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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Xue Zhang Junjie Xue Junhua Zhou Guoqiang Shi Yan Yu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Large-scale equipment presents the characteristics of technology-intensive, knowledge-intensive and complex, which brings high investment and high risk to equipment acquisition. This paper analyzes the various components of equipment acquisition to fully apply modeling and simulation technology to each stage of it. On the basis of multi-objective optimization theory, the objective function of the lowest risk and lowest cost of simulation-based acquisition (SBA) is proposed. Then establishing a multi-objective optimization model for simulation-based acquisition according to the corresponding constraints. The nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO) algorithms are used to optimize the model, and the majority voting method is used for integration. The comparison of simulation examples proves that the multi-objective optimization model oriented to SBA designed in this paper can effectively realize the optimization of each objective of SBA under cost constraints. And the feasibility and efficiency of optimization solution are guaranteed.

关键词： Analytical models Linear programming Reliability Particle swarm optimization Optimization Sorting Investment

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Modeling of service correlation for service composition in cloud manufacturing 31

Modeling of service correlation for service composition in c...

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31st European Modeling and Simulation Symposium, EMSS 2019

作者： Wang, Fei Laili, Yuanjun Zhang, Lin Xing, Chi Guo, Liqin School of Automation Science and Electrical Engineering Beihang University China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing Simulation Center China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center China

ISBN: (纸本)9788885741263

Cloud manufacturing (CMfg), integrating distributed manufacturing resources as services to cloud center, aims at intelligent, green, and economic customized manufacturing. The optimal composition of services to fulfill particular manufacturing requirement is a core issue to realize efficient cloud manufacturing. Many researchers have studied the problem considering the Quality-of-Service (QoS) of independent services. However, the correlation between services is rarely considered. In this paper, the importance of service correlation is emphasized. Two kinds of service correlation, service exclusion and service collaboration, are modeled for service composition. An improved algorithm DET, which combines Differential Evolution Algorithm (DE) with a Tabu table based on service exclusive and collaborative relationships, is designed to filter composable services and find better solutions for complex tasks. Experiments have shown the effects of service correlation on the quality of composed services and demonstrated the effectiveness of the proposed method DET compared with traditional DE. © 2019 Dime Universita di Genova, DIMEG University of Calabria.

关键词： Quality of service

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Globally optimal consensus maximization for robust visual inertial localization in point and line map

Globally optimal consensus maximization for robust visual in...

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2020 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Yanmei Jiao Yue Wang Bo Fu Qimeng Tan Lei Chen Minhang Wang Shoudong Huang Rong Xiong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou P.R. China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing P.R. China Minhang Wang is with the Application Lab Huawei Incorporated Company P.R. China Shoudong Huang is with the Center for Autonomous Systems (CAS) the University of Technology Sydney Australia

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose from a set of 3D-2D feature correspondences, of which the main challenge is the presence of outliers, especially in changing environment. In this paper, we propose a robust solution based on efficient global optimization of the consensus maximization problem, which is insensitive to high percentage of outliers. We first introduce translation invariant measurements (TIMs) for both points and lines to decouple the consensus maximization problem into rotation and translation subproblems, allowing for a two-stage solver with reduced search space. Then we show that (i) the rotation can be estimated by minimizing TIMs using only 1-dimensional branch-and-bound (BnB), (ii) the translation can be estimated by running 1-dimensional search for each of the three axes with prioritized progressive voting. Compared with the popular randomized solver, our solver achieves deterministic global convergence without requiring an initial value. Furthermore, ours is exponentially faster compared with existing BnB based methods. Finally, our experiments on both simulation and real-world datasets demonstrate that the proposed method gives accurate pose estimation even in the presence of 90% outliers (only 2 inliers).

