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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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A Mother-Son Model for Multi-Objective Non-Invasive Load Monitoring

SSRN

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SSRN 2023年

作者： Mei, Huawei Liu, Yanping Cao, Wangbin Yu, Yang North China Electric Power University Baoding071003 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding071003 China Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Baoding071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding071003 China

This paper proposes a mother-son model (MSM) to address the challenges of missing data, complex models, and limited transfer capabilities in non-intrusive load monitoring (NILM). The MSM model designs a novel transfer method based on feature inheritance to dynamically decompose multiple appliances (including unknown appliances). The mother model incorporates a six-parallel feature extraction structure to extract comprehensive features, which are then inherited by the son model. The features inherited from the mother model integrate information from multiple dimensions in the spatial domain and focus on local fine features in the temporal domain. The son model offers the capability to adjust the number of appliances and decompose unknown appliances, catering to the requirements of practical applications of NILM. Furthermore, a novel statistical metric, Ratio Squared Error (RSE), is designed to quantify the proportion of squared error in energy consumption. The experiments are carried out on five households and statistically evaluated using five statistical indicators. The results indicate that the son model exhibits superior decomposition performance and greater resilience compared to the state-of-the-art model. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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A Sub-mother UAV System Capable of Autonomous Aerial Docking

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年

作者： Li, Gang Lu, Yi He, Bin Pang, Tao Cheng, Bin Zhang, Pengpeng College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200120 China Shanghai Sunshine Rehabilitation Center Shanghai201613 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai200120 China East China Institute of Computing Technology Shanghai201822 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China College of Electronic and Information Engineering Tongji University Shanghai China

Completing the docking between two multi-rotor unmanned aerial vehicles (UAVs) in the air has great significance for expanding the application scenarios of multiple UAVs and improving their endurance capability. In this regard, this paper first designs a safe and autonomous docking scheme and constructs a sub-mother UAV *** address the issue of unstable flight in downwash airflow, this paper designs a high-performance control method suitable for the sub-mother UAV system based on Incremental Nonlinear Dynamic Inversion (INDI) and achieves robust attitude control with strong disturbance rejection capability. A UWB-based Ortho-UWB 3D positioning method is proposed in this paper with orthogonal installation, which, after being integrated with visual methods, enables high-precision 3D relative positioning during the docking process of the sub-mother UAV ***, the paper designs a complete docking process for the sub-mother UAVs and incorporates safety constraints into the docking process to ensure the safety of the docking process for both UAVs. Experiments of successfully docking the sub UAV to the mother UAV from bottom to top prove the feasibility and effectiveness of this scheme. © 2024 IEEE.

关键词： Disturbance rejection

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Text Semantic Analysis and intelligent Interaction in Parallel Dispatching systems for Technical Station

Text Semantic Analysis and Intelligent Interaction in Parall...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yuantao Jiao Jian Wang Runmei Li Gang Xiong Shichao Chen School of Automation and Intelligence Beijing Jiaotong University Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Technical station dispatching system plays an important role in cargo operation, but due to the large number of dispatching systems and complex operation, dispatchers rely on manual experience to complete the task, and many scenarios cannot be applied in the real system. Therefore, this paper utilizes the data-driven form to establish a parallel dispatching system for technical stations, semantically analyzes text data rich in dispatcher experience, completes the Computational Experiments on the textual semantic data in the artificial dispatching system, and synchronizes the Parallel Execution of data interaction with the real dispatching system.

关键词： Training Analytical models Semantics Dispatching Digital twins Synchronization Feeds

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Multi-residual 2D network integrating spatial correlation for whole heart segmentation

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Computers in Biology and Medicine 2024年 172卷 108261-108261页

作者： Huang, Yan Yang, Jinzhu Sun, Qi Yuan, Yuliang Li, Honghe Hou, Yang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Department of Radiology Shengjing Hospital of China Medical University Liaoning Shenyang China

Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy, modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on cardiac CT images, as well as the fast inference speed and low graphics processing unit (GPU) memory consumption required by practical clinical applications. Thus, we propose a multi-residual two-dimensional (2D) network integrating spatial correlation for WHS. The network performs slice-by-slice segmentation on three-dimensional cardiac CT images in a 2D encoder–decoder manner. In the network, a convolutional long short-term memory skip connection module is designed to perform spatial correlation feature extraction on the feature maps at different resolutions extracted by the sub-modules of the pre-trained ResNet-based encoder. Moreover, a decoder based on the multi-residual module is designed to analyze the extracted features from the perspectives of multi-scale and channel attention, thereby accurately delineating the various substructures of the heart. The proposed method is verified on a dataset of the multi-modality WHS challenge, an in-house WHS dataset, and a dataset of the abdominal organ segmentation challenge. The dice, Jaccard, average symmetric surface distance, Hausdorff distance, inference time, and maximum GPU memory of the WHS are 0.914, 0.843, 1.066 mm, 15.778 mm, 9.535 s, and 1905 MB, respectively. The proposed network has high accuracy, fast inference speed, minimal GPU memory consumption, strong robustness, and good generalization. It can be deployed to clinical practical applications for WHS and can be effectively extended and applied to other multi-organ segmentation fields. The source code is publicly available at https://***/nancy1984yan/MultiResNet-SC. © 2024 Elsevier Ltd

