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共 35125 条记录，以下是571-580 订阅

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AI Empowered Solar Energy: Reinforcement Learning and Comparative Analysis for Grid-Connected Photovoltaic systems Optimization 28

AI Empowered Solar Energy: Reinforcement Learning and Compar...

引用

28th ieee international conference on intelligent Engineering systems (INES)

作者： Marwa, Banawaz Ali, Zeddini Mohamed Hanen, Berriri Faouzi, Mimouni Mohamed LAS2E Elect Dept Monastir Tunisia

ISBN: (纸本)9798350367607;9798350367591

This study addresses the optimization of grid-connected photovoltaic (PV) systems, particularly focusing on overcoming challenges posed by shading conditions. Employing machine learning (ML) technology, specifically Reinforcement Learning (RL), this research conducts a comparative analysis with traditional optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for Maximum Power Point Tracking (MPPT). Simulation results, executed using Simulink/Matlab, highlight RL's superior performance in terms of convergence speed and effectiveness compared to PSO and GA, without the need for prior system knowledge. This study contributes valuable insights into the application of ML-based algorithms in enhancing PV system efficiency, paving the way for advancements in renewable energy technologies.

关键词： Reinforcement Learning Particle Swarm Optimization Genetic algorithm MPPT grid-connected photovoltaic system Shunt Active Power filter Multilayer Feed Forward Neural Network Fuzzy Logic

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Safety Management and control technology of Explosives in Infrastructure Engineering of a Super Large Underground Iron Mine 2

Safety Management and Control Technology of Explosives in In...

引用

2nd international conference on cyber-Energy systems and intelligent Energy, ICCSIE 2024

作者： Yu, Zilong Yang, Liyun Fu, Zhanhua Chang, Rungang Yu, Yonghai Beijing China Tangshan Jinyu Blasting Engineering Co. Ltd. Tangshan China

ISBN: (纸本)9798350373318

The construction period of infrastructure in extra-large underground mines often entails prolonged durations, arduous tasks, multiple blasting units, intricate personnel arrangements, high frequencies of blasting, and substantial consumption of explosives. Shougang Luannan Macheng iron mine serves as an exemplar of such a mine with 12 main and auxiliary shafts, ventilation shafts, and simultaneous ramp construction. Considering the specific conditions for blasting during the construction phase, a comprehensive management and control system has been developed and implemented. This closed-loop system strictly regulates all aspects related to explosives including procurement, transportation, storage, issuance, usage monitoring, return procedures as well as disposal processes. By employing advanced technologies such as video surveillance cameras for recording purposes along with individual equipment monitoring systems and explosive detection instruments integrated into the civil explosion safety supervision platform;effective control over explosives has been achieved resulting in outstanding safety records while also contributing to social welfare and economic benefits. This approach holds significant guidance value for managing explosives in similar mining infrastructure projects. © 2024 ieee.

关键词： Blasting

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An intelligent Robot Motion Planning Method and Application via LPPO in Unknown Environment 12

An Intelligent Robot Motion Planning Method and Application ...

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12th international conference on cyber technology in automation, control, and intelligent systems, cyber 2022

作者： Yu, Lingli Luo, Jiawei Zhou, Kaijun School of Automation Central South University Changsha410083 China Hunan Xiangjiang Artificial Intelligence Academy Changsha410083 China School of Computer and Information Engineering Hunan University of Technology and Business Changsha410205 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Aiming at the problem of motion planning in complex dynamic environment, traditional algorithm which relies on prior map information is unavailable due to the change of environment and suffer from high computational complexity. Proximal policy optimization algorithm suffers from the problem of training failure due to a bad strategy learned in a certain round. In this paper, an improved end-to-end motion planning algorithm with proximal policy optimization is proposed to address this problem by using logarithmic decay strategy dynamically selecting the clipping range and applying the algorithm to robot motion planning. It ensures a larger exploration space for the agent at the beginning of training, and limits the sudden change of the agent's strategy by attenuating the clipping range at the later stage, which improves the stability of the algorithm. We compare our proposed algorithm with several other improved algorithms in different Mujoco environments and our built environment. The results show that our proposed improved algorithm obtains higher reward values and the robot has smoother motion control while navigating obstacle avoidance. © 2022 ieee.

