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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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Defects Clustering for Mineral Sintering Surface Based on Multi-source Data Fusion

Defects Clustering for Mineral Sintering Surface Based on Mu...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Yi Yang Tengtuo Chen Liang Zhao Jiaying Gu Xiaoyu Tang Yangqianhui Zhang School of Reliability and Systems Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China College of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Software Technology Dalian University of Technology Dalian China School of Mechanical Engineering Zhejiang University Hangzhou China

During sintering of minerals, multiple categories of defects often appear on the surface of material due to hysteresis of control. Fusion of data from multiple sources is considered in this paper in a bid to identify different types of defects and to improve the utilization of collected data. Specifically, this paper establishes a heterogeneous multi-source dataset based on sintering data. And a novel deep learning approach for multi-source data fusion is introduced and extended. Based on constructed multi-source dataset and associated algorithm, the downstream task of clustering is carried out. Affiliated experimental results show that the multi-source information clustering process for mineral sintered surface defects proposed in this paper outperforms unimodal approach, and relative performance metrics have reached the level of engineering applications.

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Immobilizing ultrafine bimetallic PtAg alloy onto uniform MnO_(2) microsphere as a highly active catalyst for CO oxidation

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Chinese Chemical Letters 2021年第6期32卷 2057-2060页

作者： Shengpeng Mo Peng Peng Yinchang Pei Taiming Shen Qinglin Xie Mingli Fu Yunfa Chen Daiqi Ye College of Environment Science and Engineering Guilin University of TechnologyGuilin 541004China School of Environment and Energy South China University of TechnologyGuangzhou 510006China Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control Guilin University of TechnologyGuilin 541004China State Key Laboratory of Multi-Phase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Herein,a facile glycol reduction route is successful employed to synthesize bimetallic Pt Ag alloys with homogeneous distribution of sizes and *** studies reveal that the ultrafine Pt Ag alloys with well-defined sizes from around 3.3 nm to 5.8 nm are immobilized onto MnO_(2)microsphere,which remarkably enhances the catalytic performances for CO ***,quasi in-situ X-ray photoelectron spectroscopy(XPS)result reveals that both Mn and Pt ions on the surface of catalysts would realize alternating reduction-oxidation by CO and O_(2)molecules,and the oxygen vacancy sites could be replenished and excited by gas-phase O_(2).

关键词： Bimetallic alloys PtAg NPs MnO_(2) Quasi in-situ XPS CO oxidation

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Intention-Aware Planner for Robust and Safe Aerial Tracking

arXiv

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arXiv 2023年

作者： Ren, Qiuyu Yu, Huan Dai, Jiajun Zheng, Zhi Meng, Jun Xu, Li Xu, Chao Gao, Fei Cao, Yanjun The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou313000 China The Robotics Institute Zhejiang University Yuyao315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to specifically evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Aircraft detection

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Re-defining the performance of just-in-time learning based soft sensor for high dimensional process data

Re-defining the performance of just-in-time learning based s...

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第32届中国过程控制会议(CPCC2021)

作者： Xiaoyu Jiang Zhiqiang Ge State Key Laboratory of Industrial Control Technology Institute of Industrial Process ControlCollege of Control Science and EngineeringZhejiang University

Just-in-time learning(JITL) is a widely used online soft sensing method for time-varying *** the increase of dimensionality of industrial dataset,both reliability and usability of JITL would be seriously damaged due to the sparseness caused in the high-dimensional data *** this paper,variational autoencoder is introduced as a generative model to provide virtual samples for augmenting high-dimensional sparse *** on this,a data augmentation just-in-time learning framework is formulated and implemented with two different ***,we carefully discuss the effect of virtual data on just-in-time learning,as well as the influence of virtual data volume,virtual data ratio and other factors on the proposed ***,a real industrial example is applied to verify the effectiveness of the proposed method,in which two indicators(RMSE and R2) have been improved by an average of 22%,15%,respectively,compared to the traditional JITL approach.

关键词： Data augmentation just-in-time learning variational autoencoder soft sensor

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Driving Performance Analysis for Connected Vehicle Platoons: Velocity Perturbation and Fuel Consumption

Driving Performance Analysis for Connected Vehicle Platoons:...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Yihan Zhao Xuting Duan Jianshan Zhou Long Zhang State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Zhongguancun Laboratory School of Transportation Science and Engineering Beihang University Beijing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Beijing China

Vehicle platoon is one of the concrete manifestations of multi-vehicle cooperative control and intelligent transportation. However, the velocity perturbation in the driving process will bring about stability and safety problems. The connected autonomous vehicles (CAVs) can obtain the motion status of the lead vehicle in real time and respond to it in time, which is of far-reaching significance to platoon control. Based on this, this paper focuses on the longitudinal driving strategy of connected autonomous vehicle platoons. Specifically, we provide a discrete double-integral model controller for each vehicle in a generic scenario, and the controller performs acceleration and deceleration control at each timeslot according to the state of the vehicle in front and its own state. Simulation experiments are designed by varying the size of the platoon and the number of connected autonomous vehicles. The experimental results show that the introduction of connected autonomous vehicles can effectively suppress the platoon velocity perturbation, increase the stability of the platoon driving, and significantly reduce the fuel consumption at the same time.

