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2022 Chinese automation Congress, CAC 2022

作者： Yang, Yuelin Yu, Wanke Liu, Zepeng Zhao, Lingran Lu, Chengda Wu, Min Niu, Ruiqing China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China China University of Geosciences Institute of Geophysics and Geomatics Wuhan China

ISBN: (纸本)9781665465335

In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslide units in the training set is usually random or subjective, and there is no guarantee sample accuracy. In this paper, the deep forest algorithm combined with the twice-sample method is used to evaluate the landslide susceptibility in the Zigui Badong area of the Three Gorges Reservoir area. First, 11 landslide impact factors were selected according to the geological data to initially construct a sample set, and the cleaned data set was obtained by twice-sample. After dividing the training set and the test set, the deep forest model was used to predict the landslide susceptibility and evaluate the accuracy. The results show that the AUC of the susceptibility prediction model built using the deep forest is 1%-10% higher than that of GBDT, MLP and other methods, while the Twice-Sample method improves the AUC by 2%. © 2022 IEEE.

关键词： Landslides

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A multiple-time-scale control strategy for burden distribution and blast supply of a BF

A multiple-time-scale control strategy for burden distributi...

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Chinese control and Decision Conference, CCDC

作者： Yunpeng Guo Jianqi An Guoyu Zhao Min Wu Qingyi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

The operations of blast furnaces (BFs) are very vital for the long-term stability of the iron making process. The burden distribution and blast supply are the two major operation systems of BFs. At present, the researches are lack effective adjustment methods for the BF operations combined with burden distribution and blast supply. The burden distribution affects the iron making process on a long time scale, while the blast supply affects the iron making process on a short time scale. This paper presents a multi-time sampling-data adjustment strategy for the BF operations aiming at optimizing GUR on multiple time scales. First, this paper analyzes the relationship between the gas utilization ratio (GUR) and the burden distribution, the blast supply on multiple time scales. Then, this paper establishes a prediction model of GUR on the long time scale and the short time scale based on autoregressive integrated moving average (ARIMA). Next, this paper provides a control strategy of burden distribution and a control strategy of blast supply by a prediction model based on support vector regression (SVR). Finally, this paper makes experiments and applies this method in a real-world BF. The analysis of the results shows the control strategy of the BF operations provides a good guide on making a suitable decision for burden distribution and blast supply.

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Anti-sway control of Crane Based on Equivalent-Input-Disturbance Method and Feedback Linearization

Anti-sway Control of Crane Based on Equivalent-Input-Disturb...

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第42届中国控制会议

作者： Shuhan He Gang Yu Yongbo Li Fengyu Zhang Wangyong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation Complex Systems Huaxin Cement Technology Management (Wuhan) Co. Ltd

The anti-sway issue with crane systems is discussed in this essay. Because cranes are undriveable and nonlinear,implementing anti-sway controllers becomes much more challenging. This work suggests a crane anti-sway controller that uses feedback linearization(FL) in conjunction with the Equivalent-Input-Disturbance(EID) technique to address the issues. to reduce the problem that the feedback linearization largely relies on the model's *** crane system is first treated as a linear system, after which the unmodeled disturbances, nonlinear components, and external disturbances of the system are treated as the total disturbances of the system, and the effects of these disturbances are then compensated for using disturbance estimation. Finally, simulation experiments confirm that the maximum steady-state fluctuations of the position and angle of the anti-sway controller based on the equivalent input disturbance method and the feedback linearization method are 14 and 6.85percent, respectively, of those estimated without *** demonstrates the potency of this approach.

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Distributed multi-timescale algorithm for nonconvex optimization problem: A control perspective

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Neural Networks 2025年 186卷 107257页

作者： Shi, Xiasheng Liu, Jian Sun, Changyin School of Artificial Intelligence Anhui University Hefei230106 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Automation Southeast University Nanjing210096 China

The distributed nonconvex constrained optimization problem with equality and inequality constraints is researched in this paper, where the objective function and the function for constraints are all nonconvex. To solve this problem from a control perspective, a virtual reference-based convex penalty function is added to the augmented Lagrangian function. Then, based on the primal–dual technique, a two-timescale distributed approach is designed based on the consensus scheme. The slower subsystem aims to ensure the optimality, and the faster subsystem intends to guarantee the stability. Finally, three cases are presented to illustrate the approach's effectiveness. © 2025 Elsevier Ltd

关键词： Consensus algorithm

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Tracking Performance of Networked Systems with Limited Bandwidth and Colored Noise

Tracking Performance of Networked Systems with Limited Bandw...

