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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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A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions

A Novel Two-Stage Framework for 2D/3D Registration in Neurol...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Huang, De-Xing Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Ma, Ning Yan, Long The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China The Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing100070 China

ISBN: (纸本)9781665481090

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (612 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians. © 2022 IEEE.

关键词： Medical imaging

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Hybrid quantum-classical convolutional neural networks

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Science China(Physics,Mechanics & Astronomy) 2021年第9期64卷 1-8页

作者： Junhua Liu Kwan Hui Lim Kristin LWood Wei Huang Chu Guo He-Liang Huang Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore Forth AI Singapore 487372Singapore College of Engineering Design and ComputingUniversity of Colorado DenverColorado 80208USA Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic TechnologyGuilin 541004China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal UniversityChangsha 410081China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Shanghai Branch CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefei 201315China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou 450000China

Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or *** parallel,quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations,which could generate distributions that are hard for a classical computer to *** we propose a hybrid quantum-classical convolutional neural network(QCCNN),inspired by convolutional neural networks(CNNs)but adapted to quantum computing to enhance the feature mapping *** is friendly to currently noisy intermediate-scale quantum computers,in terms of both number of qubits as well as circuit’s depths,while retaining important features of classical CNN,such as nonlinearity and *** also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical *** demonstrate the potential of this architecture by applying it to a Tetris dataset,and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.

关键词： quantum computing quantum machine learning hybrid quantum-classical algorithm convolutional neural network

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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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Design and Visual Servoing control of a Hybrid Dual-Segment Flexible Neurosurgical Robot for Intraventricular Biopsy

arXiv

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arXiv 2024年

作者： Chen, Jian Chen, Mingcong Zhao, Qingxiang Wang, Shuai Wang, Yihe Xiao, Ying Hu, Jian Chan, Danny Tat Ming Yeung, Kam Tong Leo Chan, David Yuen Chung Liu, Hongbin The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Beijing100190 China Hong Kong Department of Biomedical Engineering City University of Hong Kong Hong Kong CAIR-HKISI-CAS HKSAR China The State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing100190 China Department of Surgery The Chinese University of Hong Kong Hong Kong The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing100190 China The School of Biomedical Engineering and Imaging Sciences King's College London LondonSE1 7EU United Kingdom

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot's ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual servoing

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Perception for Coupler Rod of Freight Train Based on Image and Point Clouds

Perception for Coupler Rod of Freight Train Based on Image a...

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Chinese Automation Congress (CAC)

作者： Renjie Guo Jun Feng Huachang Yang Dejie Luan Guodong Yang En Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Signal & Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing China

In the construction of automated marshaling yards, the automatic uncoupling operation of freight trains has always been the research and development direction. The coupler rod is the operation object of the uncoupling operation. The identification of coupler rod type and estimating position and posture are the prerequisites to realizing automatic uncoupling operation. In this paper, we propose a perception method of coupler rod by RGB image combined with the point cloud. We use an RGBD camera to synchronously collect frame-aligned RGB and depth images and generate point cloud data with colors according to the mapping relationship. Then, we use the YOLOv5 neural network to detect the collected RGB images and recognize the type of coupler rod in the image. Clear clutter in the point cloud and the bounding box is mapped to a spatial coordinate system for straight-through filtering of the point cloud. We use RANSAC for coarse registration and ICP for fine registration. The result of registration is carried out with the lower sampling point cloud model to achieve the registration of the coupler rod. Experiments show that this method can effectively segment the point cloud of the region of interest with high robustness.

关键词： Point cloud compression Image segmentation Image recognition Image color analysis Filtering Neural networks Data integration

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Impact of COVID-19-related nonpharmaceutical interventions on diarrheal diseases and zoonotic Salmonella

微生物与宿主健康（英文）

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微生物与宿主健康（英文） 2024年第5期2卷 246-256页

作者： Linlin Huang Haiyang Zhou Jiaqi Chen Chenghao Jia Abubakar Siddique Beibei Wu Haoqiu Wang Biao Tang Fang He Guoping Zhao Min Yue Department of Veterinary Medicine Zhejiang University College of Animal SciencesZhejiangChina Department of Veterinary Medicine Zhejiang University College of Animal SciencesZhejiangChina Hainan Institute of Zhejiang University HainanChina Zhejiang Provincial Center for Disease Control and Prevention ZhejiangChina Hangzhou Center for Disease Control and Prevention ZhejiangChina Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesZhejiangChina Department of Veterinary Medicine Zhejiang University College of Animal SciencesZhejiangChina ZJU-Xinchang Joint Innovation Centre(TianMu Laboratory) Gaochuang Hi-Tech ParkZhejiangChina Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesZhejiangChina CAS Key Laboratory of Synthetic Biology Institute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina Department of Microbiology and Microbial Engineering School of Life SciencesFudan UniversityShanghaiChina Department of Veterinary Medicine Zhejiang University College of Animal SciencesZhejiangChina Hainan Institute of Zhejiang University HainanChina Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesZhejiangChina State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious DiseasesNational Medical Center for Infectious DiseasesThe First Affiliated HospitalCollege of MedicineZhejiang UniversityZhejiangChina

Non-pharmaceutical interventions(NPIs)have been proven effective in reducing the spread of the coronavirus disease 2019(COVID-19),but their broader impact on gastrointestinal disorders remains poorly ***,we report an observational analysis and retrospective study that compares the incidence of acute diarrheal diseases in Hangzhou,Zhejiang,China,between 2019 and 2020,with an examination of the antimicrobial resistance and genetic spectrum of non-typhoidal Salmonella(NTS)from 2015 to 2022,before and during the COVID-19 *** findings underscore that NPIs related to COVID-19 effectively reduced the incidence of acute diarrhea,with a substantial decrease in most enteric ***,NTS exhibited an unexpected *** genomic investigations of NTS isolates re-vealed an overall reduction in antimicrobial-resistant(AMR),disinfection-tolerant,and virulent capabilities,but with marked variations detected between human and animal *** genome-based analysis confirmed a decrease in the scale of zoonotic transmission in response to NPIs,suggesting particular NTS types may contribute to human infections via alternative *** collective findings manifested that COVID-19-related NPIs had a mixed impact on NTS infections,which may inform AMR NTS mitigation policy.

