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A slim integrated with empirical study and network analysis for human error assessment in the railway driving process

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 204卷

作者： Zhou, Jian-Lan Lei, Yi Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Swinburne Univ Technol Fac Sci Engn & Technol Melbourne Vic 3122 Australia

Human error assessment is essential work to guarantee the safety of the locomotive driving process. Success Likelihood Index Method (SLIM) is a well-known approach applied to determine human error probability (HEP) for railway critical operations. However, the dependencies between performance shaping factors (PSFs) make it difficult to evaluate the accurate weights of PSFs. To overcome this deficiency, a hybrid SLIM utilizes empirical study and Complex Network to assess HEP accounting for the dependencies during the weighting procedure of PSFs. With the aid of Human Factors Analysis and Classification System, 611 accident/incident reports are investigated to explore the links, then three key parameters are tailored to acquire the weights of PSFs by network analysis. Finally, the SLIM approach is performed to evaluate HEPs of typical human errors. In order to validate this proposed method, we apply Monte Carlo simulation to obtain the state of system reliability. The calculated results are consistent with the simulation results and conform to the experience and knowledge in railway operation. The hybrid SLIM approach is useful to reduce the likelihood of occurrence of errors, and improve the overall safety level in the railway driving process and other industries.

关键词： Railway operation Human error assessment Success likelihood index method Empirical study Directed weighted complex network Fault tree analysis

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Dynamic behaviour and control strategy of high temperature proton exchange membrane electrolyzer cells (HT-PEMECs) for hydrogen production

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第51期45卷 26613-26622页

作者： Zhao, Dongqi He, Qijiao Yu, Jie Jiang, Jianhua Li, Xi Ni, Meng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan Hubei Peoples R China Hong Kong Polytech Univ Dept Bldg & Real Estate Bldg Energy Res Grp Hung HomKowloon Hong Kong Peoples R China

A fast and safe dynamic process is a key issue during the start-stop and adjustment of high temperature proton exchange membrane electrolyzer cells (HT-PEMECs). In the paper, a 2D multi-physics model is developed to investigate the dynamic process in an HT-PEMEC. First, the dynamic responses of step scheme, multistep scheme and diagonal scheme are compared. It is found that the step scheme has the fastest dynamic response, but it may cause the problem of reactant starvation. The dynamic response speed of diagonal scheme is slower than the step scheme, but it can prevent the problem of reactant starvation. Subsequently, the dynamic process is optimized with a fast dynamic response without reactant starvation. This paper proposes a fast and safe dynamic process adjustment scheme and forms a basis for subsequent control of the HT-PEMEC stack and system. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen production Electrolysis Proton exchange membrane electrolyzer cell Numerical modelling Dynamic response Optimization problem

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Exponential stability of switched positive systems with all modes being unstable

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2020年第12期30卷 4600-4610页

作者： Zhou, Zi-Yu Wang, Yan-Wu Yang, Wu Hu, Meng-Jie 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 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan Peoples R China

This article studies the exponential stability of continuous-time switched positive systems consisting of unstable subsystems. Different from the existing results, both stabilizing and destabilizing switching behaviors act in the switching sequences. By employing multiple composite copositive Lyapunov functions, sufficient condition is derived to ensure the exponential stability of the system, which evaluates the ratio of stabilizing switching behaviors to compensate the state divergence caused by either unstable subsystems or destabilizing switching behaviors. Simulations demonstrate the effectiveness of the result.

关键词： copositive Lyapunov function Filippov solutions fuzzy neutral-type neural networks general decay synchronization

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New Criteria on Global Stabilization of Delayed Memristive Neural Networks With Inertial Item

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第6期50卷 2770-2780页

作者： Zhang, Guodong Hu, Junhao Zeng, Zhigang South Cent Univ Nationalities Sch Math & Stat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

In this paper, we are concerned with global stabilization for a kind of delayed memristive neural network with an inertial term. By building a new Lyapunov functional and designing a feedback controller, we obtain some new results on global stabilization of the addressed delayed memristive inertial neural networks (MINNs). An adaptive control strategy is also designed to realize the global stabilization. Compared with the reduced-order method used in the existing literature, we consider the stabilization directly from the MINNs themselves without a reduced-order method. In addition, the new results proposed here are shown as algebraic criteria, which are easy to test. At last, some simulations are given to show the validity of the derived criteria.