关键词： Location awareness Visualization Pose estimation Search problems State estimation Optimization Rotation measurement

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A knowledge acquisition automatizing framework from literature exemplified by Na + activation energy prediction of NASICON solid-state electrolyte

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Energy Storage Materials 2025年 80卷

作者： Yue Liu Dahui Liu Zhengwei Yang Xianyuan Ge Wenxuan Yao Jie Wu Maxim Avdeev Siqi Shi State Key Laboratory of Materials for Advanced Nuclear Energy & School of Computer Engineering and Science Shanghai University Shanghai 200444 China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444 China Australian Nuclear Science and Technology Organisation Sydney 2232 Australia School of Chemistry The University of Sydney Sydney 2006 Australia State Key Laboratory of Materials for Advanced Nuclear Energy & School of Materials Science and Engineering Shanghai University Shanghai 200444 China Materials Genome Institute Shanghai University Shanghai 200444 China

Materials science literature contains vast amount of structure-activity relationship knowledge crucial for materials discovery and design. However, automatic extraction of domain knowledge from literature remains challenging due to its unstructured and heterogeneous format. Herein, we propose a framework for automating knowledge acquisition, which involves a materials entity-aware relational extraction model (MatRE) to mine triples, an approach to construct a knowledge graph (KG) for the detection of associations among triples, as well as inference and representation of structure-activity relationships in a machine learning (ML)-compatible format. We demonstrate its application in predicting sodium ion activation energy for the NASICON solid-state electrolyte (SSE) system. MatRE trained on a NASICON SSE dataset, achieves an F1-score of 0.80, and is used to extract 260,475 entity–relation triples from 1,808 scientific publications. Furthermore, embedding 24 knowledge bullets from the KG into data pre-processing and feature engineering stages improves the performance and interpretability of six common ML models by up to 25.7%. This work offers key insights into automatic knowledge acquisition from literature and heralds a new paradigm for AI-assisted materials genome engineering driven by both data and knowledge.

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Modeling of fuzzy BN prediction of a complex multi-level system considering multiple states 65

Modeling of fuzzy BN prediction of a complex multi-level sys...

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65th Annual Technical Meeting and Exposition of the institute of Environmental Sciences and technology, ESTECH 2019

作者： Wang, Xiaohong Wang, Yuqing Wang, Lizhi Wang, Jingbin School of Reliability and Systems Engineering Beihang University Beijing100191 China Unmanned System Institute Beihang University Beijing100191 China Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System of Ministry of Industry and Information Technology Beihang University Beijing100191 China AVIC Aeronautical Radio Electronics Research Institute Shanghai201100 China

Reliability modeling of a multi-level system is of great significance to ensure safety, while also presenting new requirements in order to define a full description of the system. However, in traditional reliability research, most systems are considered to have only two states, which is often inadequate and leads to inaccurate predictions. The difference between reality and analysis can be significant such that the analyses cannot meet the needs of more complex systems. In this study, to reach a full understanding for a multi-level system, a modeling framework based on a fuzzy Bayesian Network (BN) is presented. First, state probabilities of root nodes are obtained by integrating the mature prediction model at the component-level. The logical association mapping is developed to achieve a system-level prediction. Second, fuzzy fusion theory is introduced into the BN reliability model to enable it to handle fuzzy information. Finally, by modifying the conditional probability table for the higher-level nodes, all root nodes in the BN are more finely divided into multi-states. The proposed framework is applied to a solar-power unmanned system to illustrate the validity of the model. The network and results from this framework can be used to further analyze the reliability and security of a complex multi-level system with fuzzy and uncertain information in life cycle management. © 2019 institute of Environmental Sciences and technology. All rights reserved.

关键词： Bayesian networks

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Research on Change Path Optimization for Complex Mechanical Products Considering upgrade Risk

Research on Change Path Optimization for Complex Mechanical ...

