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Minimum Epsilon-Kernel Computation for Large-Scale Data Processing

Journal of Computer science & technology 2022年第6期37卷 1398-1411页

作者： Hong-Jie Guo Jian-Zhong Li Hong Gao School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Kernel is a kind of data summary which is elaborately extracted from a large *** a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate *** is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive *** this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data *** the open problem presented by Wang et *** whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its *** to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve *** prove that the MC problem and the MK problem can be Turing-reduced to each ***,we discuss the update of MK under insertion and deletion operations,***,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of *** efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic *** show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,*** speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic ***-SCMK runs up to three times faster than SCMK on synthetic datasets.

关键词： approximate query processing kernel large-scale dataset NP-hard

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Finite-Time control for Combination Spacecraft Attitude Based on LESO of Auxiliary System

Finite-Time Control for Combination Spacecraft Attitude Base...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Han, Xiao Zhou, Lifan Ma, Guangfu Chen, Xixuan Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9789811966125

This paper proposes a finite-time control method for the attitude of combined spacecraft without system parameter identification, avoiding the problems of long identification time and low identification accuracy of the combined spacecraft parameters. The method designs an auxiliary system based on the features of the model, combines the Linear Extended State Observer (LESO) and non-singular terminal sliding mode control(SMC), and achieves the finite-time attitude control of the combined spacecraft. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spacecraft

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Exploring Adversarial Robustness of Deep State Space Models 38

Exploring Adversarial Robustness of Deep State Space Models

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qi, Biqing Luo, Yiang Gao, Junqi Li, Pengfei Tian, Kai Ma, Zhiyuan Zhou, Bowen Department of Electronic Engineering Tsinghua University China Shanghai Artificial Intelligence Laboratory China Department of Control Science and Engineering Harbin Institute of Technology China School of Mathematics Harbin Institute of Technology China

Deep State Space Models (SSMs) have proven effective in numerous task scenarios but face significant security challenges due to Adversarial Perturbations (APs) in real-world deployments. Adversarial Training (AT) is a mainstream approach to enhancing Adversarial Robustness (AR) and has been validated on various traditional DNN architectures. However, its effectiveness in improving the AR of SSMs remains unclear. While many enhancements in SSM components, such as integrating Attention mechanisms and expanding to data-dependent SSM parameterizations, have brought significant gains in Standard Training (ST) settings, their potential benefits in AT remain unexplored. To investigate this, we evaluate existing structural variants of SSMs with AT to assess their AR performance. We observe that pure SSM structures struggle to benefit from AT, whereas incorporating Attention yields a markedly better trade-off between robustness and generalization for SSMs in AT compared to other components. Nonetheless, the integration of Attention also leads to Robust Overfitting (RO) issues. To understand these phenomena, we empirically and theoretically analyze the output error of SSMs under AP. We find that fixed-parameterized SSMs have output error bounds strictly related to their parameters, limiting their AT benefits, while input-dependent SSMs may face the problem of error explosion. Furthermore, we show that the Attention component effectively scales the output error of SSMs during training, enabling them to benefit more from AT, but at the cost of introducing RO due to its high model complexity. Inspired by this, we propose a simple and effective Adaptive Scaling (AdS) mechanism that brings AT performance close to Attention-integrated SSMs without introducing the issue of RO. Our code is available at Robustness-of-SSM. © 2024 Neural information processing systems foundation. All rights reserved.

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Coalition formation problem: a capability-centric analysis and general model

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science China(Information sciences) 2024年第11期67卷 180-193页

作者： Jie CHEN Miao GUO Bin XIN Qing WANG Shengyu LU Yipeng WANG Yulong DING School of Automation Beijing Institute of Technology National Key Lab of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering Tongji University

Coalition formation(CF) refers to reasonably organizing robots and/or humans to form coalitions that can satisfy mission requirements, attracting more and more attention in many fields such as multirobot collaboration and human-robot collaboration. However, the analysis on CF problems remains *** provide a valuable study reference for researchers interested in CF, this paper proposed a capabilitycentric analysis of the CF problem. The key problem elements of CF are firstly extracted by referencing the concepts of the 5W1H method. That is, objects(who) form coalitions(what) to accomplish missions(why) by aggregating capabilities(how) in a specific environment(where-when). Then, a multi-view analysis of these elements and their correlation in terms of capabilities is proposed through various logic diagrams, structure charts, etc. Finally, to facilitate a deeper understanding of capability-centric CF, a general mathematical model is constructed, demonstrating how the different concepts discussed in this analysis contribute to the overall model.

