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Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第6期28卷 1050-1061页

作者： Wu, Dongrui Tan, Xianfeng Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Multitask learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multitask optimization applies multitask learning an optimization to study how effectively and efficiently tackle the multiple optimization problems, simultaneously. Evolutionary multitasking, or multifactorial optimization, is an emerging subfield of multitask optimization, which integrates evolutionary computation and multitask learning. This article proposes a novel and easy-to-implement multitasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single-task and multitask approaches in the literature on nine benchmarks demonstrated that, on average, the MTGA outperformed all of them and had lower computational cost than six of them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.

关键词： Task analysis Biological cells Optimization Sociology Statistics Evolutionary computation Genetic algorithms Evolutionary multitasking fuzzy logic controller (FLC) genetic algorithm multifactorial optimization (MFO) multitask learning

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image Regularizations Based on the Sparsity of Corner Points

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IEEE TRANSACTIONS ON image processing 2019年第1期28卷 72-87页

作者： Liu, Haosen Tan, Shan Huazhong Univ Sci & Technol Sch Automat Minist Educ China Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Many analysis-based regularizations proposed so far employ a common prior information, i. e., edges in an image are sparse. However, in local edge regions and texture regions, this prior may not hold. As a result, the performance of regularizations based on the edge sparsity may be unsatisfactory in such regions for image-related inverse problems. These regularizations tend to smooth out the edges while eliminating the noise. In other words, these regularizations' abilities of preserving edges are limited. In this paper, a new prior that the corner points in a natural image are sparse was proposed to construct regularizations. Intuitively, even in local edge regions and texture regions, the sparsity of corner points may still exist, and hence, the regularizations based on it can achieve better performance than those based on the edge sparsity. As an example, by utilizing the sparsity of corner points, we proposed a new regularization based on Noble's corner measure function. Our experiments demonstrated the excellent performance of the proposed regularization for both image denoising and deblurring problems, especially in local edge regions and texture regions.

关键词： Corner detection image restoration nonlocal method regularization structure tensor

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Fast restoration approach for rotational motion blurred image based on deconvolution along the blurring paths

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OPTICAL ENGINEERING 2003年第12期42卷 3471-3486页

作者： Hong, HY Zhang, TX Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

We present a novel approach to restoring images blurred by rotational motions, without experiencing geometric coordinate transformations as in traditional restoration. The space-variant blur is decomposed into a series of space-invariant blurs along the blurring paths. By incorporating Bresenham's algorithm into our work, the blurred gray values of the discrete pixels can be fetched along the blurring paths in real time. Thus, the space-variant blur can be quickly removed along the blurring paths. We apply a two-stage process to restore the rectangular blurred image, which results in the proposal of two corresponding restoration algorithms. One removes the blur by deconvolution along the blurring paths, which are completely inside the rectangular image. The other is used in the case when only some of the pixels of some blurring paths are inside the rectangular image, so based on a neighborhood knowledge guide, the information of these pixels is restored with the least cost in terms of the constrained optimization estimation theory. Furthermore, these two restoration algorithms avoid iteration calculations and some time-consuming operations. To determine the blur center and the blur extents from the blurred image in a case of not knowing the rotational motion parameters, we present, based on cross correlation, an effective blur identification method, which becomes an integral part of the proposed approach. The experimental results demonstrate the efficiency of the proposed restoration algorithms and the effectiveness of the blur identification method. (C) 2003 Society of Photo-Optical Instrumentation Engineers.

关键词： image restoration rotational motion space-variant blur blurring path knowledge guide blur identification

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Lightweight Source-Free Transfer for Privacy-Preserving Motor imagery Classification

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2023年第2期15卷 938-949页

作者： Zhang, Wen Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China

Transfer learning utilizes data or knowledge in one problem to help solve a related problem. It is particularly useful in electroencephalogram (EEG)-based motor imagery (MI) classification, to handle high intrasubject and/or cross-subject variations. This article considers offline unsupervised cross-subject MI classification, i.e., we have labeled EEG trials from several source subjects, but only unlabeled EEG trials from the target subject. Existing transfer learning approaches usually make use of the source-domain data directly in target model learning. To protect the privacy of the source subjects, we propose lightweight source-free transfer (LSFT), which first generates source models locally and encapsulates them as model application programming interfaces (APIs), then constructs a virtual intermediate domain to transfer the knowledge in the source domains to the target domain, and finally performs feature adaptation learning. Compared with the existing deep transfer learning approaches, LSFT does not need to transfer from massive source data or models, is computationally efficient, and has a small number of parameters. Experiments on four benchmark MI data sets demonstrated that LSFT outperformed 13 different approaches, including several state-of-the-art transfer learning approaches that make use of the source-domain samples or model parameters directly.

关键词： Brain modeling Transfer learning Data models Covariance matrices Electroencephalography Adaptation models Computational modeling Brain-computer interface (BCI) electroencephalogram (EEG) motor imagery (MI) privacy protection transfer learning

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Multi-Source Decentralized Transfer for Privacy-Preserving BCIs

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 2710-2720页

作者： Zhang, Wen Wang, Ziwei Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Transfer learning, which utilizes labeled source domains to facilitate the learning in a target model, is effective in alleviating high intra- and inter-subject variations in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Existing transfer learning approaches usually use the source subjects' EEG data directly, leading to privacy concerns. This paper considers a decentralized privacy-preserving transfer learning scenario: there are multiple source subjects, whose data and computations are kept local, and only the parameters or predictions of their pre-trained models can be accessed for privacy-protection;then, how to perform effective cross-subject transfer for a new subject with unlabeled EEG trials? We propose an offline unsupervised multi-source decentralized transfer (MSDT) approach, which first generates a pre-trained model from each source subject, and then performs decentralized transfer using the source model parameters (in gray-box settings) or predictions (in black-box settings). Experiments on two datasets from two BCI paradigms, motor imagery and affective BCI, demonstrated that MSDT outperformed several existing approaches, which do not consider privacy-protection at all. In other words, MSDT achieved both high privacy-protection and better classification performance.

