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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Yang, Ming Feng, Honglin Wang, Xin Jiao, Xuguo Wu, Xiaoming Qin, Shuwei Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Public Security of Shandong Province Shandong Intelligent Public Security Joint Innovation Center Jinan China

ISBN: (纸本)9789819710829

In the contemporary era of information technology, the exponential surge in data has rendered colossal potential value, which would be fully unlocked through data circulation and sharing. However, when the data contains sensitive information, special attention must be paid to the data privacy and security problem for data sharing. A feasible solution is to incorporate blockchain technology into the data sharing system, and the new challenge is how to provide fair rewards for data owners to motivate them to contribute data. To address this challenge, in this paper, we first design a blockchain-based data sharing system to enhance the trust and reliability of the data shared among participants. Then we propose an incentive allocation method based on the Shapley value, which comprehensively incorporates each participant’s contribution while ensuring fairness for the allocation. Finally, multiple numerical simulations are conducted to validate the effectiveness of the proposed mechanism. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Blockchain

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Identification of Mixed Organic Pollutants in River Water Based on Graph Convolutional Networks

Identification of Mixed Organic Pollutants in River Water Ba...

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Chinese Automation Congress (CAC)

作者： Shuai Cao Jie Yu Xingyu Fan Qiuhao Zhang Fangjia Xue Dibo Hou State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the application of three-dimensional (3-D) fluorescence spectroscopy for river water pollutant detection, traditional feature extraction models often neglect wavelength information and complex intermolecular interrelationships. Moreover, experiments are frequently limited to single-substance classifications. This research addresses these limitations by proposing a novel model for organic pollutant recognition based on graph neural networks (GNNs). The proposed method begins by using the excitation-emission matrix and the wavelength information corresponding to each fluorescence intensity value as inputs. A convolutional neural network (CNN) is initially employed to extract preliminary features from the three-dimensional fluorescence spectra. Subsequently, the initial spectral features obtained during the pre-training stage are used to construct a graph neural network model. This model represents each category of organic pollutants as nodes within the graph, capturing the intrinsic connections between different pollution samples through node interactions. Finally, the model outputs a score vector for each category to classify the pollutants. To validate the proposed model, datasets comprising twelve common organic pollutants, both as single substances and mixtures, were collected. Comparative analysis with other methods demonstrates that the proposed approach achieves a high accuracy rate.

关键词： Volatile organic compounds Accuracy Water quality Fluorescence Feature extraction Water pollution Vectors Rivers Monitoring Active matrix organic light emitting diodes

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Improved Residual Reinforcement Learning for Dynamic Obstacle Avoidance of Robotic Arm

Improved Residual Reinforcement Learning for Dynamic Obstacl...

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Data Driven control and Learning Systems (DDCLS)

作者： Zhenting Liu Shan Liu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

This paper proposes an improved residual deep reinforcement learning method for robot arm dynamic obstacle avoidance and position servo. The proposed method first simplifies the state space by constructing key points and pre-trains a model capable of completing obstacle avoidance tasks using the simplified state. Then, when training with real state information, a guiding network is used to help accumulate good samples, which improves the training efficiency. To overcome the convergence difficulty of residual DQN in robot arm obstacle avoidance, this paper incorporates the action of the feedback controller into the action space and uses incremental reward values to evaluate the actions. Simulation results demonstrate that the proposed method can effectively achieve robot arm dynamic obstacle avoidance and position servo.

关键词： Training Learning systems Simulation Aerospace electronics Manipulators Deep reinforcement learning Collision avoidance

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Development of a Compact Optical Layout for a Miniature Fluorescence Spectrometer: In-Situ Detection of Organic Pollutants in Aquatic Environment

Development of a Compact Optical Layout for a Miniature Fluo...

