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Review of Power-Assisted Lower Limb Exoskeleton Robot HE Guisong (贺贵松), HUANG Xuegong (黄学功), LI Feng (李峰), WANG Huixing (汪辉兴) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 1-15.   DOI: 10.1007/s12204-022-2489-3 Abstract （1575）      PDF（pc） （1195KB）（662）       Knowledge map    Save Power-assisted lower limb exoskeleton robot is a wearable intelligent robot system involving mechanics,materials, electronics, control, robotics, and many other fields. The system can use external energy to provide additional power to humans, enhance the function of the human body, and help the wearer to bear weight that is previously unbearable. At the same time, employing reasonable structure design and passive energy storage can also assist in specific actions. First, this paper introduces the research status of power-assisted lower limb exoskeleton robots at home and abroad, and analyzes several typical prototypes in detail. Then, the key technologies such as structure design, driving mode, sensing technology, control method, energy management, and human-machine coupling are summarized, and some common design methods of the exoskeleton robot are summarized and compared. Finally, the existing problems and possible solutions in the research of power-assisted lower limb exoskeleton robots are summarized, and the prospect of future development trend has been analyzed. Reference | Related Articles | Metrics | Comments（0）

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Review of Key Technologies for Developing Personalized Lower Limb Rehabilitative Exoskeleton Robots TAO Jing, (陶璟), ZHOU Zhenhuan (周振欢) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 16-28.   DOI: 10.1007/s12204-022-2452-3 Abstract （1544）      PDF（pc） （1179KB）（2084）       Knowledge map    Save Rehabilitative training and assistance to daily living activities play critical roles in improving the life quality of lower limb dyskinesia patients and older people with motor function degeneration. Lower limb rehabilitative exoskeleton has a promising application prospect in support of the above population. In this paper, critical technologies for developing lower limb rehabilitative exoskeleton for individualized user needs are identi- fied and reviewed, including exoskeleton hardware modularization, bionic compliant driving, individualized gait planning and individual-oriented motion intention recognition. Inspired by the idea of servitization, potentials in exoskeleton product-service system design and its enabling technologies are then discussed. It is suggested that future research will focus on exoskeleton technology and exoskeleton-based service development oriented to an individual’s physical features and personalized requirements to realize better human-exoskeleton coordination in terms of technology, as well as accessible and high-quality rehabilitation and living assistance in terms of utility. Reference | Related Articles | Metrics | Comments（0）

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Multi-Agent Path Planning Method Based on Improved Deep Q-Network in Dynamic Environments LI Shuyi (李舒逸), LI Minzhe (李旻哲), JING Zhongliang∗ (敬忠良) J Shanghai Jiaotong Univ Sci    2024, 29 (4): 601-612.   DOI: 10.1007/s12204-024-2732-1 Abstract （1301）      PDF（pc） （1213KB）（583）       Knowledge map    Save The multi-agent path planning problem presents significant challenges in dynamic environments, primarily due to the ever-changing positions of obstacles and the complex interactions between agents’ actions. These factors contribute to a tendency for the solution to converge slowly, and in some cases, diverge altogether. In addressing this issue, this paper introduces a novel approach utilizing a double dueling deep Q-network (D3QN), tailored for dynamic multi-agent environments. A novel reward function based on multi-agent positional constraints is designed, and a training strategy based on incremental learning is performed to achieve collaborative path planning of multiple agents. Moreover, the greedy and Boltzmann probability selection policy is introduced for action selection and avoiding convergence to local extremum. To match radar and image sensors, a convolutional neural network - long short-term memory (CNN-LSTM) architecture is constructed to extract the feature of multi-source measurement as the input of the D3QN. The algorithm’s efficacy and reliability are validated in a simulated environment, utilizing robot operating system and Gazebo. The simulation results show that the proposed algorithm provides a real-time solution for path planning tasks in dynamic scenarios. In terms of the average success rate and accuracy, the proposed method is superior to other deep learning algorithms, and the convergence speed is also improved. Reference | Related Articles | Metrics | Comments（0）

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Multi-Robot Task Allocation Using Multimodal Multi-Objective Evolutionary Algorithm Based on Deep Reinforcement Learning MIAO Zhenhua(苗镇华), HUANG Wentao(黄文焘), ZHANG Yilian(张依恋), FAN Qinqin(范勤勤) J Shanghai Jiaotong Univ Sci    2024, 29 (3): 377-387.   DOI: 10.1007/s12204-023-2679-7 Abstract （1272）      PDF（pc） （975KB）（433）       Knowledge map    Save The overall performance of multi-robot collaborative systems is significantly affected by the multirobot task allocation. To improve the effectiveness, robustness, and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper. The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allocation problems. Moreover, a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner. Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm. The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multirobot collaborative systems in uncertain environments, and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems. Reference | Related Articles | Metrics | Comments（0）

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Video-Based Detection of Epileptic Spasms in IESS: Modeling, Detection, and Evaluation DING Lihui1, 2(丁黎辉), FU Lijun1, 3 (付立军), YANG Guang4(杨光), WAN Lin4, 5 (万林), CHANG Zhijun7(常志军) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 1-9.   DOI: 10.1007/s12204-024-2789-x Abstract （837）      PDF（pc） （712KB）（125）       Knowledge map    Save Behavioral scoring based on clinical observations remains the gold standard for screening, diagnosing,and evaluating infantile epileptic spasm syndrome (IESS). The accurate identification of seizures is crucial for clinical diagnosis and assessment. In this study, we propose an innovative seizure detection method based on video feature recognition of patient spasms. To capture the temporal characteristics of the spasm behavior presented in the videos effectively, we incorporate asymmetric convolution and convolution–batch normalization–ReLU (CBR) modules. Specifically within the 3D-ResNet residual blocks, we split the larger convolutional kernels into two asymmetric 3D convolutional kernels. These kernels are connected in series to enhance the ability of the convolutional layers to extract key local features, both horizontally and vertically. In addition, we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels efficiently. To improve the generalization ability, we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity requirements. We train and evaluate our method using the PLA IESS-VIDEO dataset, achieving an average seizure recognition accuracy of 90.59%, precision of 90.94%, and recall of 87.64%. To validate its generalization capability further, we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical centers. The final test results demonstrate that our method achieved a recall of 0.647 6, surpassing the average level achieved by human experts (0.559 5), while attaining a high F1-score of 0.721 9. These findings have substantial significance for the long-term assessment of patients with IESS. Reference | Supplementary Material | Related Articles | Metrics | Comments（0）

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot ZHAO Yanfei1，2，3(赵艳飞), XIAO Peng4 (肖鹏), WANG Jingchuan1，2，3* (王景川), GUO Rui4*(郭锐） J Shanghai Jiaotong Univ Sci    2025, 30 (1): 27-33.   DOI: 10.1007/s12204-023-2678-8 Abstract （791）      PDF（pc） （996KB）（94）       Knowledge map    Save Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties. This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots, which can assist users to implement accurate navigation (e.g., docking) in the environment without prior maps. In order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms, this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic goals. At last, comparative experiments were carried out in the real environment. Results show that our method is superior in terms of safety, comfort and docking accuracy. Reference | Related Articles | Metrics | Comments（0）

