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Shape Sensing for Single-Port Continuum Surgical robot Using FewMulticore Fiber Bragg Grating Sensors

Journal of Shanghai Jiaotong university(Science) 2023年第3期28卷 312-322页

作者：黎定佳王重阳郭伟王志东张忠涛刘浩 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China Liaoning Province Key Laboratory of Minimally Invasive Surgical Robot Shenyang 110016China University of Chinese Academy of Sciences Beijing 100049China Beijing Friendship Hospital Capital Medical UniversityBeijing 100050China Chiba Institute of Technology Narashino 275-0016ChibaJapan

We proposed a method for shape sensing using a few multicore ﬁber Bragg grating (FBG) sensors ina single-port continuum surgical robot (CSR). The traditional method of utilizing a forward kinematic model tocalculate the shape of a single-port CSR is limited by the accuracy of the model. If FBG sensors are used forshape sensing, their accuracy will be affected by their number, especially in long and ﬂexible CSRs. A fusionmethod based on an extended Kalman ﬁlter (EKF) was proposed to solve this problem. Shape reconstructionwas performed using the CSR forward kinematic model and FBG sensors, and the two results were fused usingan EKF. The CSR reconstruction method adopted the incremental form of the forward kinematic model, whilethe FBG sensor method adopted the discrete arc-segment assumption method. The fusion method can eliminatethe inaccuracy of the kinematic model and obtain more accurate shape reconstruction results using only a smallnumber of FBG sensors. We validated our algorithm through experiments on multiple bending shapes underdifferent load conditions. The results show that our method signiﬁcantly outperformed the traditional methodsin terms of robustness and effectiveness.

关键词： single-port continuum surgical robot multicoreﬁber Bragg grating(FBG) forward kinematic model extended Kalmanﬁlter(EKF) shape reconstruction

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MorNet: Asymmetric UNet-like Network with Morphological Opening and Closing for Image Segmentation 17th

MorNet: Asymmetric UNet-like Network with Morphological Open...

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17th International Conference on intelligent robotics and Applications, ICIRA 2024

作者： Li, Meng Yun, Juntong Jiang, Du Tao, Bo Li, Gongfa Liu, Rong Liu, Ying Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China School of Medicine Wuhan University of Science and Technology No. 1 Huangjia Lake University Town Wuhan430065 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

Image segmentation plays a crucial role in target recognition as it provides an essential prerequisite for identifying targets. However, UNet cannot acquire comprehensive information about the image, which limits its ability to effectively segment small and discrete objects. A new network structure, called MorNet, is proposed. It applies dilation and erosion operations in morphological opening and closing for connected and discrete region segmentation. MorNet effectively integrates global segmentation images, emphasizing small and discrete area segmentation and enhancing the segmentation of large continuous areas. This method enhances the understanding of the location of organs and diseased sites, enabling accurate segmentation. A side-out (SO) monitoring mechanism is introduced into the decoder to provide monitoring information for the original image and the predicted results. Compared with current UNet-like networks, our proposed method achieved the highest Dice scores (96.4635%, 94.0514%, 94.3444%) and PPV scores (94.2226%, 90.2361%, 91.5582%) for liver tumor, brain tumor, and liver segmentation tasks, respectively. The experimental results show that MorNet successfully combines the segmentation of connected and discrete regions. MorNet locates organs and lesions more accurately than current state-of-the-art medical image segmentation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Fluidic Ga–In liquid metal-modified cathode with improved cyclic performance and capacity of Li–O2 batteries

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Rare Metals 2022年第7期41卷 2223-2229页

作者： Ao-Hua Chai Chen-Hao Ji Dong Yuan Lian-Kun Yin Yan-Song Zhang Xiang-Qun Zhuge Zhi-Hong Luo Yi-Bing Li Kun Luo Guangxi Key Laboratory of Optical and Electronic Materials and Devices College of Materials Science and EngineeringGuilin University of TechnologyGuilin 541004China Jiangsu Province Engineering Research Center of Intelligent Manufacturing Technology for the New Energy Vehicle Power Battery School of Materials Science and EngineeringChangzhou UniversityChangzhou 213164China

The cathode passivation induced by the accumulated solid products is a serious issue which impede the application of Li-O_(2) ***,we applied Ga-In liquid metal(Ga-In LM)functionalized cathode to alleviate the cathode passivation by using the fluidity and self-healing surface of Ga-In LM microdrops,which could hardly be covered by the solid discharge products,such as Li_(2)O_(2) and LiOH during charge/discharge cycles.

