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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China School of Computing and Engineering University of Huddersfield Huddersfield U.K State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://***/flysoaryun/LF-PGVIO. IEEE

关键词： Feature extraction

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Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment 38

Addressing Spatial-Temporal Heterogeneity: General Mixed Tim...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Jiawei Zhao, Chunhui State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University China

Mixed time series (MiTS) comprising both continuous variables (CVs) and discrete variables (DVs) are frequently encountered yet under-explored in time series analysis. Essentially, CVs and DVs exhibit different temporal patterns and distribution types. Overlooking these heterogeneities would lead to insufficient and imbalanced representation learning, bringing biased results. This paper addresses the problem with two insights: 1) DVs may originate from intrinsic latent continuous variables (LCVs), which lose fine-grained information due to extrinsic discretization;2) LCVs and CVs share similar temporal patterns and interact spatially. Considering these similarities and interactions, we propose a general MiTS analysis framework MiTSformer, which recovers LCVs behind DVs for sufficient and balanced spatial-temporal modeling by designing two essential inductive biases: 1) hierarchically aggregating multi-scale temporal context information to enrich the information granularity of DVs;2) adaptively learning the aggregation processes via the adversarial guidance from CVs. Subsequently, MiTSformer captures complete spatial-temporal dependencies within and across LCVs and CVs via cascaded self- and cross-attention blocks. Empirically, MiTSformer achieves consistent SOTA on five mixed time series analysis tasks, including classification, extrinsic regression, anomaly detection, imputation, and long-term forecasting. The code is available at https://***/chunhuiz/MiTSformer. © 2024 Neural information processing systems foundation. All rights reserved.

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One-dimensional hollow porous Ru-CuO nanofibers covered with ZIF-71 for H2S gas sensing and its first-principle study

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稀有金属（英文版） 2025年第2期44卷 1170-1181页

作者： Chang-Kun Qiu Lin Wang Fei An Hao Zhang Qing-Run Li Hao-Zhi Wang Ming-Jun Li Jing-Yu Guo Pei-Lin Jia Zong-Wei Liu Liang Zhu Wei Xu Dong-Zhi Zhang State Key Laboratory of Chemical Safety SINOPEC Research Institute of Safety Engineering Co.Ltd.Qingdao 266104China College of Control Science and Engineering China University of Petroleum(East China)Qingdao 266580China

Based on the unique catalytic properties of precious metals,the introduction of precious metals into metal oxide semiconductors will greatly improve the gas-sensitive properties of *** a new type of porous material,metal-organic frameworks(MOF)can be used for gas separation and adsorption due to their adjustable pore size and acceptable thermal *** this work,the ZIF-71 MOF was synthesizedon CuO nanofibers doped with different concentrations of Ru to form a Ru-CuO@ZIF-71 nanocomposite sensor,which was then used for H2S *** sensor shows sensitivity to trace amounts of H2S gas(100 ppb),and the response is greatly enhanced at the optimal Ru doping ratio and operating *** introduction of the ZIF-71 membrane can significantly increase the selectivity of the sensor while further improving the ***,the possible sensing mechanism of the Ru-CuO@ZIF-71 sensor was *** enhancement of the H2S gas sensing prop-erties may be attributed to the catalysis of Ru and the formation of the Schottky junction at the Ru-CuO ***,the calculation based on density functional theory reveals enhanced adsorption capacities of CuO for H2S after Ru ***,the Ru-CuO@ZIF-71 sensor has strong application potential in exhaled gas detection and portable detection of H2S gas in industrial environments.

关键词： Ru-CuO nanofibers Metal-organic framework Selectivity regulation H2S gas sensor Density functional theory calculations

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Stretchable artificial vision sensor with retinomorphic transistorreservoir computing

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Nano Research 2025年第3期18卷 486-493页

作者： Tongrui Sun Xu Liu Yutong Xu Xinglei Zhao Pu Guo Junyao Zhang Ziyi Guo Yue Wu Shilei Dai Jia Huang School of Materials Science and Engineering Tongji UniversityShanghai 201804China State Key Laboratory of Petroleum Pollution Control BeijingCNPC Research Institute of Safety and Environmental TechnologyBeijing 102206China