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Review on Anti-Frost Technology Based on Microchannel Heat Exchanger YE Zhenhong(叶振鸿), WANG Wei(王炜), LI Xinhua(李新华), CHEN Jiangping(陈江平) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 161-178.   DOI: 10.1007/s12204-022-2539-x Abstract （632）      PDF（pc） （4397KB）（879）       Knowledge map    Save Frosting is an inevitable adverse phenomenon in many fields such as industrial refrigeration, cryogenics, and heat pump air conditioning, which may influence the efficiency of the equipment and increase the energy consumption of the system. The complicated louvered-fin structure and fluid-channels arrangements of the microchannel heat exchanger (HEX) will affect the heat transfer performance and frosting characteristics. First, this article analyzes different factors such as refrigerant distribution, refrigerant flow pattern, and HEX surface temperature distribution. Further, combined with the features of the microchannel HEX, the existing anti-frosting technologies and various methods of surface treatment for anti-frosting are summarized. The review focuses on the preparation of superhydrophobic surfaces and their superior properties. Furthermore, the internal mechanism is analyzed in conjunction with the relevant research of our group. Superhydrophobic character has excellent anti-frosting performance and heat transfer performance, which is of great significance for improving energy-saving and system performance. Finally, the future development of superhydrophobic surface technology is analyzed and prospected. Reference | Related Articles | Metrics | Comments（0）

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Unsupervised Oral Endoscope Image Stitching Algorithm HUANG Rong (黄荣), CHANG Qing∗ (常青), ZHANG Yang （张扬） J Shanghai Jiaotong Univ Sci    2024, 29 (1): 81-90.   DOI: 10.1007/s12204-022-2513-7 Abstract （631）      PDF（pc） （5774KB）（194）       Knowledge map    Save Oral endoscope image stitching algorithm is studied to obtain wide-field oral images through registration and stitching, which is of great significance for auxiliary diagnosis. Compared with natural images, oral images have lower textures and fewer features. However, traditional feature-based image stitching methods rely heavily on feature extraction quality, often showing an unsatisfactory performance when stitching images with few features. Moreover, due to the hand-held shooting, there are large depth and perspective disparities between the captured images, which also pose a challenge to image stitching. To overcome the above problems, we propose an unsupervised oral endoscope image stitching algorithm based on the extraction of overlapping regions and the loss of deep features. In the registration stage, we extract the overlapping region of the input images by sketching polygon intersection for feature points screening and estimate homography from coarse to fine on a three-layer feature pyramid structure. Moreover, we calculate loss using deep features instead of pixel values to emphasize the importance of depth disparities in homography estimation. Finally, we reconstruct the stitched images from feature to pixel, which can eliminate artifacts caused by large parallax. Our method is compared with both feature-based and previous deep-based methods on the UDIS-D dataset and our oral endoscopy image dataset. The experimental results show that our algorithm can achieve higher homography estimation accuracy, and better visual quality, and can be effectively applied to oral endoscope image stitching. Reference | Related Articles | Metrics | Comments（0）

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Applications of Artificial Intelligence in Cardiac Electrophysiology and Clinical Diagnosis with Magnetic Resonance Imaging and Computational Modeling Techniques ZHAN Heqing1 (詹何庆), HAN Guilai1 (韩贵来), WEI Chuan’an1 (魏传安), LI Zhiqun2* (李治群) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 53-65.   DOI: 10.1007/s12204-023-2628-5 Abstract （599）      PDF（pc） （233KB）（89）       Knowledge map    Save The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases, which are the most common cause of morbidity and mortality worldwide, have gotten a lot of attention and been widely explored in recent decades. Along the way, techniques such as medical imaging, computing modeling, and artificial intelligence (AI) have always played significant roles in above studies. In this article, we illustrated the applications of AI in cardiac electrophysiological research and disease prediction. We summarized general principles of AI and then focused on the roles of AI in cardiac basic and clinical studies incorporating magnetic resonance imaging and computing modeling techniques. The main challenges and perspectives were also analyzed. Reference | Related Articles | Metrics | Comments（0）

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Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction CHEN Kun(陈坤), ZHAO Xu(赵旭), DONG Chunyu(董春玉), DI Zichao(邸子超), CHEN Zongzhi(陈宗枝) J Shanghai Jiaotong Univ Sci    2024, 29 (3): 400-413.   DOI: 10.1007/s12204-022-2484-8 Abstract （564）      PDF（pc） （5510KB）（207）       Knowledge map    Save Visual object tracking is an important issue that has received long-term attention in computer vision.The ability to effectively handle occlusion, especially severe occlusion, is an important aspect of evaluating theperformance of object tracking algorithms in long-term tracking, and is of great significance to improving therobustness of object tracking algorithms. However, most object tracking algorithms lack a processing mechanism specifically for occlusion. In the case of occlusion, due to the lack of target information, it is necessary to predict the target position based on the motion trajectory. Kalman filtering and particle filtering can effectively predict the target motion state based on the historical motion information. A single object tracking method, called probabilistic discriminative model prediction (PrDiMP), is based on the spatial attention mechanism in complex scenes and occlusions. In order to improve the performance of PrDiMP, Kalman filtering, particle filtering and linear filtering are introduced. First, for the occlusion situation, Kalman filtering and particle filtering are respectively introduced to predict the object position, thereby replacing the detection result of the original tracking algorithm and stopping recursion of target model. Second, for detection-jump problem of similar objects in complex scenes, a linear filtering window is added. The evaluation results on the three datasets, including GOT-10k, UAV123 and LaSOT, and the visualization results on several videos, show that our algorithms have improved tracking performance under occlusion and the detection-jump is effectively suppressed. Reference | Related Articles | Metrics | Comments（0）

