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    Eye Robotic System for Vitreoretinal Surgery
    DAI Qianlin (代倩琳), XU Mengqiao (徐梦乔), SUN Xiaodong (孙晓东), XIE Le∗ (谢叻)
    J Shanghai Jiaotong Univ Sci    2022, 27 (1): 1-6.   DOI: 10.1007/s12204-021-2369-2
    Abstract483)      PDF (1040KB)(254)      
    Micro incision vitrectomy system (MIVS) is considered to be one of the most difficult tasks of eye surgery, due to its requirements of high accuracy and delicate operation under blurred vision environment. Therefore, robot-assisted ophthalmic surgery is a potential and efficient solution. Based on that consideration, a novel master-slave system for vitreoretinal surgery is realized. A 4-DOF remote center of motion (RCM) mechanism with a novel linear stage and end-effector is designed and the master-slave control system is implemented. The forward and inverse kinematics are analyzed for the controller implementation. Then, algorithms with motion scaling are also integrated into the control architecture for the purpose to enhance the surgeon’s operation accuracy. Finally, experiments on an eye model are conducted. The results show that the eye robotic system can fulfill surgeon’s motion following and simulate operation of vitrectomy, demonstrating the feasibility of this system.
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    Camera-Radar Fusion Sensing System Based on Multi-Layer Perceptron
    YAO Tong (姚 彤), WANG Chunxiang(王春香), QIAN Yeqiang(钱烨强)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 561-568.   DOI: 10.1007/s12204-021-2345-x
    Abstract314)      PDF (1189KB)(197)      
    Environmental perception is a key technology for autonomous driving. Owing to the limitations of a single sensor, multiple sensors are often used in practical applications. However, multi-sensor fusion faces some problems, such as the choice of sensors and fusion methods. To solve these issues, we proposed a machine learning-based fusion sensing system that uses a camera and radar, and that can be used in intelligent vehicles. First, the object detection algorithm is used to detect the image obtained by the camera; in sequence, the radar data is preprocessed, coordinate transformation is performed, and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed. The proposed fusion sensing system was verified by comparative experiments in a real-world environment. The experimental results show that the system can effectively integrate camera and radar data results, and obtain accurate and comprehensive object information in front of intelligent vehicles.
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    Intelligent-Assist Algorithm for Remote Shared-Control Driving Based on Game Theory
    QIAO Bangjun∗ (乔邦峻), LI Huanghe (李黄河), WU Xiaodong (吴晓东)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 615-625.   DOI: 10.1007/s12204-021-2351-z
    Abstract147)      PDF (759KB)(172)      
    Contemporary autonomous-driving technology relies on good environmental-perception systems and high-precision maps. For unknown environments or scenarios where perception fails, a human-in-the-loop remote-driving system can effectively complement common solutions, although safety remains an issue for its application. A haptic shared-control algorithm based on non-cooperative game theory is presented in this paper. The algorithm generates collision-free reference paths with model predictive control and predicts the driver’s path using a two-point preview model. Man-machine torque interaction is modeled as a Nash game, and the assist system’s degree of intervention is regulated in real time, according to assessments of collision risk and the driver’s concentration. Simulations of several representative scenarios demonstrate how the proposed method improves driving safety, while respecting driver decisions.
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    Sealing Performance of Pressure-Adaptive Seal
    LI Yuanfeng (李元丰), WANG Yiling (王怡灵), ZHANG Wanxin∗ (张万欣), LIU Jinian (刘冀念), MA Jialu (马加炉)
    J Shanghai Jiaotong Univ Sci    2022, 27 (6): 747-756.   DOI: 10.1007/s12204-022-2510-x
    Abstract430)      PDF (2268KB)(167)      
    A pressure-adaptive seal is developed to meet the demands of quick assembling and disassembling for an individual protection equipment in aerospace. The analysis model, which reflects the main characteristics of the seal structure, is built based on the finite element method and the Roth’s theory of rubber seal, and verified by the prototype test. The influences of precompression ratio, hardness of the sealing ring rubber, and friction coefficient on the sealing performance are investigated by variable parameter method. Results show that the model can describe the essential characteristics of the pressure-adaptive seal structure, which has good follow-up to the cavity pressure to achieve the purpose of pressure self-adaptive. The leakage rate correlates negatively with the precompression ratio of the sealing ring and the hardness of the sealing ring material, while is positively related to the friction coefficient between the sealing ring and the sealing edge. The maximum contact stress on sealing surface has negative correlation with the precompression ratio of the sealing ring, and positive correlation with the hardness of the seal ring material. The damage risk of the sealing ring increases with the increases of the precompression ratio of sealing ring, hardness of sealing ring material, and friction coefficient.
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    Intelligent Connected Vehicle as the New Carrier Towards the Era of Connected World
    ZHUANG Hanyang (庄瀚洋), QIAN Yeqiang (钱烨强), YANG Ming(杨 明)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 559-560.  
    Abstract341)      PDF (83KB)(164)      
    Human beings have been kept pursuing of higher efficiency and better safety to move people and things around since thousands of years ago. In modern soci-ety, vehicles are therefore invented and utilized to boost the speed and enhance the safety. In recent years, rapid development of information technology has brought hu-man into a new era of connected world. Internet and smartphones have made it extremely easy to get ac-cess to anyone from anywhere any time. In this back-ground, intelligent connected vehicles (ICVs) have been proposed and investigated. In the similar manner as the smartphones, ICVs are expected to be the next gener-ation carrier for people to get connected to the world. ICVs are equipped with novel sensors, controllers, and actuators to understand the environment, make decisions, and take actions, respectively. The word “intelligent” indicates that the vehicle should be able to handle unexpected events on the road. The word “connected” means that the information of each vehicle should be shared and considered globally. Full auton-omy and full connection are the ultimate goals of ICV industry. Unfortunately, we are still far away from this goal; therefore, continuous efforts shall be made to step further to this destination. As the ICV consists of multiple subsystems and is across different disciplines, the overall improvement re-quires the innovation in each aspect. Under this cir-cumstance, the Special Issue on Intelligent Connected Vehicle at Journal of Shanghai Jiao Tong University (Science) has been organized to broaden the perspec-tive, promote the interdisciplinary collaboration, and report the state-of-the-art works.