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2022 Chinese automation Congress, CAC 2022

作者： Zhang, Bin Jiang, Xiaowei Sun, Li Zhang, Xianhe School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan430074 China School of Electrical Engineering and Automation Hubei Normal University Huangshi435002 China

ISBN: (纸本)9781665465335

This study investigated the optimal tracking performance (OTP) of multi-input multi-output (MIMO), discrete- time networked control systems (NCSs). The limits of tracking performance (TP) under the influences of bandwidth, encoding- decoding, and additive coloured Gaussian noise (ACGN) are derived using the techniques of coprime decomposition and all-pass decomposition. The results reveal the negative impact of non-minimum phase (NMP) zeros and unstable poles of the plant as well as network communication constraints on the TP of NCSs. Finally, a numerical simulation is discussed and verifies our conclusions. © 2022 IEEE.

关键词： Networked control systems

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Robust control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

Robust Control of Feeding Speed in Coal Seam Drilling Proces...

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2024 China automation Congress, CAC 2024

作者： Liu, Xiao Chen, Luefeng Lu, Chengda Fan, Haipeng Wu, Min Pedrycz, Witold School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey

ISBN: (纸本)9798350368604

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust H∞ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller’s effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction. © 2024 IEEE.

关键词： Coal deposits

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Mdbfusion: A Visible And Infrared Image Fusion Framework Capable For Motion Deblurring

Mdbfusion: A Visible And Infrared Image Fusion Framework Cap...

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IEEE International Conference on Image Processing

作者： Jun Chen Wei Yu Xin Tian Jun Huang Jiayi Ma School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Electronic Information School Wuhan University Wuhan China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

As a vital technology in the field of image processing, numerous visible and infrared fusion methods have emerged with the aim of generating a single image containing salient targets and abundant details. However, owing to the influence of target motion or camera shake, source images may suffer from motion blur, resulting in blurred edges and unrecognizable objects. To address this issue, this paper bridges the gap between deblurring and fusion tasks and proposes a joint motion deblurring and fusion network for visible and infrared image (MDbFusion). On the one hand, we innovatively merge motion deblurring task into network design, which effectively ensures the capacity of MDbFusion to process images in the extreme condition. On the other hand, an Adaptive Weight Module (AWM) is designed to calculate contribution between visible and infrared features, which solves the channel contrast between two tasks, greatly reducing the complexity of network. Extensive experiments demonstrate that the MDb-Fusion outperforms state-of-the-art (SOTA) fusion algorithms in terms of preserving texture details and quantitative metrics. Furthermore, we also compare with a two-step fusion strategy that first deblurring then fusion, both with SOTA methods. The results reveal superiority of our framework in coupling and reciprocity between two tasks.

关键词： Measurement Couplings Adaptive systems Image edge detection Cameras complexity theory Decoding

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Smart Infrared Temperature Measurement System Based On Elastic Compute Service Platform

Smart Infrared Temperature Measurement System Based On Elast...

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2021 China automation Congress, CAC 2021

作者： Li, Yongbo Yuan, Li Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665426473

In the fight against the novel coronavirus, this paper designs a smart infrared temperature measurement system based on the Elastic Compute Service platform. In the perceptual layer part of the Internet of Things(IoT), the system uses infrared temperature sensors to quickly collect the temperature of the forehead or arm of the human body and Uses the MCU to automatically transmits the measured data to the Elastic Compute Service through the WiFi module. In the network layer part of the Internet of Things,the data written into the database of the Elastic Compute Service through the program deployed on the Elastic Compute Service. In the application layer of the Internet of Things, the remote management terminal monitors the collected body temperature data in real-time and provides quick warnings. Finally, the User can log in to the applet on his mobile phone to quickly and easily obtain personal information. © 2021 IEEE

关键词： Internet of things

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Improved Shufflenet V2 Network with Attention for Speech Emotion Recognition

SSRN

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SSRN 2024年

作者： Udeh, Chinonso Paschal Chen, Luefeng Du, Sheng Liu, Yulong Li, Min Wu, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

This paper introduces a novel algorithm for recognizing speech emotions, utilizing an enhanced version of the ShuffleNet V2 network. Previous studies typically analyze speech signals solely from the time or frequency domain, resulting in the loss of detailed information. Our algorithm initially converts speech data into an acoustic spectrogram to address this limitation, preserving rich original information across both time and frequency domains. This approach optimizes feature extraction by leveraging the capabilities of deep convolutional networks to capture local changes in images. Furthermore, we integrated the attention mechanism from ECA-Net into the ShuffleNet V2 model. This incorporation enhances the extraction of significant features, amplifying the expression of features strongly correlated with the emotional state conveyed in speech while suppressing irrelevant ones. In addition to these enhancements, we replace the ReLU activation function with Hardswish to deepen the neural network, thereby improving feature information without compromising model accuracy. Finally, the training stage incorporates the hot restart Cosine Annealing Learning Rate to optimize the network further. Simulation experiments are conducted on the enterface'05 dataset to evaluate the accuracy of the proposed algorithm. The experimental results show that the recognition rate of the method is higher than the existing algorithms. © 2024, The Authors. All rights reserved.

关键词： Human robot interaction

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