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

arXiv

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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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Study on the COVID-19 epidemic in China's Mainland between November 2022 and January 2023,with prediction of its tendency

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Journal of Biosafety and Biosecurity 2023年第1期5卷 39-44页

作者： Yao Bai Zhihang Peng Fengying Wei Zhen Jin Jinjie Wang Ximing Xu Xinyan Zhang Jun Xu Zixiong Ren Bulai Lu Zhaojun Wang Jianguo Xu Senzhong Huang Xi’an Center for Disease Control and Prevention Xi’an 710054ShaanxiChina Department of Biostatistics School of Public HealthNanjing Medical UniversityNanjing 211166JiangsuChina School of Mathematics and Statistics Fuzhou UniversityFuzhou 350116FujianChina Complex Systems Research Center Key Discipline of Computer Science and Technology of“Double-First-Class”Project of Shanxi ProvinceShanxi UniversityTaiyuan 030006ShanxiChina Nankai Institute of Economics Binhai Development InstituteNankai UniversityTianjin 300071China Children’s Hospital of Chongqing Medical University Chongqing 400010China Jinzhou Center for Disease Control and Prevention Jinzhou 121000LiaoningChina School of Statistics and Data Science Nankai UniversityTianjin 300071China Suzhou Center for Disease Control and Prevention Suzhou 215004JiangsuChina State Key Laboratory of Communicable Disease Prevention and Control National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 102206China Institute of Public Health Nankai UniversityTianjin 300071China School of Statistics and Data Science ZhiYing Research Center for Health DataNankai UniversityTianjin 300071China

The prediction system EpiSIX was used to study the COVID-19 epidemic in China's Mainland between November 2022 and January 2023,based on reported data from December 9,2022,to January 30,2023,released by The Chinese Center for Disease control and Prevention on February 1,*** kinds of reported data were used for model fitting:the daily numbers of positive nucleic acid tests and deaths,and the daily number of hospital beds taken by COVID-19 *** was estimated that the overall infection rate was 87.54%and the overall case fatality rate was 0.078%–0.116%(median 0.100%).Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023,induced by a slightly more infectious mutant strain,we predicted a possible large rebound between September and October 2023,with a peak demand of between 800,000 and 900,000 inpatient *** no such new outbreak was induced by other variants,then the current COVID-19 epidemic course in China's Mainland would remain under control until the end of ***,it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future,especially for the period between September and October 2023.

关键词： COVID-19 Epidemic Infection rate Case fatality rate Prediction

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Analysis of OODA Loop based on Adversarial for Complex Game Environments

arXiv

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arXiv 2022年

作者： Lu, Xiangri Ma, Hongbin Wang, Zhanqing State Key Laboratory of Intelligent Decision Control for Complex Systems Beijing Institute of Technology Beijing China Automated Institute Beijing Institute of Technology Beijing China

To address the problem of imperfect confrontation strategy caused by the lack of information of game environment in the simulation of non-complete information dynamic countermeasure modeling for intelligent game, the hierarchical analysis game strategy of confrontation model based on OODA ring (Observation, Orientation, Decision, Action) theory is proposed. At the same time, taking into account the trend of unmanned future warfare, NetLogo software simulation is used to construct a dynamic derivation of the confrontation between two tanks. In the validation process, the OODA loop theory is used to describe the operation process of the complex system between red and blue sides, and the four-step cycle of observation, judgment, decision and execution is carried out according to the number of armor of both sides, and then the OODA loop system adjusts the judgment and decision time coefficients for the next confrontation cycle according to the results of the first cycle. Compared with traditional simulation methods that consider objective factors such as loss rate and support rate, the OODA-loop-based hierarchical game analysis can analyze the confrontation situation more comprehensively. © 2022, CC BY.

关键词： Decision theory

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A Swing-Up and Stable control Strategy for Pendubot Based on Nonlinear Model Predictive Method

A Swing-Up and Stable Control Strategy for Pendubot Based on...

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2023 China Automation Congress, CAC 2023

作者： Wang, Lejun Liu, Yiqin Zhang, Ruihuan Huang, Zixin Cai, Zhen School of Automation School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing400065 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China School of Information Engineering Wuhan City Polytechnic Wuhan430064 China

ISBN: (纸本)9798350303759

The control objective of Pendubot is to stabilise the system in a vertically upward position by swinging up from a vertically downward position. Facing various physical constraints in practical applications, this paper proposes a swing-up stabil-isation control strategy based on a nonlinear model prediction method to accomplish the above control objective. On the basis of the system dynamics model, the forward Euler method is used to construct a nonlinear prediction model of the system. According to the control objectives and physical constraints of the system, the performance index function is designed, and then the control inputs of each sampling cycle are obtained through rolling optimisation and feedback correction, so that the system state gradually converges to the target trajectory, thus achieving the motion control objective. Based on the simulation results, it can be seen that the strategy proposed in this paper is efficient and robust under external disturbances. © 2023 IEEE.

关键词： Model predictive control

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