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    Real-Time Trajectory Planning for On-road Autonomous Tractor-Trailer Vehicles
    SHEN Qiyue (沈琦越), WANG Bing (王 冰), WANG Chunxiang∗ (王春香)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 722-730.   DOI: 10.1007/s12204-021-2362-9
    Abstract340)      PDF (1546KB)(142)      
    Tractor-trailer vehicles, which are composed of a car-like tractor towing a passive trailer, have been widely deployed in the transportation industry, and trajectory planning is a critical step in enabling such a system to drive autonomously. Owing to the properties of being highly nonlinear and nonholonomic with complex dynamics, the tractor-trailer system poses great challenges to the development of motion-planning algorithms. In this study, an indirect trajectory planning framework for a tractor-trailer vehicle under on-road driving is presented to deal with the problem that the traditional planning framework cannot consider the feasibility and quality simultaneously in real-time trajectory generation of the tractor-trailer vehicle. The indirect planning framework can easily handle complicated tractor-trailer dynamics and generate high-quality, obstacle-free trajectory using quintic polynomial spline, speed pro?le optimization, forward simulation, and properly designed cost functions. Simulations under di?erent driving scenarios and trajectories with di?erent driving requirements are conducted to validate the performance of the proposed framework.
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    Integrated Hydraulic-Driven Wearable Robot for Knee Assistance
    ZHAO Yafei (赵亚飞), HUANG Chaoyi (黄超逸), ZOU Yuging(邹玉莹), ZOUKehan(邹可涵), zoU Xiaogang(邹笑阳), XUE .Jiaqi(薛嘉琦), LI Xiaoting(李晓婷), KOH Keng Huat, WANG Xiaojun(王小军), LAI Wai Chiu King(赖伟超), HU Yong(胡勇), XI Ning(席宁), WANG Zheng(王峥)
    J Shanghai Jiaotong Univ Sci    2023, 28 (3): 289-295.   DOI: 10.1007/s12204-023-2602-2
    Abstract288)      PDF (1156KB)(135)      
    Age-related diseases can lead to knee joint misfunction, making knee assistance necessary through the use of robotic wearable braces. However, existing wearable robots face challenges in force transmission and human motion adaptation, particularly among the elderly. Although soft actuators have been used in wearable robots, achieving rapid response and motion control while maintaining portability remains challenging. To address these issues, we propose a soft-robotic knee brace system integrated with multiple sensors and a direct-drive hydraulic actuation system. This approach allows for controlled and rapid force output on the portable hydraulic system. The multi-sensor feedback structure enables the robotic system to collaborate with the human body through human physiological signal and body motion information. The human user tests demonstrate that the knee robot provides assistive torques to the knee joint by being triggered by the electromyography signal and under human motion control.
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    Travel Intention of Electric Vehicle Sharing based on Theory of Multiple Motivations
    BAO Lewen (鲍乐雯), MIAO Rui, ∗ (苗 瑞), CHEN Zhihua (陈志华), ZHANG Bo (张 博), GUO Peng (郭 鹏), MA Yuze (马宇泽)
    J Shanghai Jiaotong Univ Sci    2023, 28 (1): 1-9.   DOI: 10.1007/s12204-023-2563-5
    Abstract335)      PDF (467KB)(125)      
    Determining the travel intention of residents with shared electric vehicles (EVs) is significant for promoting the development of low-carbon transportation, considering that common problems such as high idle rate and lack of attractiveness still exist. To this end, a structural equation model (SEM) based on the theory of multiple motivations is proposed in this paper. First, the influencing motivations for EV sharing are divided into three categories: consumer-driven, program-driven, and enterprise-driven motivations. Then, the intentions of residents in Shanghai to travel with shared EVs are obtained through a survey questionnaire. Finally, an SEM is constructed to analyze quantitatively the impact of different motivations on the travel intention. The results show that consumer-driven motivations with impact weights from 0.14 to 0.63 have the overwhelming impact on travel intention, compared to program-driven motivations with impact weights from ?0.14 to 0.15 and enterprise-driven motivations with impact weights from 0.02 to 0.06. In terms of consumer-driven motivations, the weight of green travel awareness is the highest. The implications of these results on the policy to enable large-scale implementation of shared EVs are discussed from the perspectives of the resident, enterprise, and government.
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    COVID-19 Interpretable Diagnosis Algorithm Based on a Small Number of Chest X-Ray Samples
    BU Ran (卜冉), XIANG Wei∗ (向伟), CAO Shitong (曹世同)
    J Shanghai Jiaotong Univ Sci    2022, 27 (1): 81-89.   DOI: 10.1007/s12204-021-2393-2
    Abstract307)      PDF (1470KB)(125)      
    The COVID-19 medical diagnosis method based on individual’s chest X-ray (CXR) is achieved difficultly in the initial research, owing to difficulties in identifying CXR data of COVID-19 individuals. At the beginning of the study, infected individuals’ CXRs were scarce. The combination of artificial intelligence (AI) and medical diagnosis has been advanced and popular. To solve the difficulties, the interpretability analysis of AI model was used to explore the pathological characteristics of CXR samples infected with COVID-19 and assist in medical diagnosis. The dataset was expanded by data augmentation to avoid overfitting. Transfer learning was used to test different pre-trained models and the unique output layers were designed to complete the model training with few samples. In this study, the output results of four pre-trained models in three different output layers were compared, and the results after data augmentation were compared with the results of the original dataset. The control variable method was used to conduct independent tests of 24 groups. Finally, 99.23% accuracy and 98% recall rate were obtained, and the visual results of CXR interpretability analysis were displayed. The network of COVID-19 interpretable diagnosis algorithm has the characteristics of high generalization and lightweight. It can be quickly applied to other urgent tasks with insufficient experimental data. At the same time, interpretability analysis brings new possibilities for medical diagnosis.