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Chinese Automation Congress (CAC)

作者： Xingyu Fan Jie Yu Qiuhao Zhang Shuai Cao Lingke Zhang Fangjia Xue State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In recent years, the increasing presence of emerging organic pollutants in rivers, lakes, and other aquatic environments has posed significant threats to human health and ecological safety. Current monitoring technologies for these pollutants face several challenges, including the high cost of equipment and the scarcity of trained professionals. Furthermore, existing portable fluorescence sensors are limited by inadequate data, unsuitability for in-situ water environments, and the ability to detect only specific pollutants. This paper proposes a novel design for an in-situ miniature fluorescence spectrometer, utilizing a compact optical layout to monitor organic pollutants in water. The design features a high-power UV LED combined with a lens group to enhance the fluorescence excitation effect of low-concentration organic pollutants. The system components are integrated into a compact layout centered around a flow cell, significantly reducing the device's size. In experimental validation, vitamin B was used as a model organic pollutant to test the prototype's detection capabilities. Within the concentration range of 1 − 100 μg/L, the fluorescence intensity exhibited a strong linear correlation with the vitamin B concentration, achieving an R 2 value of 0.9945, comparable to that of commercial spectrometers. This portable device demonstrates excellent performance in detecting organic matter and offers significant advantages for in-situ monitoring applications, making it well-suited for real-time pollutant detection in rivers, lakes, and other aquatic environments.

关键词： Performance evaluation Optical design Layout Prototypes Fluorescence Light emitting diodes Water pollution Rivers Optical sensors Monitoring

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Tomographic Object Detection by Unsupervised Clustering

Tomographic Object Detection by Unsupervised Clustering

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Computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Fengyuan Wang Zheng Wang Yimin Wu Yandan Jiang Haifeng Ji Baoliang Wang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This work introduces clustering to electrical tomography (ET) and first compares the effectiveness of different unsupervised clustering categories in tomographic object detection. First, the one-step linear back projection (LBP) algorithm is applied to reconstruct a rough image. Then, clustering is introduced to detect and refine the objects in the image, and a higher-quality image is obtained based on the clustering results. Six unsupervised clustering algorithms belonging to different categories, including K-means, Mini Batch K-means, Agglomerative, density peak clustering (DPC), statistical information grid (STING), gaussian mixture model (GMM), are compared from the aspects of clustering evaluation index, reconstruction quality and reconstruction efficiency. Simulation was carried out based on a capacitively coupled electrical resistance tomography (CCERT) system to collect the projection data under the cases of one to three objects to be detected in the sensing region. The research results show that the quality of object detection and reconstruction can be effectively improved by post-processing the image with clustering. It is found that for the single-object distribution, the grid-based clustering algorithm STING provides images with the highest quality. While for the multi-objects distributions (two or three objects), the Agglomerative algorithm belonging to the hierarchy clustering category shows advantage in achieving good images. Concerning the real-time performance, Agglomerative clustering algorithm is also preferred with less computational time than most of the other clustering algorithms. Therefore, among the combinations investigated in this work, LBP + Agglomerative clustering has the overall best performance in topographic object detection, in regarding of the reconstruction quality and efficiency.

关键词： Image quality Resistance Clustering algorithms Object detection Tomography Real-time systems Sensors Pattern recognition Indexes Image reconstruction

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Feedback Generative Adversarial Network With Channel-Space Attention for Image-Based Optimal Path Search Planning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4293-4307页

作者： Sun, Tao Li, Jian-Sheng Zhang, Yi-Fan Ru, Xin-Feng Wang, Ke Tsinghua University Department of Automation Beijing100084 China Dalian University of Technology School of Control Science and Engineering Dalian116024 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Tongji University School of Economics and Management Shanghai200092 China

Path planning and seeking is one of the most challenging and interesting problems in the field of artificial intelligence. In this article, we propose a new image-based path planning algorithm to overcome traditional design concepts based on complex topological networks. Specifically, a novel feedback generative adversarial network learning model with channel-space attention (CSA-FGAN) is developed to search and plan an optimal path. It is worth noting that the input and output of the proposed feedback learning model are RGB images with complex spatial obstacle environment maps and RGB images with feasible paths, respectively. In particular, in order to further enhance the feature extraction ability and path prediction accuracy of image data, the feedback attention mechanism controls and improves the depth of the network by forming a closed loop of the proposed learning model. Final, three application scenario image examples are conducted to verify the superiority of the proposed path planning policy. © 2020 IEEE.

