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arXiv 2021年

作者： Peng, Gang Ren, Zhenyu Wang, Hao Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China IEEE senior member School of Automation South East University Nanjing China

To realize a robust robotic grasping system for unknown objects in an unstructured environment, large amounts of grasp data and 3D model data for the object are required, the sizes of which directly affect the rate of successful grasps. To reduce the time cost of data acquisition and labeling and increase the rate of successful grasps, we developed a self-supervised learning mechanism to control grasp tasks performed by manipulators. First, a manipulator automatically collects the point cloud for the objects from multiple perspectives to increase the efficiency of data acquisition. The complete point cloud for the objects is obtained by utilizing the hand-eye vision of the manipulator, and the TSDF algorithm. Then, the point cloud data for the objects is used to generate a series of six-degrees-of-freedom grasp poses, and the force-closure decision algorithm is used to add the grasp quality label to each grasp pose to realize the automatic labeling of grasp data. Finally, the point cloud in the gripper closing area corresponding to each grasp pose is obtained;it is then used to train the grasp-quality classification model for the manipulator. The results of data acquisition experiments demonstrate that the proposed method allows high-quality data to be obtained. The simulated results prove the effectiveness of the proposed grasp-data acquisition method. The results of performing actual grasping experiments demonstrate that the proposed self-supervised learning method can increase the rate of successful grasps for the manipulator. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

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Generating Any Number of Diversified Hidden Attractors via Memristor Coupling

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2021年第12期68卷 4945-4956页

作者： Zhang, Sen Li, Chunbiao Zheng, Jiahao Wang, Xiaoping Zeng, Zhigang Chen, Guanrong Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Key Lab Meteorol Observat & Informat Proc Nanjing 210044 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Memristors are widely used to construct multi-scroll/wing chaotic systems with complex dynamics. However, the generation of a multi-scroll/wing attractor is typically not induced by the memristor but depends on other nonlinear functions in the system, which does not take advantage of the unique features of the memristor for chaos-based applications. To address this issue, the present paper introduces a memristor coupling (MC) method to construct a novel memristive Sprott A system (MSAS) through coupling a flux-controlled memristor with multi-piecewise linear memductance into the chaotic Sprott A system. From theoretical analysis and numerical simulations, the MSAS is shown to be able to generate any number of multi-type hidden attractors, including multi-one-scroll, multi-double-scroll and multi-double-wing hidden attractors. In addition, it has two kinds of multistabilities, that is, heterogeneous multistability and homogeneous multistability. Based on these unique properties, different numbers of coexisting heterogeneous hidden attractors and coexisting homogeneous hidden attractors are derived respectively by switching the memristor initial states. These interesting dynamical properties are comprehensively investigated using nonlinear analysis tools. Furthermore, hardware experiments are implemented to demonstrate the feasibility of the MSAS and the effectiveness of the MC method. Finally, a new pseudo-random number generator (PRNG) is proposed to explore the practical applications of the MSAS. Performance evaluation results verify the high-quality randomness of the designed PRNG.

关键词： Memristors Mathematical models Couplings Chaotic communication Integrated circuit modeling Hysteresis Generators Memristor memristive Sprott A system multi-scroll wing hidden attractor heterogeneous homogeneous multistability pseudo-random number generator

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An Unsupervised Fault-Detection Method for Railway Turnouts

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第11期69卷 8881-8901页

作者： Guo, Zijian Wan, Yiming Ye, Hao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Automat Beijing 100084 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Railway turnouts require high-performance condition monitoring to prevent disastrous railway accidents. In industrial practice, turnouts' monitoring is usually done by railway workers who visually inspect the operating current curves. This results in huge labor costs and prone to human mistakes. Thus, automating the process of turnouts' monitoring via fault-detection algorithms is imperative. The available turnout field data bring three difficulties to fault detection: 1) large amounts of data do not have any labels;2) data collected in normal condition have multiple unknown modes;and 3) there are only a small number of samples in some modes. To address these difficulties, this article develops a novel unsupervised fault-detection method by using deep autoencoders, which is composed of an unknown modes' mining stage and a multimode fault-detection stage. First, unknown modes are identified through clustering and employing engineer expertise. Then, an ensemble monitoring model, consisting of local monitoring models developed with individual fault-free modes and a global monitoring model developed by merging the data in all fault-free modes, is proposed to improve the overall fault-detection performance. In addition, to construct local models for the modes with a small number of samples, a one-class transfer learning algorithm is presented. In online monitoring, the decision of a newly arrived sample exploits both local models and the global model. Using both the simulated turnout data and the field data collected from a high-speed railway in China, the efficacy and robustness of the proposed approach are demonstrated by comparisons with other methods.