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    Solution to Long-Range Continuous and Precise Positioning in Deep Ocean for Autonomous Underwater Vehicles Using Acoustic Range Estimation and Inertial Sensor Measurements
    YANG Tao (杨 涛), ZHAO Jiankang∗ (赵健康)
    J Shanghai Jiaotong Univ Sci    2022, 27 (3): 281-297.   DOI: 10.1007/s12204-022-2441-6
    Abstract197)      PDF (2619KB)(121)      
    Although advances in research into autonomous underwater vehicles (AUVs) have been made to extend their working depth and endurance, underwater experiments and missions remain to be restricted by the positioning performance of AUVs. With the Global Navigation Satellite System (GNSS) precluded due to the rapid attenuation of radio signals in underwater environments, acoustic positioning methods serve as an effective substitution. A long-range continuous and precise positioning solution for AUVs in deep ocean is proposed in this study, relying on acoustic signals from beacons at the same depth and aided by onboard inertial sensors. A signal system is investigated to provide time of arrival (TOA) estimation in a resolution of milliseconds. Without pre-knowledge or local measurement of the accurate sound speed, an AUV is enabled to continuously locate its horizontal position based on rough ranges estimated by an iterative least square (ILS) based algorithm. For better accuracy and robustness, range deviations are compensated with a reference point of known position and outliers in the trajectory are eliminated by an implementation of the extended Kalman filter (EKF) coupled with the state-acceptance filter. The solution is evaluated in simulation experiments with environmental information measured on the spot, providing an average position error from ground truth below 10 m with a standard deviation below 5 m.
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    Automatic Removal of Multiple Artifacts for Single-Channel Electroencephalography
    ZHANG Chenbei (张晨贝), SABOR Nabil, LUO Junwen (罗竣文), PU Yu (蒲 宇), WANG Guoxing (王国兴), LIAN Yong∗ (连 勇)
    J Shanghai Jiaotong Univ Sci    2022, 27 (4): 437-451.   DOI: 10.1007/s12204-021-2374-5
    Abstract228)      PDF (2934KB)(112)      
    Removing different types of artifacts from the electroencephalography (EEG) recordings is a critical step in performing EEG signal analysis and diagnosis. Most of the existing algorithms aim for removing single type of artifacts, leading to a complex system if an EEG recording contains different types of artifacts. With the advancement in wearable technologies, it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices. In this paper, an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts, i.e., ocular artifact (OA), transmission- line/harmonic-wave artifact (TA/HA), and muscle artifact (MA), from a single-channel EEG recording. The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB- MIT dataset. The experimental results show that the proposed algorithm effectively suppresses OA, MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.
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    Advances in Medicine-Engineering Crossover in Automated Anesthesia
    XU Tianyi (徐天意), XIA Ming (夏明), JIANG Hong (姜虹)
    J Shanghai Jiaotong Univ Sci    2022, 27 (2): 137-143.   DOI: 10.1007/s12204-021-2329-x
    Abstract268)      PDF (156KB)(111)      
    Medicine-engineering crossover refers to the cross-fertilization of multiple disciplines to meet clinical needs through various means, including engineering, which greatly promotes medical development. In the development of anesthesiology, improvements in anesthesia equipment and continuous innovation of anesthesia technology are all closely related to the integration of medicine and engineering. In recent years, the exploration and development of automated anesthesia equipment has led to closer integration of medicine, engineering, and other disciplines, including the development of robots in anesthesia, automated monitoring and alarm technology,automated perioperative management, and remote anesthesia. Herein, the current status of applications and development of medicine-engineering crossover in the field of automated anesthesia are discussed.
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    Collision-Free Path Planning with Kinematic Constraints in Urban Scenarios
    WANG Liang (王 亮), WANG Bing (王 冰), WANG Chunxiang∗ (王春香)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 731-738.   DOI: 10.1007/s12204-021-2363-8
    Abstract282)      PDF (2199KB)(111)      
    In urban driving scenarios, owing to the presence of multiple static obstacles such as parked cars and roadblocks, planning a collision-free and smooth path remains a challenging problem. In addition, the path-planning problem is mostly non-convex, and contains multiple local minima. Therefore, a method for combining a sampling-based method and an optimization-based method is proposed in this paper to generate a collision-free path with kinematic constraints for urban scenarios. The sampling-based method constructs a search graph to search for a seeding path for exploring a safe driving corridor, and the optimization-based method constructs a quadratic programming problem considering the desired state constraints, continuity constraints, driving corridor constraints, and kinematic constraints to perform path optimization. The experimental results show that the proposed method is able to plan a collision-free and smooth path in real time when managing typical urban scenarios.
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    Physical Characterization of Ionic Liquid-Modified Polyvinyl Alcohol and Sodium Thiocyanate Polymer Electrolytes for Electrochemical Double-Layer Capacitor Application
    AZEMTSOP Manfo Theodore , MEHRA Ram Mohan , KUMAR Yogesh , GUPTA Meenal
    J Shanghai Jiaotong Univ Sci    2023, 28 (2): 161-171.   DOI: 10.1007/s12204-021-2397-y
    Abstract277)      PDF (1140KB)(110)      
    Novel gel polymer electrolytes (GPEs) composed of polyvinyl alcohol (PVA) and sodium thiocyanate were developed via a solution casting technique. An ionic liquid (IL), 1-ethyl-3-methyl-imidazolium tricyanomethanide ([EMIM][TCM]), was doped into a polymer–salt complex system (PVA + NaSCN) to further enhance the conductivity. IL-doped polymer electrolyte (ILDPE) films were characterized using X-ray diffraction (XRD), polarized optical microscopy (POM), Fourier-transform infrared (FTIR) spectroscopy, and conductivity measurements. XRD was performed to check the degree of crystallinity and amorphicity of the ILDPE films, and the amorphicity of GPEs increased with the increase of the IL content. POM was employed to evaluate the changes in the surface morphology due to the inclusion of salt and IL in the PVA. The compositional nature of the GPE films was examined via FTIR studies. The electrical and electrochemical properties were characterized by cyclic voltammetry and electrochemical impedance spectroscopy. The maximum conductivity for the GPE film was estimated to be 1.10 × 10-5 S/cm for 6% (mass fraction) of IL in the polymer–salt complex. The ionic transference number was approximately 0.97. An electrochemical double-layer capacitor (EDLC) was built from optimized GPE films and reduced graphene oxide-based electrodes. The specific capacitance calculated from the cyclic voltammograms of the EDLC cells was 3 F/g.
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    Numerical Study on Separation of Circulating Tumor Cell Using Dielectrophoresis in a Four-Electrode Microfluidic Device
    WANG Yukuna (王雨坤), DING Xiantingb (丁显廷), ZHANG Zhinana (张执南)
    J Shanghai Jiaotong Univ Sci    2023, 28 (4): 391-.   DOI: 10.1007/s12204-022-2459-9
    Abstract146)      PDF (1462KB)(108)      
    This numerical study proposes a cell sorting technique based on dielectrophoresis (DEP) in a microfluidic chip. Under the joint effect of DEP and fluid drag, white blood cells and circulating tumor cells are separated because of different dielectric properties. First, the mathematical models of device geometry, single cell, DEP force, electric field, and flow field are established to simulate the cell motion. Based on the simulation model, important boundary parameters are discussed to optimize the cell sorting ability of the device. A proper matching relationship between voltage and flow rate is then provided. The inlet and outlet conditions are also investigated to control the particle motion in the flow field. The significance of this study is to verify the cell separating ability of the microfluidic chip, and to provide a logistic design for the separation of rare diseased cells.
