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Corrigendum to “T2V_TF: An adaptive timing encoding mechanism based transformer with multi-source heterogeneous information fusion for portfolio management: A case of the Chinese A50 stocks” [Expert Syst. Appl. 213 (2023) 119020]

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Expert systems with Applications 2024年 239卷

作者： Feng Zhou Qun Zhang Yuan Zhu Tian Li School of Information Sciences Guangdong University of Finance and Economics Guangzhou 510320 China School of Finance Guangdong University of Foreign Studies Guangzhou Guangdong 510006 China Southern China Institute of Fortune Management Guangzhou Guangdong 510006 China Institute of Financial Openness and Asset Management Guangzhou Guangdong 510006 China School of Automation China University of Geosciences Wuhan Hubei 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan Hubei 430074 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai 200433 China Department of Finance School of Business The Chinese University of Hong Kong Shatin NT Hong Kong

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Observer-based finite time H ∞ control of nonlinear discrete time-varying systems with an adaptive event-triggered Mechanism

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Journal of the Franklin Institute 2020年第16期357卷 11668-11689页

作者： Menghua Chen Hamid Reza Karimi Jian Sun School of Automation Beijing Institute of Technology Beijing 100081 China Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 China

The problem of finite time stability and synthesis is discussed for nonlinear discrete time-varying systems by using the T-S fuzzy model. To reduce the communication network resources, two adaptive event-triggered mechanisms (AETMs) are taken into consideration in sensor-to-observer (S/O) and observer-to-controller (O/C) channels. The thresholds of the AETMs are adjusted according to the estimation error rather than some fixed ones. Sufficient conditions for developing an observer-based time-varying control are obtained, which ensure the time-varying error system to be finite time stable and satisfy the H ∞ performance simultaneously. Moreover, using the singular value decomposition technique, recursive linear matrix inequalities conditions are obtained for computing the gains of the controller and observer. Finally, the superiority and applicability of the proposed method are demonstrated by examples.

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Model analysis and experimental research on hydraulic high-speed impact system 8

Model analysis and experimental research on hydraulic high-s...

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8th IEEE International Conference on Fluid Power and Mechatronics, FPM 2019

作者： Wang, Shoukun Shi, Mingxin Chen, Zhihua Beijing Institute of Technolog Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing China Key Laborator Yof Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9781728103112

ISBN: (纸本)9781728103112

High-speed impact simulation test is an important test method to study the fatigue properties or limit parameters of materials. With the advantages of great impact energy, good program control and convenient test operation, hydraulic impact is widely used in high-speed impact field. A hydraulic high speed impact simulation system is designed in this paper, which uses high pressure accumulator to supply oil and converts hydraulic energy into impact energy through servo valve controlled cylinder system. This system can simulate dynamic change process of impact velocity and acceleration of projectile, and has impact angle adjustment and end buffer function. First, the composition and working principle of this hydraulic impact simulation system is introduced, as well as the analysis of the hydraulic impact mechanism. Second, the system dynamics model of servo valve controlled cylinder based on accumulator oil is built, and the simplified model and its basic characteristics are analyzed. Then, the Influences of various parameters in impact dynamic model on impact process are also discussed. Finally, the simulation system and physical prototype system of hydraulic impact is presented, the simulation and actual impact test data and corresponding analysis conclusions are also given. © 2019 IEEE.

关键词： Software testing

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A novel online learning RECOC-KFDA method for lithologic identification in drilling process*

A novel online learning RECOC-KFDA method for lithologic ide...

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Asian control Conference

作者： Xi Chen Weihua Cao Chao Gan Min Wu School of Automation China University of Geosciences and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781728102634

Aiming to achieve a safe and efficient drilling, this paper is concerned with identification of formation lithology, which provides critical information for drilling control. Notice that it is hard to make accurate geological prediction using conventional identification approaches, due to the data characteristics of imbalanced, multi-classification and low value density, a novel reduction error correcting output code kernel fisher discriminant analysis algorithm(RECOC-KFDA) method is developed in an online manner. It consider design optimal error correcting output code(ECOC) matrix based on a reduction algorithm, and it proposed an online method to reduce the computation complexity required for updating the kernel fisher discriminant analysis(KFDA) classifiers rather than recalibrating them. Proposed method has been applied to lithologic identification in drilling site. Simulations and comparisons demonstrate that our method is superior to the existing ones for both offline training and online prediction model.

