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检索条件"任意字段=International Conference on Complex Systems: Control and Modeling Problems"

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Temperature control System of Thermoelectric Cooler Based on Improved ADRC 34

Temperature Control System of Thermoelectric Cooler Based on...

引用

34th Chinese control and Decision conference (CCDC)

作者： Shan, Xiyuan Cai, Tao Zong, Zhicheng Mu, Hongbo Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China

ISBN: (纸本)9781665478960

Aiming at the problems containing complex working conditions, modeling difficulties and long-time delay of thermoelectric cooler temperature control system, an improved ADRC control method combining ADRC and Smith predictor is proposed in this paper. ADRC deals with the disturbances and uncertain dynamics in the system, SP compensates the time delay to improve the control performance. Compared with the traditional PID controller, the proposed control method has faster response speed and stronger anti-disturbance ability, meanwhile, overcomes the dependence of Smith predictor on object parameters to a certain extent. Through simulation and experimental verification, a good control effect is obtained, which provides a positive guidance of the related application of thermoelectric cooler.

关键词： Thermoelectric cooler (TEC) Active Disturbance Rejection control (ADRC) Smith Predictor (SP) Temperature control System

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Learning from Not All Vehicles via Influence Function 26

Learning from Not All Vehicles via Influence Function

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IEEE 26th international conference on Intelligent Transportation systems (ITSC)

作者： Pang, Jinsong Long, Tingting Cui, Zhichao Xu, Linhai Li, Li Zhang, Chi Xi An Jiao Tong Univ Natl Key Lab Human Machine Hybrid Augmented Intel Natl Engn Res Ctr Visual Informat & Applicat Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China Tsinghua Univ Dept Automat BNRist Beijing 100084 Peoples R China Changan Univ Sch Elect & Control Engn Xian Peoples R China

ISBN: (纸本)9798350399462

Learning robust driving policy for complex driving scenarios brings significant challenges to autonomous driving system due to its inherent black-box properties. Recent methods learn driving policies from not only ego-vehicle but all the observed vehicles to improve sample efficiency and scenario exposure. However, our empirical studies unveil the existence of meaningless and erroneous behaviors of the observed vehicles, which may result in unexpected and dangerous driving policies. Expanding upon the notion of influence function, which gauges the effect of a training sample on the target model and its predictive ability, we delve into finely modeling the influence of each training vehicle behavior in relation to a predicted policy. Specifically, we modify the influence function to make it adaptive for sub-sample level vehicle behaviors and determine the optimal configurations that strike a balance between speed and quality when estimating the inverse Hessian-vector product trade-off. This not only avoids the expensive training cost of "leave one out" but also enhances the interpretability of the model. We optimize the dataset based on influence score, retrain the learning model, and test on driving simulations CARLA. The results show that driving score and route completion both increased.

关键词： Vehicles

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Research on new energy grid-connected human-machine cooperative training system applying parallel control and transformer modeling 3

Research on new energy grid-connected human-machine cooperat...

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Li, Miao Wang, Jing Zhuang, Yuan Training Center of Jilin Province State Grid Corporation of China Changchun China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The purpose of this paper is to propose a panoramic human-machine collaborative training system that can adapt to new energy grid-connected operation conditions, simulate various complex situations, and provide regulators with simulation exercises, intelligent deductions, decision-making references, Q&A services, which provide intelligent reference solutions for precise regulation under new energy access scenarios with high percentage of new energy under a new type of power system. In order to enhance training effectiveness and operational efficiency, it is need to apply intelligent technology and data-driven models instead of experience and human labor. Interactive Q&A application such as Chat GPT has subversively changed the implementation mode of the training process, making the traditional teaching of theory and basic knowledge easier and faster. However, these commercial Q&A systems still have limitations in terms of accuracy, security, as well as professionalism, which makes them inefficient in professional learning area. In this paper, we use the idea of parallel control and the transformer model to construct a human-computer cooperative training system adapted to new energy grid-connected electric power systems, which realize a human-computer cooperative training model that highly integrates the Q&A services with the real trainer, the real trainees, the computer simulation system. By constructing a large model of human-computer system, a training computing experiment platform, as well as a system with a closed loop of training reality, the parallel training will help training program planning, training teaching design, training arrangements, teaching interaction and other key training links, and realize automated and intelligent training design and execution. As a training and management model adapted to the situation of artificial intelligence, parallel training will bring brand new possibilities for the development of the training industry in the intellige

关键词： Decision making

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RELIABILITY ANALYSIS OF MULTI-STATE SYSTEM CONSIDERING INTERDEPENDENT PERFORMANCE CONVERSION 14

RELIABILITY ANALYSIS OF MULTI-STATE SYSTEM CONSIDERING INTER...

