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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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New Results in Stabilization of Uncertain Nonholonomic Systems:An Event-Triggered control Approach

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Journal of Systems Science & Complexity 2021年第5期34卷 1953-1972页

作者： LIU Tengfei ZHANG Pengpeng WANG Mengxi JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004China. Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

This paper reports latest developments in event-triggered and self-triggered control of uncertain nonholonomic systems in the perturbed chained *** order to tackle the effects of drift uncertain nonlinearities,nonholonomic constraints and nonsmooth aperiodic sampling in eventbased control,a novel systematic design scheme is proposed by integrating set-valued maps with stateseparation and state-scaling *** stability analysis of the closed-loop event-triggered control system is based on the cyclic-small-gain techniques that overcome the limitation of Lyapunov theory in the construction of Lyapunov functions for nonsmooth dynamical systems and enjoy inherent robustness properties due to the use of gain-based characterization of robust *** specifically,the closed-loop event-triggered control system is transformed into an interconnection of multiple input-tostate stable systems,to which the cyclic-small-gain theorem is applied for robust stability *** self-triggered mechanisms are also developed as natural extensions of the event-triggered control *** proposed event-based control design approach is new and original even when the system model is reduced to the ideal unperturbed chained ***,the proposed methodology is also applicable to a broader class of nonholonomic systems subject to state and input-dependent *** efficacy of the obtained event-triggered controllers is validated by a benchmark example of mobile robots subject to parametric uncertainties and a measurement noise such as bias in the orientation.

关键词： Event-triggered control nonholonomic systems self-triggered control stabilization

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A Reinforcement Learning Mission Supervisor with Memory for Human-multi-robot Coordination Systems 17th

A Reinforcement Learning Mission Supervisor with Memory for ...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Mo, Zhibin Zhang, Zhenyi Chen, Yutao Huang, Jie School of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China

ISBN: (纸本)9789811663192

In this paper, a novel reinforcement learning mission supervisor (RLMS) with memory is proposed for human-multi-robot coordination systems (HMRCS). The existing HMRCS are known to suffer from long decision waiting time and large mission error caused by repeated human intervention, restricting the autonomy of multi-robot systems. The proposed supervisor elaborately integrates deep-Q-network (DQN) and long-short-term memory (LSTM) knowledge base within the null-space based behavioral control (NSBC) framework, so as to achieve optimal adjustment strategy of the behavioral priority in the presence of mission conflicts, and to reduce the frequency of human intervention. In particular, the proposed RLMS with memory first memorize human intervention history when robot systems are not confident in decision making when encountering emergencies, and then reload the history information when encountering the same situation that have been tackled by human previously. Simulation demonstrates the effectiveness of proposed RLMS with memory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Linear parameter-varying model predictive control for nonlinear systems using general polytopic tubes

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Automatica 2024年 160卷

作者： Abbas, Hossam Seddik Institute for Electrical Engineering in Medicine University of Lübeck Moislinger Allee 53-55 Lübeck23558 Germany Automation and Control Group Department of Electrical and Information Engineering Faculty of Engineering Kiel University Kaiserstraße 2 Kiel24143 Germany

This paper deals with model predictive control (MPC) for nonlinear systems using linear-parameter varying (LPV) embedding of the nonlinear dynamics (LPVMPC). The proposed LPVMPC can incorporate information of the future evolution of the scheduling parameter over the MPC prediction horizon with uncertainty bounds, which are used to construct anticipated scheduling tubes for robustification. Therefore, this approach is less conservative than the methods that only consider knowledge of the bounds on the scheduling parameter's rate of variation. The scheduling tubes are employed to synthesize online general polytopic invariant state tubes. The optimization problem of the proposed LPVMPC is a single quadratic program. Recursive feasibility is proven. A numerical example is presented for demonstrating the effectiveness of the proposed LPVMPC algorithm compared to nonlinear MPC and other standard approaches. © 2023 Elsevier Ltd

关键词： Model predictive control

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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Variational Adversarial Active Learning Assisted process Soft Sensor Method 4

Variational Adversarial Active Learning Assisted Process Sof...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Dai, Yun Zhang, Ying Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Taizhou Vocational and Technical College Department of Mechanical and Electrical Engineering Taizhou318000 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Soft sensor methods have been widely applied in process industries to predict key quality variables that cannot be measured online. However, labeled samples to construct models are often limited because quality variables are difficult to be obtained. Additionally, due to the instrument of redundant sensors, the process data is high-dimensional with strong correlations. In this paper, an active learning soft sensor framework named variational adversarial active learning (VAAL) is developed to select informative unlabeled samples to enhance prediction performance. The sampling strategy of VAAL learns a latent space using a variational autoencoder (VAE) and an adversarial network trained in a way of minimax game. The VAE tries to trick the adversarial network into predicting that all samples are from the labeled pool, while the adversarial network learns how to discriminate between dissimilarities in the latent space. The Gaussian process regression model is adopted in VAAL as a base soft sensor. The prediction results of an industrial debutanizer column demonstrate the advantages of VAAL as compared to the existing active learning strategies. © 2022 IEEE.

