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Physical Mechanism Enabled Neural Network for Power system Dynamic Security Assessment

CSEE Journal of Power and Energy systems 2024年第6期10卷 2296-2307页

作者： Guozheng Wang Jianbo Guo Shicong Ma Kui Luo Xi Zhang Qinglai Guo Shixiong Fan Tiezhu Wang Weilin Hou Department of Power System China Electric Power Research InstituteBeijing 100192China School of Automation Beijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Tsinghua UniversityBeijing 100084China

Data-driven artificial intelligence technologies have emerged as increasingly fascinating tools for assessing power system ***,their inherent mechanism of inexplicability and unreliability now limits their scalability in power *** address this problem,this paper proposes a neural network design method empowered by physical mechanisms for power system security *** incorporates geometric characteristics of dynamic security regions into the network training process and constructs connections between network structure and system's unstable mode,which can perform security assessment with a neural network efficiently while ensuring physical ***,a credibility evaluation mechanism is established to ensure credibility of neural network predictions and reduce ***,effectiveness of the proposed method is verified on test *** and considerations in building a neural network with interpretable structures and credible predictions can provide a reference for machine intelligence applied in other industrial systems.

关键词： Credibility index machine intelligence neural network structure physical properties power system security assessment

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Characteristic Optimization Based on Combined Statistical Indicators and Random Forest Theory

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CSEE Journal of Power and Energy systems 2024年第6期10卷 2657-2666页

作者： Qingzhen Liu Chao Cai Lei Wu Renwu Yan School of Electric Engineering and Automation Fuzhou UniversityFuzhou 350116China Quanzhou Electric Power Supply Company State Grid Fujian Electric Power CompanyQuanzhou 362000China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ07030USA Department of Power System Fujian University of TechnologyFuzhou 350116China

In order to effectively utilize the dielectric response characteristics of transformers to diagnose the insulation state,this paper proposes a two-level hybrid optimization method for analyzing time-domain dielectric response *** optimization algorithm is based on the combined statistical indicators(CSI)and random forest(RF)*** initial feature space set is formed with 23 time-domain *** the first-level stage,statistical indices correlation,distance,and information indicators are integrated to assess the synthesis score of the characteristics,while highly redundant and lowclass discrimination characteristics are eliminated from the initial space *** the second-level stage,the Random Forest based outside bagging data theory is introduced to evaluate the least important characteristics,and the characteristics with low importance indices are excluded to obtain the final optimal feature space *** proposed method is carried out on 82 sets of data from actual dielectric response tests on oil-paper insulation ***,the final optimal feature space set,along with several other data sets,is tested via different diagnosis *** results show that the optimal feature space set obtained via the proposed method outperforms other feature space sets in terms of better adaptability and diagnosis accuracy.

关键词： Feature space optimization integrated statistical indicators oil-paper insulation state random forest time domain characteristic two-level algorithm

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CareGenic: AI/ML Based Interactive Medicines Prescription system 3

CareGenic: AI/ML Based Interactive Medicines Prescription Sy...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing technology, ODICON 2024

作者： Ahmed, Syed Ammar Umair, Shaikh Prasad, Ritesh Singh, Shashank Kumar Hati, Anirban J. Department of Electronic System Engineering National Institute of Electronics and Information Technology Aurangabad431001 India National Institute of Electronics and Information Technology Sr. Technical Assistant Department of Electronic System Engineering Aurangabad431001 India National Institute of Electronics and Information Technology Scientist B Department of Electronic System Engineering Aurangabad431001 India Scientist C Department of Electronic System Engineering National Institute of Electronics and Information Technology Aurangabad431001 India

ISBN: (纸本)9798350354379

CareGenic leverages Artificial Intelligence (AI) and Machine Learning (ML) to develop a robust system for predicting illnesses based on symptoms and recommending appropriate medications. This study addresses the growing need for intelligent healthcare solutions that can assist medical professionals and improving patient outcomes. The system utilizes a Random Forest algorithm trained on extensive healthcare datasets to predict illnesses accurately. Furthermore, it recommends medications based on the predicted illnesses, ensuring a holistic approach to patient care. Implemented on a Raspberry Pi 3 with an interactive LCD display interface, CareGenic provides an accessible and efficient tool for healthcare environments. The system's performance was evaluated using metrics such as accuracy, precision, and recall, achieving 95%, 93%, and 92%, respectively. These results demonstrate the efficacy of CareGenic in real-world scenarios. Compared to existing systems like IBM Watson Health and Google Health, CareGenic offers a more integrated approach by combining symptom-based predictions with medication recommendations. Future work will focus on enhancing the model's accuracy and expanding its capabilities to cover a broader range of illnesses and medications. This paper provides detailed insights into the system's architecture, methodology, implementation, and evaluation, highlighting the significant potential of AI and ML in revolutionizing healthcare. © 2024 IEEE.