关键词： coalition formation capability aggregation capability metric mission requirement environmental effect

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Cross-modal three-dimensional intrinsic image decomposition for hyperspectral and LiDAR image joint classification 43

Cross-modal three-dimensional intrinsic image decomposition ...

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43rd Chinese control Conference, CCC 2024

作者： Yu, Wenbo Zhang, Miao Soochow University School of Electronics and Information Engineering Suzhou215021 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China

ISBN: (纸本)9789887581581

Existing remote sensing technologies capture discriminant information of on-ground objects and materials from a distance for accurate land-cover identification. Specifically, hyperspectral and light detection and ranging (LiDAR) images record spectral and spatial information and elevation information, respectively. Aligning and fusing both RS modalities comprehensively is beneficial to improving classification performance. However, this alignment and fusion are always challenged by the enormous RS modality gap. In this paper, a cross-modal three-dimensional intrinsic image decomposition (CM3DIID) is presented for hyperspectral and LiDAR image joint classification. Intrinsic image decomposition contributes to enhancing semantic information and making RS modalities clear. Our motivation is to construct a unique cross-modal intrinsic image decomposition for three-dimensional data cubes specifically, facilitating robust discriminant information extraction. Specifically, the hyperspectral image contributes to restricting the decomposed reflectance component, making the samples with similar spectral curves very close. The LiDAR image is considered to search samples with similar elevations, which should be closed in the shading component. The whole optimization process is clearly analyzed and achieved by constructing a deep learning network. The proposed CM3DIID constructs a connection bridge between hyperspectral and LiDAR images for information switching and modality fusing. Multiple experiments are conducted on two widely used hyperspectral and LiDAR datasets to verify the effectiveness and feasibility of the proposed CM3DIID. The final results clearly prove that the proposed CM3DIID is capable of generating better classification performance than other state-of-the-art comparison techniques. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image enhancement

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A novel perylene diimide-based ionene polymer and its mixed cathode interlayer strategy for efficient and stable inverted perovskite solar cells

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Journal of Energy Chemistry 2023年第7期82卷 334-342,I0008页

作者： Daizhe Wang Cong Kang Tengling Ye Dongqing He Shan Jin Xiaoru Zhang Xiaochen Sun Yong Zhang Department of Applied Chemistry School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin 150001HeilongjiangChina CAS Key Laboratory of Renewable Energy Guangzhou Institute of Energy ConversionGuangzhou 510640GuangdongChina School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina Institute of Advanced Technology Heilongjiang Academy of SciencesHarbin 150020HeilongjiangChina

Inverted(p-i-n)perovskite solar cells(PerSCs)have attracted much attention owing to their low temperature processability,less hysteresis effect and easy integration as a subunit for the tandem *** unsatisfactory interface contacts and energy level barrier between adjacent interlayers on the cathode side are one of the key challenges for the development of p-i-n ***,perylene diimidebased(PDI)ionene polymer was synthesized and developed as a cathode interlayer(CIL)to enhance interface contact,reduce the energy level barrier and prevent the migration of *** compact PNPDI CIL with high conductivity and appropriate lowest unoccupied molecular orbital(LUMO)level,resulted in a high efficiency device(20.03%),which is higher than the control device with bathophenanthroline(Bphen)(19.48%).Bphen-based CIL shows better adjusting ability of the work function of cathode metal but exhibits poor film-forming ***,the synergistic effect of 1+1>2 can be obtained by combining Bphen and PNPDI into one *** expected,the device performance was further improved by using the mixed CIL of Bphen and PNPDI,and 21.46%power conversion efficiency(PCE)was ***’s more,the compact and hydrophobic mixed CIL dramatically enhanced the resistance to I-ions and moisture,which led to much enhanced device stability.

关键词： Perylene diimide-based polymer Ionene Perovskite solar cells Interface engineeringCathode interlayer

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Multi-perspective regional continuity alignment network for hyperspectral and LiDAR image fusion and classification 43

Multi-perspective regional continuity alignment network for ...

