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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Luo, Kun Xiong, Gang Wang, Yuxiao Hu, Bin Liu, Sheng Chen, Shichao Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences Beijng China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Beijng China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China

ISBN: (纸本)9781665433372

Regarding human-machine collaborative task, generated trajectories of a robot manipulator should be able to avoid joint limits and singularities, but the 6 Degrees of Freedom(DoF) or lower DoF manipulators encounter difficulties in many schemes. In this paper, research is implemented on the methods of trajectory planning and motion control on the newly human-machine collaborative 7-DoF Sawyer manipulator. We try to pay more attention to these approaches and find a better combination between trajectory planners and motion controllers for Sawyer manipulator. First of all, comparison is proposed between the methods about trajectory planning in task space and joint space, drawing a conclusion that the latter has the advantage of quickness and smoothness on trajectories in real time. Next, we choose to plan trajectory in the joint space by interpolating positions and orientations. Three approaches, i.e., trapezoidal velocity trajectory, cubic polynomial trajectory and basic spline trajectory, are applied to interpolate positions. Experimental results show that it results in the smoothest trajectory using basic spline trajectory planning. We also compare linear and nonlinear orientation interpolations to address the angle wrapping issue, which leads to difficulties in smooth orientation interpolation between several waypoints. Combining polynomial trajectory planning methods with the proportional-derivative (PD) motion controller and the computed-torque motion controller, we focus on the performance of the two different combinations. It is found that trajectories with computed-torque controller have shorter path with a congruous convergence on joint position, and more accurate trajectories with less overshoots in position than others. © 2021 IEEE.

关键词： Motion control

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Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multiple Data Sources

arXiv

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arXiv 2021年

作者： Geng, Guannan Xiao, Qingyang Liu, Shigan Liu, Xiaodong Cheng, Jing Zheng, Yixuan Tong, Dan Zheng, Bo Peng, Yiran Huang, Xiaomeng He, Kebin Zhang, Qiang State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing100084 China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System Science Tsinghua University Beijing100084 China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning Beijing100012 China Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China

Air pollution has altered the Earth’s radiation balance, disturbed the ecosystem and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant dataset with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://***/) that combines information from multiple data sources, including ground measurements, satellite retrievals, dynamically updated emission inventories, operational chemical transport model simulations and other ancillary data. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R2 of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM2.5 data allow us to track the day-to-day variations in PM2.5 concentrations over China in a timely manner. The long-term records of PM2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM2.5 data are publicly available through our website for sharing with the research and policy communities. © 2021, CC BY.

关键词： Air pollution

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Continuous-Time Linear Parallel control

Continuous-Time Linear Parallel Control

引用

Chinese Automation Congress (CAC)

作者： Qinglai Wei Hongyang Li Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

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： control systems controllability control theory complex systems Adaptive control Numerical analysis Linear systems

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Cross-Membrane Penetration to Nucleus of Adherent Cells Using Micropipettes Made by Borosilicate Glass and Quartz

Cross-Membrane Penetration to Nucleus of Adherent Cells Usin...

引用

IEEE International Conference on Mechatronics and Automation

作者： Xinyue Xu Xiaoming Liu Yuqing Lin Pengyun Li Fengyu Liu Xiaoqing Tang Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In the biomicromanipulation field, single-cell manipulation is receiving attention due to its potential applications in many aspects of biomedicine. Cell injection, as a typical cell micromanipulation technique, is devoted to introducing foreign substances into living cells by penetrating a fine micropipette into cell membranes. Direct access to the intracellular environment, especially the nucleus, provides an ideal way to gain insight into the dynamic processes of living cells. This paper presents the comparison of controlled penetrations of two types of adherent cells and their nuclei by two types of micropipettes, made of borosilicate glass and quartz respectively. Due to the differences in the cell properties (e.g., height, adhesion strength), two corresponding penetration strategies optimizing the puncture angle and puncture height, are proposed to improve the success rate of the puncture process and the survival rate of cells after the puncture. Real nucleus puncture experiments adopting the optimal puncture strategy for each type of cells and carried out by a semi-automatic system, indicated a better puncture effect by quartz pipettes. Through this method, functional materials such as peptides and nanoparticles can be potentially introduced to perturb the cell activities and then realize its full automation.

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

引用

Chinese control and Decision Conference, CCDC

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and structured. However, with robots gradually applied in more domains, robots often encounter working scenes which are complicated, unpredictable, and unstructured. To overcome the limitation of traditional programming method, in this paper, we apply deep reinforcement learning (DRL) method to train robot agent to obtain skill policy. As policy trained with DRL on real-world robot is time-consuming and costly, we propose a novel and simple learning paradigm with the aim of training physical robot efficiently. Firstly, our method train a virtual agent in an simulated environment to reach random target position from random initial position. Secondly, virtual agent trajectory sequence obtained with the trained policy, is transformed to real-world robot command with coordinate transformation to control robot performing reaching tasks. Experiments show that the proposed method can obtain self-adaptive reaching policy with low training cost, which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot kinematics Training Task analysis Machine learning Trajectory Neural networks

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Sliding sequential CVAE with time variant socially-aware rethinking for trajectory prediction

arXiv

引用

arXiv 2021年

作者： Zhou, Hao Ren, Dongchun Yang, Xu Fan, Mingyu Huang, Hai National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China Research Center for Autonomous Vehicles Meituan Beijing China College of Computer Science Wenzhou University Wenzhou China National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China