关键词： intelligent robots

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Learning-based Relational Object Matching Across Views

Learning-based Relational Object Matching Across Views

引用

ieee international conference on Robotics and automation (ICRA)

作者： Elich, Cathrin Armeni, Iro Oswald, Martin R. Pollefeys, Marc Stueckler, Joerg Max Planck Inst Intelligent Syst Embodied Vis Grp Tubingen Germany Max Planck ETH Ctr Learning Syst Zurich Switzerland Swiss Fed Inst Technol Comp Vis & Geometry Lab Zurich Switzerland Univ Amsterdam Amsterdam Netherlands Microsoft Mixed Real & AI Lab Zurich Switzerland

ISBN: (纸本)9798350323658

intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reconstruction, image retrieval, or place recognition can benefit from reasoning on the level of objects. While keypoint-based matching can yield strong results for finding correspondences for images with small to medium view point changes, for large view point changes, matching semantically on the object-level becomes advantageous. In this paper, we propose a learning-based approach which combines local keypoints with novel object-level features for matching object detections between RGB images. We train our object-level matching features based on appearance and inter-frame and cross-frame spatial relations between objects in an associative graph neural network. We demonstrate our approach in a large variety of views on realistically rendered synthetic images. Our approach compares favorably to previous state-of-the-art object-level matching approaches and achieves improved performance over a pure keypoint-based approach for large view-point changes.

关键词： intelligent robots

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cyber Security for Industrial control System using Deep Neural Network Model

Cyber Security for Industrial Control System using Deep Neur...

引用

2023 ieee international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Ranjeethapriya, K. Dhana Shree, K. Sowntharya, C. Anand, Sruthi Elwine, Granty Regina Sri Ramakrishna Engineering College Coimbatore India Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350305708

Machine learning, Deep Learning and cyber security have an enormous multidisciplinary convergence. Recent advances in deep learning have indicated a tremendous increase in cyber security and intrusion detection. Through the development of concepts, cyber security specialists are enhancing the safety of Deep Learning models for the cyber environment. Before the development of deep learning and machine learning, cybercriminals conducted their attacks using Petri net formalizations. However, as technology advanced, they began to employ more sophisticated methods. The focus of this paper is on deep learning CNN models for industrial cyber control systems using network traffic data. Deep transfer learning is applied by including additional characteristics either from the input data or through pre-processing methods. In addition, supervised learning is done to design a self-updating model based on the various attack variations and suspicious behaviour is detected. Finally, it is further illustrated that deep learning is being used in a variety of fields beyond the cyber security, including education, robotics, automation, and many more. © 2023 ieee.

关键词： Convolution Neural Network cyber physical systems cyber security systems Deep Learning Deep Neural Network Intrusion Detection systems

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Dynamic-Memory Event-Based Output Feedback Security control for Networked Delayed Descriptor Jump systems Against Multiple cyber-Attacks 36

Dynamic-Memory Event-Based Output Feedback Security Control ...

引用

36th Chinese control and Decision conference (CCDC)

作者： Zhu, Jingjuan Pang, Yujing Wu, Yifan Hao, Mengjuan Fu, Yanran Meng, Yanan Zhuang, Guangming Liaocheng Univ Sch Math Sci Liaocheng Shandong Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This article studies the issue of output feedback control for descriptor Markov jump systems subject to spoofing attacks and DoS attacks via dynamic-memory event-triggered mechanism. By utilizing a group of historical transmitted data, the dynamic-memory event-triggered mechanism is quoted. Moreover, by building the improved segmented mode-dependent Lyapunov-Krasovskii functional, the exponential admissibility criterion for the controlled system is obtained by right of linear matrix inequalities. Lastly, the direct current motor model is exploited to testify the availability of suggested approach.