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    Wavelet Transform-Based High-Definition Map Construction From a Panoramic Camera
    ZHUANG Hanyang (庄瀚洋), ZHOU Xuejun (周学军), WANG Chunxiang (王春香), QIAN Yuhan (钱宇晗)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 569-576.   DOI: 10.1007/s12204-021-2346-9
    Abstract238)      PDF (1242KB)(107)      
    High-definition (HD) maps are key components that provide rich topologic and semantic information for decision-making in vehicle autonomous driving systems. A complete ground orthophoto is usually used as the base image to construct the HD map. The ground orthophoto is obtained through inverse perspective transformation and image mosaicing. During the image mosaicing, multiple consecutive orthophotos are stitched together using pose information and image registration. In this study, wavelet transform is introduced to the image mosaicing process to alleviate the information loss caused by image overlapping. In the orthophoto wavelet transform, high-frequency and low-frequency components are fused using different strategies to form a complete base image with clearer local details. Experimental results show that the accuracy of the orthophotos generated using this method is improved.
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    Influence of Thermal Modification on Al-Si Coating of Hot-Stamped 22MnB5 Steel: Microstructure, Phase Transformation, and Mechanical Properties
    WANG Qiongyan (王琼燕), LIN Wenhu (林文虎),LI Fang∗ (李芳), SHEN Chen (沈忱), HUA Xueming (华学明)
    J Shanghai Jiaotong Univ Sci    2021, 26 (6): 747-756.   DOI: 10.1007/s12204-021-2267-7
    Abstract263)      PDF (3340KB)(104)      
    The hot-stamped steel with ultrahigh strength is a promising material for the fabrication of automotivecomponents. However, the coating on the sheet surface leads to a softening problem in the welded joint. Instead ofthe costly coating removal process, heat treatment is an economical and effective method for the diffusion process,which can decrease the Al concentration in the coating. In this study, a preheating treatment was carried out onAl-Si-coated 22MnB5 hot-stamped steels for the homogeneity of Al, followed by laser welding and hot stamping.The effects of the preheating on the microstructure and mechanical properties of the laser-welded joints wereinvestigated. With the preheating treatment, the Al-Si coating transformed into an Fe-Al intermetallic compoundand the difference in Al content between the coating and substrate was reduced. The Al content in the weld ofthe specimen with the preheating treatment was reduced, compared with that without the preheating treatment.The amount of δ-ferrite in the weld after laser welding was reduced largely. The distribution of long-bland-likesegregation was changed to a fine and uniform distribution. With the preheating treatment, the tensile strengthof the welded joint was significantly improved and comparable to that of the decoated joint. In conclusion, thepreheating treatment before the welding is an effective method to suppress the formation of δ-ferrite and improvethe mechanical properties of the welded joint.
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    Ant Colony Algorithm Path Planning Based on Grid Feature Point Extraction
    LI Erchao∗ (李二超), QI Kuankuan (齐款款)
    J Shanghai Jiaotong Univ Sci    2023, 28 (1): 86-99.   DOI: 10.1007/s12204-023-2572-4
    Abstract169)      PDF (1196KB)(100)      
    Aimed at the problems of a traditional ant colony algorithm, such as the path search direction and field of view, an inability to find the shortest path, a propensity toward deadlock and an unsmooth path, an ant colony algorithm for use in a new environment is proposed. First, the feature points of an obstacle are extracted to preprocess the grid map environment, which can avoid entering a trap and solve the deadlock problem. Second, these feature points are used as pathfinding access nodes to reduce the node access, with more moving directions to be selected, and the locations of the feature points to be selected determine the range of the pathfinding field of view. Then, based on the feature points, an unequal distribution of pheromones and a two-way parallel path search are used to improve the construction efficiency of the solution, an improved heuristic function is used to enhance the guiding role of the path search, and the pheromone volatilization coefficient is dynamically adjusted to avoid a premature convergence of the algorithm. Third, a Bezier curve is used to smooth the shortest path obtained. Finally, using grid maps with a different complexity and different scales, a simulation comparing the results of the proposed algorithm with those of traditional and other improved ant colony algorithms verifies its feasibility and superiority.
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    Adaptive Human-Robot Collaboration Control Based on Optimal Admittance Parameters
    YU Xinyi (禹鑫燚), WU Jiaxin (吴加鑫), XU Chengjun (许成军), LUO Huizhen (罗惠珍), OU Linlin∗ (欧林林)
    J Shanghai Jiaotong Univ Sci    2022, 27 (5): 589-601.   DOI: 10.1007/s12204-022-2460-3
    Abstract280)      PDF (1674KB)(100)      
    In order to help the operator perform the human-robot collaboration task and optimize the task performance, an adaptive control method based on optimal admittance parameters is proposed. The overall control structure with the inner loop and outer loop is first established. The tasks of the inner loop and outer loop are robot control and task optimization, respectively. Then an inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is proposed, which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator. Subsequently, the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force. The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model. The model includes the unknown dynamics of the operator and the task performance details. For relaxing the requirement of the system model, the integral reinforcement learning is employed to solve the linear quadratic regulator problem. Besides, an auxiliary force is designed to help the operator complete the specific task better. Compared with the traditional control scheme, the security performance and interaction performance of the human-robot collaboration system are improved. The effectiveness of the proposed method is verified through two numerical simulations. In addition, a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method.