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Psychological Impact of the 2022 Round COVID-19 Pandemic on China’s College Students HONG Dongyang1,3 (洪冬羊), WANG Jinxia2,3 (王金霞), ZHANG Hongyang2,3 (张虹洋), CAO Ziyang2,3 (曹紫阳), YAN Zijun 2,3 (晏紫君), ZOU Lin2,3∗ (邹琳) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 141-149.   DOI: 10.1007/s12204-022-2557-8 Abstract （560）      PDF（pc） （194KB）（258）       Knowledge map    Save In response to the new round of COVID-19 outbreaks since March 2022, universities with high outbreak rates around the country have taken quarantine measures to contain the epidemic. Evidence from previous coronavirus outbreaks has shown that people under quarantine are at risk for mental health disorders. To better understand the impacts of this round of COVID-19 quarantine on domestic college students and their responses, we conducted a systematic survey to assess the stress and anxiety, and to evaluate effective measurements in this population. We searched relevant documents and literature, and designed a questionnaire from six aspects, including psychological status, epidemic situation, study, daily life, sports, and interpersonal communication, with 51 items in total. We sent the questionnaire on the Wenjuanxing Web platform, from April 2 to 8, 2022. We evaluated the mental status according to parts of the Generalized Anxiety Disorder-7 (GAD-7) and Depression Anxiety Stress Scales-21 (DASS-21), and investigated the influencing risk factors and countermeasures. Statistical analysis was performed by using the Chi-square test and multi-variable logistic regression. In total, 508 college respondents were recruited in our survey, and the pooled prevalence of mild anxiety (GAD score  5, or DASS-21 anxiety score 8) or stress (DASS-21 pressure score 14) caused by the new round of COVID-19 pandemic quarantine was 19.69% (100/508). The prevalence of the anxiety or stress in college students with COVID-19 quarantine between different genders, regions, and majors was not significantly different. Independent risk factors for the mild anxiety or stress of undergraduates by COVID-19 quarantine included learning efficiency or duration [OR = 1.36, 95%CI (1.14—1.62), P = 0.001], based on the combined analysis of Chi-square test analysis with multi-variable logistic regression analysis. Interestingly, the mental well-beings before COVID-19 epidemic quarantine [OR = 0.22, 95%CI (0.13—0.36), P < 0.000 1], more low-intensity exercise [OR = 0.36, 95%CI (0.15—0.87), P = 0.02, high-intensity exercise as reference], and good sleep quality [OR = 0.14, 95%CI (0.07—0.30), P < 0.000 1: OR = 0.42, 95%CI (0.30—0.59), P < 0.000 1] are protective factors for alleviating the quarantinecaused anxiety or stress in Chinese college students for this round of COVID-19 epidemic quarantine. During the round of COVID-19 epidemic quarantine in 2022, a small number of college students have mild anxiety, affected by decreased learning efficiency or duration, which could be mitigated with low-intensity exercise and good sleep quality. Reference | Related Articles | Metrics | Comments（0）

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Time-Resolved Imaging in Short-Wave Infrared Region XU Yang (徐杨), LI Wanwan∗ (李万万) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 29-36.   DOI: 10.1007/s12204-022-2547-x Abstract （550）      PDF（pc） （810KB）（175）       Knowledge map    Save Compared with the conventional first near-infrared (NIR-I, 700—900 nm) window, the short-wave infrared region (SWIR, 900—1 700 nm) possesses the merits of the increasing tissue penetration depths and the suppression of scattering background, leading to great potential for in vivo imaging. Based on the limitations of the common spectral domain, and the superiority of the time-dimension, time-resolved imaging eliminates the auto-fluorescence in the biological tissue, thus supporting higher signal-to-noise ratio and sensitivities. The imaging technique is not affected by the difference in tissue composition or thickness and has the practical value of quantitative in vivo detection. Almost all the relevant time-resolved imaging was carried out around lanthanide-doped upconversion nanomaterials, owing to the advantages of ultralong luminescence lifetime, excellent photostability, controllable morphology, easy surface modification and various strategies of regulating lifetime. Therefore, this review presents the research progress of SWIR time-resolved imaging technology based on nanomaterials doped with lanthanide ions as luminescence centers in recent years. Reference | Related Articles | Metrics | Comments（0）

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Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network LONARE Savita1，2* , BHRAMARAMBA Ravi2 J Shanghai Jiaotong Univ Sci    2024, 29 (3): 509-517.   DOI: 10.1007/s12204-021-2382-5 Abstract （544）      PDF（pc） （525KB）（123）       Knowledge map    Save Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models. However, data privacy and security are always a challenge in every field where data need to be uploaded to the cloud. Federated learning (FL) is an emerging trend for distributed training of data. The primary goal of FL is to train an efficient communication model without compromising data privacy. The traffic data have a robust spatio-temporal correlation, but various approaches proposed earlier have not considered spatial correlation of the traffic data. This paper presents FL-based traffic flow prediction with spatio-temporal correlation. This work uses a differential privacy (DP) scheme for privacy preservation of participant’s data. To the best of our knowledge, this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation. The proposed framework trains the data locally at the client-side with DP. It then uses the model aggregation mechanism federated graph convolutional network (FedGCN) at the server-side to find the average of locally trained models. The results of the proposed work show that the FedGCN model accurately predicts the traffic. DP scheme at client-side helps clients to set a budget for privacy loss. Reference | Related Articles | Metrics | Comments（0）

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Significant Retest Effects in Spatial Working Memory Task MA Xianda1,2,3‡ (马显达), LAN Zhaohui1,2,3‡ (兰兆辉),CHEN Zhitang1,2,3 (陈志堂), MONISHA M L4, HE Xinyi1,2,3 (何欣怡), LI Weidong1,2,3* (李卫东) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 115-120.   DOI: 10.1007/s12204-023-2585-z Abstract （543）      PDF（pc） （567KB）（61）       Knowledge map    Save Working memory is a core cognitive function that supports goal-directed behavior and complex thought. We developed a spatial working memory and attention test on paired symbols (SWAPS) which has been proved to be a useful and valid tool for spatial working memory and attention studies in the fields of cognitive psychology, education, and psychiatry. The repeated administration of working memory capacity tests is common in clinical and research settings. Studies suggest that repeated cognitive tests may improve the performance scores also known as retest effects. The systematic investigation of retest effects in SWAPS is critical for interpreting scientific results, but it is still not fully developed. To address this, we recruited 77 college students aged 18—21 years and used SWAPS comprising 72 trials with different memory loads, learning time, and delay span. We repeated the test once a week for five weeks to investigate the retest effects of SWAPS. There were significant retest effects in the first two tests: the accuracy of the SWAPS tests significantly increased, and then stabilized. These findings provide useful information for researchers to appropriately use or interpret the repeated working memory tests. Further experiments are still needed to clarify the factors that mediate the retest effects, and find out the cognitive mechanism that influences the retest effects. Reference | Related Articles | Metrics | Comments（0）

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Transfer Learning in Motor Imagery Brain Computer Interface: A Review LI Mingai1,2,3∗ (李明爱), XU Dongqin1 (许东芹) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 37-59.   DOI: 10.1007/s12204-022-2488-4 Abstract （532）      PDF（pc） （1734KB）（638）       Knowledge map    Save Transfer learning, as a new machine learning methodology, may solve problems in related but different domains by using existing knowledge, and it is often applied to transfer training data from another domain for model training in the case of insufficient training data. In recent years, an increasing number of researchers who engage in brain-computer interface (BCI), have focused on using transfer learning to make most of the available electroencephalogram data from different subjects, effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model. This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI. In addition, according to the “what to transfer” question in transfer learning, this review is organized into three contexts: instance-based transfer learning, parameter-based transfer learning, and feature-based transfer learning. Furthermore, the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods, datasets, evaluation performance, etc. At the end of the paper, the questions to be solved in future research are put forward, laying the foundation for the popularization and in-depth research of transfer learning in BCI. Reference | Related Articles | Metrics | Comments（0）

