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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

GeoReF: Geometric Alignment Across Shape Variation for Categ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Linfang Zheng Tze Ho Elden Tse Chen Wang Yinghan Sun Hua Chen Ales Leonardis Wei Zhang Hyung Jin Chang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology China School of Computer Science. University of Birmigsham UK Department of Computer Science the University of Hong Kong China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress to-wards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrep-ancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach in-tegrates an HS-Iayer and learnable affine transformations, which aims to enhance the extraction and alignment of Geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges di-verse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive ex-periments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics. 1 1 Project page: https://***/***

关键词： Point cloud compression computer vision Shape Soft sensors Pose estimation computer architecture Predictive models

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Snap-through path in a bistable dielectric elastomer actuator

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Applied Mathematics and Mechanics(English Edition) 2022年第8期43卷 1159-1170页

作者： Wenjie SUN Wentao MA Fei ZHANG Wei HONG Bo LI School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an710048China State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Xi’an Jiaotong UniversityXi’an710049China State Key Laboratory for Manufacturing System Engineering Shaanxi Key Lab for Intelligent RobotsSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an710049China Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhenGuangdong Province518055China

The dielectric elastomer(DE)has attracted significant attention due to its desired features,including large deformation,fast response,and high energy ***,for a DE actuator(DEA)utilizing a snap-through deformation mode,most existing theoretical models fail to predict its deformation *** paper develops a new finite element method(FEM)based on the three-parameter Gent-Gent model suitable for capturing strain-stiffening *** simulation results are verified by experiments,indicating that the FEM can accurately characterize the snap-through path of a *** method proposed in this paper provides theoretical guidance and inspiration for designing and applying DEs and bistable electroactive actuators.

关键词： dielectric elastomer actuator(DEA) snap-through bistable finite element method(FEM) strain-stiffening behavior

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Research on Knowledge Graph Completion Method Based on Graph Convolutional Neural Networks 9

Research on Knowledge Graph Completion Method Based on Graph...

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9th International Conference on intelligent Computing and Signal Processing, ICSP 2024

作者： Wang, Yunyi Feng, Shiyu Wang, Renjie Feng, Ping College of Computer Science and Technology Changchun University Jilin Changchun China Ministry of Education Key Laboratory of Intelligent Rehabilitation and Barrier-free Access for the Disabled Jilin Changchun China Jilin Provincial Key Laboratory of Human Health State Identification and Function Enhancement Jilin Changchun China Jilin Rehabilitation Equipment and Technology Engineering Research Center for the Disabled Jilin Changchun China

ISBN: (纸本)9798350376548

Embedding-based knowledge graph complementation techniques have gained more apparent results. However, they still need to improve in dealing with the problem of large-scale sparse graph complementation, and graph convolutional neural network-based knowledge graph complementation methods can better solve this problem and have high research value However, the existing graph convolutional neural network models need to be improved in dealing with node information and global information. Therefore, this paper proposes to dynamically aggregate neighborhood information based on graph convolutional neural networks, where the model uses a single trained weight matrix per layer to aggregate neighborhood information;unlike previous work on convolution using global filters, this paper proposes to adaptively construct convolution filters from relational representations and apply these filters between entity representations to generate convolutional features improve the expressive power of the model;in the datasets, MRR metrics on both FB15k-237 and WN18RR are improved to different degrees compared to the baseline model. © 2024 IEEE.

关键词： Knowledge graph

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A Hybrid Marine Predator Algorithm for Thermal-aware Routing Scheme in Wireless Body Area Networks

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Journal of Bionic Engineering 2023年第1期20卷 81-104页

作者： Tao Hai Jincheng Zhou Mohammad Masdari Haydar Abdulameer Marhoon School of Computer and Information Qiannan Normal University for NationalitiesDuyun558000GuizhouChina Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000GuizhouChina Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Universiti Teknologi MARA40450Shah AlamSelangorMalaysia Department of Computer Engineering Islamic Azad UniversityUrmia BranchUrmiaIran Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq

Thermal-aware routing protocols in WBANs consider temperature factors in the routing process for preventing overheating of the tissues surrounding the sensor ***,providing an energy-efficient and thermal-aware routing in WBANs is a challenging *** deal with this problem,this article presents a novel temperature-aware routing protocol that applies Mamdani-based Fuzzy Logic Controllers(FLCs)for selecting the next forwarding node in routing data *** FLCs apply five important input factors such as the priority of the packet,and sensor node's remaining energy,temperature,distance,and link path ***,a new hybrid version of the Marine Predator Algorithm(MPA),named MPAOA is presented by combining the exploration and exploitation phases of the MPA and Arithmetic Optimization Algorithm(AOA).This algorithm is effectively applied for selecting the best possible set of fuzzy rules for FLCs and tuning their fuzzy *** experiments conducted in the Castalia simulator exhibit that the proposed temperature and priority-aware routing scheme can outperform other well-known routing schemes such as LATOR,TTRP,TAEO,ATAR,and EOCC-TARA in terms of metrics such as sensor nodes lifetime,the average temperature of the sensor nodes,and the percentage of the packets routed through non-overheated ***,it is shown that the MPAOA outperforms other algorithms such as Bat Algorithm(BA),Genetic Algorithm(GA),AOA,and MPA regarding the specified metrics.