关键词： Brain modeling Electroencephalography Feature extraction Adaptation models Data models Data privacy Computational modeling Brain-computer interfaces transfer learning privacy protection deep learning

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A Regularized Super Resolution Algorithm for Generalized Gaussian Noise

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Journal of Donghua University(English Edition) 2010年第1期27卷 25-35页

作者：陈文方向忠刘立峰蒋伟丁大为乔艳涛 Institute of Image Communication and Information Processing Shanghai Jiaotong University Computer and Information Department Shanghai University of Electric Power Key Lab oratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University

In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is *** on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost *** the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration *** convergence is thoroughly *** results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.

关键词： super resolution generalized p-Gaussian distribution regularization parameter

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Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2022年第1期13卷 16-27页

作者： Wu, Dongrui Huang, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China

Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging. Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far few have considered affective computing. This paper proposes two multi-task active learning for regression approaches, which select the most beneficial samples to label, by considering the three affect primitives simultaneously. Experimental results on the VAM corpus demonstrated that our optimal sample selection approaches can result in better estimation performance than random selection and several traditional single-task active learning approaches. Thus, they can help alleviate the data labeling problem in affective computing, i.e., better estimation performance can be obtained from fewer labeling queries.

关键词： Active learning affective computing emotion estimation multi-task learning regression greedy sampling

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control design of 60kW PEMFC generation system for residential applications

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2013年第9期14卷 679-685页

作者： Ying-ying ZHANG Ying ZHANG Xi LI Guang-yi CAO Shandong Provincial Key Laboratory of Ocean Environment Monitoring Technology Institute of Oceanographic Instrumentation Shandong Academy of Sciences Control Science and Engineering Department Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Fuel Cell Institute Department of Automation Shanghai Jiao Tong University

This paper presented a control design methodology for a proton exchange membrane fuel cell (PEMFC) generation system for residential applications. The dynamic behavior of the generation system is complex in such applications. A comprehensive control design is very important for achieving a steady system operation and efficiency. The control strategy for a 60 kW generation system was proposed and tested based on the system dynamic model. A two-variable single neuron proportional-integral (PI) decoupling controller was developed for anode pressure and humidity by adjusting the hydrogen flow and water injection. A similar controller was developed for cathode pressure and humidity by adjusting the exhaust flow and water injection. The desired oxygen excess ratio was kept by a feedback controller based on the load current. An optimal seeking controller was used to trace the unique optimal power point. Two negative feedback controllers were used to provide AC power and a suitable voltage for residential loads by a power conditioning unit. control simulation tests showed that 60 kW PEMFC generation system responded well for computer-simulated step changes in the load power demand. This control methodology for a 60 kW PEMFC generation system would be a competitive solution for system level designs such as parameter design, performance analysis, and online optimization.

关键词： Proton exchange membrane fuel cell （PEMFC） Generation system control strategy decoupling control Optimalseeking control

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A Novel Evolutionary Template-Matching Algorithm and Researches on It

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电子学报(英文版) 2006年第2期15卷 246-250页

作者： Gao, Jun Zhao, Qin Xu, Xiaohong Yang, Jing Lab of Image Information Processing Hefei University of Technology Hefei 230009 China Center for Biomimetic Sensing and Control Research Institute of Intelligent Machines Chinese Academy of Sciences Hefei 230031 China

This paper proposed a novel evolutionary template-matching algorithm and studied its convergence problem. The method regarded the image matching as a global optimization problem where the main task is to find the parameters of the affine transformation. It can greatly decrease computation amount and quickly detect the affine transformed object from noises-polluted images. Experimental results demonstrate this approach's feasibility and its potential in practical applications.

关键词：进化模板图象匹配进化计算进化策略

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A structured light-based system for human heads

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OPTICS AND LASER TECHNOLOGY 2004年第5期36卷 387-391页

作者： Wu, XF Li, DH Gang, J Zhou, Z Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China

The 3-D modeling of heads by using optical triangulation techniques is of great interest in the context of virtual reality, telecommunication and computer animation. This paper presents a structured light-based system mainly for human heads. It is named "3-D Laser Color Scanner" (3DLCS). A 3-D model is obtained with a cylindrical scan. The laser beam is switched on and off using a "light valve" and two successive CCD frames are captured, one with the laser line showing and one without. We can simplify the laser line extracting by subtracting these two images. In this system, two CCD cameras are used to avoid occlusion problems. Color information is read from the CCD when the laser light is absent. Since traditional laser scanner will miss the range data in the low-reflectance areas such as the hair area of human bead, a shape from silhouette algorithm is presented to overcome this problem. Finally, we give some results using our system. The resulting model is suitable for many applications. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： optical triangulation techniques structured light human heads image processing shape from silhouette

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