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

作者： Liu, Wei Wang, Haitao Wang, Rui Wen, Dayong Lu, Tao 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 University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9781665436786

This paper addresses the design and control of an intelligent dual-arm mobile robot named Baymax-I. The mechanism design of Baymax-I is based on modular concepts, which is mainly composed of three parts, including main body, dual-arms and a mobile chassis. The main body is designed as human-like structure, carrying multiple sensors. UR3 manipulators are adopted as two arms. Each manipulator has six degrees of freedom. A two-finger grip pawl is mounted on the end of each manipulator. A MIR mobile chassis drives the Baymax-I motion with high maneuverability. As for software systems, the robot is designed based on hierarchical structure. The bottom driving layer is used to perform specific movements. The middle layer is responsible for processing sensor data. The top layer is algorithm execution layer. In the end, three experiments including SLAM, pressing the elevator and opening the fridge are presented to verify the feasibility and effectiveness of the proposed system design and control methods of the Baymax-I robot. © 2021 IEEE.

关键词： Mobile robots

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Semi-supervised Long-tail Endoscopic Image Classification

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Chinese Medical Sciences Journal 2022年第3期37卷 171-180,I0002页

作者： Runnan Cao Mengjie Fang Hailing Li Jie Tian Di Dong School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Advaneed Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'an 710126China

Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited *** We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse ***-supervised learning algorithm FixMatch was applied based on consistency regularization and *** splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited *** The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall *** algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,*** a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average *** analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail *** SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume ***,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.

关键词： endoscopic image artificial intelligence semi-supervised learning long-tail distribution image classification

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Design of a curled hyper-redundant manipulator and the motion control with tip-following algorithm

Design of a curled hyper-redundant manipulator and the motio...

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Chinese control and Decision Conference, CCDC

作者： Aoshun Zhang En Li Feng Zhang Rui Guo Mingrui Luo Yuwei Zhang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Laboratory for Intelligent Industrial Vision CAS Beijing China State Grid Intelligent Technology Co. Ltd. Shandong China State Grid Shandong Electric Power Company Jinan Power Supply Company Shandong China

In order to meet the operational needs in some narrow environments, the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of freedom. At present, the research of hyper-redundant manipulator mainly focuses on the straight push platform. Considering the limitation of working space, this paper designed a hyper-redundant manipulator delivered by a curled turntable. According to the characteristics of turntable delivery, a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are proposed. Finally, with the algorithm applied to the physical robot we made, we successfully realized the tip-following motion control of the hyper-redundant manipulator.

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Improve the Conversion Rate and Selectivity of Aromatic Monomers in Lignin Photocatalytic Conversion Reaction Via Phase Junction Cadmium Sulphide Nanoparticles and External Hydrogen Supplier

SSRN

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SSRN 2023年

作者： Yue, Zongyang Shao, Shibo Yu, Jialin Lu, Guanchu Wei, Wenjing Huang, Yi Zhang, Kai Wang, Ke Fan, Xianfeng Institute for Materials and Processes School of Engineering The University of Edinburgh EdinburghEH9 3BF United Kingdom Petrochemical Research Institute PetroChina Company Limited Beijing102206 China Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Innovation Academy for Green Manufacture Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China Longzihu New Energy Laboratory Zhengzhou Institute of Emerging Industrial Technology Henan University Zhengzhou450000 China Beijing Key Laboratory of Emission Surveillance and Control for Thermal Power Generation North China Electric Power University Beijing102206 China

Photocatalysis has the potential for lignin valorisation to generate functionalised aromatic monomers, but its large-scale application has been limited by the slow conversion rate and the low selectivity to desirable products. In this work, record-high lignin model compound Cβ-O bonds cleavage rate and excellent selectivity to aromatic monomers has been achieved over the development of new hexagonal-cubic phase junction CdS nanoparticles, enhanced external hydrogen donor supply and hydrogen transfer, and improved separation efficiency of photo-excited electron-hole pairs. Water is employed as external hydrogen supplier, resulting in significant increase in the conversion rate of 2-Phenoxy-1-phenylethanol (PP-ol) as lignin model from 21.8% to 100% and the selectivity of aromatic monomers from ~45% to ~94% under 1h of visible light irradiation. 2-phenoxyacetophenone (PP-one) as by-product from PP-ol conversion can be further completely cleaved to aromatic monomers with assistance of TEA and reaction time is reduced from 2-12 hrs to10 min under visible-light irradiation. © 2023, The Authors. All rights reserved.

关键词： Lignin

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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Zwitterionic Liquid Hydrogel Sustained-Release Strategy for High-Performance Nanofiltration Membrane

