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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Qiu, Changlin Kong, Shihan Zhou, Chao Yu, Junzhi Institute of Automation Cas State Key Lab Management and Control for Complex Systems Beijing100190 China Naval Research Academy Beijing100072 China

ISBN: (纸本)9781728164151

In this paper, a cascade control framework is proposed a class of remote operated vehicles (ROVs) to enhance the control performance and capacity of interference resistance. Specifically, the control framework is composed of a fuzzy-based controller in primary loop and a PID neural network based controller in secondary loop. In the primary loop, fuzzy controller is implemented to avoid precise establishment of ROV model for calculating the expected velocity of the vehicle. Furthermore, in the secondary loop, a PID neural network with adaptation is adopted to accomplish the capacity of resisting disturbance. Eventually, the control framework is verified by theoretical and simulation analyses. The obtained results indicate that the proposed cascade control framework endows the ROV with the ability to guarantee the control performance and attenuate external disturbance effectively. © 2020 IEEE.

关键词： controllers

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Explanation guided cross-modal social image clustering

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INFORMATION SCIENCES 2022年 593卷 1-16页

作者： Yan, Xiaoqiang Mao, Yiqiao Ye, Yangdong Yu, Hui Wang, Fei-Yue Zhengzhou Univ Sch Informat Engn Zhengzhou 450052 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ Hants England Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

The integration of visual and semantic information has been found to play a role in increasing the accuracy of social image clustering methods. However, existing approaches are limited by the heterogeneity gap between the visual and semantic modalities, and their performances significantly degrade due to the commonly sparse and incomplete tags in semantic modality. To address these problems, we propose a novel clustering framework to discover reasonable categories in unlabeled social images under the guidance of human explanations. First of all, a novel Explanation Generation Model (EGM) is proposed to automatically boost textual information for the sparse and incomplete tags based on an extra lexical database with human knowledge. Then, a novel clustering algorithm called Group Constrained Information Maximization (GCIM) is proposed to learn image categories. In this algorithm, a new type of constraint named group level side information is unprecedentedly defined to bridge the well-known heterogeneity gap between visual and textual modalities. Finally, an interactive draw-and-merge optimization method is proposed to ensure an optimal solution. Extensive experiments on several social image datasets including NUS-Wide, IAPRTC, MIRFlickr, ESP-Game and COCO demonstrate the superiority of the proposed approach to state-of-the-art baselines. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Social image clustering Human explanation Side information Information maximization Interactive optimization

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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A Modified Line-of-Sight Method for Path Tracking Applied to Robotic Fish 17

A Modified Line-of-Sight Method for Path Tracking Applied to...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Du, Sheng Zhou, Chao Yu, Junzhi Wu, Zhengxing State Key Lab Management and Control for Complex Systems Institute of Automation Cas Beijing100190 China Naval Research Academy Beijing100072 China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164151

This paper proposes an improved line-of-sight method to navigate a two-joint-actuated tuna-like robotic fish. Based on the characteristics of thunniform swimmer, a simplified kinematic and dynamic model is constructed, and the intrinsic body swing is illustrated. To deal with the nonlinear and under-actuated dynamics of the robotic fish, the reference head angle is designed with cross-track error, which substantially simplifies the control of linear path tracking. Furthermore, with the physical constraint, co-simulation using Automatic Dynamic Analysis of Mechanical systems (ADAMS) software and MATlab simulink reveals the effectiveness. The obtained results show that the proposed method is able to make both the cross-track error and the heading error fluctuate in an acceptable range quickly. © 2020 IEEE.

关键词： MATlab

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An Integrated Data Envelopment Analysis and Non-Cooperative Game Approach for Public Transportation Incentive Subsidy Allocation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第11期23卷 21515-21530页

作者： Xie, Qiwei Wu, Xiao Dai, Qianzhi Zheng, Xiaolong Wang, Fei-Yue Beijing Univ Technol Res Base Beijing Modern Mfg Dev Beijing 100124 Peoples R China Hubei Univ Fac Math & Stat Wuhan 430062 Peoples R China Hefei Univ Technol Sch Econ Hefei 230009 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

As an important national initiative, China's public transport priority development strategy is conducive to the development of the public transport industry and urban economic construction. However, current public transport operation is inefficient due to the information asymmetry between the government and public transport enterprises, thereby inevitably generating losses. To address the issue of information asymmetry, this study designs a public transport subsidy allocation method from the perspective of incentives and discusses the allocation results using this method through a case study. The rationality of the proposed method and the scientificity and authenticity of the conclusions, are fully explained and demonstrated via the experimental simulation. Based on real published data of public transport enterprises, the proposed method can motivate public transport enterprises to improve their performance. In the case of reporting false data, the proposed method can motivate public transport enterprises to report accurate data.

关键词： Resource management Costs Government Games Industries Behavioral sciences Data envelopment analysis Subsidy allocation data envelopment analysis information asymmetry Nash non-cooperative game

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GAN-Based key Secret-Sharing Scheme in Blockchain

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第1期51卷 393-404页

作者： Zheng, Wenbo Wang, Kunfeng Wang, Fei-Yue Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

In this article, we propose a key secret-sharing technology based on generative adversarial networks (GANs) to address three major problems in the blockchain: 1) low security;2) hard recovery of lost keys;and 3) low communication efficiency. In our scheme, the proposed network plays the role of a dealer and treats the secret-sharing process as a classification issue. The key idea is to view the secret as an image during the secret-sharing process. If the user's private key is text, we can covert the key text into an image called the original image. Specifically, we first divide the original image into original subimages by the image segmentation. Next, we encode each original subimage by DNA coding. Finally, we train the proposed network to find the key secret-sharing results. Our proposed scheme is not only a significant extension of the GANs but also a new direction for the key secret-sharing technology. The simulation results show that the scheme is secure, and both flexible and efficient in communication.