Artificial visual sensors(AVSs)with bio-inspired sensing and neuromorphic signal processing are essential for next-generation intelligent *** optoelectronic devices employed in AVSs operate discretely in terms of sensing,processing,and memorization,and not ideal for applications necessitating shape deformation to achieve wide fields-of-view and deep ***,we present stretchable artificial visual sensors(S-AVS)capable of concurrently sensing and processing optical signals while adapting to shape ***,these S-AVSs use a stretchable transistor structure with a meticulously engineered photosensitive semiconductor layer,comprising an organic semiconductor,thermoplastic elastomer,and cesium lead bromide quantum dots(CsPbBr_(3) QDs).They exhibit synaptic behaviors such as excitatory postsynaptic current(EPSC)and paired-pulse facilitation(PPF)under optical signals,maintaining functionality under 30%strain and repeated *** nonlinear response and fading memory effect support in-sensor reservoir computing,achieving image recognition accuracies of 97.46%and 97.1%at 0%and 30%strain,respectively.

关键词： stretchable electronics artificial vision sensors(AVSs) retinomorphic transistors reservoir computing neuromorphic computing

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Directed Acyclic Graphs With Tears

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 972-983页

作者： Chen, Zhichao Ge, Zhiqiang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Peng Cheng Laboratory Shenzhen518000 China

Bayesian network is a frequently used method for fault detection and diagnosis in industrial processes. The basis of Bayesian network is structure learning which learns a directed acyclic graph (DAG) from data. However, the search space will scale super-exponentially with the increase of process variables, which makes the data-driven structure learning a challenging problem. To this end, the DAGs with NOTEAR methods are being well studied not only for their conversion of the discrete optimization into continuous optimization problem but also their compatibility with deep learning framework. Nevertheless, there still remain challenges for NOTEAR-based methods: 1) the infeasible solution results from the gradient descent based optimization paradigm;2) the truncation operation to promise the learned graph acyclic. In this work, the reason for challenge 1) is analyzed theoretically, and a novel method named DAGs with tears method is proposed based on mix-integer programming to alleviate challenge 2). In addition, prior knowledge is able to incorporate into the new proposed method, making structure learning more practical and useful in industrial processes. Finally, a numerical example and an industrial example are adopted as case studies to demonstrate the superiority of the developed method. © 2020 IEEE.

关键词： Numerical methods

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RAGAN: Regression Attention Generative Adversarial Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1549-1563页

作者： Jiang, Xiaoyu Ge, Zhiqiang Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Peng Cheng Laboratory Shenzhen518000 China

Despite surrounding by Big Data, we still need to learn from insufficient data in many scenarios. Building an accurate regression model for a small amount of data is a pretty tricky and exciting problem. At present, it is a promising solution to augment limited real data by generating data through generative adversarial networks (GANs). However, when GAN is used to generate labeled data in regression modeling, it lacks attention to the relationship between independent and dependent variables, resulting in poor performance of regression modeling. This article proposes a novel regression attention GAN (RA-GAN) for augmented regression modeling. Regression attention mechanisms are introduced into network parameters learning of both generator and discriminator in RA-GAN to establish a known relationship between variables. This makes RA-GAN restore the regression information during data generation. In addition, an indicator called cross regression score is designed to describe the quality of the generated data before augmented regression modeling, effectively evaluating data augmentation performance in advance. The effectiveness and superiority of the proposed methods are verified in an actual industrial soft-sensing case and a diabetes prediction case through data augmentation regression applications. © 2020 IEEE.

关键词： Generative adversarial networks

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Attitude control of flexible satellite via three-dimensional magnetically suspended wheel

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Applied Mathematics and Mechanics(English Edition) 2025年第3期46卷 555-572页

作者： J.TAYEBI Yingjie CHEN Ti CHEN Shiyuan JIA State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Intelligent Engineering and Automation Beijing University of Posts and TelecommunicationsBeijing 100876China

This paper proposes an attitude control strategy for a flexible satellite equipped with an orthogonal cluster of three-dimensional(3D)magnetically suspended wheels(MSWs).The mathematical model for the satellite incorporating flexible appendages and an orthogonal cluster of magnetically suspended reaction wheel actuators is initially *** that,an adaptive attitude controller is designed with a switching surface of variable structure,an adaptive law for estimating inertia matrix uncertainty,and a fuzzy disturbance observer for estimating disturbance ***,a Moore-Penrose-based steering law is proposed to derive the tilt angle commands of the orthogonal configuration of the 3D MSW to follow the designed control ***,numerical simulations are presented to validate the effectiveness of the proposed control strategy.