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A Novel Cable-Driven Soft Robot for Surgery LI Ru1 (李茹), CHEN Fang2 (陈方), YU Wenwei3 (俞文伟), IGARASH Tatsuo3,4, SHU Xiongpeng1 (舒雄鹏), XIE Le1,5,6∗ (谢叻) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 60-72.   DOI: 10.1007/s12204-022-2497-3 Abstract （531）      PDF（pc） （2939KB）（774）       Knowledge map    Save Robot-assisted laparoscopic radical prostatectomy (RARP) is widely used to treat prostate cancer. The rigid instruments primarily used in RARP cannot overcome the problem of blind areas in surgery and lead to more trauma such as more incision for the passage of the instrument and additional tissue damage caused by rigid instruments. Soft robots are relatively flexible and theoretically have infinite degrees of freedom which can overcome the problem of the rigid instrument. A soft robot system for single-port transvesical robot-assisted radical prostatectomy (STvRARP) is developed in this study. The soft manipulator with 10 mm in diameter and a maximum bending angle of 270? has good flexibility and dexterity. The design and mechanical structure of the soft robot are described. The kinematics of the soft manipulator is established and the inverse kinematics is compensated based on the characteristics of the designed soft manipulator. The master-slave control system of soft robot for surgery is built and the feasibility of the designed soft robot is verified. Reference | Related Articles | Metrics | Comments（0）

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Ship Pipe Layout Optimization Based on Improved Particle Swarm Optimization LIN Yan1, 2(林焰), BIAN Xuanyi1(卞璇屹), DONG Zongran3(董宗然) J Shanghai Jiaotong Univ Sci    2024, 29 (5): 737-746.   DOI: 10.1007/s12204-022-2530-6 Abstract （522）      PDF（pc） （1456KB）（207）       Knowledge map    Save Ship pipe layout optimization is one of the difficulties and hot spots in ship intelligent production design. A high-dimensional vector coding is proposed based on the research of related pipe coding and ship pipe route features in this paper. The advantages of this coding method are concise structure, strong compatibility, and independence from the gridding space. Based on the proposed coding, the particle swarm optimization algorithm is implemented, and the algorithm is improved by the pre-selected path strategy and the branch-pipe processing strategy. Finally, two simulation results reveal that the proposed coding and algorithm have feasibility and engineering practicability. Reference | Related Articles | Metrics | Comments（0）

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Augmented Reality Navigation Using Surgical Guides Versus Conventional Techniques in Pedicle Screw Placement KONG Huiyang1 (孔会扬), WANG Shuyi1 (王殊轶), ZHANG Can2 (张璨), CHEN Zan2, 3 (陈赞) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 10-17.   DOI: 10.1007/s12204-023-2689-5 Abstract （519）      PDF（pc） （1106KB）（152）       Knowledge map    Save The aim of this study was to assess the potential of surgical guides as a complementary tool to augmented reality (AR) in enhancing the safety and precision of pedicle screw placement in spinal surgery. Four trainers were divided into the AR navigation group using surgical guides and the free-hand group. Each group consisted of a novice and an experienced spine surgeon. A total of 80 pedicle screws were implanted. First, the AR group reconstructed the 3D model and planned the screw insertion route according to the computed tomography data of L2 lumbar vertebrae. Then, the Microsoft HoloLensTM 2 was used to identify the vertebral model, and the planned virtual path was superimposed on the real cone model. Next, the screw was placed according to the projected trajectory. Finally, Micron Tracker was used to measure the deviation of screws from the preoperatively planned trajectory, and pedicle screws were evaluated using the Gertzbein-Robbins scale. In the AR group, the linear deviations of the experienced doctor and the novice were (1.59±0.39) mm and (1.73±0.52) mm respectively, and the angle deviations were 2.72◦ ± 0.61◦ and 2.87◦ ± 0.63◦ respectively. In the free-hand group, the linear deviations of the experienced doctor and the novice were (2.88 ± 0.58) mm and (5.25 ± 0.62) mm respectively, and the angle deviations were 4.41◦ ± 1.18◦ and 7.15◦ ± 1.45◦ respectively. Both kinds of deviations between the two groups were significantly different (P < 0.05). The screw accuracy rate was 95% in the AR navigation group and 77.5% in the free-hand group. The results of this study indicate that the integration of surgical guides and AR is an innovative technique that can substantially enhance the safety and precision of spinal surgery and assist inexperienced doctors in completing the surgery. Reference | Related Articles | Metrics | Comments（0）

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Tree Detection Algorithm Based on Embedded YOLO Lightweight Network LV Feng(吕峰), WANG Xinyan* (王新彦), LI Lei(李磊), JIANG Quan(江泉), YI Zhengyang(易政洋) J Shanghai Jiaotong Univ Sci    2024, 29 (3): 518-527.   DOI: 10.1007/s12204-022-2451-4 Abstract （518）      PDF（pc） （2443KB）（129）       Knowledge map    Save To avoid colliding with trees during its operation, a lawn mower robot must detect the trees. Existing tree detection methods suffer from low detection accuracy (missed detection) and the lack of a lightweight model. In this study, a dataset of trees was constructed on the basis of a real lawn environment. According to the theory of channel incremental depthwise convolution and residual suppression, the Embedded-A module is proposed, which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model. According to residual fusion theory, the Embedded-B module is proposed, which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion. The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions. Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17% and 69.91% average precision values respectively for trunk and spherical tree, and 77.04% mean average precision value. The number of convolution parameters is 1.78 × 106, and the calculation amount is 3.85 billion float operations per second. The size of weight file is 7.11 MB, and the detection speed can reach 179 frame/s. This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots. Reference | Related Articles | Metrics | Comments（0）

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Electrocardiogram Signal Denoising Using Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform BALASUBRAMANIAN S1*, NARUKA Mahaveer Singh2, TEWARI Gaurav3 J Shanghai Jiaotong Univ Sci    2025, 30 (1): 66-80.   DOI: 10.1007/s12204-023-2591-1 Abstract （504）      PDF（pc） （1497KB）（73）       Knowledge map    Save Cardiovascular diseases are the world’s leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive noninvasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this article, swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms (EWTs). At first, the white Gaussian noise is added to the input ECG signal and then applied to the EWT. The ECG signals are denoised by the proposed adaptive hybrid filter. The honey badge optimization (HBO) algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters. The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian, electromyogram and electrode motion artifact noises. A comparison of the HBO approach with recursive least square-based adaptive filter, multichannel least means square, and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter. The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising. Reference | Related Articles | Metrics | Comments（0）

