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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 （613）      PDF（pc） （1195KB）（173）       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 （566）      PDF（pc） （1179KB）（460）       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-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 （320）      PDF（pc） （975KB）（171）       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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Boosting Unsupervised Domain Adaptation with Soft Pseudo-Label and Curriculum Learning ZHANG Shengjia(张晟嘉), LIN Tiancheng(林天成), XU Yi(徐奕) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 703-716.   DOI: 10.1007/s12204-022-2487-5 Abstract （221）      PDF（pc） （963KB）（95）       Knowledge map    Save By leveraging data from a fully labeled source domain, unsupervised domain adaptation (UDA) improves classification performance on an unlabeled target domain through explicit discrepancy minimization of data distribution or adversarial learning. As an enhancement, category alignment is involved during adaptation to reinforce target feature discrimination by utilizing model prediction. However, there remain unexplored problems about pseudo-label inaccuracy incurred by wrong category predictions on target domain, and distribution deviation caused by overfitting on source domain. In this paper, we propose a model-agnostic two-stage learning framework, which greatly reduces flawed model predictions using soft pseudo-label strategy and avoids overfitting on source domain with a curriculum learning strategy. Theoretically, it successfully decreases the combined risk in the upper bound of expected error on the target domain. In the first stage, we train a model with distribution alignment-based UDA method to obtain soft semantic label on target domain with rather high confidence. To avoid overfitting on source domain, in the second stage, we propose a curriculum learning strategy to adaptively control the weighting between losses from the two domains so that the focus of the training stage is gradually shifted from source distribution to target distribution with prediction confidence boosted on the target domain. Extensive experiments on two well-known benchmark datasets validate the universal effectiveness of our proposed framework on promoting the performance of the top-ranked UDA algorithms and demonstrate its consistent superior performance. 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 （205）      PDF（pc） （1213KB）（132）       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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Entity Relationship Explanation via Conceptualization XIE Chenhao(谢晨昊), LIANG Jiaqing(梁家卿), XIA Yanghua(肖仰华), HWANG Seung-won J Shanghai Jiaotong Univ Sci    2023, 28 (6): 695-702.   DOI: 10.1007/s12204-021-2394-1 Abstract （197）      PDF（pc） （608KB）（115）       Knowledge map    Save Finding an attribute to explain the relationships between a given pair of entities is valuable in many applications. However, many direct solutions fail, owing to its low precision caused by heavy dependence on text and low recall by evidence scarcity. Thus, we propose a generalization-and-inference framework and implement it to build a system: entity-relationship finder (ERF). Our main idea is conceptualizing entity pairs into proper concept pairs, as intermediate random variables to form the explanation. Although entity conceptualization has been studied, it has new challenges of collective optimization for multiple relationship instances, joint optimization for both entities, and aggregation of diluted observations into the head concepts defining the relationship. We propose conceptualization solutions and validate them as well as the framework with extensive experiments. 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 （195）      PDF（pc） （4397KB）（96）       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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Journal's online articles can be found at SpringerLink Online publication is equivalent to paper publication which can also be indexed in the database of EI Village approximately one month after the online date. Paper publications will be printed within around one year after online publication (content cannot be changed). The link is Online First Articles in our journal: https://link.springer.com/journal/12204/online-first J Shanghai Jiaotong Univ Sci    Accepted: 21 December 2023

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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 （182）      PDF（pc） （1734KB）（96）       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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Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network LIU Zengmin (刘增敏), WANG Shentao(王申涛), YAO Lixiu(姚莉秀), CAI Yunze(蔡云泽) J Shanghai Jiaotong Univ Sci    2024, 29 (3): 388-399.   DOI: 10.1007/s12204-022-2540-4 Abstract （159）      PDF（pc） （1105KB）（69）       Knowledge map    Save In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle (UAV) platform, the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied. Furthermore, a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm. For the problem of object association failure caused by UAV movement, image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm. The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform, and effectively solve the problem of association failure caused by UAV movement. Reference | Related Articles | Metrics | Comments（0）