关键词： flexible satellite three-dimensional(3D)magnetically suspended wheel(MSW) attitude control adaptive control disturbance observer

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Fast Distance Protection Scheme for Wind Farm Transmission Lines Considering R-L and Bergeron Models

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 840-852页

作者： Zepeng Hu Bin Li Yuping Zheng Tonghua Wu Jiawei He Bin Yao Yaru Sheng Wei Dai Xindong Li Electrical Engineering and Automation Tianjin UniversityTianjinChina State Key Laboratory of Smart Grid Protection and Control NARI Group CorporationNanjingChina

The distributed capacitance of the line becomes larger as the scale of wind farms,the transmission voltage level,and the transmission distance ***,the error of the traditional time-domain distance protection scheme based on the R-L model,which ignores the distributed capacitance of the line,becomes ***,the error of the time-domain fault location method based on the R-L model,especially the maximum error range,is theoretically analyzed in this *** this basis,a novel fault location method based on the RL and Bergeron models is ***,a fast time-domain distance protection scheme is *** the proposed scheme,the error in the fitting calculation is used to construct a weight matrix,and an algorithm for solving the time-domain differential equations is designed to improve the calculation speed and *** with the traditional frequency-domain distance protection scheme,the proposed scheme is independent of the power supply characteristics;thus,it is suitable for wind farm transmission *** addition,compared with the traditional method based on the R-L model,the proposed scheme effectively avoids the negative influence of the distributed capacitance of the line,which significantly improves the operating *** types of faults are simulated by PSCAD/EMTDC to verify the effectiveness and superiority of the proposed scheme.

关键词： Transmission line R-L model time-domain distance protection wind farm

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Research Progress on the Solder Joint Reliability of Electronics Using in Deep Space Exploration

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Chinese Journal of Mechanical engineering 2023年第2期36卷 16-28页

作者： Qilong Guan Chunjin Hang Shengli Li Dan Yu Ying Ding Xiuli Wang Yanhong Tian State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China Beijing Institute of Control Engineering Beijing 100190China

The spacecraft for deep space exploration missions will face extreme environments,including cryogenic temperature,intense radiation,wide-range temperature variations and even the combination of conditions mentioned *** environments will lead to solder joints degradation or even failure,resulting in damage to onboard *** research activities on high reliability solder joints using in extreme environments can not only reduce the use of onboard protection devices,but effectively improve the overall reliability of spacecraft,which is of great significance to the aviation *** this paper,we review the reliability research on SnPb solder alloys,Sn-based lead-free solder alloys and In-based solder alloys in extreme environments,and try to provide some suggestions for the follow-up studies,which focus on solder joint reliability under extreme environments.

关键词： Deep space exploration Extreme environments Solder joints Microstructure Electronics Reliability

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Few-shot object detection via class encoding and multi-target decoding

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IET Cyber-Systems and Robotics 2023年第2期5卷 1-14页

作者： Xueqiang Guo Hanqing Yang Mohan Wei Xiaotong Ye Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province HangzhouChina

The task of few‐shot object detection is to classify and locate objects through a few annotated *** many studies have tried to solve this problem,the results are still not *** studies have found that the class margin significantly impacts the classification and representation of the targets to be *** methods use the loss function to balance the class margin,but the results show that the loss‐based methods only have a tiny improvement on the few‐shot object detection *** this study,the authors propose a class encoding method based on the transformer to balance the class margin,which can make the model pay more attention to the essential information of the features,thus increasing the recognition ability of the ***,the authors propose a multi‐target decoding method to aggregate RoI vectors generated from multi‐target images with multiple support vectors,which can significantly improve the detection ability of the detector for multi‐target *** on Pascal visual object classes(VOC)and Microsoft Common Objects in Context datasets show that our proposed Few‐Shot Object Detection via Class Encoding and Multi‐Target Decoding significantly improves upon baseline detectors(average accuracy improvement is up to 10.8%on VOC and 2.1%on COCO),achieving competitive *** general,we propose a new way to regulate the class margin between support set vectors and a way of feature aggregation for images containing multiple objects and achieve remarkable *** method is implemented on mmfewshot,and the code will be available later.

关键词： Class Margin Few-Shot Object Detection Multi-Target Transformer

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