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43rd Chinese control Conference, CCC 2024

ISBN: (纸本)9789887581581

In these decades, remote sensing (RS) techniques have been widely used when facing diverse real-world tasks. These unique RS theories tend to observe on-ground objects and materials by several techniques from different perspectives. Generally, several works have verified that land cover classification tasks are always hampered when using one single RS modality owing to the lack of diverse information. In this paper, we propose a multi-perspective regional continuity aligning network (MPRCAnet) for hyperspectral and light detection and ranging (LiDAR) image fusion and classification. Our motivation is to capture and align the regional spatial-spectral continuities from different perspectives for better performance. Specifically, the whole network consists of three parallel streams, including 1) the HS spatial stream, 2) the HS spectral stream and 3) the LiDAR spatial stream. This network aims to discuss the potential of the continuous information extraction strategy in related tasks. Multiple experiments are conducted on two publicly available hyperspectral and LiDAR datasets to prove the effectiveness and feasibility of the proposed MP-RCAnet. Compared with several state-of-the-art techniques, MP-RCAnet is capable of generating fusion and classification results with higher evaluation accuracies. Its classification map has less scattered noise than other techniques. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image fusion

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Nodes2STRNet for structural dense displacement recognition by deformable mesh model and motion representation

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International Journal of Mechanical System Dynamics 2023年第3期3卷 229-250页

作者： Jin Zhao Hui Li Yang Xu Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbinChina Key Lab of Structures Dynamics Behavior and Control of the Ministry of Education Harbin Institute of TechnologyHarbinChina School of Civil Engineering Harbin Institute of TechnologyHarbinChina Department of System Design and Simulation Goldwind Science and Technology Co.Ltd.BeijingChina

Displacement is a critical indicator for mechanical systems and civil *** vision-based displacement recognition methods mainly focus on the sparse identification of limited measurement points,and the motion representation of an entire structure is very *** study proposes a novel Nodes2STRNet for structural dense displacement recognition using a handful of structural control nodes based on a deformable structural three-dimensional mesh model,which consists of control node estimation subnetwork(NodesEstimate)and pose parameter recognition subnetwork(Nodes2PoseNet).NodesEstimate calculates the dense optical flow field based on FlowNet 2.0 and generates structural control node ***2PoseNet uses structural control node coordinates as input and regresses structural pose parameters by a multilayer perceptron.A self-supervised learning strategy is designed with a mean square error loss and L2 regularization to train *** effectiveness and accuracy of dense displacement recognition and robustness to light condition variations are validated by seismic shaking table tests of a four-story-building *** studies with image-segmentation-based Structure-PoseNet show that the proposed Nodes2STRNet can achieve higher accuracy and better robustness against light condition *** addition,NodesEstimate does not require retraining when faced with new scenarios,and Nodes2PoseNet has high self-supervised training efficiency with only a few control nodes instead of fully supervised pixel-level segmentation.

关键词： structural dense displacement recognition deformable structural mesh model deep-learning-based monocular vision self-supervised learning

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Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

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Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： Ziyou Zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong State Key Laboratory of Mechanical Transmission for Advanced Equipment&College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400000China Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

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Sneak Circuit Analysis Covering Interface Circuits in Spacecraft Integrated Chips while Considering Faults 6

Sneak Circuit Analysis Covering Interface Circuits in Spacec...

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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Liu, Weiming Chen, Cen Xiao, Kaiwen Feng, Mingtao Zhang, Haonan Zhai, Guofu Harbin Institute of Technology Department of Electrical Engineering & Automation Harbin China Beijing Aerospace Automatic Control Institute Beijing China Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350356083

Sneak circuit analysis is a crucial reliability design process. In the design of spacecraft electronic systems, it is challenging to identify sneak circuits related to integrated chip interface circuits, and there are few targeted researches. In extreme working conditions, the coupling sneak circuits caused by the failure of integrated chips are difficult to identify, affecting the design optimization. The paper proposes a typical interface information specification model for integrated chips to simulate the electrical information characteristics of interfaces. And the typical failure models of intrinsic components inside the chip are determined through multi-physics field analysis. Then, based on the digital model, a sneak circuit diagnosis process based on excitation and fault injection are proposed, and a sneak circuit screening method based on Python virtual prototyping is implemented in the case study. Finally, the design constraint clues for the chip's external circuit are obtained, which provides a reference for related fault diagnosis and reliability optimization works. © 2024 IEEE.

关键词： Integrated circuit design

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