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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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Applications of MXenes in human-like sensors and actuators

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Nano Research 2023年第4期16卷 5767-5795页

作者： Jinbo Pang Songang Peng Chongyang Hou Xiao Wang Ting Wang Yu Cao Weijia Zhou Ding Sun Kai Wang Mark H.Rümmeli Gianaurelio Cuniberti Hong Liu Institute for Advanced Interdisciplinary Research(iAIR) Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of ShandongUniversity of JinanJinan 250022China Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics Dresden Technische Universität DresdenDresden 01069Germany Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of TechnologyShandong Academy of SciencesJinan 250353China School of Bioengineering Qilu University of TechnologyShandong Academy of ScienceJinan 250353China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Ministry of Education) Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266000China School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchun 130118China Institute for Complex Materials Leibniz Institute for Solid State and Materials Research Dresden(IFW Dresden)20 Helmholtz StrasseDresden 01069Germany College of Energy Soochow Institute for Energy and Materials Innovations Soochow UniversitySuzhou 215006China Key L

Human beings perceive the world through the senses of sight,hearing,smell,taste,touch,space,and *** first five senses are prerequisites for people to *** sensing organs upload information to the nervous systems,including the brain,for interpreting the surrounding ***,the brain sends commands to muscles reflexively to react to stimuli,including light,gas,chemicals,sound,and ***,as an emerging two-dimensional material,has been intensively adopted in the applications of various sensors and *** this review,we update the sensors to mimic five primary senses and actuators for stimulating muscles,which employ MXene-based film,membrane,and composite with other functional ***,a brief introduction is delivered for the structure,properties,and synthesis methods of ***,we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas,gas sensors,chemical biosensors,acoustic devices,and tactile sensors for electronic ***,the actuators of MXene-based composite are ***,future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot,which may induce prospects in accompanying healthcare and biomedical engineering applications.

关键词： MXenes sensors actuators gas sensors biosensors tactile sensors sound sensors artificial retina

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An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life prediction

arXiv

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arXiv 2021年

作者： Ayodeji, Abiodun Wang, Wenhai Su, Jianzhong Yuan, Jianquan Liu, Xinggao State Key Laboratory of Industrial Control Technology Control Department Zhejiang University Hangzhou310027 China Tianjin Jinhang Technical Physics Institute Tianjin300192 China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing100074 China

A single unit (head) is the conventional input feature extractor in deep learning architectures trained on multivariate time series signals. The importance of the fixed-dimensional vector representation generated by the single-head network has been demonstrated for industrial machinery condition monitoring and predictive maintenance. However, processing heterogeneous sensor signals with a single-head may result in a model that cannot explicitly account for the diversity in time-varying multivariate inputs. This work extends the conventional single-head deep learning models to a more robust form by developing context-specific heads to independently capture the inherent pattern of each sensor reading in multivariate time series signals. Using the turbofan aircraft engine benchmark dataset (CMAPSS), an extensive experiment is performed to verify the effectiveness and benefits of multi-head fully connected neurons, recurrent networks, convolution network, the transformer-style stand-alone attention network, and their variants for remaining useful life estimation. Moreover, the effect of different attention mechanisms on the multi-head models is also evaluated. In addition, each architecture's relative advantage and computational overhead are analyzed. Results show that utilizing the attention layer is task-sensitive and model dependent, as it does not provide consistent improvement across the models investigated. The result is further compared with five state-of-the-art models, and the comparison shows that a relatively simple multi-head architecture performs better than the state-of-the-art models. The results presented in this study demonstrate the importance of multi-head models and attention mechanisms to improved understanding of the remaining useful life of industrial assets. © 2021, CC BY.

关键词： Recurrent neural networks

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An Improved Chimp Optimization Algorithm for Short-term Hydrothermal Scheduling

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第3期49卷 666页

作者： Chen, Gonggui Wang, Shumin He, Yongsheng Shang, Wanfeng Long, Hongyu Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

The short-term hydrothermal scheduling (STHS) is a complex non-linear, non-convex and high-dimensional mathematical optimization problem. Due to the fact that the original chimp optimization algorithm (ChOA) is likely to fall into local optimum and has the disadvantage of low population diversity, an improved chimp optimization algorithm (IChOA) is proposed to solve the STHS problem. Firstly, Logistic-Tent chaotic mapping is employed to initialize the population and increase the population diversity. Then a new nonlinear convergence factor is introduced to make the optimization search process more applicable to STHS problems. Next, particle swarm algorithm (PSO) and gravitational search algorithm (GSA) are combined to enhance the shortcoming and balance the global search ability and local exploration ability of ChOA. Moreover, Cauchy mutation and opposition-based learning strategy are supplemented as perturbation interference when the optimal position is not updated to improve the ability to leap out of the local optimum. Finally, a graded optimization strategy is adopted for the constraint handing of dynamic reservoir balance and thermal power balance. Three standard hydrothermal power systems are utilized to verify the practicability and validity of the proposed method. Results of simulations reveal that IChOA has significant competitive advantages in addressing the STHS problem. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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Double deep Q-learning for anti-saturation attack problem of warship group 1

Double deep Q-learning for anti-saturation attack problem of...

