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Adaptive Type-2 Fuzzy Neural-Network control for Teleoperation Systems With Delay and Uncertainties

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第10期28卷 2543-2554页

作者： Kebria, Parham Mohsenzadeh Khosravi, Abbas Nahavandi, Saeid Wu, Dongrui Bello, Fernando Deakin Univ Inst Intelligent Syst Res & Innovat Waurn Ponds Vic 3216 Australia Huazhong Univ Sci & Technol Sch Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China Imperial Coll London Ctr Engagement & Simulat Sci London SW7 2AZ England Chelsea & Westminster Hosp Acad Surg London SW10 9NH England

Interacting with human operators, remote environment, and communication networks, teleoperation systems are considerably suffering from complexities and uncertainties. Managing these is of paramount importance for safe and smooth performance of teleoperation systems. Among the countless solutions developed by researchers, type-2 fuzzy (T2F) algorithms have shown an outstanding performance in modeling complex systems and tackling uncertainties. Moreover, artificial neural networks (NNs) are well known for their adaptive learning potentials. This article proposes an adaptive interval type-2 fuzzy neural-network control scheme for teleoperation systems with time-varying delays and uncertainties. The T2F models are developed based on the experimental data collected from a teleoperation setup over a local computer network. However, the resulted controller is evaluated on an intercontinental communication network through the Internet between Australia and Scotland. Moreover, the slave robot and the remote workspace are completely different and unforeseen. Stability and performance of the proposed control is analyzed by Lyapunov-Krasovskii method. Comprehensive comparative studies demonstrate that the proposed controller outperforms traditional techniques in experimental evaluations.

关键词： Uncertainty Robots Adaptation models Delays Data collection Adaptive systems Artificial neural networks Adaptive control neural network (NN) time delay teleoperation systems type-2 fuzzy (T2F) models uncertainties

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Distributed resource allocation: an indirect dual ascent method with an exponential convergence rate

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NONLINEAR DYNAMICS 2020年第3期102卷 1685-1699页

作者： Lin, Wen-Ting Wang, Yan-Wu Li, Chaojie Yu, Xinghuo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China UNSW Sch Elect Engn & Telecommun Kingston NSW 2052 Australia RMIT Univ Sch Engn Melbourne Vic 3000 Australia

In this paper, an indirect dual ascent method with an exponential convergence rate is proposed for a general resource allocation problem with convex objectives and weighted constraints. By introducing the indirect dual variables, the dual dynamics can be executed in a decentralized manner by all nodes over the network. In contrast to the conventional methods, consensus on all the dual variables is not required. This further leads to the fast convergence, reduced communication burden and better privacy preserving. Moreover, the exponential convergence rate of the proposed algorithm is established through the Lyapunov method and the singular perturbation theory. Application of the dynamic power dispatch problem in smart grid verifies the effectiveness and performance of the proposed algorithm.

关键词： Resource allocation Dual ascent dynamics Distributed algorithm Exponential convergence

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Optimizing Neural Network image Classification with Fractional Order Gradient Methods

SSRN

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SSRN 2023年

作者： Zhao, Haiming Chen, Jiejie Jiang, Ping Zeng, Zhigang Fan, Rui Yang, Honggang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei PolyTechnic University Huangshi435000 China Faculty of Artificial Intelligence Education Central China Normal University Wuhan430000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technology increasingly permeates various fields, the significance of optimization algorithms in neural network training has become more prominent. Typically, the training of deep learning networks relies on gradient descent optimizers based on calculus computations. Traditional integer-order calculus optimizers often fall short when dealing with complex and subtle data. To overcome these limitations, we innovatively utilized the Grünwald-Letnikov and Caputo definitions of fractional calculus to develop two new optimization algorithms: Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam). These algorithms have been proven to converge to extremal points.A key advantage of our methods is their capability in handling complex, high-dimensional tasks, such as artistic painting image classification, where they exhibit exceptional performance in recognizing and processing subtle differences. The robustness and adaptability of these algorithms were further validated through experiments on the CIFAR-100 dataset. The results demonstrate the efficacy of fractional order optimizers in enhancing training efficiency and accuracy. © 2023, The Authors. All rights reserved.