关键词： cathode hardly healing

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Enhancing IC substrate manufacturing through differential geometry and lightweight networks for etching defect detection

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Journal of manufacturing Systems 2025年 80卷 902-915页

作者： Yu, Yongxing Huang, Dan Hu, Yueming School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Engineering Research Center of Precision Electronic Manufacturing Equipment Ministry of Education and Guangdong Provincial Engineering Laboratory for Advanced Chip Intelligent Packaging Equipment South China University of Technology Guangzhou510641 China Department of Automation North China Electric Power University Baoding071003 China Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System North China Electric Power University Baoding071003 China

With the advancement of high-density interconnect technology in semiconductor manufacturing, the precision and complexity of integrated circuit (IC) substrates have significantly increased, placing higher demands on quality control. Efficient and accurate detection of complex etching defects, which often occur during manufacturing, has become critical to preventing potential product defects. A defect detection method is proposed that combines a lightweight network with differential geometry tools to address the issue of etching defects in IC substrates. First, an improved deformable model is used to rapidly extract regular circuit trace contours from complex metallographic images, and morphological processing is applied to enhance the details, achieving precise image segmentation. For under-etching defects between circuit traces, differential processing of the original and segmented images is performed to locate abnormal regions. Subsequently, an optimized lightweight network based on MobileNet, termed OMNet, is designed to achieve the rapid identification of under-etching defects in these regions. For etching defects occurring on circuit traces, the DGEtch method employs a high-precision discrete curvature calculation based on the Frenet frame to evaluate angular discontinuities in contours, enabling accurate detection of etching defects. Experimental results demonstrate that the proposed method achieves an average recall rate of over 95% and maintains a precision above 90%. It exhibits high accuracy and stability in detecting etching defects and consistently outperforms existing models, particularly in handling complex mixed defects. This study provides an effective solution for detecting complicated defects in high-density IC substrate manufacturing. © 2025 The Society of manufacturing Engineers

关键词： Inspection

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Exploring self-organization and self-adaption for smart manufacturing complex networks

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Frontiers of Engineering Management 2023年第2期10卷 206-222页

作者： Zhengang GUO Yingfeng ZHANG Sichao LIU Xi Vincent WANG Lihui WANG Key Laboratory of Industrial Engineering and Intelligent Manufacturing of Ministry of Industry and Information Technology School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an 710072China Department of Electrical and Electronic Engineering Imperial College LondonLondon SW72AZUK Department of Production Engineering KTH Royal Institute of TechnologyStockholm 10044Sweden

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch,short-cycle,and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments,which poses great challenges to manufacturing ***,recent advances in the Industrial Internet of Things(IIoT)and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart,flexible,and resilient manufacturing *** this context,this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing ***,a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple ***,the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology,which can be added to or removed from the networks in a plug-and-play ***,information,and financial assets are passed through interactive links across the ***,analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network ***,an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical *** simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method,reducing manufacturing cost,manufacturing time,waiting time,and energy consumption,with reasonable computational time.T

关键词： cyber–physical systems Industrial Internet of Things smart manufacturing complex networks self-organization and self-adaption analytical target cascading collaborative optimization

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High entropy metal oxides as dielectric layers prepared by spin-coating and application in thin film transistors

High entropy metal oxides as dielectric layers prepared by s...

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International Conference on Display technology, 2024

作者： Liang, Zhihao Wu, Weijing Fu, Xiao Ning, Honglong Luo, Dongxiang Qiu, Tian Yang, Zhao Su, Guoping Yao, Rihui Peng, Junbiao State Key Laboratory of Luminescent Materials and Devices School of Materials Sciences and Engineering South China University of Technology Guangzhou510640 China School of Chemistry and Chemical Engineering/Institute of Clean Energy and Materials/Guangzhou Key Laboratory for Clean Energy and Materials Guangzhou University Guangzhou510006 China Department of Intelligent Manufacturing Wuyi University Jiangmen529020 China State Key Laboratory of Advanced Materials and Electronic Components Fenghua Electronic Industrial Park No. 18 Fenghua Road Zhaoqing526020 China

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Temporal-Spatial Scheduling of Energy and Computation Resources for Charging and Computing Service Vehicles

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IEEE Internet of Things Journal 2025年

作者： Rong, Shichu Deng, Xiongtian Zhong, Weifeng Huang, Xumin Kang, Jiawen Xie, Shengli Yuen, Chau Guangdong University of Technology School of Automation Key Laboratory of Intelligent Detection and Manufacturing Internet of Things Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore

The growing adoption of electric vehicles (EVs) and expansion of Internet-of-Things (IoT) in-vehicle applications enhance vehicle intelligence and connectivity but also drive higher demand for both charging and computing services. Charging and Computing Stations (CCSs), integrating bidirectional chargers and edge computing servers and allowing optimal joint energy-computation management, has been taken as an effective solution to address this demand. This paper introduces a new concept called charging and computing service vehicle (CCSV) fleets, which are equipped with high-capacity batteries and edge servers, serving as mobile resources to support the stationary CCSs at different locations in a wide area. We propose a two-timescale model integrating temporal-spatial scheduling, charging/discharging management, and computation task offloading of the CCSV fleets. Our goal is to minimize the total system cost by optimizing the energy-computation coordination between the mobile CCSV fleets and the stationary CCSs. We construct an extended time-space network (TSN) with congestion nodes, providing a clearer depiction of the time-varying congestion conditions in the traffic network. For practical implementation, we develop a heuristic based on the convex-concave procedure (CCP) and penalty alternating direction method (PADM) to solve the problem quickly. Simulation results in a traffic network based on Guangzhou city demonstrate that the proposed model effectively leverages the mobility and multi-dimensional resources of the CCSV fleets to reduce the system cost significantly. © 2014 IEEE.

关键词： Traffic congestion

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Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using sEMG

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Personal and Ubiquitous Computing 2022年第4期26卷 1215-1224页

作者： Jiang, Du Li, Gongfa Sun, Ying Kong, Jianyi Tao, Bo Chen, Disi Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

In order to resolve the problem of unstable control of force in human–computer interaction based on surface EMG signals, the adaptive neural fuzzy inference system is designed to achieve the grip strength assessment. As we know, the acquisition of surface EMG signal is non-invasive, which provides a better evaluation index for rehabilitation training in the medical process. By establishing the relationship between grip force and surface electromechanical signals, the effect of rehabilitation training can be evaluated directly while reducing the types of sensors used. Firstly, the experimental equipment are introduced, which are utilized to carry out simultaneous acquisition of surface EMG signals and forces. Then, the traditional features of sEMG and the corresponding algorithms are illustrated, based on this, supplementing the energy eigenvalue with wavelet analysis and fuzzy entropy. In which, fuzzy entropy is effective in characterizing muscle fatigue that can effectively reduce the impact of muscle fatigue on force assessment. Finally, combining fuzzy logic implication and neural network, the adaptive neural fuzzy inference system is designed, which is trained by extracted feature vectors. The experimental result shows the method used in this paper can effectively predict the grip force. Further, force prediction based on sEMG can be used to guide rehabilitation therapy in virtual space, combined with an electrical stimulator. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Eigenvalues and eigenfunctions

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Preface to the proceedings of the 9th conference on spectroscopic ellipsometry (ICSE-9)

Preface to the proceedings of the 9th conference on spectros...

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作者： Liu, Shiyuan Li, Lingjie Hu, Zhigao State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Chemistry and Chemical Engineering Chongqing University Chongqing400044 China School of Physics and Electronic Science East China Normal University Shanghai200241 China

The 9th International Conference on Spectroscopic Ellipsometry (ICSE-9) was held in Beijing, China from May 22nd to 28th, 2022. This special issue of Thin Solid Films collects 38 peer-reviewed articles that were presented at ICSE-9. These articles are expected to inspire the scientific community of spectroscopic ellipsometry and related fields. © 2023

关键词： Paul Drude Award Preface Spectroscopic Ellipsometry

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An Underwater Acoustic Signal Denoising Method for Broadband Interference from Underwater Platforms Based on the Deep Neural Network Model 6

An Underwater Acoustic Signal Denoising Method for Broadband...

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6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Cao, Linxuan Lei, Zhixiong Zhou, Zihao Sun, Jie Shi, Yang School of Marine Science and Technology Northwestern Polytechnical University Shaanxi Key Laboratory of Underwater Information Technology Xi'an China Southwest China Institute of Electronic Technology Chengdu610036 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350339994

As underwater vehicles widely used in ocean observation, the self-noise generated from the underwater platforms will affect the underwater acoustic signal observation, impeding signal detection. In this paper, we proposed an ideal binary mask (IBM) based Deep Neural Network (DNN) denoising method to reduce the broadband transient interference that underwater platforms caused. First, we analysis the interference produced by the underwater glider, illustrating its hazard by a common noisy situation. Then the IBM based DNN model was train to reduce this interference. Compared with spectral subtraction and Wiener filtering method, the method we proposed has short processing time of 0.15 $s$ in average, 7 dB higher signal-to-interference ratio (SIR) gain, and more complete retention of the original signal components in low SIR situations. © 2023 IEEE.

关键词： broadband interference deep neural network ideal binary mask transient signal underwater acoustic signal denoising

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