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Digital Society and intelligent systems (DSInS), International Conference on

作者： Qin Zhang Jinlong Pan Hu Chen Yixiong Feng Zhaoxi Hong Key Laboratory of Robot System Integration and Intelligent Equipment of Huzhou Huzhou Vocatinal & Technical College Huzhou P. R. China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou P. R. China Shanghai Space Propulsion Technology Research Institute Shanghai P. R. China Shanghai Institute of Special Equipment Inspection and Technical Research Shanghai P. R. China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province Zhejiang University Hangzhou P. R. China

ISBN: (纸本)9781665406314

Affected by uncertain factors such as customer demand, product design process will face with varying degrees of upgrade risk. In order to response to change requirements and reduce the impact of component changes, change path optimization for complex products considering upgrade risk is proposed. The goal of this work is to optimize change paths with multi-objective programming. The optimization model is constructed with the goal of minimizing the change complexity, the minimizing change propagation intensity and minimizing the change time. The improved whale algorithm is proposed to solve the model. A set of non-dominated solutions is obtained which can guide decision makers to choose the appropriate scheme. Finally, a product change path optimization for an elevator is given to verify the effectiveness of the proposed method.

关键词： Whales Pareto optimization Product design Elevators Mechanical products Complexity theory intelligent systems

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Publisher Correction: Compact angle-resolved metasurface spectrometer

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Nature materials 2025年第4期24卷 650页

作者： Guiyi Cai Yanhao Li Yao Zhang Xiong Jiang Yimu Chen Geyang Qu Xudong Zhang Shumin Xiao Jiecai Han Shaohua Yu Yuri Kivshar Qinghai Song Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen People's Republic of China. Pengcheng Laboratory Shenzhen People's Republic of China. Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen People's Republic of China. shumin.xiao@***. Pengcheng Laboratory Shenzhen People's Republic of China. shumin.xiao@***. National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology Harbin People's Republic of China. shumin.xiao@***. National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology Harbin People's Republic of China. Pengcheng Laboratory Shenzhen People's Republic of China. yush@***. Nonlinear Physics Center Research School of Physics Australian National University Canberra Australian Capital Territory Australia. yuri.kivshar@anu.edu.au. Qingdao Innovation and Development Center Harbin Engineering University Qingdao People's Republic of China. yuri.kivshar@anu.edu.au. Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen People's Republic of China. qinghai.song@***. Pengcheng Laboratory Shenzhen People's Republic of China. qinghai.song@***. Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan People's Republic of China. qinghai.song@***.

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Compound Asynchronous Exploration and Exploitation

Compound Asynchronous Exploration and Exploitation

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2019 2nd International Conference on Mechanical,Electronic and Engineering technology(MEET 2019)

作者： Jie Bai Li Liu Yaobing Wang Haoyu Zhang Jianfei Li Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering College of Engineering Peking University Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering

Data efficiency has always been a significant key topic for deep reinforcement learning. The main progress has been on sufficient exploration and effective exploitation. However, the two are often discussed separately. Profit from distributed systems, we propose an asynchronous approach to deep reinforcement learning by combining exploration and exploitation. We apply our framework to off-the-shelf deep reinforcement learning algorithms, and experimental results show that our algorithm is superior in final performance and efficiency.

关键词： Deep Reinforcement Learning Exploration and Exploitation Asynchronous Methods

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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2-Entity RANSAC for robust visual localization in changing environment

arXiv

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arXiv 2019年

作者： Jiao, Yanmei Wang, Yue Fu, Bo Ding, Xiaqing Tan, Qimeng Chen, Lei Xiong, Rong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China

Visual localization has attracted considerable attention due to its low-cost and stable sensor, which is desired in many applications, such as autonomous driving, inspection robots and unmanned aerial vehicles. However, current visual localization methods still struggle with environmental changes across weathers and seasons, as there is significant appearance variation between the map and the query image. The crucial challenge in this situation is that the percentage of outliers, i.e. incorrect feature matches, is high. In this paper, we derive minimal closed form solutions for 3D-2D localization with the aid of inertial measurements, using only 2 point matches or 1 point match and 1 line match. These solutions are further utilized in the proposed 2-entity RANSAC, which is more robust to outliers as both line and point features can be used simultaneously and the number of matches required for pose calculation is reduced. Furthermore, we introduce three feature sampling strategies with different advantages, enabling an automatic selection mechanism. With the mechanism, our 2-entity RANSAC can be adaptive to the environments with different distribution of feature types in different segments. Finally, we evaluate the method on both synthetic and real-world datasets, validating its performance and effectiveness in inter-session scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

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