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Medical Image Encryption Based on Fisher-Yates Scrambling and Filter Diffusion HUANG Jiaxin (黄佳鑫), GUO Yali (郭亚丽), GAO Ruoyun (高若云),LI Shanshan∗ (李珊珊) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 136-152.   DOI: 10.1007/s12204-023-2618-7 Abstract （498）      PDF（pc） （8077KB）（85）       Knowledge map    Save A medical image encryption is proposed based on the Fisher-Yates scrambling, filter diffusion and S-box substitution. First, chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system, which is used for the scrambling, substitution and diffusion processes. The three-dimensional Fisher-Yates scrambling, S-box substitution and diffusion are employed for the first round of encryption. The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round. Then, three-dimensional filter is applied to diffusion for further useful information hiding. The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters. It improves resisting ability of plaintext attacks. The security analysis shows that the algorithm is effective and efficient. It can resist common attacks. In addition, the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research. Reference | Related Articles | Metrics | Comments（0）

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Biomechanical Analysis of Scoliosis Orthopedic Force Loading with Human Avoidance Effect ZHU Ye1 (朱晔), REN Dong1 (任东), ZHANG Shuang2 (张爽), CAO Qian3 (曹倩) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 187-196.   DOI: 10.1007/s12204-023-2620-0 Abstract （494）      PDF（pc） （1836KB）（59）       Knowledge map    Save Due to the lack of human avoidance analysis, the orthosis cannot accurately apply orthopedic force during orthopedic, resulting in poor orthopedic effect. Therefore, the relationship between the human body’s active avoidance ability and force application is studied to achieve accurate loading of orthopedic force. First, a high-precision scoliosis model was established based on computed tomography data, and the relationship between orthopedic force and Cobb angle was analyzed. Then 9 subjects were selected for avoidance ability test grouped by body mass index calculation, and the avoidance function of different groups was fitted. The avoidance function corrected the application of orthopedic forces. The results show that the optimal correction force calculated by the finite element method was 60 N. The obese group had the largest avoidance ability, followed by the standard group and the lean group. When the orthopedic force was 60 N, the Cobb angle was reduced from 33.77◦ to 20◦, the avoidance ability of the standard group at 50 N obtained from the avoidance function was 20.28% and 10.14 N was actively avoided. Therefore, when 50 N was applied, 60.14 N was actually generated, which can achieve the orthopedic effect of 60 N numerical simulation analysis. The avoidance effect can take the active factors of the human body into consideration in the orthopedic process, so as to achieve a more accurate application of orthopedic force, and provide data reference for clinicians in the orthopedic process. Reference | Related Articles | Metrics | Comments（0）

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Positional Information is a Strong Supervision for Volumetric Medical Image Segmentation ZHAO Yinjie1 (赵寅杰), HOU Runpingg1 (侯润萍), ZENG Wanqin2 (曾琬琴), QIN Yulei1 (秦玉磊), SHEN Tianle2 (沈天乐), XU Zhiyong2 (徐志勇), FU Xiaolong2* (傅小龙), SHEN Hongbin1* (沈红斌) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 121-129.   DOI: 10.1007/s12204-023-2614-y Abstract （493）      PDF（pc） （482KB）（62）       Knowledge map    Save Medical image segmentation is a crucial preliminary step for a number of downstream diagnosis tasks. As deep convolutional neural networks successfully promote the development of computer vision, it is possible to make medical image segmentation a semi-automatic procedure by applying deep convolutional neural networks to finding the contours of regions of interest that are then revised by radiologists. However, supervised learning necessitates large annotated data, which are difficult to acquire especially for medical images. Self-supervised learning is able to take advantage of unlabeled data and provide good initialization to be finetuned for downstream tasks with limited annotations. Considering that most self-supervised learning especially contrastive learning methods are tailored to natural image classification and entail expensive GPU resources, we propose a novel and simple pretext-based self-supervised learning method that exploits the value of positional information in volumetric medical images. Specifically, we regard spatial coordinates as pseudo labels and pretrain the model by predicting positions of randomly sampled 2D slices in volumetric medical images. Experiments on four semantic segmentation datasets demonstrate the superiority of our method over other self-supervised learning methods in both semisupervised learning and transfer learning settings. Codes are available at https://github.com/alienzyj/PPos. Reference | Related Articles | Metrics | Comments（0）

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Adjacent Segment Biomechanical Changes After Implantation of Cage Plus Plate or Zero-Profile Device in Different Segmental Anterior Cervical Discectomy and Fusion YE Peng (叶鹏), FU Rongchang∗ (富荣昌), WANG Zhaoyao (王召耀) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 166-174.   DOI: 10.1007/s12204-023-2633-8 Abstract （490）      PDF（pc） （930KB）（86）       Knowledge map    Save Cage plus plate (CP) and zero-profile (Zero-P) devices are widely used in anterior cervical discectomy and fusion (ACDF). This study aimed to compare adjacent segment biomechanical changes after ACDF when using Zero-P device and CP in different segments. First, complete C1—C7 cervical segments were constructed and validated. Meanwhile, four surgery models were developed by implanting the Zero-P device or CP into C4—C5 or C5—C6 segments based on the intact model. The segmental range of motion (ROM) and maximum value of the intradiscal pressure of the surgery models were compared with those of the intact model. The implantation of CP and Zero-P devices in C4—C5 segments decreased ROM by about 91.6% and 84.3%, respectively, and increased adjacent segment ROM by about 8.3% and 6.82%, respectively. The implantation of CP and Zero-P devices in C5—C6 segments decreased ROM by about 93.3% and 89.9%, respectively, while increasing adjacent segment ROM by about 4.9% and 4%, respectively. Furthermore, the implantation of CP and Zero-P devices increased the intradiscal pressure in the adjacent segments of C4—C5 segments by about 4.5% and 6.7%, respectively. The implantation of CP and Zero-P devices significantly increased the intradiscal pressure in the adjacent segments of C5—C6 by about 54.1% and 15.4%, respectively. In conclusion, CP and Zero-P fusion systems can significantly reduce the ROM of the fusion implant segment in ACDF while increasing the ROM and intradiscal pressure of adjacent segments. Results showed that Zero-P fusion system is the best choice for C5—C6 segmental ACDF. However, further studies are needed to select the most suitable cervical fusion system for C4—C5 segmental ACDF. Therefore, this study provides biomechanical recommendations for clinical surgery. Reference | Related Articles | Metrics | Comments（0）

