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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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A Novel Fault Classification Method for Process Industries Incorporating Multi-sphere Support Tensor data Description and Hybrid Synchronization Optimization

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IFAC-PapersOnLine 2023年第2期56卷 8073-8078页

作者： Chuanfang Zhang Kaixiang Peng Jie Dong Liang Ma Xueyi Zhang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 PR China

Process monitoring is important for ensuring the safe running of process industries. From the perspective of pattern recognition, fault detection can be regarded as a binary classification problem, while fault classification can be regarded as a multi-class classification issue. As the extension of one-class classifier, multi-sphere support vector data description (MSVDD) are oriented to vector data and cannot deal with tensor data directly. Moreover, MSVDD cannot deal with feature selection and parameter optimization synchronously. In order to deal with above problems, a faut classification method based on multi class support tensor data description (MSTDD) is proposed in this paper. Based on sooty tern optimization algorithm (STOA) and beetle antennae search (BAS), a hybrid synchronization optimization (HSO) strategy is used to select features and optimize parameters at the same time. Application to the Tennessee Eastman process (TEP) verifies the classification performance of HSO-MSTDD comparing to MSTDD.

关键词： Fault classification multi-sphere support tensor data description sooty tern optimization algorithm beetle antennae search hot strip mill process

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data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Lin Zou, Qingzhi Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Due to the exceptional performance of Transform-ers in 2D medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%. © 2024 IEEE.

关键词： Image segmentation

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Reduced clinical severity during 2022 Shanghai Spring epidemic of SARS-CoV-2 omicron BA.2 variant infection——an integrated account of virus pathogenicity and vaccination effectiveness

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National Science Review 2024年第4期11卷 24-27页

作者： Xingyue Wu Yao Chen Kangli Cao Yao Shen Xueling Wu Yilin Yang Zhongshu Kuang Qingrun Li Zhenzhen Lu Yichen Jia Mian Shao Guorong Gu Xiangwei Wang Ye Yao Ying Wang Shaodie Chen Zhigao Yu Wei Wei Longfei Ding Lulu Lan Tianwen Gu Xiangyu Long Jian Sun Lingyu Xing Jiayuan Shen Yi Han Yue Luo Sucheng Mu Mengna Lin Xiaoyan Zhang Rong Zeng Jianqing Xu Guoping Zhao Lihong Huang Zhenju Song Department of Emergency Medicine Clinical Center for Bio-Therapy Department of Biostatistics and Department of UrologyShanghai Key Laboratory of Lung Inflammation and Injury Zhongshan Hospital Fudan University Department of Respiratory and Critical Care Medicine Shanghai Pudong Hospital Fudan University Department of Pulmonology Renji Hospital Shanghai Jiao Tong University School of Medicine Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai Public Health Clinical Center Fudan University Department of Biostatistics School of Public Health Key Laboratory of Public Health Safety of Ministry of Education and Shanghai Institute of Infectious Disease and Biosecurity School of Public Health Fudan University Shanghai Institute of Immunology Department of Immunology and Microbiology Shanghai Institute of Virology Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of EducationSchool of Medicine Shanghai Jiao Tong University State Key Laboratory of Genetic Engineering Fudan Microbiome CenterSchool of Life Sciences Fudan University Bio-Med Big Data Center Shanghai Institute of Nutrition and Health Chinese Academy of Sciences

The emergence of SARS-Co V-2 variant of concern omicron BA.2 led to high infection incidence rates as well as reduced severity of COVID-19 during the Shanghai Spring epidemic of 2022 [1]. We assessed the role of viral pathogenicity and the effectiveness of inactivated vaccines in a retrospective observational study including 10 258 confirmed COVID-19cases identified between April and June of 2022, from four COVID-19 designated hospitals located in two areas of Shanghai, namely the West Bund and the East Bund, where the populations’ demographic characteristics varied significantly (Table S1, Fig. S1).

关键词： sars-cov-2 effectiveness epidemic shanghai integrated spring clinical variant virus account

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Cooperative differential games guidance laws for multiple attackers against an active defense target

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Chinese Journal of Aeronautics 2022年第5期35卷 374-389页

作者： Fei LIU Xiwang DONG Qingdong LI Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of ChinaChongqing UniversityChongqing 400044China

This paper is concerned with a scenario of multiple attackers trying to intercept a target with active *** types of agents are considered in the guidance:The multiple attackers,the target and the defender,where the attackers aim to pursuit the target from different directions and evade from the defender *** guidance engagement is formulated in the framework of a zero-sum two-person differential game between the two opposing teams,such that the measurements on the maneuver of the target or estimations on the defending strategy of the defender can be *** of the attackers resides in two aspects:redundant interception under the threat of the defender and the relative intercept geometry with the *** miss distances,the relative intercept angle errors and the costs of the agents are combined into a single performance index of the *** formulation enables a unitary approach to the design of guidance laws for the *** minimize the control efforts and miss distances for the attackers,an optimization method is proposed to find the best anticipated miss distances to the defender under the constraint that the defender is endowed with a capture *** simulations with two cases are conducted to illustrate the effectiveness of the proposed cooperative guidance law.

