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2022 29th International Conference on Nuclear engineering, ICONE 2022

作者： Zhang, Pengbin Xing, Kunpeng Zou, Tao Yang, Zhijia Wang, Jingyang Wang, Hongrui State Key Laboratory of Robotics Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd Shenzhen China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791886472

Nuclear power is one of several significant clean energy and its production capacity is increasing rapidly. This paper proposed a novel PID self-tuning method based on closed-loop identification used in nuclear power plants to deal with the turbine's load tracking problem. A setpoint-shifting method is adopted to achieve the informativity of the closed-loop system, and the PID parameters are designed through an optimization method. For validating the proposed method, this paper modeled a pressurized-water reactor (PWR) nuclear power plant, a complex system consisting of three circuits, through the first principle. The primary circuit contains three main devices: a nuclear reactor, a pressurizer, and a steam generator. The models of these three devices are provided by previous literature separately. The whole model is generated through mass and energy balance. This mechanism model is used as the plant model, while the identified model is used as the control model. The initial PID parameters are set through open-loop identification using an external excitation. Then the proposed method works online to track the turbine's load in time. The effectiveness of the proposed method is illustrated by simulations. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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RESEARCH ON THE PID PARAMETER SELF-TUNING OF THE PRESSURIZER control system OF NUCLEAR POWER PLANT 29

RESEARCH ON THE PID PARAMETER SELF-TUNING OF THE PRESSURIZER...

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2022 29th International Conference on Nuclear engineering, ICONE 2022

作者： Wang, Hongrui Xing, Kunpeng Zou, Tao Yang, Zhijia Wang, Jingyang Zhang, Pengbin State Key Laboratory of Robotics Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd Shenzhen China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791886373

The pressurizer is an essential part of the safe operation of the nuclear power plant. It contains both the steam phase and the liquid phase, which requires simultaneous control of the pressure and water level, and the control system is strongly coupled. When using the PID control method, it is difficult to tune the parameters, and the difficulty is exacerbated when the set value fluctuates. This paper aims at the multi-input and multioutput pressurizer control system, based on the particle swarm optimization to realize the PID controller parameters self-tuning, adopting the time and absolute error integral as the fitness function to evaluate the merits of the PID parameters, and constantly iterating to update and obtain the PID parameters that better satisfy the current working conditions. For coupled pressurizer control systems, the PID parameter tuning method using particle swarm optimization algorithms can give a group of PID controller parameters that make the system operation stable despite the frequent fluctuation of the set value, and compared with the traditional PID controller parameter tuning method, this method can realize the PID parameter self-tuning, eliminating the trouble of manual tuning. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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Advancing Orchard Fruit Detection: An Innovative Agricultural Foundation Model Approach

Advancing Orchard Fruit Detection: An Innovative Agricultura...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University MI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

ISBN: (纸本)9798331302214

Fruit harvesting poses a significant labor and financial burden on the fruit industry, which underscore the urgent need for advancements in robotic harvesting solutions. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated across several metrics, outperforming existing state-of-the-art algorithms in our MetaFruit, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Orchards

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Low-Carbon Optimization Dispatch of Multi-Integrated Energy system Based on Kriging Metamodel and Stackelberg Game

Low-Carbon Optimization Dispatch of Multi-Integrated Energy ...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Zhaoqing Zhang Yonghui Sun Fan Sheng Chenxu Yin Denis N. Sidorov Alexey B. Iskakov School of Electrical and Power Engineering Hohai University Nanjing China Melentiev Energy System Institute Russian Academy of Sciences Irkutsk Russia School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350386127

ISBN: (纸本)9798350386134

Integrated energy system (IES) is an advanced configuration that combines electrical energy, thermal energy, and renewable energy, into a unified system. In recent years, the architecture of IES has gradually changed from vertical integration to interactive competition, and its distributed characteristics have become increasingly evident. Therefore, traditional centralized optimization strategies can no longer meet the current practical situation. In this paper, a low-carbon optimization dispatch for IES based on Stackelberg game and kriging metamodel is proposed. In order to address this problem, firstly, three typical models containing hydrogen blending gas technology and a kind of ladder-type carbon trading mechanism considering reward and punishment (RP-LCTM) are established. Secondly an integrated energy manager (IEM) is introduced and a Stackelberg game model with the IEM as the leader and three typical IESs as followers is established. Finally, the kriging metamodel is used to optimize the transaction electricity price. During the game process, the IPSO algorithm is used to optimize electricity prices in key price regions. At last, an experimental example is utilized to demonstrate the effectiveness of the proposed method.

关键词： Renewable energy sources Electricity Hydrogen Fitting Games Emissions trading Optimal scheduling Optimization Thermal energy

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Towards Supervisory control Theory in Tactical Environments: A Stackelberg Game Approach

Towards Supervisory Control Theory in Tactical Environments:...