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Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University WANG Qingwen (王庆稳)，CUI Tingting (崔婷婷)，DENG Peiwen* (邓珮雯) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 841-856.   DOI: 10.1007/s12204-022-2418-5 Abstract （157）      PDF（pc） （1829KB）（299）       Knowledge map    Save This article aims to provide reference for medicine-engineering interdisciplinary research. Targeted at the scientific literature and patent literature published by Shanghai Jiao Tong University, this article attempts to set up co-occurrence matrix of medicine-engineering institutional information which was extracted from address fields of the papers, so as to construct the medicine-engineering intersection datasets. The dataset of scientific literature was analyzed using bibliometrics and visualization methods from multiple dimensions, and the most active factors, such as trends of output, journal and subject distribution, were identified from the indicators of category normalized citation impact (CNCI), times cited, keywords, citation topics and the degree of medicineengineering interdisplinary. Research on hotspots and trends was discussed in detail. Analyses of the dataset of patent literature showed research themes and measured the degree for technology convergence of medicineengineering. 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 （147）      PDF（pc） （5510KB）（63）       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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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 （136）      PDF（pc） （5774KB）（58）       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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CT Image Segmentation Method of Composite Material Based on Improved Watershed Algorithm and U-Net Neural Network Model XUE Yongboa (薛永波)，LIU Zhaob (刘钊)， LI Zeyanga (李泽阳)，ZHU Pinga* (朱平) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 783-792.   DOI: 10.1007/s12204-021-2385-2 Abstract （133）      PDF（pc） （1655KB）（41）       Knowledge map    Save In the study of the composite materials performance, X-ray computed tomography (XCT) scanning has always been one of the important measures to detect the internal structures. CT image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process, which is of great significance to the study of material performance. This study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT images. In the core layer area, area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed algorithm. In the transition layer area, a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation result. Furthermore, a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is proposed. It is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials, and the accuracy of segmentation is significantly improved in comparison with the original method, which ensures the accuracy and robustness of the subsequent fiber feature extraction process 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 （132）      PDF（pc） （810KB）（48）       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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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 （128）      PDF（pc） （1349KB）（58）       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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Stagewise Training for Hybrid-Distorted Image Restoration HOU Shujuan* (侯舒娟)，ZHU Wenping (朱文萍)，LI Hai (李海) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 793-801.   DOI: 10.1007/s12204-022-2453-2 Abstract （120）      PDF（pc） （1221KB）（51）       Knowledge map    Save Image restoration is the problem of restoring a real degraded image. Previous studies mostly focused on single distortion. However, most of the real images experience multiple distortions, and single distortion image restoration algorithms can not effectively improve the image quality. Moreover, few existing hybrid distortion image restoration algorithms can not deal with single distortion. Therefore, an end-to-end pipeline network based on stagewise training is proposed in this paper. Specifically, the network selects three typical image restoration tasks: denoising, inpainting, and super resolution. The whole training process is divided into single distortion training, hybrid distortion training of two types, and hybrid distortion training of three types. The design of loss function draws on the idea of deep supervision. Experimental results prove that the proposed method is not only superior to other methods in hybrid-distorted image restoration, but also suitable for single distortion image restoration. Reference | Related Articles | Metrics | Comments（0）

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Working Fluid Distribution and Charge Regulation Control in Organic Rankine Cycle 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): 188-201.   DOI: 10.1007/s12204-022-2538-y Abstract （119）      PDF（pc） （1116KB）（35）       Knowledge map    Save Charge-based studies, in particular investigations of mass distribution, are still almost absent, although the efficiency of the organic Rankine cycle (ORC) has attracted a great deal of scholarly attention. This paper aims to provide a new perspective on the intrinsic relationship among the mass distribution, phase-zone distribution in the heat exchanger (HEX), charge of working fluid (WF), rotation speed of the pump (RSP), and system performance. A comprehensive ORC simulation model is presented by linking each component’s sub-models, including the independent models for HEX, pump, and expander in an object-oriented fashion. The visualization study of mass distribution of the WF in the system is investigated under different working conditions. Furthermore, the volume and mass of the gas phase, two-phase and liquid phase of WF in the HEX and their variation rules are analyzed in-depth. Finally, the strategies of charge reduction considering HEX areas and pipe sizes are investigated. The results show that the model based on the interior-point method provides high levels of accuracy and robustness. The mass ratio of the WF is concentrated in the liquid receiver, especially in the regenerator, which is 32.9% and 21.9% of the total mass, respectively. Furthermore, 2.4 kg (6.9%) WF in the system gradually migrates to the hightemperature side as the RSP increases while 6.1 kg (17.4%) WF migrates to the low-temperature side, especially to the condenser, as the charge in the system increases. Output power and efficiency both decrease gradually after the peak due to changes in RSP and charge. Last, reducing heat transfer areas of the condenser and regenerator is the most effective way to reduce WF charge. 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 （113）      PDF（pc） （2939KB）（63）       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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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 （108）      PDF（pc） （194KB）（24）       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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Social Network Analysis of COVID-19 Research and the Changing International Collaboration Structure QIN Ye1 (秦野), CHEN Rongrong2∗ (陈蓉蓉) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 150-160.   DOI: 10.1007/s12204-022-2558-7 Abstract （106）      PDF（pc） （3127KB）（30）       Knowledge map    Save Research in Information Science and interdisciplinary areas suggested the formation of a growing network of international research collaboration. The massive transmission of COVID-19 worldwide especially after the identification of the Omicron variant could fundamentally alter the factors shaping the network’s development. This study employs network analysis methods to analyze the structure of the COVID-19 research collaboration from 2020 to 2022, using two major academic publication databases and the VOSviewer software. A novel temporal view is added by examining the dynamic changes of the network, and a fractional counting method is adopted as methodological improvements to previous research. Analysis reveals that the COVID-19 research network structure has undergone substantial changes over time, as collaborating countries and regions form and re-form new clusters. Transformations in the network can be partly explained by key developments in the pandemic and other social-political events. China as one of the largest pivots in the network formed a relatively distinct cluster, with potential to develop a larger Asia-Pacific collaboration cluster based on its research impact. Reference | Related Articles | Metrics | Comments（0）