关键词： WBANs Multi-hop routing Temperature Priority Fuzzy logic Bio-inspiration

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Visible to near-infrared photodetector based on organic semiconductor single crystal

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红外与毫米波学报 2025年第1期44卷 46-51页

作者： LI Xiang HU Jin-Han ZHONG Zhi-Peng CHEN Yu-Zhong WANG Zhi-Qiang SONG Miao-Miao WANG Yang ZHANG Lei LI Jian-Feng HUANG Hai School of Mathematics and Physics Lanzhou Jiaotong UniversityLanzhou 730070China State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronic and PerceptionInstitute of Optoelectronic and Department of Material ScienceFudan UniversityShanghai 200433China School of Materials Science and Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

Organic semiconductor materials have shown unique advantages in the development of optoelectronic devices due to their ease of preparation,low cost,lightweight,and *** this work,we explored the application of the organic semiconductor Y6-1O single crystal in photodetection ***,Y6-1O single crystal material was prepared on a silicon substrate using solution droplet casting *** optical properties of Y6-1O material were characterized by polarized optical microscopy,fluorescence spectroscopy,etc.,confirming its highly single crystalline performance and emission properties in the near-infrared *** based on Y6-1O materials with different thicknesses were then fabricated and *** was found that the devices exhibited good visible to near-infrared photoresponse,with the maximum photoresponse in the near-infrared region at 785 *** photocurrent on/off ratio reaches 10^(2),and photoresponsivity reaches 16 mA/*** was also found that the spectral response of the device could be regulated by gate voltage as well as the material thickness,providing important conditions for optimizing the performance of near-infrared *** study not only demonstrates the excellent performance of organic phototransistors based on Y6-1O single crystal material in near-infrared detection but also provides new ideas and directions for the future development of infrared detectors.

关键词： near-infrared photodetector organic semiconductor Y6-1O single crystal spectral response

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Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL

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IEEE Transactions on Communications 2024年第6期73卷 4272-4289页

作者： Zhao, Yikun Zhou, Fanqin Feng, Lei Sun, Yao Li, Wenjing Lim, Wei Yang Bryan Xiong, Zehui Mao, Shiwen Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Nanyang Technological University Alibaba Group and Alibaba-NTU Joint Research Institute Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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A novel rock bolting system exploiting steel particles

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International Journal of Mining science and technology 2022年第5期32卷 1045-1058页

作者： Xiaowei Feng Fei Xue Valter Carvelli Tongyang Zhao Fengzhen He Dehua Wang State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology Shandong University of Science and TechnologyQingdao 266590China School of Mines China University of Mining and TechnologyXuzhou 221116China Key Laboratory of Rock Mechanics and Geohazards of Zhejiang Province College of Civil EngineeringShaoxing UniversityShaoxing 312000China Department A.B.C Politecnico di MilanoMilan 20133Italy

The effectiveness of rock bolting in ground control has been extensively investigated,mainly for resin based *** coupling materials are needed to have good mechanical performance and to reduce the economic *** study proposed a new bolting system exploiting steel particles as coupling *** applicability of this system was assessed by laboratory and field pullout tests,assisted by digital imaging correlation(DIC),infrared thermography(IRT)and acoustic emission(AE).The results indicated that,for a 20 mm diameter bolt,the suitable steel particle size and corresponding inner diameter of borehole were 1.4 and 28 mm,*** bolts installed in steel tubes,the particles improved the loading capacity compared to the resin bonded *** pullout tests on cement blocks indicated that steel particles can be effective for hard rock,whilst resin was a better choice for bolting of soft *** understanding was obtained by pullout tests in engineering fields,which demonstrated that the steel particles coupled bolts can provide favorable effects in hard rock mass,while the effects were negligible when installed in extremely soft coal *** wide set of multi-technique measurements helped to understand the mechanisms involved in the performance of the bolting system with coupling steel particles.

关键词： Rock bolt Steel particle Pullout test Digital image correlation Infrared thermography Field test

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An Improved Dissimilarity based Approach to Semantic Similarity Calculation 9

An Improved Dissimilarity based Approach to Semantic Similar...

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9th International Conference on Dependable systems and Their Applications, DSA 2022

作者： Guan, Hui Ma, Tianyu Wang, Guangwei Shenyang University of Chemical Technology Department of Computer Science and Technology Liaoning ShenYang China Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry Liaoning Shenyang China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

With the trend that the computational process of semantic similarity more and more mimics the human thought process, it becomes very important to consider the difference between semantics. In this paper, we review commonly used semantic similarity models, discuss the limitations of our previous models, and improve the information content (IC) computation model based on wordnet ontology. In turn, different strategy is employed. The results show that the model proposed in this paper has high computational accuracy. © 2022 IEEE.

关键词： Ontology

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A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery

arXiv

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arXiv 2023年

作者： Zhang, Hanlei Xu, Hua Wang, Xin Long, Fei Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China

New intent discovery is of great value to natural language processing, allowing for a better understanding of user needs and providing friendly services. However, most existing methods struggle to capture the complicated semantics of discrete text representations when limited or no prior knowledge of labeled data is available. To tackle this problem, we propose a novel clustering framework, USNID, for unsupervised and semi-supervised new intent discovery, which has three key technologies. First, it fully utilizes unsupervised or semi-supervised data to mine shallow semantic similarity relations and provide well-initialized representations for clustering. Second, it designs a centroid-guided clustering mechanism to address the issue of cluster allocation inconsistency and provide high-quality self-supervised targets for representation learning. Third, it captures high-level semantics in unsupervised or semi-supervised data to discover fine-grained intent-wise clusters by optimizing both cluster-level and instance-level objectives. We also propose an effective method for estimating the cluster number in open-world scenarios without knowing the number of new intents beforehand. USNID performs exceptionally well on several benchmark intent datasets, achieving new state-of-the-art results in unsupervised and semi-supervised new intent discovery and demonstrating robust performance with different cluster numbers. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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