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IEEE Conference on Decision and control

作者： Bohan Cui Alessandro Giua Xiang Yin Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

In this paper, we propose a new framework for supervisory control of discrete-event systems in tactical environments. In contrast to the standard supervisory control theory, where the environments are considered fully adversarial, we consider the possibility of the presence of attackers who have their own objectives that may not necessarily be in opposition to the specification of the supervisor. We formulate this scenario as a Stakelberg game in the leader-follower setting, where the designer proposes a supervisor, and the attacker takes a best response to the supervisor. We characterize the solution to the Stakelberg supervisory control problem as having both cooperative and antagonistic solutions. Moreover, we provide an effective algorithm for synthesizing a cooperative supervisor that enables both players to achieve their objectives. Our work makes an initial step forward from the traditional zero-sum setting of supervisory control theory to the non-zero-sum setting. Examples are provided to illustrate our results.

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Modal Fusion Sign Language Recognition Based on Swin Transformer Architecture

Modal Fusion Sign Language Recognition Based on Swin Transfo...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Yujie Gao Yadong Li Bin Liu Yifan Tian Minfeng Zhu Jian Luo School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Control Theory &Applications in Complicated System Tianjin University of Technology Tianjin China

Sign language plays an important role in information transmission and emotional communication between the hearing-impaired and the outside world. It is expressed through a rapid and complex combination of gestures, body postures and facial expressions. In this paper, Google's MediaPipe framework is used to extract and process key point information of hands. Depth features are used to supplement the RBG diagram. A framework of skeleton sensing multi-modal recognition suitable for complex scenes is proposed. This can be used to deal with the problem of sign language recognition of multi-modal information in different scenes. However, most of the existing sign language recognition methods are traditional convolutional networks and LSTM. Although they have excellent achievements in image recognition and video recognition, their application in sign language recognition does not conform to the characteristics of sign language. In this paper, a Swin Transformer model is used to process both depth information and RGB information. Finally, statistical data fusion method was used to fuse the depth prediction results with RGB prediction results. Experimental analysis shows that this method performs well in sign language recognition in complex scenes compared with existing methods.

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Adversarially Robust Reinforcement Learning for Urban Traffic Signal control

Adversarially Robust Reinforcement Learning for Urban Traffi...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Muhammad Shafly Hamzah Endra Joelianto Herman Yoseph Sutarto Adiyana Putri Instrumentation and Control Master Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi Bandung Indonesia Doctoral Program of Engineering Physics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems.

关键词： Training Adaptive systems Uncertainty Scalability Urban areas Traffic control Robustness Standards Traffic congestion Testing

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A Method for Evading Penetration and Guidance of the Hypersonic Missile

A Method for Evading Penetration and Guidance of the Hyperso...

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第43届中国控制会议

作者： Zhi Lin Jianglong Yu Hong Jiang Xiwang Dong Zhang Ren Qingke Tan Rui Lv Zhuo Liang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Institute of Unmanned System Beihang University Institute of Artificial Intelligence Beihang University China Academy of Launch Vehicle Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the problem of evading penetration of the hypersonic missile,this paper proposes a method to control the overload and maneuver time of the hypersonic missile for evasive ***,the kinematic model,the kinetic model,and the attack-defense confrontation model of the hypersonic missile and interceptor are ***,the evaluation function model of the hypersonic missile penetration effect is established,and a method to determine the penetration parameters with the maximum value of evaluation function is ***,the critical parameters of penetration,including the optimal overload and maneuver time,are obtained through online ***,the error conditions of environmental noise and time delay are introduced into the critical parameters,and the Monte Carlo simulation verifies that the evasive penetration method can improve the success rate of penetration of a single hypersonic missile against two interceptors.

关键词： Hypersonic missile attack-defense confrontation overload maneuver time Monte Carlo Simulation

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Flight Parameter Prediction and Fault Propagation based on Machine Learning and Symbolic Directed Graph

Flight Parameter Prediction and Fault Propagation based on M...

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International Symposium on Autonomous systems (ISAS)

作者： Wenzhuo Li Kun Guo Yuehua Cheng Hengsong Hu Cheng Xu Ziquan Yu College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Institute of Mechanical and Electrical Engineering Beijing China

Fault influence and propagation analysis of flight vehicle systems is an important element of flight vehicle health management and also an important problem to be solved. A fault influence model based on data-driven is established, including a prediction model of flight parameters under fault, a dynamic influence path, and an influence degree model. Based on the historical experimental data, a long and short-term memory neural network (LSTM) model is proposed to predict the time-series data of each flight parameter of the flight vehicle under fault; based on the prediction results, a symbolic directed graph (SDG) is used to describe the fault of the flight vehicle system, and then introduce the concept of a compatible path with time-series characteristics to describe the dynamic propagation process of the fault. The case shows that the method proposed in this paper enables qualitative and quantitative analysis of the fault influence, and can reasonably describe the fault propagation path and influence characteristics.

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Deep Learning Networks for Handwritten Bangla Character Recognition

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IAENG International Journal of Applied Mathematics 2023年第4期53卷 1页

作者： Begum, H. Islam, M.M. Eva, H.S. Emon, N.H. Siddique, F.A. Department of Electrical and Electronic Engineering East West University A/2 Aftabnagar Dhaka Bangladesh Department of Electrical Engineering and Computer Science South Dakota State University United States Engineering and Design Department Reverie Power and Automation Engineering Limited Bangladesh Program in Smart System Integrated Solutions Aalto University Finland

In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Classification (of information)

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