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Performance Improvement of Multiband Triangular Microstrip Patch Antenna Using Frequency Selective Surface MAHENDRAN Krishnakumar∗, GAYATHRI Rajaraman J Shanghai Jiaotong Univ Sci    2024, 29 (2): 316-321.   DOI: 10.1007/s12204-022-2492-8 Abstract （106）      PDF（pc） （798KB）（19）       Knowledge map    Save Today’s antennas have to operate in multiple resonant frequencies to satisfy the need of recent advances in communication technologies. This paper presents split ring resonator based triangular multiband antenna whose antenna performance is enhanced with the help of frequency selective surfaces (FSSs). The antenna has multiple resonances at S, C, and X bands. An array of 4 × 3 crisscross-shaped unit cells are arranged to form the FSS layer. The antenna is fed with a microstrip line feeding technique. The proposed antenna operates at 3.5 GHz, 4.1 GHz, 5.5 GHz, 9.4 GHz, and 9.8 GHz with a better return loss and gain. Simulated and measured results yield a good match. Reference | Related Articles | Metrics | Comments（0）

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Multiple Detection Model Fusion Framework for Printed Circuit Board Defect Detection WU Xingl(武星), ZHANG Qingfeng(张庆丰), WANG Jianjia(王健嘉), YAO Junfeng(姚骏峰), Guo Yike.(郭毅可) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 717-727.   DOI: 10.1007/s12204-022-2471-0 Abstract （103）      PDF（pc） （1870KB）（58）       Knowledge map    Save The printed circuit board (PCB) is an indispensable component of electronic products, which determines the quality of these products. With the development and advancement of manufacturing technology, the layout and structure of PCB are getting complicated. However, there are few effective and accurate PCB defect detection methods. There are high requirements for the accuracy of PCB defect detection in the actual production environment, so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method (DDMV) and the defect detection by multi-model learning method (DDML). With the purpose of reducing wrong and missing detection, the DDMV and DDML integrate multiple defect detection networks with different fusion strategies. The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets. The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score, and the area under curve value of DDML is also higher than that of any other individual detection model. Furthermore, compared with DDMV, the DDML with an automatic machine learning method achieves the best performance in PCB defect detection, and the F1-score on the two datasets can reach 99.7% and 95.6% respectively. 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 （101）      PDF（pc） （1310KB）（41）       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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Off-Grid Sparse Bayesian Inference with Biased Total Grids for Dense Time Delay Estimation WEI Shuang (魏爽)， LI Wenyao (李文瑶)，SU Ying* (苏颖)， LIU Rui (刘睿) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 763-771.   DOI: 10.1007/s12204-022-2464-z Abstract （98）      PDF（pc） （831KB）（32）       Knowledge map    Save For dense time delay estimation (TDE), when multiple time delays are located within a grid interval, it is difficult for the existing sparse Bayesian learning/inference (SBL/SBI) methods to obtain high estimation accuracy to meet the application requirements. To solve this problem, this paper proposes a method named off-grid sparse Bayesian inference - biased total grid (OGSBI-BTG), where a mesh evolution process is conducted to move the total grids iteratively based on the position of the off-grid between two grids. The proposed method updates the off-grid dictionary matrix by further reconstructing an optimum mesh and offsetting the off-grid vector. Experimental results demonstrate that the proposed approach performs better than other state-of-the-art SBI methods and multiple signal classification even when the grid interval is larger than the gap of true time delays. In this paper, the time domain model and frequency domain model of TDE are studied. Reference | Related Articles | Metrics | Comments（0）

