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Stability-Constrained Contingency Analysis for Modern Power systems

IFAC-PapersOnLine 2024年第25期58卷 114-119页

作者： Rajan Ratnakumar Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC 29634 USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa.

Modern power systems comprise diverse nonlinear components, and an increasingly large number of low inertia renewable power sources necessitate the modernization of conventional power system security measures. Contingency analysis (CA), a routine process for power system operators, ensures grid security under unforeseen circumstances by identifying potential issues and enabling proactive measures for uninterrupted power flow. Electromechanical oscillations (EMOs) in the power system that are a threat to stability must be regularly monitored and mitigated. An online hierarchical EMO index integrating time and frequency response analysis can be utilized for system stability assessment. The integration of an EMO index threshold into contingency analysis is presented in this paper to enhance system security. This new approach is referred to as the stability-constrained contingency analysis (SCCA). Typical results for a modified two-area, four-machine power system with large solar photovoltaic plants simulated on a real-time digital simulator (RTDS) are presented. These results demonstrate that SCCA flags potential issues that can arise from EMOs for certain contingencies, whereas traditional CA does not, as it solely considers bus voltage limits and line ratings.

关键词： Contingency analysis electromechanical oscillations security stability

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Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Design of a Remote Controlled Puppet Robot Imitating a Manual Driven Puppet Using Deep Learning Pose Detection

Design of a Remote Controlled Puppet Robot Imitating a Manua...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Hamed Hamzeh Kiarash Shahroozi Ahmad Nabipour Parham Kazemi Mohammad Malek-zahedi Mehdi Hallajian Hadi Moradia Advanced Robotics and Intelligent Systems Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Traditional puppet manipulation systems often require human operators to be physically present in tight and cramped locations. This leads to challenges in the positioning and effective operation of puppets, particularly in the presence of children in educational settings. In this paper, we present the design, development, and integration of a cost-effective robotic system for remotely manipulating puppets in real-time. Additionally, the system integrates a YOLOv8 Pose Detection model, trained on a custom dataset with an accuracy of 91.4%, enabling real-time gesture recognition and allowing the robot to be controlled by imitating the motions of a manually operated puppet. The system demonstrates the potential for many applications, including interactive entertainment, teleoperation, and education.

关键词： Hands Mechatronics Education Entertainment industry Manuals Gesture recognition Real-time systems Maintenance Robots intelligent control

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Artificial Intelligence Techniques With Digital Twin for Fault Diagnosis in Interconnected systems: A Review

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IEEE Access 2025年 13卷 91860-91874页

作者： Imen Nakti Majdi Mansouri Rami Al-Hmouz Atef Khedher Laboratory of Advanced Technology and Intelligent Systems ENISO University of Sousse Sousse Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman

As industrial systems become more complex and interconnected, diagnosing faults accurately and in real time has become increasingly challenging. This paper explores how combining artificial intelligence with digital twin technology can address these challenges. We focus on developing hybrid Artificial intelligence models that leverage diverse data sources to enhance fault detection and diagnosis, enabling secure and distributed diagnostics. Digital twins, virtual models of physical systems, are shown to enhance predictive maintenance and decision-making by providing real-time system insights. What sets our work apart is the way we integrate these technologies to create scalable, adaptive, and context-aware diagnostic solutions. We demonstrate the potential of this approach across applications such as smart grids, manufacturing, and autonomous systems. Our goal is to provide researchers and practitioners with a practical and forward-looking framework for developing intelligent, reliable fault diagnosis systems in today’s data-rich industrial environments.

关键词： Artificial intelligence Real-time systems Sensors Fault detection Fault diagnosis Interconnected systems intelligent sensors Smart grids Digital twins Adaptation models

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A Spatial-temporal Model for Tourism Demand Forecasting 23

A Spatial-temporal Model for Tourism Demand Forecasting

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Dong, Yunxuan Zhou, Binggui Yang, Guanghua Hou, Fen Ma, Shaodan School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China University of Macau Department of Electrical and Computer Engineering 999078 China

ISBN: (纸本)9781665494571

Accurate forecasting of tourism demand is important to the development of tourism. However, the difficulties in recognizing complex spatial and temporal features make it challenging to accurately forecast tourism demand. In addition, existing methods are not practical and flexible enough since they usually established multiple models for different scenic spots. In this paper, we propose a novel method for tourism demand forecasting based on the fully connected long short-term neural network, which enables simultaneous identification of spatial and temporal features for better forecasting accuracy. To enhance the practicality and flexibility of our method, we propose to establish one general model for multiple scenic spots. Experimental results demonstrate that the proposed method outperforms other models in the daily tourism demand forecasting for the Wanshan Archipelago, an emerging tourism spot in Zhuhai, China. © 2021 IEEE.