关键词： Liquid crystal displays

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Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm

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Neural Computing and Applications 2024年第18期36卷 10613-10635页

作者： Hassan, Mohamed H. Kamel, Salah Selim, Ali Shaheen, Abdullah Yu, Juan El-Sehiemy, Ragab Ministry of Electricity and Renewable Energy Cairo Egypt Department of Electrical Engineering Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electrical Power Engineering Faculty of Engineering Suez University Suez43533 Egypt State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Electrical Engineering Chongqing University Chongqing China Department of Electrical Engineering Faculty of Engineering Kafrelsheikh University Kafrelsheikh33516 Egypt

This article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm. © The Author(s) 2024.

关键词： Electric load dispatching

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Fractional Order Iteration for Gradient Descent Method Based on Event-Triggered Mechanism

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Journal of systems Science & Complexity 2023年第5期36卷 1927-1948页

作者： LU Jiajie WANG Yong FAN Yuan Department of Automation University of Science and Technology of ChinaHefei 230026China Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China

In this work,a novel gradient descent method based on event-triggered strategy has been proposed,which involves integer and fractional order ***,the convergence of integer order iterative optimization method and the stability of its associated system with integrator dynamics are *** on this result,a fractional order iteration approach has been developed by modelling the system with fractional order ***,to reduce the comsumption of computation,a feedback based event-triggered mechanism has been introduced to the gradient descent *** convergence of this new event-triggered optimization algorithm is guaranteed by using a Lyapunov method,and Zeno behavior is proved to be avoided ***,the effectiveness and advantages of the proposed algorithms are verified by numerical simulations.

关键词： Event-triggered mechanism fractional order iteration gradient descent Zeno behavior

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Moving computational multi-domain method for modelling the flow interaction of multiple moving objects

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Advances in Aerodynamics 2022年第1期4卷 78-95页

作者： Momoha Nishimura Masashi Yamakawa Shinichi Asao Seiichi Takeuchi Mehdi Badri Ghomizad Department of Mechanical and System Engineering Kyoto Institute of TechnologyKyotoJapan Department of Mechanical Engineering College of Industrial TechnologyHyogoJapan

This study proposes a method for modelling the flow interaction of multiple moving objects where the flow field variables are communicated between multiple separate moving computational *** of using the conventional approach with a single fixed computational domain covering the whole flow field,this method advances the moving computational domain(MCD)method in which the computational domain itself moves in line with the motions of an object *** computational domains created around each object move independently,and the flow fields of each domain interact where the flows *** eliminates the spatial restriction for simulating multiple moving ***,a shock tube test verifies that the overset implementation and grid movement do not adversely affect the results and that there is communication between the grids.A second test case is conducted in which two spheres are crossed,and the forces exerted on one object due to the other’s crossing at a short distance are *** results verify the reliability of this method and show that it is applicable to the flow interaction of multiple moving objects.

关键词： Crossing Moving boundary problems Compressible flow Moving mesh method

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A Multi-Bit ECRAM-Based Analog Neuromorphic system With High-Precision Current Readout Achieving 97.3% Inference Accuracy

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IEEE Transactions on Biomedical Circuits and systems 2025年第3期19卷 590-604页

作者： Um, Minseong Kang, Minil Eom, Kyeongho Kwak, Hyunjeong Noh, Kyungmi Lee, Jimin Son, Jeonghoon Kwon, Jiseok Kim, Seyoung Lee, Hyung-Min Korea University School of Electrical Engineering Seoul02841 Korea Republic of Korea University Department of Semiconductor System Engineering Seoul02841 Korea Republic of Pohang University of Science and Technology Department of Materials Science and Engineering Pohang37673 Korea Republic of The Catholic University of Korea School of Information Communications and Electronic Engineering Bucheon14662 Korea Republic of

This article proposes an analog neuromorphic system that enhances symmetry, linearity, and endurance by using a high-precision current readout circuit for multi-bit nonvolatile electro-chemical random-access memory (ECRAM). For on-chip training and inference, the system uses activation modules and matrix processing units to manage analog update/read paths and perform precise output sensing with feedback-based current scaling on the ECRAM array. The 250nm CMOS neuromorphic chip was tested with a 32 × 32 ECRAM synaptic array, achieving linear and symmetric updates and accurate read operations. The proposed circuit system updates the 32 × 32 ECRAM across 100 levels, maintaining consistent synaptic weights, and operates with an output error rate of up to 2.59% per column. It consumes 5.9 mW of power excluding the ECRAM array and achieves 97.3% inference accuracy on the MNIST dataset, close to the software-confirmed 97.78%, with only the final layer (64 × 10) mapped to the ECRAM. © 2007-2012 IEEE.