关键词： image classification

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A Koopman operator approach for machinery health monitoring and prediction with noisy and low -dimensional industrial time series

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NEUROCOMPUTING 2020年 406卷 204-214页

作者： Cheng, Cheng Ding, Jia Zhang, Yong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Intelligent Control & Image Proc Wuhan 430074 Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200240 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China

Data-driven methods for machinery health monitoring and prediction, such as machine learning and statistical pattern recognition techniques, normally requires high quality (less noised), frequently-sampled and large volume time-series data to estimate proper models between system inputs and outputs. However, most of the industrial time-series are highly noisy and only few types of sensory data are available, which challenges the estimation accuracy. This paper proposes a data-driven spectral decomposition framework (denoted as Koopman-CBM) for the machinery health monitoring and prediction problem. Specifically, considering noisy industrial signals in the form of one dimensional time-series, we use the higher-order dynamic mode decomposition (DMD) embeds time-lagged snapshots to increase the spatial complexity of low-dimensional time series and use the total-least-square algorithm to compensate the effect of measurement noise, thereby, extracting accurate dynamical features. The obtained de-noised model characteristics (i.e., eigenvalues, eigenfunctions, Koopman operator) is effective in predicting the system health stages. In parallel, using the Koopman operator as the linear predictor associated with the nonlinear dynamics, we can then perform remaining useful life (RUL) predictions with high accuracy. The experimental validation of the proposed framework is carried out on a rolling bearing datasets for degradation health-stage inspection and RUL prediction. Results show that– compared with other mainstream methods– our approach is capable of identifying the critical degradation stages and achieving higher RUL prediction accuracies.

关键词： Remaining useful life prediction Bearing health monitoring Koopman operator Dynamic mode decomposition Diagnostics and prognostics

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Remote sensing image ship detection based on feature pyramid 11

Remote sensing image ship detection based on feature pyramid

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11th International Symposium on Multispectral image processing and Pattern Recognition (MIPPR) - Pattern Recognition and Computer Vision

作者： Zou, Lamei Li, Changfeng Yang, Weidong Zhou, Shiyang Nie, Shiwei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Peoples R China

ISBN: (纸本)9781510636385

We present a two-stage method for remote sensing image ship detection. The proposed approach efficiently detects ships in remote sensing images. Firstly, a light-weight classification network is used to classify different regions. In second stage, we design a detection framework to detect ships in sub-images, which are considered to contain object in the first stage. To solve the scale problems in object detection, our detection network is built on feature pyramid network, but we explicitly assign object into corresponding feature maps based on size. In our proposed framework, instead of using anchors, we predict object center point and the offsets to bounding box. The experiment results show that our proposed method has a good performance in terms of speed and accuracy.

关键词： remote sensing image object detection feature pyramid network feature pyramid network anchor-free

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A New and Fair Peer-to-Peer Energy Sharing Framework for Energy Buildings

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IEEE TRANSACTIONS ON SMART GRID 2020年第5期11卷 3817-3826页

作者： Cui, Shichang Wang, Yan-Wu Shi, Yang Xiao, Jiang-Wen 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 Univ Victoria Dept Mech Engn Victoria BC V8W 3P1 Canada

With the rapid development of energy buildings, advanced energy management is urgently demanded for a green society. In this paper, focusing on the coordinated energy management for a building community, we present a new and fair peer-to-peer energy sharing framework to realize an economic and sustainable building community. Specifically, in the building-centric peer-to-peer mode, buildings directly share their energy supplies/demands and offer the related payments within the community under the constraints of community energy and payment balance. We propose a non-cooperative energy sharing game for the selfish buildings, and we further show that a generalized Nash equilibrium of the game is independent of the energy sharing payments. Consequently, we firstly derive the energy sharing profiles by seeking the equilibrium. Since the buildings' energy sharing payments are mutually coupled and influenced, we propose a cost reduction ratio distribution model to determine the payments to ensure the fairness in the sense that buildings can get as large cost reductions and similar cost reduction ratios as possible. Simulation results show that all buildings can reduce their energy costs and have smoother and smaller net demand profiles on the main grid, thus making the proposed schemes and algorithms promising in real applications.

关键词： Buildings Peer-to-peer computing Games Temperature measurement Renewable energy sources Schedules Energy buildings peer-to-peer energy sharing game equilibrium distributed optimization

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Automatic labelling of brain tissues in MR images through spatial indexes based hybrid atlas forest

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IET image processing 2020年第12期14卷 2728-2736页

作者： Liu, Hong Xu, Lijun Song, Enmin Jin, Renchao Hung, Chih-Cheng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Educ Minist Image Proc & Intelligent Control Key Lab Wuhan 430074 Hubei Peoples R China Hubei Univ Sch Comp Sci & informat Engn Wuhan 430074 Hubei Peoples R China Kennesaw State Univ Marietta GA USA

