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Preparation of nano-sized tungsten carbide via fluidized bed

Chinese Journal of Chemical Engineering 2020年第3期28卷 923-932页

作者： Feng Pan Zhan Du Shaofu Li Jun Li Meiju Zhang Maoqiao Xiang Qingshan Zhu State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of Sciences(CAS)Beijing 100190China Information Research Institute of Emergency Management Department Beijing 100084China University of Chinese Academy of Sciences Beijing 100049.China

Ultrafine or nano-sized of tungsten carbide(WC)is the key material to prepare ultrafine grained cemented *** this paper,nano-sized WC powders were directly prepared by using industrial nano-needle violet tungsten oxide(WO2.72)as the raw material,a fluidized bed as the reactor,and CO as the carbonization *** relationship between particle sizes and reaction temperatures,residence times,atmospheres has been investigated *** addition,the physical–chemical indexes(such as residual oxygen,total carbon and free carbon)of the products were *** results indicated that the particle size of WC increased with the increase of temperature from 800 to 950°*** the residence time increased,the particle size decreased gradually,and then increased due to slight *** introduction of hydrogen reduced the carbonization rate,and is not beneficial to obtaining nano-sized *** that satisfy the standard were obtained when WO2.72 reacted with CO at 850°C,900°C and 950°C for 3.0 h,2.5 h and 2.0 h,*** particle sizes of the three samples calculated from the specific surface area were 46.4 nm,53.2 nm and 52.1 nm,respectively.

关键词： Nanoparticles Violet tungsten oxide Nano tungsten carbide Fluidized-bed Reduction-carbonization Process control

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control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries

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Journal of Biosafety and Biosecurity 2021年第2期3卷 76-81页

作者： Rongzhang Hao Yewu Zhang Zhidong Cao Jing Li Qing Xu Lingling Ye Xudong Guo Tao Zheng Hongbin Song Chinese PLA Center for Disease Control and Prevention BeijingChina Department of Toxicology and Sanitary Chemistry School of Public HealthCapital Medical UniversityBeijingChina National Center for Public Health Surveillance and Information Service Chinese Center for Disease Control and PreventionBeijingChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina State Key Laboratory of Pathogen and Biosecurity Beijing Institute of Microbiology and EpidemiologyBeijingChina Division of Biological Safety Service Academy of Military Medical SciencesAcademy of Military Science of Chinese PLABeijingChina

COVID-19 is the most severe pandemic globally since the 1918 influenza *** responding to this once-in-a-century global pandemic is a worldwide challenge that the international community needs to jointly face and *** study reviews and discusses the key measures taken by major countries in 2020 to fight against COVID-19,such as lockdowns,social distancing,wearing masks,hand hygiene,using Fangcang shelter hospitals,large-scale nucleic acid testing,close-contacts tracking,and pandemic information monitoring,as well as their prevention and control *** hope it can help improve the efficiency and effectiveness of pandemic prevention and control in future.

关键词： COVID-19 control strategies Effect comparison

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Optimization of Robot End-Effector Load Gravity Compensation for Unmanned Live Work in Electric Power Distribution Networks Based on Improved PSO Algorithm

Optimization of Robot End-Effector Load Gravity Compensation...

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2023 China Automation Congress, CAC 2023

作者： Chen, Jinge Chen, Xin Hu, Chenyu Huang, Guangxian Sun, Shu Jian, Xu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering-Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China State Grid Hubei Electric Power Co. Ltd. Xianning Power Supply Company Distribution Department Xianning437000 China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Equipment Technology Center Wuhan430077 China

ISBN: (纸本)9798350303759

In the field of electric power distribution network operation, most tasks involve contact operations. A key technology to enable the flexible operation of robots in live distribution network tasks is the installation of six-dimensional force sensors on the robot's end flange. These sensors allow the robot to perceive external environmental contact forces. However, the presence of the end load interferes with the accurate perception of external forces. Aiming at the gravity compensation of robot end load, an optimization algorithm of gravity compensation based on particle swarm optimization is proposed. In this method, the optimal solution model is established with the objective of minimum sum of squared errors, and the particle swarm optimization algorithm is used to estimate the mounting declination angle of the force sensor and improve the precision of gravity compensation. The experiments show that the proposed method can effectively reduce the maximum gravity compensation error and the average gravity compensation error in each direction after optimization, in which the maximum gravity compensation error of the resultant force is reduced by 9.11% and the average gravity compensation error of the resultant force is reduced by 12.34%. © 2023 IEEE.