关键词： CMOS integrated circuits

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Investigation of Path Planning Algorithms Using Artificial Intelligence in Intralogistics 3rd

Investigation of Path Planning Algorithms Using Artificial I...

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3rd International Conference on Cognitive Mobility, CogMob 2024

作者： Molnár-Major, Petra Zöldy, Máté Faculty of Transportation Engineering and Vehicle Engineering Department of Material Handling and Logistics System Budapest University of Technology and Economics Budapest Hungary Faculty of Transportation Engineering and Vehicle Engineering Department of Automotive Technologies Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9783031817984

Material handling is a key activity for intralogistics companies, as it is the only way to overcome the spatial distances within a site. However, the planning of material handling is a complex task in which several aspects need to be considered to achieve an efficient and optimal allocation of tasks. With the new Industry 4.0 technologies, real-time dynamic planning can now be implemented. This paper investigates intralogistics routing algorithms with a particular focus on artificial intelligence solutions. We also define an evaluation criteria framework that can provide a suitable starting point for comparing different algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Motion planning

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Curve Monitoring and Alert system for Smart Transportation 1

Curve Monitoring and Alert System for Smart Transportation

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Natarajan, Yuvaraj Sri Preethaa K, R. Viswanathan, Shrinithi Jayarathinam, Sandhiya Balasubramaniam, Vikas Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kyungpook National University Department of Robot and Smart System Engineering Daegu Korea Republic of

ISBN: (纸本)9798350372748

The proposed IoT-based Smart Curve Monitoring and Alert system is designed to improve road safety through the fusion of embedded systems, machine learning and edge computing technologies. The proposed system is primarily powered by ESP32 microcontroller, continuously gathers data from an array of sensors, including accelerometers, gyroscopes and a potentiometer to set the speed limit. This collected data is then subjected to analysis by a Convolutional Neural Network (CNN) model, which has been trained to recognize driving patterns, especially when navigating curves. Here, CNN classifies the vehicle's behavior as safe and risky, and if a risky behavior is identified, it triggers an alert and dispatches relevant data to a NodeMCU device. The NodeMCU plays a crucial role in the system by monitoring the communication tasks. It receives the alert from the ESP32, validates the vehicle's speed against the preset limit, and, if necessary, activates a GSM module to send an immediate notification to designated guardian. To ensure real-time monitoring and awareness, all pertinent information will be displayed on the LCD screen, including the vehicle's behavior classification, current speed, speed limit and alert status. By addressing reckless driving behaviors, particularly on curves, the system contributes significantly to safer roads and reinforces the importance of responsible driving practices. © 2024 IEEE.

关键词： Convolution

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An authenticated spectrum acquirement and transmission of encrypted images over fusion centre

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Multimedia Tools and Applications 2024年 1-24页

作者： V, Thanikaiselvan Y, PiyaDessi S, Renuga devi Shanthini, S. Suba Mahalingam, Hemalatha S, Aashiq Banu R, Dhivya V.M, Padmapriya Amirtharajan, Rengarajan School of Electronics Engineering Vellore Institute of Technology Vellore Vellore India School of Information Technology Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia System Engineering Department Military Technological College Muscat Oman Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & amp Electronics Engineering SASTRA Deemed University 613 401 Thanjavur India

These days, spectrum insufficiency is the most common issue. If the number of smartphone users who access the radio spectrum grows, the radio spectrum becomes increasingly scarce. Certain types of technology equipment are designed to work only within a specific spectrum range, and there is rivalry in specifications for various other applications. As a result, this paper effectively exploits a licensed user’s unused spectrum to reduce the crowded spectrum and eradicate the Radio spectrum shortage. Licensed primary users (PUs) and secondary users (SUs) would be accepted into a Fusion Centre (FC) based on a spectrum framework with authentication. The approved PUs begin by updating their information to FC1;thenceforth, encryption is performed on the information uploaded to FC1. This information from FC1 is stored in Cloud Storage after encryption using the Blowfish algorithm & De-oxy DNA, a fast encryption algorithm. Following that, FC2, connected to the same cloud environment, downloads the PU data uploaded. The spectrum is then allocated to the authenticated SU linked to FC2 based on signal conditions and priority. This proposed method also avoids any delay in the network and encryption by acquiring a fast encryption algorithm, making the system faster and avoiding the Primary User Emulation (PUE) attack. Later, the time taken for the Tiny Encryption Algorithm (TEA) and the Blowfish/DNA algorithm are compared to validate the run time for different input images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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