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arXiv 2018年

作者： Wu, Dongrui Lin, Chin-Teng Huang, Jian Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan430074 China Faculty of Engineering and Information Technology University of Technology Sydney Australia

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Regression analysis

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Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated images for the Development and Benchmarking of Rice Segmentation Algorithms

arXiv

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arXiv 2025年

作者： Zhou, Junchi Wang, Haozhou Kato, Yoichiro Nampally, Tejasri Rajalakshmi, P. Balram, M. Katsura, Keisuke Lu, Hao Mu, Yue Yang, Wanneng Gao, Yangmingrui Xiao, Feng Chen, Hongtao Chen, Yuhao Li, Wenjuan Wang, Jingwen Yu, Fenghua Zhou, Jian Wang, Wensheng Hu, Xiaochun Yang, Yuanzhu Ding, Yanfeng Guo, Wei Liu, Shouyang Engineering Research Center of Plant Phenotyping Ministry of Education Jiangsu Collaborative Innovation Center for Modern Crop Production Academy for Advanced Interdisciplinary Studies Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University Nanjing China Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology Professor Jayashankar Telangana Agricultural State University Hyderabad India Graduate School of Agriculture Kyoto University Kyoto Japan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene Research Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Center for Geospatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China School of Information and Electrical Engineering Shenyang Agricultural University Shenyang China Rice Research Institute Jilin Academy of Agricultural Sciences Changchun China Institute of Crop Sciences National Key Facility for Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences Beijing China Yuan Long Ping High-Tech Agriculture Co. Ltd. Changsha China

Developing computer vision–based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at ***. © 2025, CC BY.

关键词： Semantic Segmentation

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T-S Fuzzy Logic control with Genetic Algorithm Optimization for Pneumatic Muscle Actuator

T-S Fuzzy Logic Control with Genetic Algorithm Optimization ...

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International Conference on Modelling, Identification and control (ICMIC)

作者： Cheng Chen Jian Huang Dongrui Wu Zhikang Song Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Beijing Advanced Innovation Center of Intelligent Robots and Systems Beijing China School of Automation Huazhong University of Science and Technology Wuhan China

Based on the three elements model of pneumatic muscle actuators(PMA), this paper proposed a T-S fuzzy logic control with genetic algorithm optimization and achieved the trajectory tracking control of PMA. To guarantee the stability of control system, the Lyapunov direct method was used. And the LMI Toolbox of Matlab was used in this paper to solve linear matrix inequalities(LMls) and calculate the state feedback gains. Finally, the results of experiment demonstrated that, T-S fuzzy logic control with genetic algorithm optimization can achieve desired control performance, which overcome the chattering of trajectory tracking, reduced tracking error effectively and improved the accuracy of control.

关键词： Genetic algorithms Fuzzy logic Mathematical model Optimization Muscles Actuators Stability analysis

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Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

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Journal of Physics: Conference Series 2019年第1期1229卷

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education China

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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Simulating evolutional symport/antiport by evolution-communication and vice versa in tissue p systems with parallel communication 18th

Simulating evolutional symport/antiport by evolution-communi...

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18th International Conference on Membrane Computing, CMC 1018

作者： Adorna, Henry Alhazov, Artiom Pan, Linqiang Song, Bosheng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China University of the Philippines Diliman Quezon City1101 Philippines Institute of Mathematics and Computer Science Academy of Science of Moldova Academiei 5 Chişinău2028 Moldova School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

We aim to compare functionality of symport/antiport with embedded rewriting to that of symport/antiport accompanied by rewriting, by two-way simulation, in case of tissue P systems with parallel communication. A simul... 详细信息

ISBN: (纸本)9783319733586

关键词： Tissue

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Robust Semi-global Coordinated Tracking of Saturated Networked Systems

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IFAC-PapersOnLine 2017年第1期50卷 8303-8308页

作者： Wang X. Su H. Cai Y. Department of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China

This paper focuses on the robust semi-global coordinated tracking of general linear systems subject to input saturation together with input additive disturbance and dead zone. A fully distributed algorithm which relating only with the number of the following agents and the relative state information of its neighbors is first constructed, and then sufficient conditions guaranteeing the robust semi-global consensus tracking and the robust semi-global swarm tracking are provided, respectively, on the basis of the low-and-high feedback technique. Finally, numerical simulations are performed to illustrate the effectiveness of the theoretical results. © 2017

关键词： dead zone input additive disturbance input saturation Networked system robust coordinated tracking

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SPATIALLY ATTENTIVE CORRELATION FILTERS FOR VISUAL TRACKING

SPATIALLY ATTENTIVE CORRELATION FILTERS FOR VISUAL TRACKING

引用

IEEE International Conference on image processing

作者： Huai Qin Zhixiong Pi Changqian Yu Changxin Gao Jin-Gang Yu Nong Sang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Huazhong University of Science and Technology Wuhan Hubei CN South China University of Technology Guangzhou China

Although correlation filter based trackers have recently demonstrated excellent performance, they still suffer from the boundary effects. The cosine window is introduced to alleviate the boundary affects, which however may result in poor performance in case of occlusion or fast motion. To address this problem, we propose a simple yet effective framework, which builds a spatially attentive model with multiple features to guide the detection of the correlation filter based trackers. The proposed method not only can breakthrough the spatial extent of cosine window, but also can provides prior information about the target object. Moreover, to model a robust object prior, we propose a generic strategy for adaptive fusion and update of multiple features. Extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our methods compared to the state-of-the-art trackers.

关键词： Target tracking Correlation Adaptation models Color Training Mathematical model

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Multi-view fuzzy logic system with the cooperation between visible and hidden views

arXiv

引用

arXiv 2018年

作者： Zhang, Te Deng, Zhaohong Wu, Dongrui Wang, Shitong School of Digital Media Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China

Multi-view datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis. Given a multi-view dataset, traditional learning algorithms usually decompose it into several single-view datasets, from each of which a single-view model is learned. In contrast, a multi-view learning algorithm can achieve better performance by cooperative learning on the multi-view data. However, existing multi-view approaches mainly focus on the views that are visible and ignore the hidden information behind the visible views, which usually contains some intrinsic information of the multi-view data, or vice versa. To address this problem, this paper proposes a multi-view fuzzy logic system, which utilizes both the hidden information shared by the multiple visible views and the information of each visible view. Extensive experiments were conducted to validate its effectiveness. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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A double-integrator system for distributed optimization of convex cost functions 29

A double-integrator system for distributed optimization of c...

引用

第29届中国控制与决策会议

作者： Ngoc-Tu Tran Xuan Feng Wen-Yuan Xu Na Zhan Xi Wang School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control(Huazhong University of Science and Technology) Ministry of Education Hubei Electric Power Company

ISBN: (纸本)9781509046584

In this paper,the distributed optimization problem is investigated under a second-order multi-agent *** the proposed algorithm,each agent solves the optimization via local computation and information exchange with its neighbors through the communication ***,in comparison with the existing second-order distributed optimization algorithms,the proposed algorithm is much simpler due to one coupled information exchange among the agents is *** achieve the optimization,the distributed algorithm is proposed based on the consensus method and the gradient *** optimal solution of the problem is thus obtained with the design of Lyapunov function and the help of LaSallel's Invariance Principle.A numerical simulation example and comparison of proposed algorithm with existing works are presented to illustrate the effectiveness of the theoretical result.

关键词： Second-order multi-agent systems Distributed optimization Consensus Lyapunov function Gradient algorithm

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Protecting privacy of users in brain-computer interface applications

arXiv

引用

arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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