关键词： coronavirus disease 2019(COVID-19) non-pharmaceutical interventions(NPIs) non-typhoidal Salmonella(NTS) zoonotic transmission

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Silver modified Cu/SiO2 catalyst for the hydrogenation of methyl acetate to ethanol

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Chinese Journal of Chemical Engineering 2020年第6期28卷 1612-1622页

作者： Zhiheng Ren Muhammad Naeem Younis Hui Zhao Chunshan Li Xiangui Yang Erqiang Wang Gongying Wang Chengdu Institute of Organic Chemistry Chinese Academy of SciencesChengdu 610041China National Engineering Laboratory for VOCs Pollution Control Material&Technology University of Chinese Academy of SciencesBeijing 101408China CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex SystemsThe National Key Laboratory of Clean and Efficient Coking TechnologyBeijing Key Laboratory of Ionic Liquids Clean ProcessInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China Zhongke Langfang Institute of Process Engineering Langfang 065000China Zhongke Langfang Institute of Process Engineering Zhengzhou 450000China School of Chemical Science University of Chinese Academy of SciencesBeijing 100049China

A series of silver modified Cu/SiO2 catalysts were synthesized with ammonia-evaporation method and applied in vapor-phase hydrogenation of methyl acetate to *** influence of additive‘Ag’on the structural evolution of catalyst was systematically studied by several characterization techniques,such as N2 adsorption–desorption,N2O titration,PXRD,FTIR,in-situ FTIR,H2-TPR,H2-TPD,XPS and *** showed that incorporation of a small amount of Ag could enhance the structural stability,and the strong interaction between Cu and Ag species was conducive to increase the dispersion of copper species and create a suitable Cu+/(Cu0+Cu+)ratio,which was proposed to be responsible for the improved catalytic *** maximum conversion of MA(94.1%)and selectivity of ethanol(91.3%)over optimized Cu-0.5 Ag/SiO2 and 120 h on stream without deactivation under optimal conditions demonstrates its excellent stability.

关键词： Copper Silver Hydrogenation Methyl acetate Ethanol Kinetic

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Adaptive Fusional Localization for Robot Fish Based on Dynamic-weight Fuzzy Inference

Adaptive Fusional Localization for Robot Fish Based on Dynam...

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Chinese Automation Congress (CAC)

作者： Yuzhuo Fu Xiaocun Liao Ben Lu Qianqian Zou Zhuoliang Zhang Yaming Ou Chao Zhou State Key Laboratory of Management and Control for Complex Systems Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426480

Accurate localization of robots in a specific environment often requires the cooperation of multiple sensors, and how to establish a more general data fusion model is always a difficult problem. For the localization of robot fish in an indoor pool environment, this paper proposes an adaptive fusional algorithm based on fuzzy inference of dynamic weights. This paper firstly constructs a confidence probability table of sensors’ signals based on the calibration data sets of BLE and UWB nodes at different distances, as the basis for updating the weights of the BLE or UWB nodes; secondly, the data obtained by each node in a single sampling period in the robot fish movement is vectorized to form a judgment matrix, and then the estimated distance is obtained by fuzzy inference together with the sensor weight; finally, the coordinates are calculated by the four-point positioning method. In this paper, more than 40 sets of experiments have been carried out with a simplified carangidae-like robot fish. The results show that the average positioning error is about 0.189m, which is 88.3% and 31.8% lower than that of using BLE only and using UWB only. In this paper, the fusional positioning method based on statistics combines the advantages of different sensors to reduce the data scale and achieve data denoising while fusing sensor data, which provides a reference for indoor positioning of robot fish and multi-sensor data fusion.

关键词： Location awareness Fuzzy logic Robot kinematics Heuristic algorithms Data integration Sensor fusion Probability

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Parameter estimation survey for multi-joint robot dynamic calibration case study

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Science China(Information Sciences) 2019年第10期62卷 102-116页

作者： Shaolin ZHANG Shuo WANG Fengshui JING Min TAN The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Accurate model parameters are the basis of robot dynamics. Many linear and nonlinear models have been proposed to calibrate the inertial parameters and friction parameters of multi-joint ***, methods of choosing a model and calculating its parameters still have few summaries. This paper reviews typical linear/nonlinear models and different calculation methods for robot dynamic *** simulations, the features of different methods are analyzed, including torque error, parameter error, model adaptability, solution time, and anti-interference ability of the calibration results. Finally, an experiment performed on a six-degree-of-freedom industrial manipulator is used as an example to illustrate how to select the model for a specified robot. These comparisons and experiments provide references for the parameter calibration of multi-joint robots.

关键词： dynamic parameter calibration friction calibration robot dynamics industrial manipulator dynamic models

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