关键词： Graphics processing unit

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Pedestrian Trajectory Prediction by Modeling the Interactions using Social LSTM Extensions

Pedestrian Trajectory Prediction by Modeling the Interaction...

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Chinese Automation Congress (CAC)

作者： Zeze Si Hongxia Zhao Haina Tang Fenghua Zhu Yisheng Lv Hao Lu State Key Laboratory for Management and Control of Complex Systems Institute of Autamation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academic of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction.

关键词： Automation Aggregates Predictive models Prediction algorithms Trajectory Convolutional neural networks Task analysis

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Dynamics Modeling with Realistic Constraints for Robust Tracking Control of Manipulator

SSRN

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SSRN 2022年

作者： Zuo, Guoyu Li, Mi Liu, Lu Li, Jiangen Huang, Gao Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

To meet the requirements of precision assembly in industrial scenarios, an efficient, accurate and robust modeling control method has become an important research objective. This paper proposes a novel manipulator control method with three-level constraints, called the real deep Lagrangian network (Real-DeLaN). In this method, real data (also called physical data) are collected from the physical manipulator and utilized to train the Lagrangian dynamics network model to improve the migration ability from simulation to reality. The torque output of the dynamic model is corrected by real-time calculation of friction in the network. The contact force on the end effector of the manipulator is compensated based on the principle of virtual displacement work. Furthermore, a robust controller based on the dynamics model is designed, and the Lyapunov theorem is used to prove that the controlled system can achieve three different kinds of stability. The experimental results show that Real-DeLaN can better control the joints to perform trajectory tracking, reduce the friction error of the manipulator, and show better anti-interference ability. The robust controller further improves the robustness and tracking control effect of this method. Moreover, the method has higher practicability than other existing methods due to its good adaptability and migration ability. © 2022, The Authors. All rights reserved.

关键词： Manipulators

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An Actor-Critic Framework Deep Reinforcement Learning Approach to High-speed Train Timetable Rescheduling Problem

An Actor-Critic Framework Deep Reinforcement Learning Approa...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zhilong Tian Runmei Li Gang Xiong Fenghua Zhu School of Automation and Intelligence Beijing Jiaotong University Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

High-speed trains are inevitably affected by emergencies in their daily operation, which may cause the trains to fail to run according to the original planned timetable. Therefore, how to adjust the timetable of subsequent trains in emergencies is crucial for railway operations. This paper introduces a deep reinforcement learning algorithm based on an actor-critic network, which minimizes the total delay time by defining the departure order as an action. Numerical experiments are performed on the timetable of Beijing-Shanghai high-speed railway line.

关键词： Optimization models Deep reinforcement learning Rail transportation Dispatching Digital twins Delays Optimization

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A Cyclic Small Phase Theorem for MIMO LTI systems*

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IFAC-PapersOnLine 2023年第2期56卷 1883-1888页

作者： Chao Chen Wei Chen Di Zhao Jianqi Chen Li Qiu Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Mechanics and Engineering Science & State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Control Science and Engineering & Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between -π and π. The proposed theorem complements the celebrated small gain theorem.

关键词： Small phase theorem segmental phase cyclic feedback systems stability analysis lead-lag compensation

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Application of graph-curvature features in computer-aided diagnosis for histopathological image identification of gastric cancer

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intelligent Medicine 2024年第3期4卷 141-152页

作者： Ruilin He Chen Li Xinyi Yang Jinzhu Yang Tao Jiang Marcin Grzegorzek Hongzan Sun Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110016China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110016China School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China Institute for Medical Informatics University of LuebeckLuebeckGermany Department of Knowledge Engineering University of Economics in KatowiceKatowicePoland Shengjing Hospital of China Medical University ShenyangLiaoning 110136China

Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological *** The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for ***,the U-net was applied to segment the ***,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was *** third step was to extract the graph-curvature features of the histopathological image according to the MST ***,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be *** During the experiment,we used various methods for *** the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were *** found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological *** the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern *** the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and *** comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.

关键词： Gastric cancer Graph-curvature feature Image identification

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