关键词： Active defense Cooperative guidance Differential games Minimum effort optimization Relative intercept angle

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Enhancing Point Cloud Sampling Quality with Dual-Branch Fusion Networks

Enhancing Point Cloud Sampling Quality with Dual-Branch Fusi...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Jin, Yi Wang, Xu Hu, Mengxia Yu, Hui Li, Yidong Wang, Tao Feng, Songhe Lang, Congyan State Key Laboratory of Intelligent Vehicle Safety Technology China School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing China China Automotive Engineering Research Institute Co. Ltd. China School of Psychology & Neuroscience University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9781665410205

Task-oriented point cloud sampling methods have attracted considerable attention for their ability to adaptively select important point sets based on downstream tasks, achieving an excellent balance between data simplification and task performance. However, existing task-oriented sampling models, primarily based on single-branch designs, struggle to fully extract features from input point clouds that comprehensively reflect multi-dimensional key information, thus limiting their sampling performance. In this paper, we introduce a dual-branch sampling network, named DBS-NET, which conducts crucial point sampling from both the global and local importance perspectives separately before merging them, thereby preserving multi-dimensional key information of the input data during the sampling process. Qualitative and quantitative experimental results demonstrate the competitive performance of DBS-NET on the classification benchmark task. © 2024 IEEE.

关键词： Importance sampling

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A Federated Learning Based Intelligent Fault Diagnosis Framework for Manufacturing Processes with Intraclass and Interclass Imbalance

SSRN

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SSRN 2024年

作者： Ma, Liang Shi, Fuzhong Peng, Kaixiang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

data-based fault diagnosis technology is an important measure to improve the operation safety, stability, and reliability of manufacturing processes, which is a key entry point and innovation power to promote the intelligent manufacturing as well as improve the operation efficiency. Class-balanced datasets often used for modeling and fault diagnosis by the traditional data-based methods. However, in practical engineering applications, manufacturing processes often produce multiple classifications of imbalance data, bringing great challenges to the promotions and applications of the classical data-based methods. To this end, an intelligent fault diagnosis framework is proposed for manufacturing processes, of which the issues on the intraclass and interclass imbalance have been focused. Specifically, considering the non-independently identically distribution characteristics among different fault data and the low recognition rates of minority fault samples, a new cost sensitive convolutional neural network is constructed as a base classifier by coordinating the cross entropy loss function with a specific cost sensitive index. Subsequently, a federated learning based aggregation algorithm is designed to optimize the participation weights of local classifiers with the purpose of cooperating with multiple classifiers to improve the model generalization performance. Finally, the validity of the proposed framework is demonstrated by a typical hot rolling process with different forms of imbalance fault data. The simulation results show that superior diagnosis performance can be achieved compared with some comparative algorithms in each scenario. © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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A Multi-source Information Fusion Method for Mobile Robot Visual-inertial Navigation 20

A Multi-source Information Fusion Method for Mobile Robot Vi...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Xu, Changzhen Zhang, Sen Jiang, Kaice Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education 30 Xueyuan Road Haidian District Beijing100083 China University of Science and Technology Beijing School of Automation and Electrical Engineering 30 Xueyuan Road Haidian District Beijing100083 China

ISBN: (纸本)9798350320831

Vision sensors have the advantages of low cost and low structural complexity. However, in certain challenging scenarios, such as high dynamic range and high-speed motion, tracking failures and inaccurate positioning may occur. While a single sensor excels in specific aspects of SLAM technology, the integration of multiple sources of information has become crucial in the field of vision-based SLAM. To address the limitations of pure visual sensors in the SLAM system, a visual-inertial fusion strategy based on multi-sensor fusion is proposed. By incorporating inertial navigation units, the SLAM system gains absolute scale information, and establishing temporal alignment between camera frames enhances calculations. This approach improves the accuracy and robustness of the mobile robot's positioning and mapping system, enabling the complementary utilization of angular velocity and acceleration data from the IMU and visual camera sensor. Experimental results demonstrate significant enhancements compared to the ORBSLAM2 algorithm, with absolute errors reduced by approximately 25% to 60% and relative errors improved by 70% to 90%. Moreover, the proposed method achieves higher accuracy in calculating attitude changes between adjacent frames. © 2023 IEEE.

关键词： Mobile robots

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