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Prediction of Pediatric Sepsis Using a Deep Encoding Network with Cross Features CHEN Xiao1,2 (陈潇), ZHANG Rui1,2 (张瑞), TANG Xinyi1,2 (汤心溢), QIAN Juan3∗ (钱娟) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 131-140.   DOI: 10.1007/s12204-022-2499-1 Abstract （98）      PDF（pc） （1153KB）（29）       Knowledge map    Save Sepsis poses a serious threat to health of children in pediatric intensive care unit. The mortality from pediatric sepsis can be effectively reduced through in-time diagnosis and therapeutic intervention. The bacilliculture detection method is too time-consuming to receive timely treatment. In this research, we propose a new framework: a deep encoding network with cross features (CF-DEN) that enables accurate early detection of sepsis. Cross features are automatically constructed via the gradient boosting decision tree and distilled into the deep encoding network (DEN) we designed. The DEN is aimed at learning sufficiently effective representation from clinical test data. Each layer of the DEN filtrates the features involved in computation at current layer via attention mechanism and outputs the current prediction which is additive layer by layer to obtain the embedding feature at last layer. The framework takes the advantage of tree-based method and neural network method to extract effective representation from small clinical dataset and obtain accurate prediction in order to prompt patient to get timely treatment. We evaluate the performance of the framework on the dataset collected from Shanghai Children’s Medical Center. Compared with common machine learning methods, our method achieves the increase on F1-score by 16.06% on the test set. Reference | Related Articles | Metrics | Comments（0）

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Cross-Modal Entity Resolution for Image and Text Integrating Global and Fine-Grained Joint Attention Mechanism ZENG Zhirian(曾志贤),CAO Jianjun*(曹建军),WENG Nianfeng(翁年凤)，YUAN Zhen(袁震)，YU Xu(余旭) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 728-737.   DOI: 10.1007/s12204-022-2465-y Abstract （97）      PDF（pc） （1951KB）（45）       Knowledge map    Save In order to solve the problem that the existing cross-modal entity resolution methods easily ignore the high-level semantic informational correlations between cross-modal data, we propose a novel cross-modal entity resolution for image and text integrating global and fine-grained joint attention mechanism method. First, we map the cross-modal data to a common embedding space utilizing a feature extraction network. Then, we integrate global joint attention mechanism and fine-grained joint attention mechanism, making the model have the ability to learn the global semantic characteristics and the local fine-grained semantic characteristics of the cross-modal data, which is used to fully exploit the cross-modal semantic correlation and boost the performance of cross-modal entity resolution. Moreover, experiments on Flickr-30K and MS-COCO datasets show that the overall performance of R@sum outperforms by 4.30% and 4.54% compared with 5 state-of-the-art methods, respectively, which can fully demonstrate the superiority of our proposed method. Reference | Related Articles | Metrics | Comments（0）

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Retinal Vessel Segmentation via Adversarial Learning and Iterative Refinement GU Wen (顾闻), XU Yi∗ (徐奕) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 73-80.   DOI: 10.1007/s12204-022-2479-5 Abstract （95）      PDF（pc） （914KB）（39）       Knowledge map    Save Retinal vessel segmentation is a challenging medical task owing to small size of dataset, micro blood vessels and low image contrast. To address these issues, we introduce a novel convolutional neural network in this paper, which takes the advantage of both adversarial learning and recurrent neural network. An iterative design of network with recurrent unit is performed to refine the segmentation results from input retinal image gradually. Recurrent unit preserves high-level semantic information for feature reuse, so as to output a sufficiently refined segmentation map instead of a coarse mask. Moreover, an adversarial loss is imposing the integrity and connectivity constraints on the segmented vessel regions, thus greatly reducing topology errors of segmentation. The experimental results on the DRIVE dataset show that our method achieves area under curve and sensitivity of 98.17% and 80.64%, respectively. Our method achieves superior performance in retinal vessel segmentation compared with other existing state-of-the-art methods. Reference | Related Articles | Metrics | Comments（0）