关键词： Dynamic-memory event-triggered mechanism Descriptor systems Markov jump systems Static output feedback control Multiple cyber-attacks

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Design Process and Development of automation for Smart Buildings 1

Design Process and Development of Automation for Smart Build...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sethi, Gunjan Khurana, Deepika Verma, Monika Pant, Surmadhur Chauhan, Priyanka Bhatt, Bhaskar Department of Computer Science and Engineering Delhi Technical Campus Uttar Pradesh Greater Noida India IIMT College of Engineering Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Shivalik College of Engineering Department of ECE Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Mechanical Engineering Dwarahat Almora India

ISBN: (纸本)9798331518578

In order to create spaces that are secure, cozy, energy-efficient, and visually beautiful, smart buildings are becoming more and more sophisticated cyber-physical systems. However, as functional complexity, financial concerns, and market demands increase, traditional design methodologies are becoming less effective. Current framework's plan and activity have been totally changed by the presentation of automated smart building, which ensures expanded security, better inhabitant solace, and energy effectiveness. With an emphasis on MATLAB-based reproductions, this study offers a careful assessment of the plan interaction and improvement procedures for computerizing savvy structures. The study delineates how MATLAB makes it simpler to depict, reproduce, and investigate savvy building frameworks with certainty. Through the mix of numerous advancements, like sensors, regulators, and correspondence conventions, the research shows the turn of events and check of a mechanized smart building framework. The intelligent building's building automation system prioritizes a humanized interface and uses a combination of mechanical, electrical, and fire control systems, access control systems, and parking management systems to give building occupants a convenient and comfortable place to live and work. The discoveries show prominent additions in functional viability, natural adaptability, and energy utilization. The conventional heuristic and ad hoc design paradigms are becoming ineffective and inadequate for the future due to cost, the requirement to handle growing functional complexity, adaptability, supply chain fragmentation, and time-to-market pressure. © 2025 ieee.

关键词： Smart homes

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Robust H∞ Consensus control for Heterogeneous Multi-agent systems Based on Time-delay and Switching Topology 4

Robust H∞ Consensus Control for Heterogeneous Multi-agent S...

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4th ieee international conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Du, Jiayi Liu, Jia Cao, Runxi Zhang, Yijing Zhang, Yunxi Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350363586

This study proposes a consensus control strategy for heterogeneous multi-agent systems characterized by uncertain parameters and external disturbances, to address the intricate robust control challenges posed by time-varying delays and dynamic network topologies. A distributed control protocol is design to make the states of the heterogeneous multi-agent system be consistent. Then the stability of the control protocol is analyzed by Lyapunov-Krasovskii stability theory and integral inequality techniques, and the sufficient conditions for the system to achieve robust H∞ consistency are obtained. Finally, the results of the simulations verify the accuracy of the theoretical analysis presented in this paper. © 2024 ieee.

关键词： Multi agent systems

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Cluster consensus control for general linear systems with arbitrary convergence time 6

Cluster consensus control for general linear systems with ar...

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ieee 6th international conference on Industrial cyber-Physical systems (ICPS)

作者： Ren, Lu Li, Xiaofeng Sun, Mengwei Zhao, Dong Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei Peoples R China

ISBN: (纸本)9798350311259

This paper investigates the cluster consensus problem for general linear systems with arbitrary convergence time. By using the optimal control theory, we design a novel distributed controller with time-varying feedback gain for guaranteeing the cluster consensus can be reached within the pre-specified time. It is noteworthy that the underlying topology of each cluster is assumed to be directed graphs containing a directed spanning tree and the convergence time can be determined arbitrarily without depending on the initial condition or system parameters. Then, a rigorous analysis is provided to derive su fficient conditions in terms of topology structure. Finally, a numerical example is presented to validate the e ffectiveness of our result.

关键词： Multi-agent systems cluster consensus fast convergence finite-time optimal control

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Estimate-Based Output Feedback Stabilization of a Class of Nonlinear systems Against DoS Attacks 2

Estimate-Based Output Feedback Stabilization of a Class of N...

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2nd international conference on cyber-Energy systems and intelligent Energy, ICCSIE 2024

作者： Li, Yulong Sun, Mengze Wang, Yingchun College Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350373318

In this paper, the global stabilization of networked control systems experiencing nonlinear uncertainty and suscep-tible to denial-of-service (DoS) attacks is demonstrated. This stabilization is achieved through the utilization of a resilient event-triggering mechanism operating under extended triangular linear growth conditions, employing feedback control techniques with discrete-time output feedback controllers and discrete-time resilient event-triggering conditions. The relationship between the inactivity/activity period of DoS attacks, resilient event-triggering mechanism (RETM) parameters, non-measurable states and sampling period in the model is also quantitatively discussed. Since the observer and controller of this controller are discrete, it can be easily implemented by computer. Finally, the effectiveness of the proposed method is verified by an examples. © 2024 ieee.

关键词： Denial-of-service attack

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