关键词： Error compensation

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AI Graphical Programming Learning Platform for Children

AI Graphical Programming Learning Platform for Children

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2021 China Automation Congress, CAC 2021

作者： Zhao, Hongxia Liu, Xiwei Gong, Xiaoyan Li, Qiang Jing, Sifeng Xue, Yaofeng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China University of Chinese Academy of Sciences Beijing China Shanghai Engineering Research Center of Digital Education Equipment Department of Education Information Technology East China Normal University Shanghai China

ISBN: (纸本)9781665426473

In this era of rapid development of artificial intelligence, the truth of competition in the future is the talent. Countries all over the world attach great importance to the programming and artificial intelligence education of children. In the future, the theoretical development and practical application of this emerging field will usher in a period of rapid development. This paper presents an artificial intelligence graphical programming learning platform based on Jetson Nano, which combines artificial intelligence education with modular software programming, provides students with a more open, intelligent, and interesting programming environment through the cross-platform development mode of "cloud service + intelligent hardware", and adopts visual programming to reduce the difficulty of artificial intelligence programming. The platform can fully tap learning resources, reduce education costs, and make artificial intelligence education more intuitive and humanized. © 2021 IEEE

关键词： Computer graphics

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GPR and SPSO-CG based gait pattern generation for subject-specific training

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Science China(Information Sciences) 2021年第8期64卷 244-246页

作者： Weiqun WANG Weiguo SHI Shixin REN Zeng-Guang HOU Xu LIANG Jiaxin WANG Liang PENG The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Dear editor,Gait training has been proved effective for recovery of walking ability for nerve injury patients caused by stroke, spinal cord injury(SCI), traumatic brain injury(TBI), etc. The traditional gait training methods, usually carried out by hands, are laborious and time-consuming, and hence, the training intensity is difficult to be maintained and the rehabilitation results are not satisfactory.

关键词： GPR and SPSO-CG based gait pattern generation for subject-specific training

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Benefit from public unlabeled data: A Frangi filtering-based pretraining network for 3D cerebrovascular segmentation

arXiv

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arXiv 2023年

作者： Shi, Gen Lu, Hao Hui, Hui Tian, Jie School of Engineering Medicine School of Biological Science and Medical Engineering Beihang University Beijing100191 China Ministry of Industry and Information Technology of China Beijing100191 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academic of Science Beijing10086 China CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing100190 China

The precise cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) data is crucial for clinically computer-aided diagnosis. However, the sparse distribution of cerebrovascular structures in TOF-MRA results in an exceedingly high cost for manual data labeling. The use of unlabeled TOF-MRA data holds the potential to enhance model performance significantly. In this study, we construct the largest preprocessed unlabeled TOF-MRA datasets (1510 subjects) to date. We also provide three additional labeled datasets totaling 113 subjects. Furthermore, we propose a simple yet effective pertraining strategy based on Frangi filtering, known for enhancing vessel-like structures, to fully leverage the unlabeled data for 3D cerebrovascular segmentation. Specifically, we develop a Frangi filtering-based preprocessing workflow to handle the large-scale unlabeled dataset, and a multi-task pretraining strategy is proposed to effectively utilize the preprocessed data. By employing this approach, we maximize the knowledge gained from the unlabeled data. The pretrained model is evaluated on four cerebrovascular segmentation datasets. The results have demonstrated the superior performance of our model, with an improvement of approximately 3% compared to state-of-the-art semi- and self-supervised methods. Furthermore, the ablation studies also demonstrate the generalizability and effectiveness of the pretraining method regarding the backbone structures. The code and data have been open source at: https://***/shigen-StoneRoot/FFPN. © 2023, CC BY.