关键词： Motion planning

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controllability of General Linear Discrete Multi-Agent Systems with Directed and Weighted Signed Network

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Journal of Systems science & Complexity 2022年第6期35卷 2107-2130页

作者： ZHAO Lanhao JI Zhijian LIU Yungang LIN Chong Institute of Complexity Science College of AutomationQingdao UniversityQingdao 266071China Shandong Key Laboratory of Industrial Control Technology Qingdao UniversityQingdao 266071China School of Control Science and Engineering Shandong UniversityJinan 250061China Institute of Complecity Science College of AutomationQingdao UniversityQingdao 266071China

This paper investigates the controllability of general linear discrete-time multi-agent systems with directed and weighted signed networks by using graphic and algebraic *** nondelay and delay cases are considered *** the case of no time delay,the upper bound condition of the controllable subspace is given by using the equitable partition method,and the influence of coefficient matrix selection of individual dynamics is *** the case of single delay and multiple delays,the equitable partition method is extended to deal with time-delay systems,and some conclusions are *** particular,some simplified algebraic criteria for controllability of systems with time delay are obtained by using augmented system method and traditional algebraic controllability criteria.

关键词： controllability equitable partition multi-agent system signed network time-delays

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FeCoP sub-nanometric-sheets for electrocatalzing overall water splitting

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Nano Research Energy 2024年第4期3卷 68-76页

作者： Long Zhao Kexin Meng Yibo Guo Qingsheng Wu Quanjing Zhu Tao Zhou Yongqing Fu Ming Wen School of Chemical Science and Engineering School of Environmental Science and EngineeringShanghai Key Laboratory of Chemical Assessment and SustainabilityState Key Laboratory of Pollution Control and Resource ReuseTongji UniversityShanghai 200092China College of Bioengineering East China University of Science and TechnologyShanghai 200237China Faculty of Engineering and Environment Northumbria UniversityNewcastle upon Tyne NE99UK

Renewable electrical energy for electrolysis water can achieve green industrial chains for hydrogen ***,finding efficient electrocatalysts remains a challenge for green ***,sub-nanometric FeCoP nanosheets with average thickness of 0.9 nm is constructed through 2D self-assembly driven by cavitation effect of ultrasonics and following *** from abundant active sites,enhanced H_(2)O molecular adsorption kinetics,and highly enhanced structural stability,the subcrystalline FeCoP shows excellent electrocatalytic activities of hydrogen evolution reaction(HER)and oxygen evolution reactions(OER).Ultralow overpotential of 37 mV is achieved at 10 mA·cm^(-2) for *** the FeCoP catalyst was used as both cathode and anode for overall water splitting using renewable electrical energy,green hydrogen produced is directly applied for hydrogen fuel cell to drive fan for more than 10 *** calculation indicates that subcrystalline FeCoP more easily adsorbs H_(2)O than crystalline one and thus speeds up the kinetics of Volmer step in HER process.

关键词： green hydrogen energy subcrystalline overall water splitting sub-nanometric-sheets

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Knowledge Distillation on Driving Intention Generator: Learn Human-like Semantic Reasoning

Knowledge Distillation on Driving Intention Generator: Learn...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Wang, Hejun Dong, Jiajun Wang, Yunkai Jiao, Yanmei Xiong, Rong Zhejiang University The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China Hangzhou Normal University The School of Information Science and Engineering Hangzhou China

ISBN: (纸本)9798350327182

Semantic information plays a significant role in autonomous vehicles. However, it has remained open how to economically compute and efficiently incorporate semantic information into other frequently applied processes, such as construction of local navigation map, driving decision making and trajectories planning. To address this issue, we introduce Knowledge Distillation to convey semantic reasoning capability. Following the existing intention-guided framework, a semantically incorporated module associates given navigation instruction with acquired visual perception to synthesize driving intention. Our major contribution is to teach the driving intention generator how to propagate considering semantics implicitly. Specifically, we construct a semantic supervisor identical to the driving intention generator. The supervisor transfers semantic reasoning ability to the generator during training process, with which the generator is expected to perform stably and reliably even in extremely sophisticated surroundings or under frequently inaccurate navigation instruction. Through exhaustive experimental validation, our model demonstrates a superior robustness and adaptability compared to the state-of-the-art. Finally, we explain the intrinsic mechanism of our model with visualization. © 2023 IEEE.

关键词： Navigation

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