引用

14th international conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Lu, He Wang, Yan Zhou, Yu Jia, Heping School of Economics & Management North China Electric Power University Beijing China Beijing Kedong Electric Power Control System Co. Ltd. Beijing China Qinghai Electric Power Trading Center Co. Ltd. Qinghai China

ISBN: (纸本)9781837242672

Multi-state systems characterized by interdependent performance conversion represent a complex category where system components exist in multiple states, each with distinct performances that are interdependent. Significant research has focused on modeling and analyzing such systems, as their reliability analysis is vital for numerous practical applications. This paper introduces a system model for multi-state systems with interdependent performance conversion. Additionally, it expands the universal generating function (UGF) method to analyze the system reliability of the proposed model, encompassing both reliability probability and expected instantaneous performance deficiency (EIPD). To demonstrate the efficacy of the proposed model and method, two numerical examples are presented, one featuring linear interdependence and the other non-linear interdependence. © The Institution of Engineering & Technology 2024.

关键词： Reliability analysis

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Convection-UNet: A Deep Convolutional Neural Network for Convection Detection based on the Geo High-speed Imager of Fengyun-4B

Convection-UNet: A Deep Convolutional Neural Network for Con...

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2023 international conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Wang, Yufei Xiao, Baihua Chinese Academy of Sciences University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350346596

Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the timely detection of such weather systems, which is crucial to saving lives and property. However, previous methods based on channel feature extraction and threshold filtering did not make full use of information in satellite images, which led to limitations on such complex problems as strong convection detection. In this study, we propose a novel framework of a deep learning-based model Convection-UNet to detect convection. We use channel 4 to 7 of FY-4B GHI that we select according to the microphysical properties of convection as input and radar reflectivity as label. We combine the detailed training time and test time data augmentation strategies and build a deep neural network to automatically extract spatial context features and achieve end-to-end learning. Results show that the performance of our method far exceeds the previous channel extraction combined with threshold filtering methods such as BT and BTD at least 0.24 on Fi-measure. We also show that our channel selection and data augmentation strategies are of great significance to detect convection. © 2023 IEEE.

关键词： Feature extraction

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RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator control

RMBench: Benchmarking Deep Reinforcement Learning for Roboti...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Xiang, Yanfei Wang, Xin Hu, Shu Zhu, Bin Huang, Xiaomeng Wu, Xi Lyu, Siwei Tsinghua Univ Inst Global Change Studies Dept Earth Syst Sci Minist Educ Key Lab Earth Syst Modeling Beijing 100084 Peoples R China SUNY Albany Albany NY 12222 USA SUNY Buffalo Buffalo NY 14222 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Microsoft Res Asia Beijing Peoples R China Chengdu Univ Informat Technol Chengdu Peoples R China

ISBN: (纸本)9781665491907

Reinforcement learning is used to tackle complex tasks with high-dimensional sensory inputs. Over the past decade, a wide range of reinforcement learning algorithms have been developed, with recent progress benefiting from deep learning for raw sensory signal representation. This raises a natural question: how well do these algorithms perform across different robotic manipulation tasks? To objectively compare algorithms, benchmarks use performance metrics. Benchmarks use objective performance metrics to offer a scientific way to compare algorithms. In this paper, we introduce RMBench, the first benchmark for robotic manipulations with high-dimensional continuous action and state spaces. We implement and evaluate reinforcement learning algorithms that take observed pixels as inputs and report their average performance and learning curves to demonstrate their performance and training stability. Our study concludes that none of the evaluated algorithms can handle all tasks well, with soft Actor-Critic outperforming most algorithms in terms of average reward and stability, and an algorithm combined with data augmentation potentially facilitating learning policies. Our code is publicly available at https://***/xiangyanfei212/RMBench- 2022. git, including all benchmark tasks and studied algorithms.

关键词： Reinforcement learning

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Algorithm of Robust control for Multi-stand Rolling Mill Strip Based on Stochastic Multi-swarm Multi-agent Optimization

Algorithm of Robust Control for Multi-stand Rolling Mill Str...

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international conference on Data Science, Computation and Security, IDSCS 2023

作者： Nikitina, Tatyana Kuznetsov, Borys Ruzhentsev, Nikolay Havrylenko, Olena Dergachov, Kostiantyn Volosyuk, Valerii Shmatko, Oleksandr Popov, Anatoliy Zhyla, Simeon Pavlikov, Vladimir Tserne, Eduard Averyanova, Yuliya Sushchenko, Olha Zaliskyi, Maksym Solomentsev, Oleksandr Ostroumov, Ivan Kuzmenko, Nataliia Educational Scientific Professional Pedagogical Institute Ukrainian Engineering Pedagogical Academy Bakhmut Ukraine Anatolii Pidhornyi Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine Kharkiv Ukraine National Aerospace University H.E. Zhukovsky harkiv Aviation Institute" Kharkiv Ukraine National Aviation University Kyiv Ukraine

ISBN: (纸本)9789819709748

A multi-stand rolling mill is a complex dynamic systems operation that requires accurate measurements of input material and precise automatic control of the formation process. In this paper, an algorithm of robust control by tension, thickness, and rolled streak loop on a multi-stand rolling mill based on a robust meta-model of the random processes of the longitudinal variation in rolled strip thickness is presented. Both problems of robust meta-model and robust control design reduced to zero-sum antagonistic vector games solutions calculated based on multi-swarm stochastic metaheuristic Archimedes optimization algorithm. The paper also presents a computer simulation of a designed robust system for a 3-stand 2-h tandem cold rolling mill. The utilization of the proposed algorithms gives the opportunity to decrease the change in strip tension and thickness in the inter-stand spaces of the rolling mill and decrease the system sensitivity in case of its parameter variation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Random processes