关键词： Monitoring Fault detection Data models Rail transportation Fault diagnosis Data mining Support vector machines Deep autoencoders (DAEs) ensemble monitoring model railway turnouts transfer learning (TL) unknown modes' mining unsupervised fault detection

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CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition

arXiv

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arXiv 2025年

作者： Kan, Siyuan Wu, Huanyu Cui, Zhenyao Huang, Fan Xu, Xiaolong Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Wuhan Institute of Digital Engineering Wuhan430074 China Shanghai Jiao Tong University Shanghai200240 China

Emotion recognition is an important component of affective computing, and also human-machine interaction. Unimodal emotion recognition is convenient, but the accuracy may not be high enough;on the contrary, multi-modal emotion recognition may be more accurate, but it also increases the complexity and cost of the data collection system. This paper considers cross-modal emotion recognition, i.e., using both electroencephalography (EEG) and eye movement in training, but only EEG or eye movement in test. We propose cross-modal contrastive representation distillation (CMCRD), which uses a pre-trained eye movement classification model to assist the training of an EEG classification model, improving feature extraction from EEG signals, or vice versa. During test, only EEG signals (or eye movement signals) are acquired, eliminating the need for multi-modal data. CMCRD not only improves the emotion recognition accuracy, but also makes the system more simplified and practical. Experiments using three different neural network architectures on three multi-modal emotion recognition datasets demonstrated the effectiveness of CMCRD. Compared with the EEG-only model, it improved the average classification accuracy by about 6.2%. © 2025, CC BY.

关键词： Modal analysis

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Exemplar-Based Recursive Instance Segmentation With Application to Plant image Analysis

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IEEE TRANSACTIONS ON image processing 2020年 29卷 389-404页

作者： Yu, Jin-Gang Li, Yansheng Gao, Changxin Gao, Hongxia Xia, Gui-Song Yu, Zhu Liang Li, Yuanqing South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Hubei Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China

Instance segmentation is a challenging computer vision problem which lies at the intersection of object detection and semantic segmentation. Motivated by plant image analysis in the context of plant phenotyping, a recently emerging application field of computer vision, this paper presents the exemplar-based recursive instance segmentation (ERIS) framework. A three-layer probabilistic model is first introduced to jointly represent hypotheses, voting elements, instance labels, and their connections. Afterward, a recursive optimization algorithm is developed to infer the maximum a posteriori (MAP) solution, which handles one instance at a time by alternating among the three steps of detection, segmentation, and update. The proposed ERIS framework departs from previous works mainly in two respects. First, it is exemplar-based and model-free, which can achieve instance-level segmentation of a specific object class given only a handful of (typically less than 10) annotated exemplars. Such a merit enables its use in case that no massive manually-labeled data is available for training strong classification models, as required by most existing methods. Second, instead of attempting to infer the solution in a single shot, which suffers from extremely high computational complexity, our recursive optimization strategy allows for reasonably efficient MAP-inference in full hypothesis space. The ERIS framework is substantialized for the specific application of plant leaf segmentation in this work. Experiments are conducted on public benchmarks to demonstrate the superiority of our method in both effectiveness and efficiency in comparison with the state-of-the-art.

关键词： image segmentation Probabilistic logic Object detection Optimization Shape Computer vision Computational modeling Instance segmentation exemplar-based Hough voting plant leaf segmentation plant phenotyping

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RFSDAF: A New Spatiotemporal Fusion Method Robust to Registration Errors

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Hou, Shuwei Sun, Wenfang Guo, Baolong Li, Xiaodong Zhang, Jianhua Xu, Changzhi Li, Xiaobo Shao, Yingzhao Li, Cheng Xidian Univ Inst Intelligent Control & Image Engn Xian 710071 Peoples R China China Acad Space Technol Xian Xian 710100 Peoples R China Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Peoples R China Chinese Acad Sci Key Lab Environm & Disaster Monitoring & Evaluat Innovat Acad Precis Measurement Sci & Technol Wuhan 430077 Peoples R China

