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2022 Chinese automation Congress, CAC 2022

作者： Xu, Pengju Gan, Chao Wang, Luzhao Cao, Weihua School of Automation China University of Geosciences Wuhan430074 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 No.3 Exploration Institute of Geology and Mineral Resources of Shandong Province Yantai264000 China

ISBN: (纸本)9781665465335

In this paper, a multi-feature extraction-based image identification method for rock debris in the drilling process is proposed, involving three main parts (trainable feature extractor, strong feature extraction, and classification). In trainable feature extractor, abstract features are obtained by extracting the full connection layer of Convolutional Neural Network (CNN). In strong feature extraction, the method uses Gray-Level Co-occurrence Matrix (GLCM) and Color Coherence Vector (CCV) to get the strong feature. In classification, the extracted abstract features and strong features are concatenated and fed into the Support Vector Machine (SVM). Comparison results with two well-known methods indicated the effectiveness of the proposed method. © 2022 IEEE.

关键词： Feature extraction

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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Multi-Batch Production Scheduling for Distributed Serial Lines with Unreliable Machines and Finite Buffers

Multi-Batch Production Scheduling for Distributed Serial Lin...

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第40届中国控制会议

作者： Longzhu Huang Zhiyang Jia Xiaohan Wang Jingchuan Chen School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

In the modern manufacturing environment, the production systems develop towards a flexible direction due to the market demands of multiple varieties and small batch-based customized products. To make better use of the existing resources and to improve production efficiency, distributed manufacturing mode begins to appear. This paper concentrates on a multi-type production scheduling problem for distributed serial lines. Specifically, unreliable machines and finite buffers are considered,which are more realistic in the practical production. The mathematical model for the production system with two Bernoulli machines is formulated first. Then, under the hypothetical model mentioned above, the main work of this article is to propose the exact and approximate analyses to calculate the performance indicators. Based on the analysis of the model, the genetic algorithm is applied to optimize the maximum completion time on the distributed serial lines for multi-batch-based production tasks. Finally, the feasibility and effectiveness of the mathematical model and the proposed algorithm are verified through numerical experiments.

关键词： Bernoulli machine Intelligent manufacturing Production scheduling Transient analysis

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Acoustically Driven Micropipette for Hydrodynamic Manipulation of Mouse Oocytes

Acoustically Driven Micropipette for Hydrodynamic Manipulati...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Zhaofeng Zuo Xiaoming Liu Zhuo Chen Yuyang Li Xiaoqing Tang Dan Liu Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and School of Mechatronical Engineering 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 Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micromanipulation techniques that can achieve controlled fine operations at the micro scale play an important role in biomedical fields including embryo engineering, gene engineering, drug screening, and cell analysis. However, micromanipulation of biological micro-objects, such as cells and micro tissues, suffers from mechanical damage and low efficiency. Several techniques have been introduced to manipulate cells more easily, but most of them are restricted by expensive devices, limited work area, and potential damage to cellular structure. Here we develop a hydrodynamic manipulation method to rotate and transport mouse oocytes, which utilizes acoustic waves and micropipette to generate acoustic radiation force and excite microstreaming. This method can accomplish rotational and translational operations precisely and controllably. We tested the process of trapping, rotation, and transportation of the mouse oocytes, and measured rotational and translational speed with a range of applied voltage. The method was able to shorten the cost time of delivery and posture adjustment before oocyte injection. Our study provides an easy-to-use technique for oocyte manipulation without contact, and it has the potential to be universally applied in many cellular studies.

关键词： Visualization Voltage measurement Three-dimensional displays Force Transportation Hydrodynamics Biology

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Activity and Relationship Modeling Driven Weakly Supervised Object Detection

Activity and Relationship Modeling Driven Weakly Supervised ...

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International Conference on Pattern Recognition

作者： Yinlin Li Yang Qian Xu Yang Yuren Zhang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China ByteDance Beijing China

This paper presents a weakly supervised object detection method based on activity label and relationship modeling, which is motivated by the assumption that configuration of human and object are similar in same activity, and joint modeling of human, active object and activity could leverage the recognition of them. Compared to most weakly supervised method taking object as independent instance, firstly, active human and object proposals are learned and filtered based on class activation map of multi-label classification. Secondly, a spatial relationship prior including relative position, scale, overlaps etc are learned dependent on action. Finally, a multi-stream object detection framework integrating the spatial prior and pairwise ROI pooling are proposed to jointly learn the object and action class. Experiments are conducted on HICO-DET dataset, and our approach outperforms the state of the art weakly supervised object detection methods.

关键词： Training Object detection Gaussian distribution Pattern recognition Proposals

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A parallel framework for scene understanding 1

A parallel framework for scene understanding

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

作者： Zhang, Wenwen Wang, Jiangong Wang, Kunfeng School of Software Engineering Xi'An Jiaotong University Xi'an710049 China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9781665433372

Different related computer vision tasks make it possible to understand and analyze the scene totally. However, there is no useful framework combining different tasks for scene understanding. In this paper, we investigate the current state of scene understanding from different aspects relying on different computer vision tasks and propose an effective framework based on the theory of parallel vision to combine different tasks for scene understanding. With the maintained artificial scene throughout time from the proposed framework, we can understand the scene in an elegant way. To explain our proposed framework, a case study is given. © 2021 IEEE.