关键词： Artificial neural networks Circuit stability Stability analysis Synchronization Asymptotic stability Adaptive control Adaptive control memristor neural networks (NNs) stabilization time-varying delays

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A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Viewpoints

Research Square

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Research Square 2021年

作者： Peng, Gang Liao, Jinhu Guan, Shangbin Yang, Jin 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 China School of Automation South East University Nanjing China

In the field of intelligent manufacturing, robot grasping and sorting is an important content. However, in the traditional 2D camera-based robotic arm grasping method, the grasping efficiency is low and the grasping accuracy is low when facing the scene of stacking and occlusion. Insufficiency and other issues, a dual perspective-based deep reinforcement learning promotion and capture method is proposed. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual viewpoints is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. © 2021, CC BY.

关键词： Manipulators

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Multidomain Features Fusion for Zero-Shot Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2020年第6期4卷 764-773页

作者： Liu, Zhihao Zeng, Zhigang Lian, Cheng Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430074 Peoples R China

Given a novel class instance, the purpose of zero-shot learning (ZSL) is to learn a model to classify the instance by seen samples and semantic information transcending class boundaries. The difficulty lies in how to find a suitable space for zero-shot recognition. The previous approaches use semantic space or visual space as classification space. These methods, which typically learn visual-semantic or semantic-visual mapping and directly exploit the output of the mapping function to measure similarity to classify new categories, do not adequately consider the complementarity and distribution gap of multiple domain information. In this paper, we propose to learn a multidomain information fusion space by a joint learning framework. Specifically, we consider the fusion space as a shared space in which different domain features can be recovered by simple linear transformation. By learning a n-way classifier of fusion space from the seen class samples, we also obtain the discriminative information of the similarity space to make the fusion representation more separable. Extensive experiments on popular benchmark datasets manifest that our approach achieves state-of-the-art performances in both supervised and unsupervised ZSL tasks.

关键词： image classification image retrieval semantics transfer learning zero-shot learning

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Iron oxide magnetic nanoparticles based low-field MR thermometry

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NANOTECHNOLOGY 2020年第34期31卷 345101页

作者： Zhang, Yapeng Guo, Silin Zhang, Pu Zhong, Jing Liu, Wenzhong 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 TU Braunschweig Inst Elect Measurement Sci & Fundamental Elect En Braunschweig Germany

This paper reports on a highly accurate approach of magnetic resonance (MR) thermometry using iron oxide magnetic nanoparticles (MNPs) as temperature sensors. An empirical model for the description of the temperature dependentR(2)relaxation rate is proposed by taking into account the temperature sensitivity of the MNP magnetization. The temperature sensitivity of the MNP magnetization (eta) and the temperature sensitivity of theR(2)relaxation rate (kappa) are simulated with the proposed empirical models to investigate their dependence on the magnetic field and the particle size. Simulation results show the existence of optimal magnetic fieldsH(o eta)andH(o kappa)that maximize the temperature sensitivities eta and kappa. Furthermore, simulations and experiments demonstrate that the optimal magnetic fieldH(o eta)(H-o kappa) decreases with increasing the particle size. Experiments on temperature dependentR(2)relaxation rate are performed at different magnetic fields for MNP samples with different iron concentrations. Experimental results show that the proposed MR thermometry using MNPs as temperature sensors allows a temperature estimation accuracy of about 0.05 degrees C. We believe that the achieved approach of highly accurate MR thermometry is of great interest and significance to biomedicine and biology.