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Performance and Optimization of Air Source Heat Pump Water Heater with Cyclic Heating LI Fan(李凡), LU Gaofeng(陆高锋), DING Yunxiao(丁云霄), ZHENG Chunyuan(郑春元), LI Bin(李斌), ZHAI Xiaoqiang(翟晓强) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 179-187.   DOI: 10.1007/s12204-022-2500-z Abstract （487）      PDF（pc） （1349KB）（136）       Knowledge map    Save A new type of microchannel condenser applied in the air source heat pump water heater (ASHPWH) with cyclic heating was proposed in this study. The operating performance of the ASHPWH was first tested. Then,the structure of the microchannel condenser was optimized with the implement of vortex generators. Finally, a numerical model of the ASHPWH was established and the optimized microchannel condenser was studied. The experimental results showed that the average coefficient of performance (COP) of the 1 HP (735 W) ASHPWH reached 3.48. In addition, the optimized microchannel condenser could be matched with a 3 HP (2 430 W) ASHPWH with an average heating capacity of 10.30 kW, and achieving an average COP of 4.24, 14.6% higher than the limit value in the national standard. Reference | Related Articles | Metrics | Comments（0）

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Reward Function Design Method for Long Episode Pursuit Tasks Under Polar Coordinate in Multi-Agent Reinforcement Learning DONG Yubo1 (董玉博), CUI Tao1 (崔涛), ZHOU Yufan1 (周禹帆), SONG Xun2 (宋勋), ZHU Yue2 (祝月), DONG Peng1∗ (董鹏) J Shanghai Jiaotong Univ Sci    2024, 29 (4): 646-655.   DOI: 10.1007/s12204-024-2713-4 Abstract （482）      PDF（pc） （567KB）（323）       Knowledge map    Save Multi-agent reinforcement learning has recently been applied to solve pursuit problems. However, it suffers from a large number of time steps per training episode, thus always struggling to converge effectively, resulting in low rewards and an inability for agents to learn strategies. This paper proposes a deep reinforcement learning (DRL) training method that employs an ensemble segmented multi-reward function design approach to address the convergence problem mentioned before. The ensemble reward function combines the advantages of two reward functions, which enhances the training effect of agents in long episode. Then, we eliminate the non-monotonic behavior in reward function introduced by the trigonometric functions in the traditional 2D polar coordinates observation representation. Experimental results demonstrate that this method outperforms the traditional single reward function mechanism in the pursuit scenario by enhancing agents’ policy scores of the task. These ideas offer a solution to the convergence challenges faced by DRL models in long episode pursuit problems, leading to an improved model training performance. Reference | Related Articles | Metrics | Comments（0）

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TshFNA-Examiner: A Nuclei Segmentation and Cancer Assessment Framework for Thyroid Cytology Image KE Jing1(柯晶), ZHU Junchao2 (朱俊超), YANG Xin1(杨鑫), ZHANG Haolin3 (张浩林), SUN Yuxiang1(孙宇翔), WANG Jiayi1(王嘉怡), LU Yizhou4(鲁亦舟), SHEN Yiqing5(沈逸卿), LIU Sheng6(刘晟), JIANG Fusong7(蒋伏松), HUANG Qin8(黄琴) J Shanghai Jiaotong Univ Sci    2024, 29 (6): 945-957.   DOI: 10.1007/s12204-024-2743-y Abstract （480）      PDF（pc） （2836KB）（326）       Knowledge map    Save Examining thyroid fine-needle aspiration (FNA) can grade cancer risks, derive prognostic information, and guide follow-up care or surgery. The digitization of biopsy and deep learning techniques has recently enabled computational pathology. However, there is still lack of systematic diagnostic system for the complicated gigapixel cytopathology images, which can match physician-level basic perception. In this study, we design a deep learning framework, thyroid segmentation and hierarchy fine-needle aspiration (TshFNA)-Examiner to quantitatively profile the cancer risk of a thyroid FNA image. In the TshFNA-Examiner, cellular-intensive areas strongly correlated with diagnostic medical information are detected by a nuclei segmentation neural network; cell-level image patches are catalogued following The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) system, by a classification neural network which is further enhanced by leveraging unlabeled data. A cohort of 333 thyroid FNA cases collected from 2019 to 2022 from I to VI is studied, with pixel-wise and image-wise image patches annotated. Empirically, TshFNA-Examiner is evaluated with comprehensive metrics and multiple tasks to demonstrate its superiority to state-of-the-art deep learning approaches. The average performance of cellular area segmentation achieves a Dice of 0.931 and Jaccard index of 0.871. The cancer risk classifier achieves a macro-F1-score of 0.959, macro-AUC of 0.998, and accuracy of 0.959 following TBSRTC. The corresponding metrics can be enhanced to a macro-F1-score of 0.970, macro-AUC of 0.999, and accuracy of 0.970 by leveraging informative unlabeled data. In clinical practice, TshFNA-Examiner can help cytologists to visualize the output of deep learning networks in a convenient way to facilitate making the final decision. Reference | Related Articles | Metrics | Comments（0）

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Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction ZHU Changsheng(朱昶胜), ZHU Lina∗ (朱丽娜) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 297-308.   DOI: 10.1007/s12204-022-2477-7 Abstract （479）      PDF（pc） （1282KB）（299）       Knowledge map    Save Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy. However, owing to the stochastic and intermittent of wind speed, predicting wind speed accurately is difficult. A new hybrid deep learning model based on empirical wavelet transform, recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper. The empirical wavelet transformation is applied to decompose the original wind speed series. The long short term memory network and the Elman neural network are adopted to predict low-frequency and highfrequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy. The error correction strategy based on deep long short term memory network is developed to modify the prediction errors. Four actual wind speed series are utilized to verify the effectiveness of the proposed model. The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction. Reference | Related Articles | Metrics | Comments（0）

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Comfort of Autonomous Vehicles Incorporating Quantitative Indices for Passenger Feeling PENG Shiwei1 (彭诗玮), ZHANG Xi1∗ (张希), ZHU Wangwang1 (朱旺旺), DOU Rui2 (窦瑞) J Shanghai Jiaotong Univ Sci    2024, 29 (6): 1063-1070.   DOI: 10.1007/s12204-022-2531-5 Abstract （476）      PDF（pc） （659KB）（120）       Knowledge map    Save At present, most of the studies on autonomous vehicles mainly focus on improving driving safety and efficiency, while less consideration is given to the comfort of passengers. Therefore, in order to gain and optimize quantitative indices for the ride experience of autonomous vehicles, this paper proposes an evaluation method for the correlation between driving behavior and passenger comfort with bidirectional long short-term memory network and attention mechanism. By collecting subjective feeling scores of passengers under different driving styles, and measuring the pressure level with skin conductance response and heart rate variability, the comprehensive quantitative indices of passenger comfort caused by driving behavior are evaluated. Based on this, a personalized comfort evaluation model for passengers with different driving style preferences is established. The results obtained from experiments in open road and closed test areas have validated the effectiveness and feasibility of the method proposed in this paper. Reference | Related Articles | Metrics | Comments（0）