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    Parameter Identification of Magic Formula Tire Model Based on Fibonacci Tree Optimization Algorithm
    FENG Shilin (冯世林), ZHAO Youqun (赵又群), DENG Huifan (邓汇凡), WANG Qiuwei(王秋伟), CHEN Tingting (陈婷婷)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 647-657.   DOI: 10.1007/s12204-021-2354-9
    Abstract313)      PDF (1130KB)(95)      
    The magic formula (MF) tire model is a semi-empirical tire model that can precisely simulate tire behavior. The heuristic optimization algorithm is typically used for parameter identification of the MF tire model. To avoid the defect of the traditional heuristic optimization algorithm that can easily fall into the local optimum, a parameter identification method based on the Fibonacci tree optimization (FTO) algorithm is proposed, which is used to identify the parameters of the MF tire model. The proposed method establishes the basic structure of the Fibonacci tree alternately through global and local searches and completes optimization accordingly. The global search rule in the original FTO was modified to improve its efficiency. The results of independent repeated experiments on two typical multimodal function optimizations and the parameter identification results showed that FTO was not sensitive to the initial values. In addition, it had a better global optimization performance than genetic algorithm (GA) and particle swarm optimization (PSO). The root mean square error values optimized with FTO were 5.09%, 10.22%, and 3.98% less than the GA, and 6.04%, 4.47%, and 16.42% less than the PSO in pure lateral and longitudinal forces, and pure aligning torque parameter identi?cation. The parameter identification method based on FTO was found to be effective.
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    Multi-Object Tracking Strategy of Autonomous Vehicle Using Modified Unscented Kalman Filter and Reference Point Switching
    WANG Muyuan∗ (王木塬), WU Xiaodong (吴晓东)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 607-614.   DOI: 10.1007/s12204-021-2350-0
    Abstract299)      PDF (1070KB)(91)      
    In this study, a multi-object tracking (MOT) scheme based on a light detection and ranging sensor was proposed to overcome imprecise velocity observations in object occlusion scenarios. By applying real-time velocity estimation, a modified unscented Kalman filter (UKF) was proposed for the state estimation of a target object. The proposed method can reduce the calculation cost by obviating unscented transformations. Additionally, combined with the advantages of a two-reference-point selection scheme based on a center point and a corner point, a reference point switching approach was introduced to improve tracking accuracy and consistency. The state estimation capability of the proposed UKF was verified by comparing it with the standard UKF in single-target tracking simulations. Moreover, the performance of the proposed MOT system was evaluated using real traffic datasets.
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    Application of Digital Medicine in Addiction
    WU Xiaojun (吴萧俊), DU Jiang (杜江), JIANG Haifeng (江海峰), ZHAO Min (赵敏)
    J Shanghai Jiaotong Univ Sci    2022, 27 (2): 144-152.   DOI: 10.1007/s12204-021-2391-4
    Abstract234)      PDF (185KB)(90)      
    Digital medicine plays an important role in disease assessment, psychological intervention, and relapse management in mental illnesses. Patients with substance use disorders can be easily affected by the environment and negative emotions, inducing addiction and relapse. However, due to social discrimination, stigma, or economic issues, they are unwilling to go to the hospital for treatment, making it difficult for health workers to track their health changes. Additionally, mental health resources in China are insufficient. Digital medicine aims to solve these problems. This article reviews digital medicine in the field of addiction, hoping to provide a reference for the future exploration of more individualized and effective digital medicine.
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    Safety Protection Method of Rehabilitation Robot Based on fNIRS and RGB-D Information Fusion
    LI Dong (李栋), FAN Yulin (樊钰琳), L v Na (吕娜), CHEN Guodong∗ (陈国栋), WANG Zheng (王正), CHI Wenzheng (迟文政)
    J Shanghai Jiaotong Univ Sci    2022, 27 (1): 45-54.   DOI: 10.1007/s12204-021-2365-6
    Abstract269)      PDF (2503KB)(90)      
    In order to improve the safety protection performance of the rehabilitation robot, an active safety protection method is proposed in the rehabilitation scene. The oxyhemoglobin concentration information and RGB-D information are combined in this method, which aims to realize the comprehensive monitoring of the invasion target, the patient’s brain function movement state, and the joint angle in the rehabilitation scene. The main focus is to study the fusion method of the oxyhemoglobin concentration information and RGB-D information in the rehabilitation scene. Frequency analysis of brain functional connectivity coefficient was used to distinguish the basic motion states. The human skeleton recognition algorithm was used to realize the angle monitoring of the upper limb joint combined with the depth information. Compared with speed and separation monitoring, the protection method of multi-information fusion is safer and more comprehensive for stroke patients. By building the active safety protection platform of the upper limb rehabilitation robot, the performance of the system in different safety states is tested, and the safety protection performance of the method in the upper limb rehabilitation scene is verified.
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    Curvature Adaptive Control Based Path Following for Automatic Driving Vehicles in Private Area
    SHI Qiang (师 强), ZHANG Jianlin (张建林), YANG Ming∗ (杨 明)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 690-698.   DOI: 10.1007/s12204-021-2359-4
    Abstract342)      PDF (1349KB)(89)      
    Path following refers to traveling along the desired path with automatic steering control, which is a crucial technology for automatic driving vehicles. Roads in private areas are highly irregular, resulting in a large curvature variation, which reduces the control accuracy of the path following. A curvature adaptive control (CAC) based path-following method was proposed to solve the problem mentioned above. Speci?cally, CAC takes advantage of the complementary characteristics in response to the path curvature ?uctuation of pure pursuit and front-wheel feedback and by combining the two methods further enhances the immunity of the control accuracy in response to a curvature ?uctuation. With CAC, the quantitative indices of the path curvature ?uctuation and control accuracy were constructed. The model between the path curvature ?uctuation and a dynamic parameter was identi?ed using the quantitative index of the control accuracy as the optimization target. The experimental results of a real vehicle indicate that the control accuracy of path following is further enhanced by its immunity in response to curvature ?uctuation improved by the CAC. In addition, CAC is easy to deploy and requires low demand for hardware resources.
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    Developing High-Precision Maps for Automated Driving in China: Legal Obstacles and the Way to Overcome Them
    ZHANG Taolue∗ (张韬略), TU Huizhao (涂辉招), QIU Wei (邱 炜)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 658-669.   DOI: 10.1007/s12204-021-2355-8
    Abstract215)      PDF (285KB)(88)      
    A high-precision map (HPM) is the key infrastructure to realizing the function of automated driving (AD) and ensuring its safety. However, the current laws and regulations on HPMs in China can lead to serious legal compliance problems. Thus, proper measures should be taken to remove these barriers. Starting with a complete view of the current legal obstacles to HPMs in China, this study first explains why these legal obstacles exist and the types of legal interests they are trying to protect. It then analyzes whether new technology could be used as an alternative to resolve these concerns. Factors such as national security, AD industry needs, and personal data protection, as well as the ?exibility of applying technology, are discussed and analyzed hierarchically for this purpose. This study proposes that China should adhere to national security and AD industry development, pass new technical regulations that redefine the scope of national security regarding geographic information in the field of HPMs, and establish a national platform under the guidance and monitoring of the government to integrate scattered resources and promote the development of HPMs via crowdsourcing. Regarding the legal obstacles with higher technical plasticity, priority should be given to technical solutions such as “available but invisible” technology. Compared with the previous research, this study reveals the current legal barriers in China that have different levels of relevance to national security and different technical plasticity. It also proposes original measures to remove them, such as coordinating national security with the development of the AD industry, reshaping the boundary of national security and industrial interests, and giving priority to technical solutions for legal barriers that have strong technical plasticity.