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

作者： Li, Wei Xiong, Gang Tao, Hao Han, Yunjun Wang, Fang Dong, Xisong Shen, Zhen Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan430064 China Shandong University Jinan China

ISBN: (纸本)9781665433372

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios. © 2021 IEEE.

关键词： Reinforcement learning

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Online progressive instance-balanced sampling for weakly supervised object detection

arXiv

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arXiv 2022年

作者： Chen, Minghao Tian, Yunong Li, Zhishuo Li, En Liang, Zize The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China Yuquan Road Beijing100049 China Engineering Laboratory of Industrial Vision and Intelligent Equipment Technology Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China

Based on multiple instance detection networks (MIDN), plenty of works have contributed tremendous efforts to weakly supervised object detection (WSOD). However, most methods neglect the fact that the overwhelming negative instances exist in each image during the training phase, which would mislead the training and make the network fall into local minima. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this paper. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module combining random sampling and IoU-balanced sampling progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances for making the network focus on positive instances. Extensive experimental results on the PASCAL VOC 2007 and 2012 datasets demonstrate the proposed method can significantly improve the baseline, which is also comparable to many existing state-of-the-art results. In addition, compared to the baseline, the proposed method requires no extra network parameters and the supplementary training overheads are small, which could be easily integrated into other methods based on the instance classifier refinement paradigm. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Target Assignment Optimization of Heterogeneous Sensor Networks in Urban Environment

Target Assignment Optimization of Heterogeneous Sensor Netwo...

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第三十九届中国控制会议

作者： Chen Chen Cheng Zihao Xu Wei School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

Preventing and controlling Low-Slow-Small(LSS) targets are problems to be solved due to their good concealment in urban environment. Consequently, the problem of target assignment is studied in this paper. Firstly, a target assignment model based on heterogeneous sensor networks is proposed. The characteristics of LSS targets and the influence of environmental factors such as climate and terrain are considered on this model. At the same time, the detection capabilities of different detection nodes and collaborative tracking capabilities are also considered. Secondly, an intelligent optimization algorithm GSAPSO based on PSO is proposed to maximize tracking effect of the sensors. Finally, the simulations are used to compare the GSAPSO algorithm with the traditional PSO and GAPSO algorithms. The results demonstrate that the proposed algorithm has better tracking effect.

关键词： Target assignment Urban environment Heterogeneous sensor networks PSO

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Closed-form integration of IMU error state covariance for optimization-based Visual-Inertial state Estimator

Closed-form integration of IMU error state covariance for op...

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第三十九届中国控制会议

作者： Xingbo Wang Zhihong Peng Lele Xi School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visualinertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagation in pre-integration process, yielding improved accuracy of IMU residual information matrix. Our visual-inertial state estimator is based on a tightly-coupled, sliding-window optimization framework, which jointly estimate the IMU states and landmarks and performing marginalization to limit the computational cost. Finally, the system are validated in park environment dataset, the result shows our proposed method is effective.

关键词： visual-inertial state estimator pre-integration

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Trajectory generation for quadrotor while tracking a moving target in cluttered environment

Trajectory generation for quadrotor while tracking a moving ...

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第三十九届中国控制会议

作者： Lele Xi Zhihong Peng Lei Jiao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

In this paper, we present a trajectory generation method for quadrotor while tracking a moving target with relative pattern. Compared to existing methods, safe flying zone, vehicle’s physical limits and smoothness are considered to ensure obstacles avoidance and real tracking flight. To tackle with the cluttered environment, a parallel Particle Swarm Optimization algorithm is applied to calculate the safe feasible waypoints for quadrotor with consideration of target’s predicted state and the realistic tracking pattern, as well as safety. Then, a multiple waypoints constraints motion planning method is applied and embed into a cost function for solving the problem of trajectories generation and robust tracking for quadrotor via convex optimization approach, ensuring the real-time planning, with the geometrical constraint of safe flight corridor for quadrotor. The effectiveness of proposed method is verified by numerical simulation experiments.

关键词： Target tracking Trajectory generation Waypoints constraint Convex optimization

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Generative Adversarial Network for Image Deblurring Using Content Constraint Loss

Generative Adversarial Network for Image Deblurring Using Co...

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第32届中国控制与决策会议

作者： Ye Ji Yaping Dai Junjie Ma Kaixin Zhao Yanyan Cheng School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

The research of image deblurring plays an important role in the digital image *** order to reduce image blurring problems,a content constraint loss（CCL） function in the generative adversarial network（GAN）is *** SSIM loss and the perceptual loss constitute the CCL function,which makes the trained generative model *** CCL function as the content constraint loss component and the adversarial loss component constitute the total *** total loss is optimized by the iterative training to further improve the stability of the network model,and the image blurring will be *** the test experiment of the open source image dataset MNIST,CIFAR10/100 and CELEBA,the CCL function is used as the content constraint loss component of the generative adversarial network,the effect of image deblurring has obvious promotion in the structural similarity measure and visual appearance.

关键词： Image deblurring Generative adversarial network Content constraint loss Adversarial loss

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