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Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method TIAN Yuanyuan (田圆圆), JIN Yanrui (金衍瑞), LI Zhiyuan (李志远), LIU Jinlei (刘金磊), LIU Chengliang∗ (刘成良) J Shanghai Jiaotong Univ Sci    2024, 29 (1): 120-130.   DOI: 10.1007/s12204-022-2537-z Abstract （91）      PDF（pc） （1081KB）（27）       Knowledge map    Save The objective of this study is to construct a multi-department symptom-based automatic diagnosis model. However, it is difficult to establish a model to classify plenty of diseases and collect thousands of diseasesymptom datasets simultaneously. Inspired by the thought of “knowledge graph is model”, this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data, and incrementally injecting it into the knowledge graph. Therefore, incremental learning and injection are used to solve the data collection problem, and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems. First, an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion. Then, an adaptive neural network model is constructed for each dataset, and the data is used for statistical initialization and model training. Finally, the weights and biases of the learned neural network model are updated to the knowledge graph. It is worth noting that for the incremental process, we consider both the data and class increments. We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets. Compared with the classical model, the proposed model improves the diagnostic accuracy of the three datasets by 5%, 2%, and 15% on average, respectively. Meanwhile, the model under incremental learning has a better ability to resist forgetting. 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 （90）      PDF（pc） （1612KB）（20）       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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Color Prediction Model of Gray Hybrid Multifilament Fabric WANG Yujuan1 (王玉娟)，LI Wengang2 (李文刚)，LIU .Jianyong3 (刘建勇),CHEN Guangxue4 (陈广学),WANG Jun1*(汪军) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 802-808.   DOI: 10.1007/s12204-021-2326-0 Abstract （86）      PDF（pc） （705KB）（21）       Knowledge map    Save To facilitate the product design of hybrid multifilament fabric prior to spinning, a color prediction model was proposed. The monofilaments in the multifilament were assumed to have a square cross-section and stacked vertically. The prediction model considered the reflectance, transmittance and arrangement of the monofilaments in the fabric. To test the reflectance and transmittance of the monofilament with the Datacolor spectrophotometer, films with the same material and thickness as the monofilaments were made. Twenty kinds of multifilaments with different blending ratios and fineness were produced and woven into fabrics. The color difference between the fabric color tested by the spectrophotometer and predicted by the new model and classical Kubelka-Munk (K-M) theory was calculated and compared. The result shows that the average color difference obtained by the new model was 1.02 Color Measurement Committee (CMC) (2 : 1) units, which was less than that of 1.78 CMC (2 : 1) units obtained by the K-M theory. Through Spearman correlation analysis, the fabric lightness and the multifilament fineness had a significant influence on calculated color difference, and the color difference decreased with increases of them. Finally, the surface color of a fabric was reproduced, indicating the model can be used to characterize the phenomenon of uneven color mixing on the fabric surface. Reference | Related Articles | Metrics | Comments（0）

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Active Magnetic Compensation Based on Parametric Resonance Magnetometer GUO Yang1 (郭阳), LI Shaoliang2 (李绍良), HUANG Yiming1 (黄艺明), LUO Manruo1 (骆曼箬), LIU Hua1* (刘华) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 280-289.   DOI: 10.1007/s12204-022-2524-4 Abstract （84）      PDF（pc） （1976KB）（20）       Knowledge map    Save Based on the parametric resonance magnetometer (PRM) theory, this paper establishes an experimental system of PRM. The experimental results are consistent with the theoretical predictions. A PRM has been developed with sensitivity of 0.5 pT/Hz1/2, which can detect the magnitude of residual magnetic field; furthermore, a proportion-integration-differentiation (PID) closed-loop magnetic compensation system of the residual magnetic field also has been realized. Compared with open-loop compensation, the PID closed-loop compensation reduces the average value of the residual magnetic field in the z-axis direction from 0.024 4 nT to −0.002 3 nT, and the mean-square error from 0.208 3 nT to 0.069 1 nT. In the same way, the average value of the residual magnetic field in the y-axis direction is reduced from 0.081 6 nT to −0.004 2 nT, and the mean-square error from 0.131 6 nT to 0.046 1 nT. The magnitude of residual magnetic fields in both directions is decreased to the order of picotesla (pT). In addition, based on the signal waveforms of the magnetometer, a method of verifying the effect of magnetic compensation is proposed. Reference | Related Articles | Metrics | Comments（0）