The multi-atlas-based methods are widely applied in the automatic labelling in magnetic resonance (MR) images. However, most multi-atlas-based methods require that all atlases be registered to the target image accurately to have a correct label propagation. In this study, the authors introduce the term spatial indexes and construct a hybrid atlas forest model to gather the labelling information from all atlases without propagating labels from every single atlas. Furthermore, a new automatic labelling method using the hybrid atlas forest model based on spatial indexes is proposed. In the proposed framework, an atlas is chosen arbitrarily as a reference image and the spatial indexes are constructed on this image space. Then, the samples are selected from all atlases in the dataset based on the spatial indexes to construct a samples pool. Finally, the hybrid atlas forest model will be trained on the samples pool and used to predict the labelling of the target. Experiments are conducted on two public datasets to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method reduces the requirement of strong dependence on precise registration and improve the accuracy of labelling.

关键词： medical image processing biomedical MRI image registration brain biological tissues multiatlas-based methods magnetic resonance images correct label propagation labelling information single atlas automatic labelling method spatial index based hybrid atlas forest brain tissues MR images target image hybrid atlas forest model image space image registration

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Distributed leaderless impulsive consensus of non-linear multi -agent systems with input saturation

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NONLINEAR ANALYSIS-HYBRID SYSTEMS 2020年 36卷

作者： Liu, Xiaolu Chen, Duxin Liu, Zhiwei Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Three Gorges Univ Hubei Prov Collaborat Innovat Ctr New Energy Micr Yichang 443002 Peoples R China Southeast Univ Sch Math Nanjing 211096 Peoples R China

This paper aims to propose dynamic consensus protocols for a class of non-linear saturated multi-agent systems. Due to the existence of non-linear dynamics of each agent, the system does not satisfy the condition named asymptotically null controllable with bounded controls (ANCBC). As a result, the widely-adopted method of low-gain feedback to solve consensus control problems for linear multi-agent systems with input saturation cannot be used. To step back, even if the system is linear and meets the ANCBC condition, the existing low-gain method or that combined with adaptive method can only make the system eventually reach zero consensus, which is unable to bring the system to dynamic consensus. In addition, from the view of energy conservation, this study proposes the impulsive control method to reduce the energy consumption of the controller. By taking advantages of the stability theory of impulsive systems, as well as the properties of Laplacian matrix and convex hull, corresponding distributed dynamic consensus protocols are proposed. Still, the shape reference set is introduced to assess the attraction domain of the leaderless MASs. Finally, the performance of the proposed anti-saturation impulsive control algorithm is demonstrated by extensive numerical experiments. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems Impulsive control Consensus Non-linear systems Input saturation

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Trusted-Region Subsequence Reduction for Designing Resilient Consensus Algorithms

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第1期8卷 259-268页

作者： Zhai, Yang Liu, Zhi-Wei Ge, Ming-Feng Wen, Guanghui Yu, Xinghuo Qin, Yuzhen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Southeast Univ Dept Syst Sci Sch Math Nanjing 211189 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia Univ Calif Riverside Dept Mech Engn Riverside CA 92521 USA

Existing resilient consensus algorithms are mainly developed based on the mean subsequence reduced (MSR) method, which relies on the assumption that there exist at most f malicious agents in the entire network or each neighborhood (i.e., f-total or f-local model). However, in some practical cases, it may be impossible to estimate an appropriate upper bound on the number of malicious agents. This paper proposes a novel method, called trusted-region subsequence reduction (TSR), for designing resilient consensus algorithm without the f-total/local model assumption. The main idea of the TSR method is to filter out the received information beyond a dynamic trusted region, determined by the current relative positions of the neighboring trusted nodes. Based on the TSR method, we design a sampled-data resilient consensus algorithm for double-integrator multi-agent networks. A necessary and sufficient graph-theoretic condition is obtained to achieve resilient consensus. Finally, simulations are conducted to illustrate the effectiveness of the proposed algorithm and the faster convergence rate of the TSR-based algorithm than the classical MSR-based algorithm.

关键词： Consensus algorithm Topology Convergence Germanium Upper bound Task analysis Mechanical engineering Multi-agent network resilient consensus malicious attack trusted-region subsequence reduction

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An Efficient Peer-to-Peer Energy-Sharing Framework for Numerous Community Prosumers

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第12期16卷 7402-7412页

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network.

关键词： Real-time systems Peer-to-peer computing Games Energy states Informatics Renewable energy sources Optimization Distributed optimization energy sharing equilibrium peer-to-peer prosumer

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