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robotics, control and Automation (ICRCA), International Conference on

作者： Jintao Wan Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

Acquiring high-quality 3D surfaces of indoor scenes is essential for applications in VR/ AR and robotics. Recent advancements in neural implicit surface learning and differ-entiable volume rendering methods have significantly improved the accuracy and completeness of geometry. In our work, we propose a volumn feature rendering method for fast and detailed surface reconstruction from muti-view RGB-D frames. We adopt a hybrid scene representation utilizing muti-resolution hash encoding and two shallow MLPs for fast convergence. We suggest improvements by replacing standard volume rendering with vol-ume feature rendering to optimize speed. Additionally, we employ an adaptive sampling strategy guided by dynamic occupancy grids to accelerate training. We introduce a curvature term to promote surface smoothness and hole filling in untexture areas. Experiments on standard benchmarks show that our method can process room-scale scenes in approximately 10 minutes, achieving a 2x speedup compared to Go-Surf [14] in a memory-efficient way, while maintaining comparable or superior reconstruction quality.

关键词： Training Geometry Surface reconstruction Three-dimensional displays Accuracy Rendering (computer graphics) Surface treatment

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ES-MSCNN: An Echo state Multi-Scale Convolutional Neural Network for Multi-Step Prediction of Wastewater Quality Variables

ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Net...

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robotics, control and Automation Engineering (RCAE), International Conference on

作者： Yue Sun Yiqi Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Given the limitations of hardware sensors together with strong nonlinearities and nexus between variables in wastewater treatment processes (WWTPs), the measurement of key quality variables is usually inaccurate or even impossible. Moreover, due to the short data sampling period, the single-output single-step prediction model cannot meet the actual demand. Therefore, this paper proposes a new soft sensor, termed as the Echo state Multi-Scale Convolutional Neural Network (ES-MSCNN), designed for accurate multivariate multi-step prediction. ES-MSCNN first generates an echo state representation (ESR) containing temporal information through an echo state network (ESN). Subsequently, a convolutional neural network (CNN) is employed to avoid the shallowness of ESN and further extract high-dimensional feature information from the data. Besides, a multi-scale learning strategy is used in the convolutional module to capture multi-timescale features and improve the multivariate prediction accuracy. Validation on the benchmark simulation model 2 platform and comparison with several models indicate that the proposed ES-MSCNN has higher accuracy and stronger robustness in multi-step prediction tasks with multi-input and multi-output.

关键词： Accuracy Soft sensors Predictive models Feature extraction Prediction algorithms Robustness Convolutional neural networks Wastewater Wastewater treatment Robots

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The Trust Evaluation and Anomaly Detection Model of Industrial control Equipment Based on Multiservice and Multi-attribute 7

The Trust Evaluation and Anomaly Detection Model of Industri...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Xin, Chen Xianda, Liu Yiheng, Jiang Chen, Wang Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang Institute of Automation Shenyang China

ISBN: (纸本)9781665409506

In the industrial control system, in order to solve the problem that the installation of smart devices in a transparent environment are faced with the unknown attack problems, because most of the industrial control equipment to detect unknown risks, Therefore, by studying the security protection of the current industrial control system and the trust mechanism that should be widely used in the Internet of things, this paper presents the abnormal node detection mode based on comprehensive trust applied to the industrial control system scenarios. This model firstly proposes a model, which fuses direct and indirect trust values into current trust values through support algorithm and vector similarity algorithm, and then combines them with historical trust values, and gives the calculation method of each trust value. Finally, a method to determine abnormal nodes based on comprehensive trust degree is given to realize a detection process for all industrial control nodes. By analyzing the real data case provided by Melbourne Water, it is concluded that this model can improve the detection range and detection accuracy of abnormal nodes. It can accurately judge and effectively resist malicious behavior and also have a good resistance to aggression. © 2021 IEEE.

关键词： Anomaly detection

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization

A Depression Level Classification Model Based on EEG and GCN...

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第43届中国控制会议

作者： Zhongyi Zhang Chenyang Xu Licheng Jin Huirang Hou Qinghao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Electroencephalography(EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network(GCN) to identify levels of depression. Additionally,to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art(SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module.

关键词： Depression Level EEG Graph Convolution Network Domain Generalization

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Robust Output Voltage Tracking with Prescribed Performance for Single-Phase UPS Inverters

Robust Output Voltage Tracking with Prescribed Performance f...

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Chinese Automation Congress (CAC)

作者： Jialei Luo Wei Liu Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper deals with the robust output voltage tracking problem with prescribed performance for single-phase voltage source uninterruptible power supply inverters. We construct two linear internal models to reproduce the steady-state information, based on which we transform our problem into the robust stabilization problem with prescribed performance of an augmented system. Then, we incorporate a novel error transformation into the existing framework for the robust output regulation problem to design a stabilization controller, based on which we synthesize our control law to make the tracking error converge to zero asymptotically while always satisfying the prescribed performance.

关键词： Automation Power supplies Transforms Inverters Regulation Steady-state Voltage control Nonlinear systems Uninterruptible power systems Lyapunov methods

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AGV state Monitoring Based on Global and First-person View Fusion in Digital Workshops 12

AGV State Monitoring Based on Global and First-person View F...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Zhang, Sichao Liang, Wei Li, Gengyu Yuan, Xudong An, Haibo Zhang, Yinlong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Science Beijing100049 China School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of Cetc Jiangsu Nanjing210007 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

AGV state monitoring is becoming increasingly important for production system safety in digital workshops. This paper develops a global and first-person view fusion to monitor the AGV (Automated Guided Vehicle) running state in the complex digital workshops. Based on the affine transformation model, the AGV running states, i.e., position, velocity and orientation are estimated by the global camera with the assistance of ArUco markers, which are pasted on the front side of the AGV. Besides, the first-person view camera is used to implement pedestrian detection and distance measurement based on the YOLOv3 model. It is noticeable that the pitch angle is considered for distance measurement, and an improved triangular model for distance measurement is proposed. The effectiveness of the proposed method has been verified in our built platform. © 2022 IEEE.