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    Further Result on the Observer Design for One-Sided Lipschitz Systems
    YANG Ming1 (杨 明), HUANG Jun1∗ (黄 俊), ZHANG Wei2 (章 伟)
    J Shanghai Jiaotong Univ Sci    2022, 27 (6): 817-822.   DOI: 10.1007/s12204-020-2252-6
    Abstract178)      PDF (328KB)(86)      
    This paper investigates the problem of observer design for a class of control systems. Different from current works, the nonlinear functions in the system only satisfy the property of the one-sided Lipschitz (OSL) condition but not quadratic inner-boundedness (QIB). Moreover, the case where the OSL constant is negative is specially investigated. Firstly, a full-order observer is constructed for the original system. Then, a reduced-order observer is also designed by using the decomposition method. The advantage and effectiveness of the proposed design scheme are shown in a numerical simulation.
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    Electric vehicle charging situation awareness for charging station ultra-short-term load forecast
    SHI Yiwei1 (史一炜), LIU Zeyu1 (刘泽宇), FENG Donghan1∗ (冯冬涵), ZHOU Yun1∗ (周 云), ZHANG Kaiyu2 (张开宇), LI Hengjie3 (李恒杰)
    J Shanghai Jiaotong Univ Sci    2023, 28 (1): 28-38.   DOI: 10.1007/s12204-023-2566-2
    Abstract172)      PDF (1518KB)(83)      
    Electric vehicles (EVs) are expected to be key nodes connecting transportation–electricity–communication networks. Advanced automotive electronics technologies enhance EVs’ perception, computing, and communication capacity, which in turn can boost the operational efficiency of intelligent transportation systems (ITSs). EVs couple the ITS to the power system, providing a promising solution to charging congestion and transformer overload via navigation and forecasting approaches. This study proposes a privacy-preserving EV charging situation awareness framework and method to forecast the ultra-short-term load of charging stations. The proposed method only relies on public information from commercial service providers. In the case study, data are powered by the Baidu LBS cloud and EV-SGCC platform, and the experiment is conducted within an area of Pudong New District in Shanghai. Based on the results, the charging load of charging stations can be adequately forecasted more than 1 min ahead with low communication and computing power requirements. This research provides the basis for further studies on operation optimization and electricity market transaction of charging stations.
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    Development of a Robotic Cochlear Implantation System
    CHEN Ziyun (陈子云), XIE Le (谢叻), DAI Peidong (戴培东), ZHANG Tianyu (张天宇)
    J Shanghai Jiaotong Univ Sci    2022, 27 (1): 7-14.   DOI: 10.1007/s12204-021-2381-6
    Abstract359)      PDF (1384KB)(83)      
    Traditional cochlear implantation surgery has problems such as high surgical accuracy requirement and large trauma, which cause the difficulty of the operation and the high requirements for doctors, so that only a few doctors can complete the operation independently. However, there is no research on robotic cochlear implantation in China. In response to this problem, a robotic cochlear implantation system is proposed. The robot is controlled by robot operating system (ROS). A simulation environment for the overall surgery is established on the ROS based on the real surgery environment. Through the analysis of the kinematics and the motion planning algorithm of the manipulator, an appropriate motion mode is designed to control the motion of the manipulator, and perform the surgery under the simulation environment. A simple and feasible method of navigation is proposed, and through the model experiment, the feasibility of robotic cochlear implantation surgery is verified.
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    Airframe Damage Region Division Method Based on Structure Tensor Dynamic Operator
    CAI Shuyu∗ (蔡舒妤), SHI Lizhong (师利中)
    J Shanghai Jiaotong Univ Sci    2022, 27 (6): 757-767.   DOI: 10.1007/s12204-022-2498-2
    Abstract226)      PDF (1607KB)(80)      
    In order to improve the accuracy of damage region division and eliminate the interference of damage adjacent region, the airframe damage region division method based on the structure tensor dynamic operator is proposed in this paper. The structure tensor feature space is established to represent the local features of damage images. It makes different damage images have the same feature distribution, and transform varied damage region division into consistent process of feature space division. On this basis, the structure tensor dynamic operator generation method is designed. It integrates with bacteria foraging optimization algorithm improved by defining double fitness function and chemotaxis rules, in order to calculate the parameters of dynamic operator generation method and realize the structure tensor feature space division. And then the airframe damage region division is realized. The experimental results on different airframe structure damage images show that compared with traditional threshold division method, the proposed method can improve the division quality. The interference of damage adjacent region is eliminated. The information loss caused by over-segmentation is avoided. And it is efficient in operation, and consistent in process. It also has the applicability to different types of structural damage.
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    Improvement of Physical Fitness Test Assessment Criteria Based on fNIRS Technology: Taking Pull-Up as an Example
    GONG Bin(巩斌), YU Xianghua(禹香华), FANG Yu (方宇), WANG Zheng (王正), YANG Hao (杨皓), CHEN Guodong (陈国栋), L Ü Na (吕娜)
    J Shanghai Jiaotong Univ Sci    2022, 27 (2): 219-225.   DOI: 10.1007/s12204-021-2367-4
    Abstract202)      PDF (1855KB)(78)      
    Pull-up, as an important physical fitness test event of the “National Student Physical Health Standard”, is known as a difficult physical fitness test event. To improve the assessment criteria of pull-ups, this paper uses the functional near-infrared spectroscopy (fNIRS) to monitor the changes and activation of oxyhemoglobin (HbO) signals in the brain motor cortex of people with different body mass indexes (BMIs) during the pullup assessment. Then the relationship between BMIs and evaluation criteria is discussed. After collecting and analyzing experimental data of 18 recruited college students, it is found that the number of pull-ups performed by people with different BMIs is different when they reach the peak state of brain activation. The results of the study indicate that different assessment criteria should be adopted for different BMI groups. It is suggested that the BMI should be introduced as one of the test indexes in the examination of pull-ups event in “National Student Physical Health Standard”.
