregators and system operators for scheduling and optimizing flexibility actions. © 2024 IEEE.

关键词： Demand response

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Chaotic Dynamics of Switched Reluctance Motor Drives: A State-of-the-Art Review

Chaotic Dynamics of Switched Reluctance Motor Drives: A Stat...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Al Aman, Yaheya Sahoo, N.C. School of Electrical and Computer Sciences Indian Institute of Technology Bhubaneswar Department of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798331540821

Complex nonlinear systems have been very often found to exhibit unpredictable, chaotic behavior as they become extremely sensitive to initial conditions. In electric motor drives, which are highly nonlinear systems, occurrence of chaos is inevitable and often causes undesirable phenomena in the system. This article begins with a structured introduction to chaotic phenomena in nature. Then it focuses on a state-of-the-art review on occurrence of chaos in switched reluctance motor drive, whose main source of nonlinearity is the nonlinear magnetization characteristics due to the doubly-salient structure of the motor. Analysis tools such as phase portraits, bifurcation diagrams, and Lyapunov exponents are also discussed to detect the presence of chaos in the motor. Some applications of chaos have also been reported. Also, some possible future works based on the research gaps in the literature are identified. © 2024 IEEE.

关键词： Reluctance motors

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PIDSNeRF: pose interpolation depth supervision neural radiance fields for view synthesis from challenging input

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Jianxin Shao, Haijian Deng, Xing Jiang, Yingtao School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States

Recently, Neural Radiance Fields(NeRF) have shown remarkable performance in the task of novel view synthesis through multi-view. The present study introduces an advanced optimization framework, termed Pose Interpolation Depth Supervision Neural Radiance Fields (PIDSNeRF), designed to address the challenges encountered by NeRF in novel view synthesis. These challenges manifest as artifacts, texture details loss, and geometric inconsistencies, particularly in scenes characterized by different lighting conditions, Textureless regions, and sparse images as inputs. The principle of PIDSNeRF involves interpolating virtual camera positions in the 3D space domain based on known cameras, Subsequently, the sparse point clouds formed during camera pose estimation is re-projected onto these virtual poses, employing an inverse angle weighting strategy, thereby generating depth-supervised rays. Furthermore, we propose a depth diffusion method that spreads depth-supervised rays along the pixel plane, forming Gaussian functions with different variances based on the diffusion distances. Finally, depth supervision during volume rendering is achieved by optimizing the Kullback-Leibler(KL) divergence between the weight distribution of sampled points along each ray and the probability density function of the corresponding Gaussian function along that ray. The above process enables a more comprehensive optimization of multi-resolution grid features that align with the sampled points along the rays. The experiments on the DTU, LLFF, and DL3DV-10K datasets demonstrate, PIDSNeRF can synthesize complete novel views within minutes, and the various metrics of the synthesized images achieve the best performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Probability density function

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