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Formal Analysis of SA-TEK 3-Way Handshake Protocols XU Sen* (徐森),YANG Shuo (杨硕)，ZHANG Kefei (张克非) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 753-762.   DOI: 10.1007/s12204-021-2340-2 Abstract （83）      PDF（pc） （1977KB）（29）       Knowledge map    Save IEEE 802.16 is the standard for broadband wireless access. The security sublayer is provided within IEEE 802.16 MAC layer for privacy and access control, in which the privacy and key management (PKM) protocols are specified. In IEEE 802.16e, SA-TEK 3-way handshake is added into PKM protocols, aiming to facilitate reauthentication and key distribution. This paper analyzes the SA-TEK 3-way handshake protocol, and proposes an optimized version. We also use CasperFDR, a popular formal analysis tool, to verify our analysis. Moreover, we model various simplified versions to find the functions of those elements in the protocol, and correct some misunderstandings in related works using other formal analysis tools. Reference | Related Articles | Metrics | Comments（0）

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Numerical Simulation of Radial Ultrasonic Assisted MIG Welding Arc HONG Lei1 (洪蕾), XIAO Hao1 (肖皓), YE Jia2 (叶佳), MA Guohong1* (马国红) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 330-338.   DOI: 10.1007/s12204-021-2380-7 Abstract （81）      PDF（pc） （1906KB）（22）       Knowledge map    Save The numerical simulation of arc was carried out for both conventional melt inert gas (MIG) welding and ultrasonic assisted melt inert gas (U-MIG) welding. Based on the model established by Fluent, the arc shape, temperature field, and potential distribution were simulated. The study found that the shape of the arc changed when ultrasonic was added radially; the high-temperature area of the arc stretched, and the temperature peak increased. But as the current increased, the increase in temperature decreased. In addition, under the same conditions, the potential of U-MIG decreased and the pressure on the workpiece increased. To verify the accuracy of the simulation results, welding experiments under identical conditions were carried out, and a high-speed camera was used to collect dynamic pictures of the arc. The simulation results were in a favorable agreement with the experimental results, which provided a certain reference value for ultrasonic assisted arc welding. Reference | Related Articles | Metrics | Comments（0）

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Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting GAO Jincheng (高锦程)，ZHAO Yisheng* (赵宜升)，CHEN Jiafa (陈加法)，CHEN Zhonghui (陈忠辉) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 822-830.   DOI: 10.1007/s12204-021-2336-y Abstract （80）      PDF（pc） （980KB）（15）       Knowledge map    Save Aiming at excessive users existing in a pico base station (PBS) in the multi-layer heterogeneous networks, the resource allocation problem of maximizing the energy efficiency of the networks is investigated in this paper. By deploying a relay node with energy harvesting function, the data of some users in the PBS can be transferred to an adjacent idle PBS. The bandwidth and transmitting power of users and the relay node are both considered to formulate the resource allocation optimization problem. The objective is to maximize the energy efficiency of the whole heterogeneous networks under the constraints of the user’s minimum data rate and energy consumption. The suboptimal solution is obtained by using the particle swarm optimization (PSO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the adopted methods have higher energy efficiency than the conventional fixed power and bandwidth method. In addition, the time complexity of the adopted methods is relatively low. Reference | Related Articles | Metrics | Comments（0）

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Optimization of Group Multiattribute Decision-Making Model in Commercial Space Investment ZHANG Yiming (张-鸣)，HOU Junjie1* (侯俊杰)，ZHONG Shaowen2 (钟少文) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 831-840.   DOI: 10.1007/s12204-021-2400-7 Abstract （79）      PDF（pc） （291KB）（48）       Knowledge map    Save A group multiattribute decision-making model was proposed by implementing prospect theory, multiattribute decision-making, group decision-making and entropy methods for the optimization in commercial space investment. First, the decision-making function was decided using prospect theory by the preference of each expert to reach the comprehensive prospect value based on different investment options; second, expert decision weights were reached according to entropy method; third, the expert group decision-making information was congregated according to the group decision-making congregation algorithm to reach the most optimized investment option; finally, an example was given to demonstrate the feasibility and effectiveness of the method. This model comprehensively takes the advantages of many methods to congregate experts’ experiences and avoid the subjective influences, thus providing a scientific decision-making approach for the commercial space investment. Reference | Related Articles | Metrics | Comments（0）