关键词： Forecasting

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Detecting Defects of Wooden Boards by Improved YOLOv4-tiny Algorithm 17th

Detecting Defects of Wooden Boards by Improved YOLOv4-tiny A...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Qiu, Yang Ai, Zhijie Lin, Ye Xu, Zhezhuang Liu, Xinxiang College of Electrical Engineering and Automation Fuzhou University Fuzhou350000 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9789811663192

Wooden boards are widely used as raw materials for furniture. Detecting defects on the wooden is significant to improve the quality of products. In this paper, we propose an improved YOLOv4-tiny target detection algorithm with feature extraction and utilization tailored for wood defect recognition. The proposed algorithm replace the residual network of the original algorithm with the Res2Net network to enhance the feature extraction ability, and increase the different scales to improve the prediction accuracy. A testbed is deployed to collect the images of wooden boards, and the performance of the proposed algorithm is evaluated on the testbed. Compared with the original YOLOv4-tiny algorithm, the mean average precision for detecting the three types of defects deadknot, insectpest and crack was 80.06% which was an increase of 5%. Compared with other methods of wood defect detection, the proposed algorithm is more accurate and faster. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Testbeds

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The Optimized Model of Hybrid Grid Connected System for Power Generation of the University of Lahore, Pakistan

The Optimized Model of Hybrid Grid Connected System for Powe...

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2022 International Conference on electrical engineering and Sustainable Technologies, ICEEST 2022

作者： Khan, Muhammad Ahmad Zain Yousaf, Muhammad Raza, Ali Tahir, Muhammad Faizan Ali, Mazhar Qadeer, Abdul Tianjin University School of Electrical and Information Engineering Tianjin China Hubei University of Automotive Technology School of Electrical and Information Engineering Shiyan China University of Engineering and Technology Department of Electrical Engineering Lahore Pakistan South China University of Technology School of Electric Power Guangzhou China Tianjin University School of Electrical and Electronic Information Control Engineering Tianjin China Wuhan University School of Electrical Engineering and Automation Wuhan China

ISBN: (纸本)9781665462099

Energy is essential for a country's economic sector to be stable and prospering, but unfortunately, Pakistan's energy crisis is the country's biggest roadblock to increasing its economy. Industries need a constant supply;even minor supply delays can cost businesses millions of dollars. The primary reason for load shedding in Pakistan is the country's reliance on costly fossil fuels, whose prices are volatile and continue to increase. Pakistan has great potential for Renewable Energy Sources (RESs) such as wind, solar, micro-hydro/canal fall, biomass, biogas, tidal/ocean, geothermal, and biodiesel. This study aims to contribute to ongoing research on hybrid grid-connected systems by highlighting the optimal modeling and sizing and considering numerous techno-economic aspects such as system stability, capacity shortage constraint, Battery State of Charge (SoC), Photovoltaic (PV) power production, Net Present Cost (NPC), and PV array monitoring systems. The Hybrid Optimization Model for Electric Renewables (HOMER) software is used to analyze the six different scenarios employing the techno-economic factors, sensitivity analysis, and environmental consequences. The proposed system shows a significant decrease in energy costs and environmental emissions. © 2022 IEEE.

关键词： Sensitivity analysis

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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Event-Triggered SMC for Networked Markov Jumping Systems With Channel Fading and Applications: Genetic Algorithm

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IEEE Transactions on Cybernetics 2023年第10期53卷 6503-6515页

作者： Qi, Wenhai Zhang, Ning Zong, Guangdeng Su, Shun-Feng Yan, Huaicheng Yeh, Ruey-Huei Qufu Normal University School of Engineering Rizhao276826 China Chengdu University School of Information Science and Engineering Chengdu610106 China Tiangong University School of Control Science and Engineering Tianjin300387 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education Shanghai200237 China National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan

The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model. © 2013 IEEE.

关键词： Markov processes

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