关键词： Computer aided diagnosis

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Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

arXiv

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arXiv 2024年

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence Anhui University Hefei230031 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Computer Engineering Indiana University Purdue University IndianapolisIN46202 United States

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. Copyright © 2024, The Authors.

关键词： Forecasting

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Hand Position Tracking based on Optimized Consistent Extended Kalman Filter

Hand Position Tracking based on Optimized Consistent Extende...

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Chinese control and Decision Conference, CCDC

作者： Lin Tian Wenchao Xue Long Cheng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Key Laboratory of Systems and Control National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665478977

This paper proposes a hand position tracking algorithm based on optimized consistent extended Kalman filter (CEKF). By introducing the previous work of the authors and analyzing the parameter of the original CEKF algorithm, the key parameter that is negatively correlated with the degree of the estimation of uncertain dynamics is determined. Then, two metaheuristic methods, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) are used to optimize the original CEKF algorithm. To quantify the performance of the algorithms, the root-mean-square error (RMSE) is employed as the performance index. Finally, the numerical simulation and practical experiment of the hand position tracking are carried out, and the optimized algorithm achieves 9.52% and 10.94% improvements of the performance, respectively.

关键词： Heuristic algorithms Metaheuristics Stability criteria Estimation Numerical simulation Performance analysis Kalman filters

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Locomotion control of a hybrid propulsion biomimetic underwater vehicle via deep reinforcement learning

Locomotion control of a hybrid propulsion biomimetic underwa...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Tiandong Wang, Rui Wang, Yu Wang, Shuo Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665436786

This paper presents a novel deep reinforcement learning (DRL) method to solve the locomotion control problem of the biomimetic underwater vehicle (BUV) with hybrid propulsion, in order to meet the challenge of intractable multi-fins coordination and the complex hydrodynamic model. The system overview of the BUV, named RoboDact, with two flexible long fins and a double-joint fishtail as hybrid propulsion, is introduced. After that, the locomotion control problem is modeled as a Markov decision process (MDP) to be solved. Therefore, the locomotion control method based on soft actor-critic (SAC, a novel DRL algorithm) is proposed. The simulation environment is established based on the kinetic model for interaction. Finally, the feasibility and effectiveness of the proposed control method is demonstrated after extensive simulations. It will provide rich insights into the coordination control of biomimetic underwater vehicles. © 2021 IEEE.

关键词： Reinforcement learning

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Fault Diagnosis for Wind Turbine Flange Bolts Based on One-Dimensional Depthwise Separable Convolutions

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The International Journal of Intelligent control and systems 2024年第1期29卷 42-47页

作者： Yongchao Liu Shuqing Dong Qingfeng Wang Wenhe Cai Ruizhuo Song Qinglai Wei North China Electric Power Test and Research Institute China Datang Corporation Science and Technology Research Institute Company LimitedBeijing 100040China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100040China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

In this paper,a new bolt fault diagnosis method is developed to solve the fault diagnosis problem of wind turbine flange bolts using one-dimensional depthwise separable *** main idea is to use a one-dimensional convolutional neural network model to classify and identify the acoustic vibration signals of bolts,which represent different bolt damage *** the methods of knock test and modal simulation,it is concluded that the damage state of wind turbine flange bolt is related to the natural frequency distribution of acoustic vibration *** is found that the bolt damage state affects the modal shape of the structure,and then affects the natural frequency distribution of the bolt vibration ***,the damage state can be obtained by identifying the natural frequency distribution of the bolt acoustic vibration *** the present one-dimensional depth-detachable convolutional neural network model,the one-dimensional vector is first convolved into multiple channels,and then each channel is separately learned by depth-detachable convolution,which can effectively improve the feature quality and the effect of data *** the perspective of the realization mechanism of convolution operation,the depthwise separable convolution operation has fewer parameters and faster computing speed,making it easier to build lightweight models and deploy them to mobile devices.

关键词： Wind turbine flange bolts one-dimensional convolutional neural network(1DCNN)model depthwise separable convolutions damage identification

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