关键词： Encoding Computational modeling Kernel Geology Drilling machines Data models Diversity reception

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A Computer-Aided Tuning Method for Microwave Filters by Combing T-S Fuzzy Neural Networks and Improved Space Mapping

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Computer Modeling in Engineering & Sciences 2018年第9期116卷 433-453页

作者： Shengbiao Wu Weihua Cao Can Liu Min Wu School of Automation China University of GeoscienceWuhanHubei430074China Nanchang Institute of Science and Technology.Nanchang Jiangxi330100China The Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuhanHubei430074China

A computer-aided tuning method that combines T-S fuzzy neural network(TS FNN)and offers improved space mapping(SM)is presented in this *** method consists of three main ***,the coupling matrix is effectively extracted under the influence of phase shift and cavity loss after the initial ***,the surrogate model is realized by using a T-S FNN based on subspace ***,the mapping relationship between the actual and the surrogate models is established by the improved space mapping algorithm,and the optimal position of the tuning screws are found by updating the input and output parameters of the surrogate ***,the effectiveness of different methods is verified by an experiment with a nine order cross coupled *** results show that,compared to a back propagation neural network method based on electromagnetic simulation and an SM method based on a least squares support vector machine,the proposed method has obvious advantages in terms of tuning accuracy and tuning time.

关键词： Computer-aided tuning T-S FNN S-parameters coupling matrix

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Parallel Building Process Management system

Parallel Building Process Management System

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Chinese automation Congress (CAC)

作者： Sheng Liu Xisong Dong Gang Xiong Zhen Shen Bin Hu Xiaoyu Chen State Key Laboratory for Management and Control of Complex System Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Institution of Intelligent Manufacturing Qingdao Academy of Intelligent Industries Qingdao China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426480

With the development of industrialized building and intelligence technology, it becomes necessary and possible to realize building process intelligence management. In recent years, digital twins and parallel management get more and more attentions in academic research and industrial applications. This paper addresses how parallel building system performs intelligence management on industrialized building process. Firstly, actual building model and artificial building model are created according to actual building system. Then business driving engine and simulation engine drive actual building model and artificial building model, respectively. Participants receive and send messages by software interfaces. Business driving engine changes the status of actual building model and sends the changes to simulation engine. Simulation engine updates the status of artificial building model and runs artificial building model with various parameters. Then it sends the parameter with the best effect to business driving engine. Business driving engine sends recommendations to participants according to the best parameter. Thus, building process intelligence management is achieved.

关键词： automation Digital twin Buildings User interfaces Software Engines Business

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Signal discrimination with wavelet algorithm and Pearson correlation-based algorithm in φ-OTDR systems

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Dai Li Mo Zhi Hua Yu Yun Fei Cai Rui Liu School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan 430074 China

This paper presents a novel method of distinguishing signal, the way expected to reduce the nuisance alarm rate since the high nuisance alarm rate will restrict the capability of phase-sensitive optical time-domain reflection technology. The proposed method includes two parts: wavelet positioning mutation to obtain the perturbation area and Pearson correlation algorithm to directly convert the intensity of the perturbation into a useful amplitude. This technique avoids the use of irrelevant data in these differential signals and provides a simple and feasible new approach for distinguishing signal and optimizing the positioning speed of φ-OTDR systems.

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CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10236-10241页

作者： Luefeng Chen Min Li Xuzhi Lai Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k -fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.

关键词： Convolution Neural Networks Broad Learning Emotion Recognition Human-Robot Interaction

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A Bioinspired retinal neural network for accurately extracting small-target motion information in cluttered backgrounds

arXiv

引用

arXiv 2021年

作者： Huang, Xiao Qiao, Hong Li, Hui Jiang, Zhihong School of Mechatronical Engineering Advanced Innovation Center for Intelligent Robots and Systems Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of’Hand-Eye-Brain’ Interaction Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

—Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks. Inspired by the neural circuitry of elementary motion vision in the mammalian retina, this paper proposes a bioinspired retinal neural network based on a new neurodynamics-based temporal filtering and multiform 2-D spatial Gabor filtering. This model can estimate motion direction accurately via only two perpendicular spatiotemporal filtering signals, and respond to small targets of different sizes and velocities through adjusting the dendrite field size of spatial filter. Meanwhile, an algorithm of directionally selective inhibition is proposed to suppress the target-like features in the moving background, which can reduce the influence of background motion effectively. Extensive synthetic and real-data experiments show that the proposed model works stably for small targets of a wider size and velocity range, and has better detection performance than other bioinspired models. Additionally, it can also extract the information of motion direction and motion energy accurately and rapidly. Copyright © 2021, The Authors. All rights reserved.

关键词： Ophthalmology

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A Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds

arXiv

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arXiv 2021年

—Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit of elementary motion vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (ASNN) that contains three main contributions. Firstly, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural network is modeled as a push-pull structure formed by ON and OFF pathways, which responds strongly to approaching motion while insensitivity to lateral motion. Finally, a method of directionally selective inhibition is introduced, which is able to suppress the translational backgrounds effectively. Extensive synthetic and real robotic experiments show that the proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making. © 2021, CC BY.

关键词： Mammals

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