关键词： Smart cities Neural networks Demand forecasting Transportation Predictive models

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Progress in self-powered sensors—Moving toward artificial intelligent and neuromorphic system

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Nano Research 2023年第9期16卷 11801-11821页

作者： Feng Wen Chan Wang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore National University of Singapore Suzhou Research Institute(NUSRI) Suzhou 215123China NUS Graduate School-Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingapore 119077Singapore

Wearable and flexible electronics are shaping our life with their unique advantages of light weight,good compliance,and desirable *** marching into the era of Internet of Things(IoT),numerous sensor nodes are distributed throughout networks to capture,process,and transmit diverse sensory information,which gives rise to the demand on self-powered sensors to reduce the power ***,the rapid development of artificial intelligence(AI)and fifth-generation(5G)technologies provides an opportunity to enable smart-decision making and instantaneous data transmission in IoT *** to continuously increased sensor and dataset number,conventional computing based on von Neumann architecture cannot meet the needs of brain-like high-efficient sensing and computing applications *** electronics,drawing inspiration from the human brain,provide an alternative approach for efficient and low-power-consumption information ***,this review presents the general technology roadmap of self-powered sensors with detail discussion on their diversified applications in healthcare,human machine interactions,smart homes,*** leveraging AI and virtual reality/augmented reality(VR/AR)techniques,the development of single sensors to intelligent integrated systems is reviewed in terms of step-by-step system integration and algorithm *** order to realize efficient sensing and computing,brain-inspired neuromorphic electronics are next briefly ***,it concludes and highlights both challenges and opportunities from the aspects of materials,minimization,integration,multimodal information fusion,and artificial sensory system.

关键词： wearable and flexible electronics self-powered sensors virtual reality/augmented reality(VR/AR)and artificial intelligence(AI)-enhanced system neuromorphic electronics

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Computational Investigation of Multiband EMNZ Metamaterial Absorber for Terahertz Applications

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computers, Materials & Continua 2022年第5期71卷 3905-3920页

作者： Ismail Hossain Md Samsuzzaman Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mandeep Singh Jit Singh Mohammad Tariqul Islam Space Science Center Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaMalaysia

This study presents an Epsilon Mu near-zero(EMNZ)nanostructured metamaterial absorber(NMMA)for visible regime *** resonator and dielectric layers are made of tungsten(W)and quartz(fused),where the working band is expanded by changing the resonator layer’s *** to perfect impedance matching with plasmonic resonance characteristics,the proposed NMMA structure is achieved an excellent absorption of 99.99%at 571 THz,99.50%at 488.26 THz,and 99.32%at 598 THz *** absorption mechanism is demonstrated by the theory of impedance,electric field,and power loss density distributions,*** geometric parameters are explored and analyzed to show the structure’s performance,and a near-field pattern is used to explain the absorption mechanism at the resonance frequency *** numerical analysis method describes that the proposed structure exhibited more than 80%absorbability between 550 and 900 *** computer Simulation Technology(CST Microwave Studio 2019)software is used to design the proposed ***,CSTHFSS interference is validated by the simulation data with the help of the finite element method(FEM).The proposed NMMA structure is also exhibits glucose concentration sensing capability as *** the proposed broadband absorber may have a potential application in THz sensing,imaging(MRI,thermal,color),solar energy harvesting,light modulators,and optoelectronic devices.

关键词： Metamaterial absorber terahertz applications multiband EM near zero nanostructured visible regime applications

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Connectivity in the era of the (I)IoT: about security, features and limiting factors of reconfigurable intelligent surfaces

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Discover Internet of Things 2023年第1期3卷 16页

作者： Lipps, Christoph Herbst, Jan Klingel, Steffen Franke, Lars Wolff, Alexander Rüb, Matthias Reddy, Rekha Rahm, Marco Schotten, Hans Dieter Intelligent Networks Research Group German Research Center for Artificial Intelligence Kaiserslautern67663 Germany Institute for Wireless Communication and Radio Positioning RPTU Kaiserslautern-Landau Kaiserslautern67663 Germany Department of Electrical and Computer Engineering Research Center OPTIMAS RPTU Kaiserslautern-Landau Kaiserslautern67663 Germany

What are the key drivers of the (Industrial) Internet of Things ((I)IoT)? Alongside the interconnection of the various devices, sensors, and actuators, it is above all the data collected and transmitted that is of particular significance, the (I)IoT itself is the source and recipient of various types of data. Within this, there are two main aspects that need to be considered: (i) the transmission of data with appropriate latency and reliability requirements, and (ii) the safeguarding of this data. Both requirements are taken into account during the development of the Sixth Generation (6G) Wireless systems. This is conceived to deliver higher data rates, reduced cost deployment, and enhanced reliability while providing universal connectivity. Hence, to assist such a vast network domain, (new) 6G-enabling technologies are developed, which include Reconfigurable intelligent Surfaces (RISs), that are capable of modifying and reconfiguring the wireless propagation channels. This work describes concepts on how security, in particular Physical Layer Security (PhySec) methods, can be supported by RISs. Furthermore, an effective impedance surface model is presented, allowing efficient simulation of beamsplitting and shaping properties of RISs. Finally, the applicability of the use cases is mentioned, with a particular focus on Visible Light Communication (VLC). © The Author(s) 2023.

关键词： Internet of things

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