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Distributed Photovoltaic Real-Time Output Estimation Based on Graph Convolutional Networks CHEN Liyue1 (陈利跃), HONG Daojian2 (洪道鉴), HE Xing3* (何星), LU Dongqi2 (卢东祁), ZHANG Qian2 (张乾), XIE Nina2 (谢妮娜), XU Yizhou2 (徐一洲), YING Huanghao2 (应煌浩) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 290-296.   DOI: 10.1007/s12204-022-2522-6 Abstract （78）      PDF（pc） （1160KB）（26）       Knowledge map    Save The rapid growth of distributed photovoltaic (PV) has remarkable influence for the safe and economicoperation of power systems. In view of the wide geographical distribution and a large number of distributed PV power stations, the current situation is that it is difficult to access the current dispatch data network. According to the temporal and spatial characteristics of distributed PV, a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid. The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations, which makes the corresponding output estimation algorithm have higher accuracy. Reference | Related Articles | Metrics | Comments（0）

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Numerical Investigation on Dynamic Response Characteristics of Fluid-Structure Interaction of Gas-Liquid Two-Phase Flow in Horizontal Pipe WANG Zhiwei(王志伟), HE Yanping(何炎平), LI Mingzhi（李铭志）, QIU Ming(仇明), HUANG Chao(黄超), LIU Yadong（亚东），WANG Zi(王梓) J Shanghai Jiaotong Univ Sci    2024, 29 (2): 237-244.   DOI: 10.1007/s12204-022-2469-7 Abstract （76）      PDF（pc） （1576KB）（35）       Knowledge map    Save Fluid-structure interaction (FSI) of gas-liquid two-phase flow in the horizontal pipe is investigated numerically in the present study. The volume of fluid model and standard k-ε turbulence model are integrated to simulate the typical gas-liquid two-phase flow patterns. First, validation of the numerical model is conducted and the typical flow patterns are consistent with the Baker chart. Then, the FSI framework is established to investigate the dynamic responses of the interaction between the horizontal pipe and gas-liquid two-phase flow. The results show that the dynamic response under stratified flow condition is relatively flat and the maximum pipe deformation and equivalent stress are 1.8 mm and 7.5 MPa respectively. Meanwhile, the dynamic responses induced by slug flow, wave flow and annular flow show obvious periodic fluctuations. Furthermore, the dynamic response characteristics under slug flow condition are maximum; the maximum pipe deformation and equivalent stress can reach 4 mm and 17.5 MPa, respectively. The principal direction of total deformation is different under various flow patterns. Therefore, the periodic equivalent stress will form the cyclic impact on the pipe wall and affect the fatigue life of the horizontal pipe. The present study may serve as a reference for FSI simulation under gas-liquid two-phase transport conditions. Reference | Related Articles | Metrics | Comments（0）

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Predicting Stock Closing Price with Stock Network Public Opinion Based on AdaBoost-AAFSA-Elman Model and CEEMDAN Algorithm ZHU Changsheng1 (朱昶胜)，KANG Lianghe1.3* (康亮河)，FENG Wenfang2 (冯文芳) J Shanghai Jiaotong Univ Sci    2023, 28 (6): 809-821.   DOI: 10.1007/s12204-021-2337-x Abstract （76）      PDF（pc） （953KB）（31）       Knowledge map    Save To solve low prediction accuracy of Elman in predicting stock closing price, the model of adaptive boosting (AdaBoost)-improved artificial fish swarm algorithm (AAFSA)-Elman based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed. By adding different white noise to the original data, CEEMDAN algorithm is used to decompose attributes serial selected by Boruta algorithm and text mining. To optimize the weight and threshold of Elman, self-adaption step length and view scope are used to improve artificial fish swarm algorithm (AFSA). AdaBoost algorithm is used to compose 5 weak AAFSA-Elman predictors into a strong predictor by continuous iteration. Experiments show that the mean absolute percentage error (MAPE) of AdaBoost-AAFSA-Elman model reduces from 4.9423% to 1.2338%. This study provides an experimental method for the prediction of stock closing price based on network public opinio. 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 （74）      PDF（pc） （1282KB）（31）       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）