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Direct Ink Writing Method of Fractal Wearable Flexible Sensor Based on Conductive Graphene/Polydimethylsiloxane Ink CHEN Junling1, 2, 3 (陈俊伶), GAO Feiyang1, 3 (高飞扬), ZHANG Liming1, 3 (张黎明), ZHENG Xiongfei1, 3(郑雄飞) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 18-26.   DOI: 10.1007/s12204-023-2687-7 Abstract （473）      PDF（pc） （1712KB）（106）       Knowledge map    Save Flexible electronic technology has laid the foundation for complex human-computer interaction system, and has attracted great attention in the field of human motion detection and soft robotics. Graphene has received an extensive attention due to its excellent electrical conductivity; however, how to use it to fabricate wearable flexible sensors with complex structures remains challenging. In this study, we studied the rheological behavior of graphene/polydimethylsiloxane ink and proposed an optimal graphene ratio, which makes the ink have a good printability and conductivity at the same time. Then, based on the theory of Peano fractal layout, we proposed a two-dimensional structure that can withstand multi-directional tension by replacing the traditional arris structure with the arc structure. After that, we manufactured circular arc fractal structure sensor by adjusting ink composition and printing structure through direct ink writing method. Finally, we evaluated the detection performance and repeatability of the sensor. This method provides a simple and effective solution for fabricating wearable flexible sensors and exhibits the potential to fabricate 3D complex flexible electronic devices. Reference | Related Articles | Metrics | Comments（0）

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Data Augmentation of Ship Wakes in SAR Images Based on Improved CycleGAN YAN Congqiang1,2 (鄢丛强), GUO Zhengyun3,4 (郭正玉), CAI Yunze1,2∗∗ (蔡云泽) J Shanghai Jiaotong Univ Sci    2024, 29 (4): 702-711.   DOI: 10.1007/s12204-024-2746-8 Abstract （462）      PDF（pc） （1418KB）（174）       Knowledge map    Save The study on ship wakes of synthetic aperture radar (SAR) images holds great importance in detecting ship targets in the ocean. In this study, we focus on the issues of low quantity and insufficient diversity in ship wakes of SAR images, and propose a method of data augmentation of ship wakes in SAR images based on the improved cycle-consistent generative adversarial network (CycleGAN). The improvement measures mainly include two aspects: First, to enhance the quality of the generated images and guarantee a stable training process of the model, the least-squares loss is employed as the adversarial loss function; Second, the decoder of the generator is augmented with the convolutional block attention module (CBAM) to address the issue of missing details in the generated ship wakes of SAR images at the microscopic level. The experiment findings indicate that the improved CycleGAN model generates clearer ship wakes of SAR images, and outperforms the traditional CycleGAN models in both subjective and objective aspects. Reference | Related Articles | Metrics | Comments（0）

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AlgoTime-Varying Formation-Containment Tracking Control for Unmanned Aerial Vehicle Swarm Systems with Switching Topologies and a Non-Cooperative Target WU Xiaojing∗(武晓晶), CAO Tongyao (曹童瑶), ZHEN Ran (甄然), LI Zhijie (李志杰) J Shanghai Jiaotong Univ Sci    2024, 29 (4): 689-701.   DOI: 10.1007/s12204-024-2728-x Abstract （455）      PDF（pc） （1627KB）（287）       Knowledge map    Save This paper studies the time-varying formation-containment tracking control problems for unmanned aerial vehicle (UAV) swarm systems with switching topologies and a non-cooperative target, where the UAV swarm systems consist of one tracking-leader, several formation-leaders, and followers. The formation-leaders are required to accomplish a predefined time-varying formation and track the desired trajectory of the tracking-leader, and the states of the followers should converge to the convex hull spanned by those of the formation-leaders. First, a formation-containment tracking protocol is proposed with the neighboring relative information, and the feasibilit condition for formation-containment tracking and the algebraic Riccati equation are given. Then, the stability of the control system with the designed control protocol is proved by constructing a reasonable Lyapunov function. Finally, the simulation examples are applied to verify the effectiveness of the theoretical results. The simulation results show that both the formation tracking error and the containment error are convergent, so the system can complete the formation containment tracking control well. In the actual battlefield, combat UAVs need to chase and attack hostile UAVs, but sometimes when multiple UAVs work together for military interception, formationcontainment tracking control will occur. Reference | Related Articles | Metrics | Comments（0）

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Fast Four-Stage Local Motion Planning Method for Mobile Robot HUANG Shan(黄山), HUANG Hongzhong(黄洪钟), ZENG Qi(曾奇) J Shanghai Jiaotong Univ Sci    2024, 29 (3): 428-435.   DOI: 10.1007/s12204-022-2423-8 Abstract （450）      PDF（pc） （1810KB）（110）       Knowledge map    Save Mobile robot local motion planning is responsible for the fast and smooth obstacle avoidance, which is one of the main indicators for evaluating mobile robots’ navigation capabilities. Current methods formulate local motion planning as a unified problem; therefore it cannot satisfy the real-time requirement on the platform with limited computing ability. In order to solve this problem, this paper proposes a fast local motion planning method that can reach a planning frequency of 500 Hz on a low-cost CPU. The proposed method decouples the local motion planning as the front-end path searching and the back-end optimization. The front-end is composed of the environment topology analysis and graph searching. The back-end includes dynamically feasible trajectory generation and optimal trajectory selection. Different from the popular methods, the proposed method decomposes the local motion planning into four sub-modules, each of which aims to solve one problem. Combining four submodules, the proposed method can obtain the complete local motion planning algorithm which can fast generate a smooth and collision-free trajectory. The experimental results demonstrate that the proposed method has the ability to obtain the smooth, dynamically feasible and collision-free trajectory and the speed of the planning is fast. Reference | Related Articles | Metrics | Comments（0）

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Multi-Channel Based on Attention Network for Infrared Small Target Detection ZHANG Yanjun(张彦军), WANG Biyun(王碧云)，CAI Yunze (蔡云泽) J Shanghai Jiaotong Univ Sci    2024, 29 (3): 414-427.   DOI: 10.1007/s12204-023-2616-9 Abstract （449）      PDF（pc） （1697KB）（129）       Knowledge map    Save Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking systems. However, the complex background,the strong noise, and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets. A multi-channel based on attention network is proposed in this paper, aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model, high complexity and poor detection performance of deep learning algorithms. First, given the difficulty in extracting the features of infrared multiscale and small dim targets, the multiple channels are designed based on dilated convolution to capture multiscale target features. Second, the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target features. In addition, the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion block. Finally, it is verified that, compared with other state-of-the-art methods based on the datasets SIRST and MDFA, the proposed algorithm further improves the detection effect, and the model size and computational complexity are smaller. Reference | Related Articles | Metrics | Comments（0）