关键词： Computer vision

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Restoring After Central Nervous System Injuries:Neural Mechanisms and Translational Applications of Motor Recovery

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Neuroscience Bulletin 2022年第12期38卷 1569-1587页

作者： Zhengrun Gao Zhen Pang Yiming Chen Gaowei Lei Shuai Zhu Guotao Li Yundong Shen Wendong Xu Department of Hand Surgery Huashan HospitalFudan UniversityShanghai 200040China The National Clinical Research Center for Aging and Medicine Fudan UniversityShanghai 200040China Department of Hand and Upper Extremity Surgery Jing’an District Central HospitalFudan UniversityShanghai 200040China Institutes of Brain Science Fudan UniversityShanghai 200040China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200040China Institute of Automation State Key Laboratory of Management and Control for Complex SystemsChinese Academy of SciencesBeijing 100190China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226000China Research Unit of Synergistic Reconstruction of Upper and Lower Limbs after Brain Injury Chinese Academy of Medical SciencesShanghai 200400China

Central nervous system(CNS)injuries,including stroke,traumatic brain injury,and spinal cord injury,are leading causes of long-term *** is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated *** studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote ***,the ability to increase plasticity in the injured brain is restricted and difficult to ***,over several decades,researchers have been prompted to enhance the compensation by the unaffected *** experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor *** addition,several clinical treatments have been designed to restore ipsilateral motor control,including brain stimulation,nerve transfer surgery,and brain–computer interface ***,we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.

关键词： Stroke Traumatic brain injury Spinal cord injury Brain-computer interface system Neuroplasticity Ipsilateral motor control Axon regrowth

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Kinematics Analysis of a Manipulator for the Power Transmission Line Maintenance Robot

Kinematics Analysis of a Manipulator for the Power Transmiss...

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Chinese control Conference (CCC)

作者： Minghao Chen En Li Feng Zhang Rui Guo Zize Liang Yuwei Zhang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shandong Luneng Intelligence Technology Co. Ltd. Jinan China The State Grid Shandong Electric Power Company Jinan China

With the remarkable development of the power grid, traditional manual maintenance modes cannot meet the requirement of efficiency and reliability. Robotic technology is applied for power line maintenance to bring down risks and ensure inspectors' safety. Based on the power transmission line maintenance platform, a 5-DOF manipulator is designed to perform the maintenance tasks for the quad bundle conductors. Then the kinematics analysis of the manipulator is analyzed. The manipulator doesn't meet the Pieper criterion. So an improved adaptive particle swarm optimization (APSO) is proposed for figuring out the inverse kinematics. The weights of the pose and the constraint of the prismatic joint are introduced to the algorithm. What's more, half of the particles are initialized around the incipient value of joints and half of the particles are randomly initialized with the constraint of joints in this algorithm. Results show that the manipulator is able to work in the environment of quad bundle conductors and the proposed algorithm can improve the precision of inverse kinematics and prevent the leap of joints.

关键词：

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Deep Convolutional Correlation Filter Learning Toward Robust Visual Object Tracking

Deep Convolutional Correlation Filter Learning Toward Robust...

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第34届中国控制与决策会议

作者： Tayssir Bouraffa Zihang Feng Yuxuan Wang Liping Yan Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Artificial Intelligence Beijing University of Posts and Telecommunications

Recently,convolutional neural network has been pervasively adopted in visual object tracking for its potential in discriminating the target from the surrounding *** of the visual object trackers extract deep features from a specific layer,generally from the last convolutional ***,these trackers are less effective,especially when the target undergoes drastic appearance variations caused by the presence of different challenging situations,such as occlusion,illumination change,background clutter and so *** this research paper,a novel tracking algorithm is developed by introducing an elastic net constraint and a contextual information into the convolutional network to successfully track the desired target throughout a video *** features are extracted from the shallow and the deep convolutional layers to further improve the tracking accuracy and *** the deep convolutional layers capture important semantic information,they are more robust to the target appearance *** for the shallow convolutional layers,they encode significant spatial details,which are more accurate to precisely localize the desired ***,Peak-Strength Context-Aware correlation filters are embedded to each convolutional layer output that produce multi-level convolutional response maps to collaboratively identify the estimated position of the target in a coarse-to-fine *** and qualitative experiments are performed on the widely used benchmark,the OTB-2015 dataset that shows impressive results compared to the state-of-the-art trackers.

关键词： Visual tracking convolutional neural network correlation filters hierarchical features

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Research on Short-term Wind Speed Prediction Based on Adaptive Hybrid Neural Network with Error Correction

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2023年第4期50卷 1290页

作者： Long, Hongyu He, Yunlong Xiang, Wei Guan, Zhenqi Tan, Hao Yu, Jianbo Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China

As a clean and efficient renewable energy, wind power is increasingly valued by countries all over the world. However, due to the inherent volatility, intermittency, and uncontrollability of wind power, the secure, stable, and economical operation of the power grid will face unprecedented pressure with wind power integration on a vast scale. Wind power predictions and management are significantly impacted by wind speed. Therefore, it is crucial to propose an accurate, stable, and efficient wind speed prediction method. In this paper, the original wind speed data is denoised using singular spectrum analysis to solve the problem of strong randomness and volatility of wind speed data. Then, the denoised data is processed using wavelet analysis, and the high-frequency and low-frequency sequences are respectively predicted using CNN-BiLSTM and CNN-BP, while the weights are optimized using the PSO algorithm. Finally, the prediction accuracy is further improved by incorporating the error correction module. In the two datasets used for testing, the outcomes of the trial indicate that the wind speed prediction model proposed in this paper performs extremely well in short-term wind speed prediction, superior to other models. The five evaluation indices (MAE, MSE, MAPE, RMSE, and R2) for the first dataset are respectively 0.166, 0.055, 0.05, 0.234, and 0.953. Similar results were obtained for the second dataset. Furthermore, The ablation tests also show that each module suggested in this research is successful in enhancing prediction accuracy. © 2023, International Association of Engineers. All rights reserved.

关键词： Error correction

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