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Universal enzymatic numerical P systems with small number of enzymatic variables

Science China(Information Sciences) 2018年第9期61卷 38-49页

作者： Zhiqiang ZHANG Tingfang WU ANDrei P■UN Linqiang PAN Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Department of Computer Science Faculty of Mathematics and Computer ScienceUniversity of Bucharest Bioinformatics Department National Institute of Research and Development for Biological Sciences School of Electric and Information Engineering Zhengzhou University of Light Industry

Numerical P systems(for short, NP systems) are distributed and parallel computing models inspired from the structure of living cells and economics. Enzymatic numerical P systems(for short, ENP systems) are a variant of NP systems, which have been successfully applied in designing and implementing controllers for mobile robots. Since ENP systems were proved to be Turing universal, there has been much work to simplify the universal systems, where the complexity parameters considered are the number of membranes, the degrees of polynomial production functions or the number of variables used in the *** the number of enzymatic variables, which is essential for ENP systems to reach universality, has not been investigated. Here we consider the problem of searching for the smallest number of enzymatic variables needed for universal ENP systems. We prove that for ENP systems as number acceptors working in the all-parallel or one-parallel mode, one enzymatic variable is sufficient to reach universality; while for the one-parallel ENP systems as number generators, two enzymatic variables are sufficient to reach *** results improve the best known results that the numbers of enzymatic variables are 13 and 52 for the all-parallel and one-parallel systems, respectively.

关键词： bio-inspired computing numerical P system mobile robot membrane controller universality

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An improved proxy-based sliding mode control method for a wearable wrist exoskeleton

IPPTA: Quarterly Journal of Indian Pulp and Paper Technical ...

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IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association 2018年第1期30卷 458-467页

作者： Tu, Xikai Wu, Sai Li, Jialu Huang, Ming School of Mechanical Engineering Hubei University of Technology Wuhan China School of Industrial Design Hubei University of Technology Wuhan China School of Mechatronic Engineering Jiangsu Institute of Technology Changzhou China MOE Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China

In order to improve the safety and adaptability during the robot-assisted rehabilitation of wrist joint, a wearable wrist exoskeleton rehabilitation device driven by pneumatic muscles is developed, and its control method is a new modified proxy-based sliding mode control (PSMC) strategy. PSMC created by Ryo Kikuuwe and Hideo Fujimoto, combines good tracking with control safety so it is well suitable for pneumatic muscle actuators. This paper build the dynamics models and compares the two kinds of modified PSMC with PSMC methods applied to the wrist rehabilitation exoskeleton. Experimental results demonstrate that the improved PSMC method performs better on tracking and less little degradation on safety when the system lies in a condition of uncertain resistance and changeable load. © 2018 Indian Pulp and Paper Technical Association. All rights reserved.

关键词： Sliding mode control

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Face Recognition with Convolutional Neural Networks and subspace learning 2

Face Recognition with Convolutional Neural Networks and subs...

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2nd International Conference on image, Vision and Computing, ICIVC 2017

作者： Wan, Lihong Liu, Na Huo, Hong Tao, Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781509062379

Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture. Then, two types of subspace learning methods, namely, linear discriminate analysis (LDA) and whitening principal component analysis (WPCA), are respectively introduced to learn the subspace of the activation vectors for face recognition under multiple samples per subject and single sample per subject circumstances. The goals of applying subspace learning to the activation vectors are obtaining compact representation (dimensionality reduction) and performance improvement. Experiments on two face databases (CMU PIE and FERET) demonstrate the effectiveness of VGG-Face + LDA and VGG-Face + WPCA, compared with state-of-the-art methods. © 2017 IEEE.

关键词： Principal component analysis

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One-pass multi-task networks with cross-task guided attention for brain tumor segmentation

arXiv

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arXiv 2019年

作者： Zhou, Chenhong Ding, Changxing Wang, Xinchao Lu, Zhentai Tao, Dacheng School of Electronic and Information Engineering South China University of Technology Guangzhou510000 China Department of Computer Science Stevens Institute of Technology Hoboken United States Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China UBTech Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia

Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation. Despite its outstanding performance, however, this method leads to undesired system complexity and also ignores the correlation among the models. To handle these flaws in the MC approach, we propose in this paper a light-weight deep model, i.e., the One-pass Multi-task Network (OM-Net) to solve class imbalance better than MC does, while requiring only one-pass computation for brain tumor segmentation. First, OM-Net integrates the separate segmentation tasks into one deep model, which consists of shared parameters to learn joint features, as well as task-specific parameters to learn discriminative features. Second, to more effectively optimize OM-Net, we take advantage of the correlation among tasks to design both an online training data transfer strategy and a curriculum learning-based training strategy. Third, we further propose sharing prediction results between tasks, which enables us to design a cross-task guided attention (CGA) module. By following the guidance of the prediction results provided by the previous task, CGA can adaptively recalibrate channel-wise feature responses based on the category-specific statistics. Finally, a simple yet effective post-processing method is introduced to refine the segmentation results of the proposed attention network. Extensive experiments are conducted to demonstrate the effectiveness of the proposed techniques. Most impressively, we achieve state-of-the-art performance on the BraTS 2015 testing set and BraTS 2017 online validation set. Using these proposed approaches, we also won joint third place in the BraTS 2018 challenge among 64 participating teams. The code is publicly available at https://***/chenhong-zhou/OM-Net. Copyright © 2019, The Author

关键词： Magnetic resonance imaging

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

arXiv

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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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Multi-objective optimization for ladle tracking of aluminium tapping based on NSGA-II 1

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12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Zhou, Kaibo Zou, Yutao Wang, Hongting Xu, Gaofeng Guo, Sihai School of Automation Huazhong University of Science and Technology Wuhan430074 China MOE Key Laboratory of Image Processing and Intelligence Control Wuhan430074 China School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (数字)9789811071799

ISBN: (纸本)9789811071782

In order to realize the optimization of ladle tracking of aluminium tapping, a mathematical model, which takes the grade of aluminium, the energy required for transportation and the optimum ratio of aluminium liquid into account, is established. The traditional method optimizes the impurity content and the transport distance based on a single objective optimization, but it requires the empirical values of the weight coefficients. The paper proposes a modified multi-objective optimization model with the elitist non-dominated sorting genetic algorithm (NSGA-II). In the crossover operator process and the mutation operator process, the separately improved methods are introduced based on the ladle tracking problem of aluminium tapping, which replaces the Simulated Binary Crossover (SBX) in the original NSGA-II algorithm into Partially Matched Crossover (PMX) based on natural number coding and uses exchange mutation (EM) operator. Finally, the practical production data of the aluminium factory is used to verify the validity and practicability of the method, and the results show that this method can obtain a feasible solution for the user to choose suitable solutions, and avoid the defects of selecting empirical weighting coefficient. © Springer Nature Singapore Pte Ltd 2017.

关键词： Multiobjective optimization

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Fault diagnosis in aluminium electrolysis using a joint method based on kernel principal component analysis and support vector machines 12th

Fault diagnosis in aluminium electrolysis using a joint meth...

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12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Zhou, Kaibo Xu, Gaofeng Wang, Hongting Guo, Sihai School of Automation Huazhong University of Science and Technology Wuhan430074 China MOE Key Laboratory of Image Processing and Intelligence Control Wuhan430074 China School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9789811071782

As a key part of aluminium smelting, the operational conditions of aluminium electrolytic cells are of great significance for the stability of the aluminium electrolysis process. As a result, developing a effective process monitoring and multiple fault diagnosis model is essential. Traditional multi-classification methods such as neural networks and multiple support vector machines (multi-SVM) have good effects. However, the connatural limitations of these methods limit the prediction accuracies. To solve this problem, a hierarchical method for multiple fault diagnosis based on kernel principal component analysis (KPCA) and support vector machines (SVM) is proposed in this paper. Firstly, test statistics, such as the comprehensive index , the squared prediction error (SPE), and Hotellings T-squared (T2), are used for fault detection. To separate faults preliminarily, traditional K-means clustering as transition layer is applied to the principal component scores. Next, anode effect is recognized and classified by the established SVM prediction model. Compared with multi-SVM-based classification methods, the proposed hierarchical method can diagnosis different faults with a higher precision. The prediction accuracy can reach about 90%. © Springer Nature Singapore Pte Ltd 2017.

关键词： Support vector machines

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H∞ consensus control of fractional-order multi-agent systems over the directed graph

H∞ consensus control of fractional-order multi-agent system...

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第37届中国控制会议

作者： Lin Chen Wen-Sen Wang Hao Wu Yan Lei Jiang-Wen Xiao 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 State grid shanxi electric power research institute

In this paper, the H∞ consensus of fractional-order multi-agent systems with directed communication graph is investigated. It's the first time to introduce the H∞ control to investigate the consensus problem of the fractional-order multi-agent systems. In view of Mittag-Leffler stability theory and fractional Lyapunov directed method, a sufficient condition is presented to guarantee all the agents reach consensus with the desired H∞ performance. Finally, the results are verified by several numerical simulations.

关键词： Consensus H∞ control Multi-agent systems Fractional-order

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Active learning for regression using greedy sampling

arXiv

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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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