Spatiotemporal fusion is commonly used in remote sensing to generate fine spatial-temporal resolution images. Among most present spatiotemporal fusion methods, the registration error results in a mismatch in the reflectance and class abundance information within the MODIS pixel to be analyzed during the spatiotemporal fusion. In this article, a new spatiotemporal fusion model, robust flexible spatiotemporal data fusion (RFSDAF), is proposed;in other words, this method is robust to registration errors. The RFSDAF method uses a multiscale fusion strategy that is adaptive to different degrees of coregistration error. It incorporates multiscale information, which extends the analysis of the reflectance and class fraction from per-MODIS pixel to inter-MODIS pixels, and it is robust to coregistration errors. This method does not require high-accuracy preprocession coregistration algorithms and can automatically reduce the effect of the registration error to a great extent. The RFSDAF method is compared with four spatiotemporal image fusion algorithms, and the effectiveness of the method in resolving the registration error is demonstrated using both a simulated dataset and two actual satellite datasets. The experimental results show that the RFSDAF method can better reduce the impact of the registration error than the spatial and temporal adaptive reflectance fusion model (STARFM) and FSDAF, which adopts the phase cross-correlation algorithm to preprocess and coregister the MODIS-Landsat image through real image experiments. Consequently, the RFSDAF method has a great robustness for real spatiotemporal image fusion with registration errors and has the potential for monitoring land surface dynamics.

关键词： Remote sensing Spatiotemporal phenomena Earth Artificial satellites MODIS Reflectivity Geology Flexible spatiotemporal data fusion (FSDAF) multiscale analysis registration error spatiotemporal fusion

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Sea Surface Temperature Prediction With Memory Graph Convolutional Networks

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Zhang, Xiaoyu Li, Yongqing Frery, Alejandro C. Ren, Peng China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Victoria Univ Wellington Sch Math & Stat Wellington 6140 New Zealand Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710126 Peoples R China

We develop a memory graph convolutional network (MGCN) framework for sea surface temperature (SST) prediction. The MGCN consists of two memory layers: one graph layer and one output layer. The memory layer captures SST temporal changes via temporal convolution units and gate linear units. The graph layer encodes SST spatial changes in terms of characteristics derived from graph Laplacian. The output layer encapsulates information from the previous layers and produces SST prediction results. The MGCN characterizes both the temporal and spatial changes, rendering a comprehensive SST prediction strategy. We use daily mean SST data for two areas near the Bohai Sea and the East China Sea for experimental evaluations and validate that the MGCN performs better than other traditional machine learning methods for nearshore SST prediction. In addition, we test the MGCN on weekly and monthly mean SST datasets and validate that the MGCN is robust and suitable for SST prediction.

关键词： Convolution Ocean temperature Predictive models Sea surface Training Numerical models Mathematical model Graph convolutional network sea surface temperature (SST) spatiotemporal prediction

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An LBP encoding scheme jointly using quaternionic representation and angular information

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NEURAL COMPUTING & APPLICATIONS 2020年第9期32卷 4317-4323页

作者： Lan, Rushi Lu, Huimin Zhou, Yicong Liu, Zhenbing Luo, Xiaonan South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Intelligent Proc Comp Image & Gra Guilin Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu Fukuoka Japan Univ Macau Dept Comp & Informat Sci Macau Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Peoples R China

Local descriptors play a crucial role in numerous computer vision and pattern recognition applications. This paper proposes a novel local descriptor, called the quaternionic local angular binary pattern (QlabP), for color image classification. QlabP is based on the quaternionic representation (QR) of color images such that it is able to handle all color components holistically as well as consider their relations. Using QR, the quaternionic angular information is further developed to account for more color characteristics. We provide two ways to derive the quaternionic angular information from different perspectives. A pattern encoding operation is finally conducted on the obtained angular information to obtain QlabP. The effectiveness of QlabP has successfully been evaluated by comparing with several state-of-the-art descriptors.

关键词： Quaternionic representation (QR) Local binary pattern (LBP) Quaternionic angular information image classification

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Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

arXiv

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arXiv 2020年

作者： Liu, Ziang Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

—In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria – informativeness, representativeness, and diversity – have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Distributed second order sliding mode secondary frequency control for HVDC system

Distributed second order sliding mode secondary frequency co...

引用

Chinese Automation Congress (CAC)

作者： Yang-Ming Dou Ming Chi Zhi-Wei Liu Key Laboratory of Image Processing and Intelligent Control Ministry of Education College of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper studies the secondary frequency control among non-synchronous AC areas interconnected by High Voltage Direct Current (HVDC). A distributed second order sliding mode control scheme is adopted to secondary frequency control of HVDC transmission systems to adjust the frequency of power grid to rated value, which solves the frequency disturbance caused by load power change of the DC power grid. And on this basis, the power generation in each region is reasonably distributed, so as to minimize the cost of power generation. Finally, the stability of the system is proved on an appropriate sliding manifold.

关键词： Frequency control HVDC transmission Power system stability Economics Sliding mode control Power system dynamics Manifolds

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