Pedestrian trajectory prediction is a key technology in many applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore. Second, the prediction results of multiple pedestrians might be impractical in the prediction horizon, i.e., the predicted trajectories might collide with each other. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The cascaded CVAE module first estimates the future trajectories in a sequential pattern. Specifically, each CVAE concatenates the past trajectories and the predicted points so far as the input and predicts the location at the following time step. Then, the socially-aware regression module generates offsets from the estimated future trajectories to produce the socially compliant final predictions, which are more reasonable and accurate results than the estimated trajectories. Moreover, considering the large model parameters of the cascaded CVAE module, a slide CVAE module is further exploited to improve the model efficiency using one shared CVAE, in a slidable manner. Experiments results demonstrate that the proposed method exhibits improvements over state-of-the-art method on the Stanford Drone Dataset (SDD) and ETH/UCY of approximately 38.0% and 22.2%, respectively. © 2021, CC BY.

关键词： Trajectories

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Hexagonally ordered microbowl arrays decorated with ultrathin CuInS_(2) nanosheets for enhanced photoelectrochemical performance

引用

Journal of Energy Chemistry 2020年第12期29卷 134-142页

作者： Ming Li Le Chen Yanjie Su Huan Yin Kexiang Hu State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyKey Laboratory of Optoelectronic Information Processing of Guangxi Colleges and Uniy/ersitiesGuilin 541004GuangxiChina Key Laboratory for Thin Film and Microfabrication of the Ministry of Education Department of Micro/Nano ElectronicsSchool of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China National and Local Joint Engineering Laboratory of Internet Technology on Mine School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221008JiangsuChina School of Physics and Telecommunication Engineering Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulin 537400GuangxiChina

This paper demonstrates the design and fabrication of three-dimensional(3 D) hexagonally ordered microbowl arrays(MBAs) decorated with Cu In S2 nanosheets for enhanced photoelectrochemical(PEC) performance. The 3 D MBAs are fabricated by a micro-fabrication technique. The ultrathin CuInS2 nanosheets are grown on the 3 D electrodes by solvothermal transformation of Cu film. The photocurrent density of 3 D photocathode(CuInS2@MBAs) is about two times higher than that of the planar counterpart(CuInS2@Planar). The improved PEC performance can be ascribed to the elevated light trapping ability and the increased surface area for loading photocatalysts. In addition, CdS quantum dots as cocatalysts are modified onto the Cu In S2 nanosheets to further enhance the PEC activity because the formed p-n heterojunction can accelerate the separation of photogenerated carriers. As a result, the 3 D photocathode of CuInS2/CdS@MBAs shows an optimal incident photon to current efficiency of 10% at the wavelength of400 nm. It is believed that this work can be generalized to design other hierarchical 3 D photoelectrodes for improved solar water splitting.

关键词： Microbowl array CuInS_(2) Nanosheet Photocathode Photoelectrochemical performance

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Active Vibration Isolation for A Parallel Wheel-legged Robot Based on Dynamic Model

Research Square

引用

Research Square 2020年

作者： Guo, Fei Wang, Shoukun Yue, Binkai Wang, Junzheng State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Beijing100081 China

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A Multi-Objective Optimization Algorithm: Power system Hybrid Static Economic Dispatch with Wind Energy

SSRN

引用

SSRN 2022年

作者： Qu, Linan Li, Lingling Tseng, Ming-Lang Chen, Chao Lim, Ming K. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Nanjing210003 China Institute of Innovation and Circular Economy Asia University Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Adam Smith Business School University of Glasgow United Kingdom

This study proposes a novel multi-objective optimization based on improved pathfinder algorithm (IPFA) algorithm and contributes to build a hybrid static dynamic economic dispatch with wind energy. The novel IPFA optimizes the built hybrid static economic dispatch model, while considering inequality and equality constraints during the model solving process. Especially, new energy system reduces the fuel consumption and operation cost of the system;however, the randomness of renewable energy output destroys the stability of the system. The upper limit of wind power grid connection is limited by the permeability constraint, and the constraints such as forbidden operation area constraint and slope rate constraint are considered. The solution speed and stability of the IPFA algorithm are improved through introducing Kent map initialization, nonlinear adaptation factor, and following correction strategy. The validity of the built model and the optimization performance of the proposed method is verified by two cases. The test results reveal that the operation cost obtained by the IPFA considering wind energy is 9.3% and 7.5% lower than that without considering wind energy. The novel method contributes in ensuring the economy and stability of the power system and improving the utilization rate of renewable energy. © 2022, The Authors. All rights reserved.

关键词： Electric load dispatching

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In-Situ 3D Printing Monitoring in Dynamic Environments via Self-Supervised Deep Neural Network Adaptation

In-Situ 3D Printing Monitoring in Dynamic Environments via S...

引用

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Fang Wang Gang Xiong Qihang Fang Zhen Shen Di Wang Li Wan Xisong Dong Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China Beijing Ten Dimensions Technology Co. Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.

关键词： Adaptation models Lighting Artificial neural networks Self-supervised learning Three-dimensional printing Feature extraction Robustness Monitoring Image reconstruction Defect detection

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