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    Lightweight Method for Vehicle Re-identification Using Reranking Algorithm Based on Topology Information of Surveillance Network
    ZOU Yue (邹 悦), LI Lin (李 霖), YANG Xubo (杨旭波)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 577-586.   DOI: 10.1007/s12204-021-2347-8
    Abstract235)      PDF (1353KB)(78)      
    As an emerging visual task, vehicle re-identification refers to the identification of the same vehicle across multiple cameras. Herein, we propose a novel vehicle re-identification method that uses an improved ResNet-50 architecture and utilizes the topology information of a surveillance network to rerank the final results. In the training stage, we apply several data augmentation approaches to expand our training data and increase their diversity in a cost-effective manner. We reform the original RestNet-50 architecture by adding non-local blocks to implement the attention mechanism and replacing part of the batch normalization operations with instance batch normalization. After obtaining preliminary results from the proposed model, we use the reranking algorithm, whose core function is to improve the similarity scores of all images on the most likely path that the vehicle tends to appear to optimize the final results. Compared with most existing state-of-the-art methods, our method is lighter, requires less data annotation, and offers competitive performance.
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    Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin
    LI Lin (李 霖), HU Zeyu(胡泽宇), YANG Xubo (杨旭波)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 587-597.   DOI: 10.1007/s12204-021-2348-7
    Abstract260)      PDF (2627KB)(77)      
    Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field, mainly due to the wide variety of anomaly cases and the complexity of surveillance videos. In this study, a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed. First, detecting vehicles based on deep learning is implemented, and Kalman filtering and feature matching are used to track vehicles. Subsequently, the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine, and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene. The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis. The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems. In addition, the implementation and analysis process show the usability, generalization, and effectiveness of the proposed framework.
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    Risk Prediction Model of Gallbladder Disease in Shanghai Middle-Aged and Elderly People Based on Neural Networks
    YUAN Xiaoqi (袁筱祺), ZHU Lelan (朱乐兰), XU Qiongfan(徐琼凡), GAO Wei (高玮)
    J Shanghai Jiaotong Univ Sci    2022, 27 (2): 153-159.   DOI: 10.1007/s12204-021-2386-1
    Abstract247)      PDF (345KB)(77)      
    This paper discusses the risk factors related to gallbladder disease in Shanghai, improves the accuracy of risk prediction, and provides a theoretical basis for scientific diagnosis and universality of gallbladder disease.We selected 3 462 data of middle-aged and elderly health check-up patients in a general hospital in Shanghai,and divided into gallbladder disease group according to color doppler ultrasound diagnosis results. Single-factor analysis screened out 8 important risk factors, which were used as an analysis variable of multi-layer perceptron neural network and binary logistic regression to construct the prediction model of gallbladder disease. The prediction accuracy of the multi-layer perceptron neural network risk prediction model is 76%. The area under the receiver operating characteristic curve (AUC) is 0.82, the maximum Youden index is 0.44, the sensitivity is 79.51, and the specificity is 64.23. The prediction accuracy of the multi-layer perceptron neural network model is better than that of the binary logistic regression prediction model. The overall prediction accuracy of the binary logistic regression prediction model is 75.60%, the AUC is 0.81, the maximum Youden index is 0.42, the sensitivity is 74.48, and the specificity is 57.60. In the objective risk prediction of gallbladder disease in middle-aged and elderly people in Shanghai, the risk prediction model based on the multi-layer perceptron neural network has a better prediction performance than the binary logistic regression model, which provides a theoretical basis for preventive treatment and intervention.
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    SeRN: A Two-Stage Framework of Registration for Semi-Supervised Learning for Medical Images
    JIA Dengqiang* (贾灯强), LUO Xinzhe (罗鑫喆), DING Wangbin (丁王斌),HUANG Liqin (黄立勤), ZHUANG Xiahai (庄吓海)
    J Shanghai Jiaotong Univ Sci    2022, 27 (2): 176-189.   DOI: 10.1007/s12204-021-2383-4
    Abstract169)      PDF (2406KB)(75)      
    Significant breakthroughs in medical image registration have been achieved using deep neural networks (DNNs). However, DNN-based end-to-end registration methods often require large quantities of data or adequate annotations for training. To leverage the intensity information of abundant unlabeled images, unsupervised registration methods commonly employ intensity-based similarity measures to optimize the network parameters.However, finding a sufficiently robust measure can be challenging for specific registration applications. Weakly supervised registration methods use anatomical labels to estimate the deformation between images. High-level structural information in label images is more reliable and practical for estimating the voxel correspondence of anatomic regions of interest between images, whereas label images are extremely difficult to collect. In this paper, we propose a two-stage semi-supervised learning framework for medical image registration, which consists of unsupervised and weakly supervised registration networks. The proposed semi-supervised learning framework is trained with intensity information from available images, label information from a relatively small number of labeled images and pseudo-label information from unlabeled images. Experimental results on two datasets (cardiac and abdominal images) demonstrate the efficacy and efficiency of this method in intra- and inter-modality medical image registrations, as well as its superior performance when a vast amount of unlabeled data and a small set of annotations are available. Our code is publicly available at
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    Dynamic Obstacle Avoidance for Application of Human-Robot Cooperative Dispensing Medicines
    WANG Zheng (王正), XU Hui (许辉), L v Na (吕娜), TAO Wei∗ (陶卫), CHEN Guodong (陈国栋), CHI Wenzheng (迟文正), SUN Lining (孙立宁)
    J Shanghai Jiaotong Univ Sci    2022, 27 (1): 24-35.   DOI: 10.1007/s12204-021-2366-5
    Abstract312)      PDF (2378KB)(73)      
    For safety reasons, in the automated dispensing medicines process, robots and humans cooperate to accomplish the task of drug sorting and distribution. In this dynamic unstructured environment, such as a humanrobot collaboration scenario, the safety of human, robot, and equipment in the environment is paramount. In this work, a practical and effective robot motion planning method is proposed for dynamic unstructured environments. To figure out the problems of blind zones of single depth sensor and dynamic obstacle avoidance, we first propose a method for establishing offline mapping and online fusion of multi-sensor depth images and 3D grids of the robot workspace, which is used to determine the occupation states of the 3D grids occluded by robots and obstacles and to conduct real-time estimation of the minimum distance between the robot and obstacles. Then, based on the reactive control method, the attractive and repulsive forces are calculated and transformed into robot joint velocities to avoid obstacles in real time. Finally, the robot’s dynamic obstacle avoidance ability is evaluated on an experimental platform with a UR5 robot and two KinectV2 RGB-D sensors, and the effectiveness of the proposed method is verified.