关键词： MR thermometry iron oxide magnetic nanoparticles low-field NMR R(2)relaxation rate

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Distributed Supervisory Secondary control for a DC Microgrid

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2020年第4期35卷 1736-1746页

作者： Liu, Xiao-Kang Wang, Yan-Wu Lin, Pengfeng Wang, Peng 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 Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This article investigates the current sharing and voltage regulation problem of a DC microgrid by a distributed supervisory control method. In the existing secondary control methods, current sharing feedback control loop and voltage regulation feedback control loop are linearly integrated to shift the droop equation. However, the linear combination of two loops may easily offset the control effect of each other, as the control dimension is less than the objective dimension. This fact motivates us to design a distributed supervisory secondary control (DSSC) scheme to achieve a more flexible configuring of each converter. The DSSC consists of two candidate controllers and a supervisor. The two controllers refer to current sharing configuring and DC bus voltage regulation respectively, and the supervisor is to orchestrate the switching between candidate controllers based on the system environment of DC microgrid. Under the proposed DSSC, each converter selects to configure the current sharing or regulates the DC bus voltage according to its need. Theoretical stability analysis is rigorously conducted. Finally, both simulation and experiment results are presented to demonstrate the effectiveness of DSSC.

关键词： Microgrids Voltage control Feedback loop Stability analysis Communication networks Switches DC microgrid switching control current sharing voltage regulation hybrid system

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The post-disaster emergency planning problem with facility location and people/resource assignment

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KYBERNETES 2020年第10期49卷 2385-2418页

作者： Wang, Jian Situ, Chenqi Yu, Mingzhu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Shenzhen Univ Dept Transportat Engn Coll Civil & Transportat Engn Shenzhen Peoples R China

Purpose This paper aims to study a dynamic post-disaster emergency planning (PDEP) problem in an integrated network through the investigation of the selection of shelters, medical centers and distribution centers (DCs), and the allocation of evacuees and injured people. The resource and people assignment in multiple periods are considered. Design/methodology/approach A mathematical formulation is provided for the PDEP problem. The authors decompose the model into two sub-models as follows: the primary model is an integer programming model and the subproblem is a nonlinear programming model with continuous variables. The simulated annealing is used to solve the primary problem, and particle swarm optimization (PSO) mixed with beetle antennae search (BAS) is used to solve the subproblem. Findings The paper finds that BAS can increase the stability of PSO and keep the advantages of PSO's rapid convergence. By implementing these algorithms on emergency planning after the Wenchuan earthquake that happened in China in 2008, this paper finds that the priority of different levels of injured people is influenced by several factors. Even within the same disaster, the priority of different levels of injured can be inconsistent because of the differences in resource levels. Originality/value The authors integrate the shelters, medical centers and DCs as a system, and simultaneously, consider evacuees and injured people and different resource assignments. The authors divide the injured people into three levels and use survival rate function to simulate the survival conditions of different people. The authors provide an improved PSO algorithm to solve the problem.

关键词： Integrated emergency network Location and allocation model Post-disaster emergency planning Priority of injured people

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A Distributed Iterative Learning Framework for DC Microgrids: Current Sharing and Voltage Regulation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2020年第2期4卷 119-129页

作者： Liu, Xiao-Kang Jiang, He Wang, Yan-Wu He, Haibo Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

With the penetration of computation intelligence, an increasing number of learning methods are developed into power engineering, such as dc microgrid applications. This paper establishes a distributed iterative learning framework to solve the current sharing and voltage regulation problem in an islanded dc microgrid from the perspective of game theory. The control objectives of dc microgrid include not only achieving the desired output current dispatch, but also regulating the voltage of dc bus to its rated value. Based on the two objectives, local performance indexes are established and an N-player game is formulated. Each source aims to minimize its own performance index and to achieve the current sharing objective simultaneously. Under the framework of game theory, a distributed iterative learning algorithm is designed based on the Bellman optimality principle and subsequently carried out using the approximate dynamic programming technique. The proposed algorithm is data based where it does not require to have the accurate model parameters of the dc microgrid and it ensures that the dc microgrid falls into a Nash equilibrium. Furthermore, a rigorous convergence analysis of the proposed algorithm is given. To demonstrate the effectiveness of the proposed method, simulation examples are presented on a tested dc microgrid.

关键词： Iterative learning approximate dynamic programming Nash equilibrium DC microgrid distributed control

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