关键词： Cameras

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Research on optical properties of human hepatoma cells using terahertz time-domain spectroscopy

Research on optical properties of human hepatoma cells using...

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2023 International Symposium on Ultrafast Phenomena and Terahertz Waves, ISUPTW 2023

作者： Guan, Hanxiao Qiu, Weihang Ma, Yehao Tian, Liangfei Cao, Yuqi Zhang, Guangxin College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou China Ningbo University of Technology Robotics Institute Ningbo China

ISBN: (纸本)9798350317473

Terahertz technology is a non-invasive and label-free inspection method, promising for applications in various fields, especially in biomedical diagnosis. Terahertz radiation is capable for cell physiological and pathological status recognition due to its sensitivity to cell composition and structural information of biomacromolecular. In this paper, the transmission properties of glass, polymethyl methacrylate and polypropylene in THz regime were compared and polypropylene was selected as the petri dish material for cell adhesion due to its high transmission of terahertz wave. The time-domain and frequency-domain spectra, refractive index and absorption coefficient of hepatoma cell line Huh7 and normal cell line MIHA were analyzed. The experimental results showed significant distinction between the terahertz characteristic spectra of hepatoma cells and normal cells. This work can be helpful for applying terahertz technology in cancer detection at the cellular level. © 2023 IEEE.

关键词： Terahertz waves

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Microanalysis of a ductile iron by microchip laser-induced breakdown spectroscopy

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Plasma Science and Technology 2021年第10期23卷 179-185页

作者： Wei Wang Lanxiang Sun Peng Zhang Liming Zheng Lifeng Qi Jinchi Wang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016People’s Republic of China Key Laboratory of Networked Control System Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016People's Republic of China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People's Republic of China

Laser beams with ns pulse width are generally employed as an excitation source in the process of detecting inclusions and elemental segregation on a workpiece surface by microanalysis of the laser-induced breakdown *** addition,the ablation crater interval of laser sampling on the sample surface is generally 20μm or *** is difficult to detect the morphology of inclusions smaller than 50μm in diameter and the micro-segregation of ***,in this work,when the laser ablation crater is 10μm and the sampling resolution of the laser on the sample surface is 5μm,the morphology and distribution of spherical inclusions(20–60μm)in ductile iron can be detected according to the difference of the Fe spectrum on the Fe matrix and the spheroidal ***,the distribution of micro-segregation of Mg and Ti elements in ductile iron was also studied.

关键词： micro-laser-induced breakdown spectroscopy micro-LIBS mapping inclusion segregation ductile iron

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Electric Vehicle Routing Problem with Capacitated Charging Stations and Dynamic Electricity Prices

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IEEE Internet of Things Journal 2025年

作者： He, Zhiye Tian, Yuning Miao, Haoyu Yao, Canqi Yang, Zaiyue Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities School of System Design and Intelligent Manufacturing Shenzhen518055 China The Hong Kong Polytechnic University Department of Logistics and Maritime Studies Hong Kong Hong Kong

With rapid electrification of transportation systems in recent years, the electric vehicle routing problem (EVRP) has received increasing attention. In order to serve a set of valuable transportation tasks using a fleet of electric vehicles, EVRP intends to jointly determine their routes and the charging schedules, with the aim of optimizing service reward and charging cost under state of charge (SOC) constraints of battery. EVRP is a difficult nonlinear integer program, and due to its intrinsic NP-hardness early EVRP studies often oversimplify the charging process and assume unlimited capacity of charging stations and constant electricity price. The contribution of this paper lies in twofold. First, to better characterize the practical charging process, this paper introduces a new graph model that can take the charging station capacity and dynamic electricity price into full consideration. Second, we design a series of transformation techniques, including reformulation SOC dynamics, Lagrangian relaxation, exact linear programming relaxation, etc, which eventually convert the original EVRP into an approximate linear program. Extensive simulations across 14 instances demonstrate that, the proposed approach is capable of solving large-scale instances over 500 nodes, and is 100 times faster than traditional analytic methods while achieving very close optimality. While comparing with the popular heuristic method ALNS (Adaptive Large Neighborhood Search), our method is several times faster and obtains much better optimality. © 2014 IEEE.

关键词： Charging stations

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Novel vision-LiDAR fusion framework for human action recognition based on dynamic lateral connection

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IET Cyber-systems and robotics 2024年第4期6卷 21-31页

作者： Fei Yan Guangyao Jin Zheng Mu Shouxing Zhang Yinghao Cai Tao Lu Yan Zhuang The School of Control Science and Engineering Dalian University of TechnologyDalianChina Technology Centre of Dalian Customs Dalian CustomsDalianChina ZX‐YZ School of Network Science Haikou University of EconomicsHainanChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina

In the past decades,substantial progress has been made in human action ***,most existing studies and datasets for human action recognition utilise still images or videos as the primary ***-based approaches can be easily impacted by adverse environmental *** this paper,the authors propose combining RGB images and point clouds from LiDAR sensors for human action recognition.A dynamic lateral convolutional network(DLCN)is proposed to fuse features from *** RGB features and the geometric information from the point clouds closely interact with each other in the DLCN,which is complementary in action *** experimental results on the JRDB-Act dataset demonstrate that the proposed DLCN outperforms the state-of-the-art approaches of human action *** authors show the potential of the proposed DLCN in various complex scenarios,which is highly valuable in real-world applications.

关键词： neural network-based pedestrian identification sensor fusion

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