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    Teleoperated Puncture Robot System: Preliminary Design and Workspace Analysis
    HU Bo (胡博), LIN Yanping∗ (林艳萍), CHEN Shihang (陈士行), WANG Fang (汪方), MA Xiaojun (马小军), CAO Qixin (曹其新)
    J Shanghai Jiaotong Univ Sci    2022, 27 (1): 15-23.   DOI: 10.1007/s12204-021-2368-3
    Abstract330)      PDF (1455KB)(69)      
    Radiofrequency ablation (RFA) guided by X-ray images aims to relieve herniated disc pain with minimal invasiveness and fast recovery. It requires an accurate and fast positioning of the puncture needle. We propose a teleoperated robotic system for percutaneous puncture to support RFA. We report the kinematics modelling and workspace analysis of the proposed system, which comprises preliminary and accurate positioning mechanisms. Preliminary positioning mechanism automatically drives the needle to the puncture area, and accurate positioning is then achieved by teleoperation under the guidance of X-ray images. We calculate the teleoperation workspace of the robot system using a spatial search algorithm and quantitatively analyze the optimal structural parameters aiming to maximize the workspace. The workspace of the proposed robot system complies with clinical requirements to support RFA.
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    Action-aware Encoder-Decoder Network for Pedestrian Trajectory Prediction
    FU Jiawei∗ (傅家威), ZHAO Xu (赵 旭)
    J Shanghai Jiaotong Univ Sci    2023, 28 (1): 20-27.   DOI: 10.1007/s12204-023-2565-3
    Abstract209)      PDF (775KB)(69)      
    Accurate pedestrian trajectory predictions are critical in self-driving systems, as they are fundamental to the response- and decision-making of ego vehicles. In this study, we focus on the problem of predicting the future trajectory of pedestrians from a first-person perspective. Most existing trajectory prediction methods from the first-person view copy the bird’s-eye view, neglecting the differences between the two. To this end, we clarify the differences between the two views and highlight the importance of action-aware trajectory prediction in the first-person view. We propose a new action-aware network based on an encoder-decoder framework with an action prediction and a goal estimation branch at the end of the encoder. In the decoder part, bidirectional long short-term memory (Bi-LSTM) blocks are adopted to generate the ultimate prediction of pedestrians’ future trajectories. Our method was evaluated on a public dataset and achieved a competitive performance, compared with other approaches. An ablation study demonstrates the effectiveness of the action prediction branch.
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    Stochastic Model Predictive Control Approach to Autonomous Vehicle Lane Keeping
    ZHANG Chenzhi (张晨之), ZHUANG Cheng (庄 诚), ZHENG Xueke (郑学科), CAI Runze (蔡润泽), LI Mian (李 冕)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 626-633.   DOI: 10.1007/s12204-021-2352-y
    Abstract256)      PDF (702KB)(68)      
    In real-world scenarios, the uncertainty of measurements cannot be handled effciently by traditional model predictive control (MPC). A stochastic MPC (SMPC) method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper. A probabilistic system is constructed by considering the variance of states. The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps. First, the cost function is separated into mean and variance components. The mean component is calculated online, whereas the variance component can be calculated offline. Second, Cantelli’s inequality is adopted for the deterministic reformulation of constraints. Consequently, the original probabilistic problem is transformed into a quadratic programming problem. To validate the feasibility and effectiveness of the proposed control method, we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario. The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.
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    Cooperative Adaptive Cruise Control Using Delay-Based Spacing Policy: A Robust Adaptive Non-Singular Terminal Sliding Mode Approach
    WANG Weiyang (王维旸), CUI Ke (崔 科), GU Lizhong(顾立忠), LU¨ Xinjun (吕新军)
    J Shanghai Jiaotong Univ Sci    2021, 26 (5): 634-646.   DOI: 10.1007/s12204-021-2353-x
    Abstract228)      PDF (1152KB)(68)      
    This study proposes two speed controllers based on a robust adaptive non-singular terminal sliding mode control approach for the cooperative adaptive cruise control problem in a connected and automated vehicular platoon. The delay-based spacing policy is adopted to guarantee that all vehicles in the platoon track the same target velocity profile at the same position while maintaining a predefined time gap. Factors such as nonlinear vehicle longitudinal dynamics, engine dynamics with time delay, undulating road profiles, parameter uncertainties, and external disturbances are considered in the system modeling and controller design. Different control objectives are assigned to the leading and following vehicles. Then, controllers consisting of a sliding mode controller with parameter adaptive laws based on the ego vehicle’s state deviation and linear coupled state errors, and a Smith predictor for time delay compensation are designed. Both inner stability and strong string stability are guaranteed in the case of nonlinear sliding manifolds. Finally, the effectiveness of the proposed controllers and the benefits of 44.73% shorter stabilization time, 11.20% less speed overshoot, and virtually zero steady-state inner vehicle distance deviation are illustrated in a simulation study of a seven-vehicle platoon cooperative adaptive cruise control and comparison experiments with a coupled sliding mode control approach.
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    Interactive Liver Segmentation Algorithm Based on Geodesic Distance and V-Net
    KANG Jie* (亢洁), DING Jumin (丁菊敏), LEI Tao (雷涛),FENG Shujie (冯树杰), LIU Gang (刘港)
    J Shanghai Jiaotong Univ Sci    2022, 27 (2): 190-201.   DOI: 10.1007/s12204-021-2379-0
    Abstract201)      PDF (3921KB)(65)      
    Convolutional neural networks (CNNs) are prone to mis-segmenting image data of the liver when the background is complicated, which results in low segmentation accuracy and unsuitable results for clinical use. To address this shortcoming, an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed. The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results. An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation, which significantly improves the efficiency of segmentation. Results from simulation of the liver tumor segmentation challenge (LiTS) dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention, and enable a fast, interactive liver image segmentation that is convenient for doctors.
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