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A Review of IoT-Driven Innovations in Supply Chain Management 3

A Review of IoT-Driven Innovations in Supply Chain Managemen...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Chandankhede, Arpit Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Meghe Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Meghe Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Supply Chain Management (SCM) worldwide makes major advancements through the Internet of Things (IoT) by enabling real-time monitoring and prediction analytics and automatic decision capabilities in worldwide deliver chains. Through IoT technology integration with Artificial Intelligence (AI) and blockchain and cloud computing the supply chain solves major operational problems including monitoring difficulties and waste reduction and regulatory compliance requirements and operation leadership deficits. The implementation of IoT for supply chain management encounters major adoption barriers from high installation costs combined with security risks and operability complications as well as limited scalability capacity that affects especially small to medium enterprises (SMEs) and developing markets. The evaluation paper examines the shifting impact of IoT in supply chain management which enhances performance metrics and brings greater clarity and strengthens supply chain resilience. A scientist studied existing papers and case studies to reveal several essential advantages which include better inventory management, predictive analysis systems and more effective supply-chain collaborations. The analysis emphasizes effective solutions for security privacy and moral and environmental issues to guarantee sustainable expanded IoT adoption. This paper delivers practical knowledge alongside best practices to help organizations adopt IoT systems which create adaptable efficient deliver chains with sustainability at their core thus achieving long-term market benefits in today's complex global marketplace. © 2025 IEEE.

关键词： Inventory control

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Field-Oriented control Based on Nonlinear Techniques for Wind Energy Conversion systems 15

Field-Oriented Control Based on Nonlinear Techniques for Win...

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15th international conference on Electronics, Computers and Artificial Intelligence, ECAI 2023

作者： Gasmi, Hamza Benbouhenni, Habib Bizon, Nicu Mendaci, Sofiane Université 8 Mai Department of Electronic and Telecommunication Guelma1945 Algeria Nisantasi University Faculty of Engineering and Architecture Department of Electrical & Electronics Engineering Istanbul34481742 Turkey University of Pitesti Faculty of Electronics Communication and Computers Pitesti110040 Romania Department of Electrotechnic and Automatic Guelma24000 Algeria

ISBN: (纸本)9798350321388

This work represents the modeling, control, comparison, and simulation of the doubly fed induction generator (DFIG) based wind energy conversion system. In order to control the reactive and active power, synergetic control and super twisting algorithm are suggested. These two methods are used to overcome the problems of field-oriented control (FOC). So, it is necessary to conduct a comparative study between the two proposed strategies to know the best way to control the generator. Therefore, Matlab software is used to perform this comparison, where the focus is on current quality, ripple reduction ratio, power, dynamic response, robustness, and degree of complexity. According to the observations, the super-twisting algorithm outperforms the synergetic control in terms of minimizing harmonic current distortion and significantly reducing ripples in reactive and active power, which also granted acceptable solidity towards parametric variations. © 2023 IEEE.

关键词： Vector control (Electric machinery)

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Transformative control Optimization in PMSG-Based Wind Energy systems: A Deep Reinforcement Learning Approach 3

Transformative Control Optimization in PMSG-Based Wind Energ...

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3rd international conference on Electrical Machines and Drives, ICEMD 2023

作者： Khademhamedani, Bagher Izadi, Masoud Izadi, Nahid Azarshab, Amin Najari, Ali Zaker, Behnam Hamedan University of Technology Department of Electrical Engineering Hamedan Iran Hamedan Azad University Department of Electrical Engineering Hamedan Iran Petroleum University of Technology Instrumentation and Automation Engineering Department Iran Sapienza University of Rome Department of Electrical Engineering Roma Italy Politecnico di Torino Department of Automotive Engineering Torino Italy

ISBN: (纸本)9798350393743

This paper addresses the optimization of control strategies in Permanent-Magnet Synchronous Generator (PMSG)-based wind energy systems, with a specific focus on the Proportional-Integral (PI) controller. Traditionally, PI controller coefficients are optimized using methods like the response surface method and artificial neural networks. However, this research introduces a transformative approach by applying deep reinforcement learning (DRL), inspired by pioneering work in continuous control. The paper compares DRL with established optimization methods such as Particle Swarm optimization (PSO), Krill Herd, and Grey Wolf. DRL exhibits adaptability to dynamic environments, continuous learning, effective handling of complex nonlinearities, autonomous decision-making, and real-time adaptation. These qualities position DRL as a versatile and efficient choice for optimizing control in PMSG-based wind energy systems. The paper is organized into sections, including system modeling and control methodology, exploration of the Krill Herd algorithm, and the empirical comparison of DRL with the Grey Wolf optimization algorithm. The findings emphasize DRL's transformative contributions to wind energy research, marking a pioneering step towards enhancing efficiency and sustainability in PMSG-based wind energy systems. © 2023 IEEE.

关键词： Reinforcement learning

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