关键词： DNA Gallium nitride Blockchain Cryptography Generative adversarial networks Encoding Training Classification generative adversarial network (GAN) image segmentation secret sharing

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Adaptive Neuro-fuzzy Algorithm for Pitch control of Variable-speed Wind Turbine

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INTERNATIONAL JOURNAL OF control AUTOMATION AND systems 2022年第11期20卷 3788-3798页

作者： Asghar, Aamer Bilal Naveed, Khazina Xiong, Gang Wang, Yong COMSATS Univ Islamabad Dept Elect & Comp Engn Lahore Campus Islamabad Pakistan COMSATS Univ Islamabad Dept Comp Sci Lahore Campus Islamabad Pakistan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

With increasing size of wind turbines (WTs), the power regulation and fatigue loads on WT structures emerge as major problems to wind power industry. Pitch angle is scheduled above the rated wind speed to keep the power captured by variable-speed wind turbine (VSWT) around its rated value and release the fatigue load on WT structure. In this paper, a hybrid intelligent learning based adaptive neuro-fuzzy algorithm is proposed to schedule the pitch angle of 2 Megawatt (MW) VSWT. The artificial neural network (ANN) trains the parameters of fuzzy membership functions (MFs) using least squares estimator method in forward pass and back propagation gradient descent method in backward pass. The simulation is done in MATlab and results are compared with multilayer perceptron feed-forward neural network (MLPFFNN) and fuzzy logic-based pitch controllers. The results indicate the effectiveness of proposed neuro-fuzzy algorithm which outperforms the other two methods.

关键词： ANFIS fuzzy logic control MLPFFNN pitch control wind turbine (WT)

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Time-/Event-Triggered Adaptive Neural Asymptotic Tracking control for Nonlinear systems With Full-state Constraints and Application to a Single-Link Robot

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第11期33卷 6690-6700页

作者： Zhang, Jiaming Niu, Ben Wang, Ding Wang, Huanqing Zhao, Ping Zong, Guangdeng Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Bohai Univ Sch Math & Phys Jinzhou 121000 Peoples R China Qufu Normal Univ Sch Engn Rizhao 276826 Peoples R China

This study proposes the time-/event-triggered adaptive neural control strategies for the asymptotic tracking problem of a class of uncertain nonlinear systems with full-state constraints. First, we design a time-triggered strategy. The effect caused by the residuals of the estimation via radial basis function (RBF) neural networks (NNs), and the reasonable upper bounds on the first derivative of the reference signal and the derivative of each virtual control, can be eliminated by designing appropriate adaptive laws and utilizing the basic properties of RBF NNs. Moreover, the construction of the barrier Lyapunov functions (BLFs) in this work ensures the compliance of the full-state constraints and also holds the asymptotic output tracking performance. Then, based on the time-triggered strategy, we further design a relative threshold event-triggered strategy. The proposed event-triggered adaptive neural controller can solve the main control objective of this work, that is: 1) the full-state constraint requirements of the system are not violated and 2) the output signal asymptotically tracks the reference signal. Compared with the traditional method, the event-triggered strategy can improve the utilization of communication channels and resources and has greater practical significance. Finally, an example of single-link robot under the proposed two strategies illustrates the validity of the constructed controllers.

关键词： Artificial neural networks Nonlinear systems control systems Adaptive systems Backstepping Neurons Task analysis Asymptotic tracking control barrier functions full-state constraints neural networks (NNs) time- event-triggered control uncertain nonlinear systems

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Continuous-Time Linear Parallel control

Continuous-Time Linear Parallel Control

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Chinese Automation Congress (CAC)

作者： Wei, Qinglai Li, Hongyang Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781728176871

This paper presents a continuous-time linear parallel control by modeling the derivative of system control with respect to time. The framework and properties of continuous-time linear parallel control are first presented. Then, considering the controllability, the pole assignment theorems will be analyzed. Finally, two examples show the correctness of continuous-time linear parallel control.

关键词： Parallel controller parallel systems parallel systems theory continuous-time linear systems

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ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第11期23卷 20217-20229页

作者： Zhang, Wenwen Wang, Jiangong Wang, Yutong Wang, Fei-Yue Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China

Recognizing and locating objects by algorithms are essential and challenging issues for Intelligent Transportation systems. However, the increasing demand for much labeled data hinders the further application of deep learning-based object detection. One of the optimal solutions is to train the target model with an existing dataset and then adapt it to new scenes, namely Unsupervised Domain Adaptation (UDA). However, most of existing methods at the pixel level mainly focus on adapting the model from source domain to target domain and ignore the essence of UDA to learn domain-invariant feature learning. Meanwhile, almost all methods at the feature level ignore to make conditional distributions matched for UDA while conducting feature alignment between source and target domain. Considering these problems, this paper proposes the ParaUDA, a novel framework of learning invariant representations for UDA in two aspects: pixel level and feature level. At the pixel level, we adopt CycleGAN to conduct domain transfer and convert the problem of original unsupervised domain adaptation to supervised domain adaptation. At the feature level, we adopt an adversarial adaption model to learn domain-invariant representation by aligning the distributions of domains between different image pairs with same mixture distributions. We evaluate our proposed framework in different scenes, from synthetic scenes to real scenes, from normal weather to challenging weather, and from scenes across cameras. The results of all the above experiments show that ParaUDA is effective and robust for adapting object detection models from source scenes to target scenes.

关键词： Adaptation models Representation learning Feature extraction Task analysis Semantics Generative adversarial networks Object detection Object detection unsupervised domain adaptation distribution alignment domain-invariant representation

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