SSRN

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SSRN 2022年

作者： Xiao, Luqi Bai, Ju Lai, Wei Jiao, Yang Wu, Qi Liu, Hongyan Zhang, Yizhou Shan, Linglong Luo, Shuangjiang School of Rare Earths University of Science and Technology of China Hefei230026 China CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Beijing Key Laboratory of Ionic Liquids Clean Process Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China Ganjiang Innovation Academy Chinese Academy of Sciences Ganzhou341000 China Key Laboratory of Organic Compound Pollution Control Engineering Ministry of Education School of Environmental and Chemical Engineering Shanghai University Shanghai200444 China Langfang Green Industrial Technology Center Langfang065000 China

controlling amine monomer diffusion behavior plays a crucial role in fabricating well-defined architecture and high-performance nanofiltration (NF) membranes through interfacial polymerization. Herein, a zwitterionic liquid hydrogel transition layer was introduced to the sustained-release amine monomer to the regulated interfacial polymerization process to fabricate a high-performance NF membrane. The hydrogel (Gel) was fabricated via in-situ one-step UV photo-initiated free-radical polymerization between 1-vinyl-3-ethylimidazolium chloride ([VEIm][Cl]) and 3-sulfopropyl methacrylate potassium salt (SMP) on PES substrate membrane surface. Then polyamide layer formed on the Gel layer surface under the regulation of mitigating the amine diffusion behavior. Low-field time-domain NMR (LF-NMR) analysis and water-absorbing demonstrated that the Gel-PA membrane showed high water-membrane interaction. Positron annihilation lifetime spectroscopy (PALS) also confirmed the Gel-PA composite membrane possessed enlarged free volume . Membrane surface and cross-sectional SEM morphologies also showed the Gel-PA membrane with a less ridge-and-valley loose structure and thinner thickness. Combined with the uptake ability of the Gel layer, the obtained Gel-PA composite membrane displayed a high permeance of 13.22 L m -2 h -1 bar -1 , a high Na 2 SO 4 rejection of 98.23%, a favorable NaCl/Na 2 SO 4 selectivity of 49.75, and excellent long-term operational stability . This sustained-release strategy provides a facial and feasible approach to controlling the IP process and obtaining high-performance nanofiltration membranes. © 2022, The Authors. All rights reserved.

关键词： Nanofiltration

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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IEEE International Conference on Robotics and Biomimetics

作者： Tian-Yu Xiang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Hong-Jun Yang Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The school of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau

ISBN: (纸本)9781665481106

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios.

关键词： Training Convolution Computational modeling Biological system modeling Streaming media Brain modeling Electroencephalography

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Collaborative Learning of High-Precision Quantum control and Tomography

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Physical Review Applied 2021年第1期16卷 014056-014056页

作者： Hai-Jin Ding Bing Chu Bo Qi Re-Bing Wu Department of Automation Tsinghua University Beijing 100084 People’s Republic of China School of Electronic and Computer Science University of Southampton SO17 1BJ United Kingdom Key Laboratory of Systems and Control ISS and National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science CAS Beijing 100190 People’s Republic of China

In the hardware implementation of quantum computation, the control pulses for quantum states and gate operations must be calibrated to correct errors induced by unknown drifts of system parameters. The calibration can be iteratively done by checking the control precision, but the required randomized benchmarking or quantum tomography is experimentally expensive. In this paper, we propose that the control calibration and the quantum tomography can be taken as two separate but collaborative learning processes. Combining the gradient-descent pulse engineering and the adaptive tomography, the resulting c-GRAPE algorithm can substantially reduce the high cost of measurements without sacrificing the control accuracy. The effectiveness is demonstrated by numerical simulation examples.

关键词： Machine learning Quantum control Quantum tomography

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A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 961-968页

作者： Xiuqin Shang Dayong Shen Fei-Yue Wang Timo R. Nyberg the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of Sciences (CAS)Beijing 100190China the Qingdao Academy of Intelligent Industries Qingdao 266109China IEEE the Beijing Enineering Research Center of Intelligent Systems and Technology Institute of AutomationCASBeijing 100190China the Cloud Computing Center CASDongguan 523808China the School of Science at Aalto University AaltoFinland

We study the fabric spreading and cutting problem in apparel *** the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment *** reducing the production costs,the number of lays that corresponds to the frequency of using the cutting beds should be *** propose an iterated greedy algorithm for solving the fabric spreading and cutting *** algorithm contains a constructive procedure and an improving *** the constructive procedure creates a set of lays in sequence,and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay *** improving loop will run until it cannot obtain any smaller lay set or the time limit is *** experiment results on 500 cases show that the proposed algorithm is effective and efficient.

关键词： Construction and improvement cutting and packing fabric spreading and cutting heuristic algorithm

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Lightweight Double Attention-Fused Networks for Intraoperative Stent Segmentation 23rd

Lightweight Double Attention-Fused Networks for Intraoperati...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhou, Yan-Jie Xie, Xiao-Liang Hou, Zeng-Guang Zhou, Xiao-Hu Bian, Gui-Bin Liu, Shi-Qi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030597245

In endovascular interventional therapy, the fusion of preoperative data with intraoperative X-ray fluoroscopy has demonstrated the potential to reduce radiation dose, contrast agent and processing time. Real-time intraoperative stent segmentation is an important pre-requisite for accurate fusion. Nevertheless, this task often comes with the challenge of the thin stent wires with low contrast in noisy X-ray fluoroscopy. In this paper, a novel and efficient network, termed Lightweight Double Attention-fused Network (LDA-Net), is proposed for end-to-end stent segmentation in intraoperative X-ray fluoroscopy. The proposed LDA-Net consists of three major components, namely feature attention module, relevance attention module and pre-trained MobileNetV2 encoder. Besides, a hybrid loss function of both reinforced focal loss and dice loss is designed to better address the issues of class imbalance and misclassified examples. Quantitative and qualitative evaluations on 175 intraoperative X-ray sequences demonstrate that the proposed LDA-Net significantly outperforms simpler baselines as well as the best previously-published result for this task, achieving the state-of-the-art performance. © 2020, Springer Nature Switzerland AG.

关键词： Stents

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