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Identification of Steady State and Transient State YU Sheng (于生), LI Xiangshun∗ (李向舜) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 261-270.   DOI: 10.1007/s12204-022-2516-4 Abstract （443）      PDF（pc） （1612KB）（86）       Knowledge map    Save Identification of steady state and transient state plays an important role in modeling, control, optimization, and fault detection of industrial processes. Many existing methods for state identification are not satisfactory in practical applications due to problems of ideal hypothesis, too many parameters, and poor robustness. In this paper, a novel state identification approach is proposed. The problem of state identification is transformed into finding the noise band of differential signal. For practical application, automatic selection of noise band amplitude is proposed to make the method convenient to be used. Problems of gross errors, low signal-to-noise ratio and online identification are considered. And comparison with other two methods shows that the proposed method has better identification performance. Simulations and experiments also prove the effectiveness and practicability of the proposed method. Reference | Related Articles | Metrics | Comments（0）

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Explosion Hazard Analysis of Liquefied Petroleum Gas Transportation GAO Sida1 (高思达),HAO Lin 1* (郝琳), ZHU Zhenxing2* (朱振兴), WEI Hongyuan1 (卫宏远) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 252-260.   DOI: 10.1007/s12204-022-2536-0 Abstract （442）      PDF（pc） （1310KB）（197）       Knowledge map    Save This paper presents a quantitative risk analysis of liquefied petroleum gas (LPG) transportation. An accident that happened on June 13, 2020, on the highway near Wenling, China is studied as a case. In this accident, LPG carried by a tank truck on the highway leaked and caused a large explosion, which led to 20 deaths. Different methods are combined to calculate the consequence of the accident. Multi-energy model and rupture of vessel model are employed to calculate the overpressure; the simulation result of the multi-energy model is closer to the damage caused by the accident. The safety distances in accidents of LPG transport storage tanks of different capacities are calculated in this study; the results show that the damage of explosion will increase with the filling degree of the tank. Even though the filling degree is 90% (value required by law), the 99% fatality rate range will reach 42 m, which is higher than regulated distance between road and building. The social risk of the tank truck has also been calculated and the results show that the risk is not acceptable. The calculating method used in this study could evaluate the risk of LPG tanker more accurately, which may contribute to the establishment of transportation regulation so that losses from similar accidents in the future could be reduced. Reference | Related Articles | Metrics | Comments（0）

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Coordination Design of a Power-Assisted Ankle Exoskeleton Robot Based on Active-Passive Combined Drive HE Guisong (贺贵松), HUANG Xuegong* (黄学功),LI Feng（李峰) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 197-208.   DOI: 10.1007/s12204-023-2589-8 Abstract （441）      PDF（pc） （2027KB）（101）       Knowledge map    Save With the continuous escalation of modern war, soldiers need to transport more combat materials to the combat area. The limited load-bearing capacity of soldiers seriously restricts their carrying capacity and mobility. It is urgent to develop a power-assisted exoskeleton robot suitable for individual combat. In the past, most power-assisted exoskeleton robots were driven by motors. This driving method has an excellent powerassisted effect, but the endurance is often insufficient. In view of this shortcoming, this study designed an ankle exoskeleton robot based on an active-passive combined drive through simulation analysis of human motion. It used OpenSim software to simulate and verify that the addition of spring could achieve a good effect. At the same time, according to the gait characteristics of the human body, the gait planning of an exoskeleton robot was carried out. Afterwards, theoretical analysis explained that the cooperation among spring, motor and wearer could be realized in this gait. Finally, the assisting ability and driving coordination of the active-passive combination driven ankle exoskeleton robot were verified through experiments. Reference | Related Articles | Metrics | Comments（0）

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Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network XU Wangwang1，2 (徐旺旺), XU Liangfeng1，2 (许良凤), LIU Ninghui3(刘宁徽), LU Na3(律娜) J Shanghai Jiaotong Univ Sci    2025, 30 (1): 91-106.   DOI: 10.1007/s12204-024-2705-4 Abstract （440）      PDF（pc） （1716KB）（77）       Knowledge map    Save Breast cancer is a serious and high morbidity disease in women, and it is the main cause of cancer death in China. However, getting tested and diagnosed early can reduce the risk of cancer. At present, there are clinical examinations, imaging screening and biopsies, among which histopathological examination is the gold standard. However, the process is complicated and time-consuming, and misdiagnosis may exist. This paper puts forward a classification framework based on deep learning, introducing multi-attention mechanism, selecting kernel convolution instead of ordinary convolution, and using different weights and combinations to pay attention to the accuracy index and growth rate of the model. In addition, we also compared the learning rate regulators. Error function can fine-tune the learning rate to achieve good performance, using label softening to reduce the loss error caused by model error recognition in the label, and assigning different category weights in the loss function to balance the positive and negative samples. We used the BreakHis data set to automatically classify histological images into benign and malignant, four categories and eight subtypes. Experimental results showed that the accuracy of binary classifications ranged from 98.23% to 99.50%, and that of multipl classifications ranged from 97.89% to 98.11%. Reference | Related Articles | Metrics | Comments（0）

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Pressure Pulse Response of High Temperature Molten Salt Check Valve Hit by Crystal Particles LI Shuxun (李树勋), SHEN Hengyun* (沈珩云), LIU Bincai (刘斌才),HU Yinggang (胡迎港), MA Tingqian (马廷前) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 271-279.   DOI: 10.1007/s12204-023-2601-3 Abstract （439）      PDF（pc） （2060KB）（118）       Knowledge map    Save In view of the problem that crystalline particles cause wall vibration at a low temperature, based on two-phase flow model, computational fluid dynamics is used to conduct the numerical simulation of internal flows when the valve openings are 20%, 60% and 100% respectively. The molten salt flow may be changed under strict conditions and produce forced vibration of the inner parts of molten salt particle shock valve body. Euler two-phase flow model is used for different molten salt sizes to extract temporal pressure pulse information and conduct statistical data processing analysis. The influence of the molten salt crystallization of molten salt particles on the flow and pressure pulse strength is analyzed. The results show that the crystallization of molten salt has a serious impact on the vibration of the valve body, especially in the throttle rate. The valve oscillation caused by the pressure pulsation mostly occurs from the small opening rate. As the opening increases, the pressure pulse threshold and its change trend decrease. Reference | Related Articles | Metrics | Comments（0）

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CBF-Based Distributed Model Predictive Control for Safe Formation of Autonomous Mobile Robots MU Jianbin∗ (穆建彬), YANG Haili (杨海丽), HE Defeng (何德峰) J Shanghai Jiaotong Univ Sci    2024, 29 (4): 678-688.   DOI: 10.1007/s12204-024-2747-7 Abstract （432）      PDF（pc） （969KB）（253）       Knowledge map    Save A distributed model predictive control (DMPC) method based on robust control barrier function (RCBF) is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment. The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivitymaintenance. RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements, and security constraints are achieved through a combination. Then, the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation. To ensure safe control, the optimization problem is integrated with the DMPC method. Finally, the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives. Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security. Reference | Related Articles | Metrics | Comments（0）