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26th International Conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo 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 School of Future Technology 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

ISBN: (纸本)9798331521950

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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Estimation and compensation of periodic disturbance using internal-model-based equivalent-input-disturbance approach

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Science China(Information Sciences) 2022年第8期65卷 150-163页

作者： Qicheng MEI Jinhua SHE Zhentao LIU Min WU School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper presents an improved equivalent-input-disturbance(EID) approach to deal with periodic disturbances. The approach has two degrees of freedom. One is an improved EID compensator, in which a repetitive controller is inserted in this study. The other is a conventional servo system for a reference *** improved EID compensator estimates and compensates for periodic disturbances without steady-state error, and the servo system ensures a satisfactory tracking performance. The improved EID compensator is designed using the linear-matrix-inequality(LMI) method. Three parameters in an LMI are selected using the particle-swarm-optimization(PSO) algorithm. The state-feedback gain of the conventional servo system is designed using the linear-quadratic-regulator(LQR) method. Simulation results of a rotational control system demonstrate the validity of the approach and its advantage over others.

关键词： equivalent input disturbance (EID) linear matrix inequality (LMI) linear quadratic regulator (LQR) particle swarm optimization (PSO) periodic disturbance repetitive control

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A Multi-view Non-negative Matrix Factorization Single-cell Clustering Algorithm for Missing Data

A Multi-view Non-negative Matrix Factorization Single-cell C...

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2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022

作者： Yu, Ailian Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

A significant study focus on the field of bioinformatics has been on how to quickly and accurately accomplish the cell clustering task using computational approaches for single-cell RNA sequencing (scRNA-seq) data. Technical issues with scRNA-seq data include missing values, high noise levels, and a large batch impact. While the single-cell data recovery method based on data filling can enhance data quality, it will subjectively alter the distribution of data. To address the above-mentioned issues, we propose a non-negative matrix factorization clustering algorithm (scMVNMF) based on multi-view data of scRNA-seq. Under the assumption of essentially unchanging original data distribution, it can not only identify the typical low-dimensional representation patterns of multi-view data but also produce final explicable clustering findings. The results of the experiments demonstrate that the suggested algorithm has some advantages over the filling approach in terms of clustering tasks and can maintain a certain level of stability when the quality of single-cell data varies. © 2022 IEEE.

关键词： Non-negative matrix factorization

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Ti, Yaqi Li, Changhe Yang, Shengxiang 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 Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350314588

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space. © 2023 IEEE.

关键词： Nutrition

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A semantic segmentation method based on improved u-net network 4

A semantic segmentation method based on improved u-net netwo...

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4th International Conference on advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021

作者： Li, Penghui Zhang, Lijun Qiao, Jinlong Wang, Xiangguo China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan China

ISBN: (纸本)9781665415965

Semantic segmentation is the most important branch of image processing. Semantic segmentation based on deep learning occupies the mainstream method. CNN, FCN, U-NET and DEEPLAB series have achieved good results in the field of image semantic segmentation. The improvement methods of each network also emerge in an endless stream, but the emphasis of improving the network focuses on the feature transfer and feature fusion between network layers in each method. In this paper, based on u-net, we use deep convolution, residual autonomic force and dendrite network to improve the network model. Deep convolution can reduce the amount of calculation, deepen the depth of network, extract more abundant image information, and transmit information between network layers in a more refined way. Dendritic networks are different from ordinary neural networks. Ordinary neural networks process information as models of nerve cell bodies. The role of neural dendrites is also important in the process of information processing. Dendritic network is used as a model of neural dendrites to improve the ability of information processing. The improved network in this paper was tested on the VOC2012 dataset, and the experimental results showed that PA, mAP and IoU were 93.36%, 92.28% and 68.26% respectively. The experimental results showed that the improved network in this paper had improved in all indicators. © 2021 IEEE.

关键词： Semantic Segmentation

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Reinforcement learning-enhanced dynamic multi-objective evolutionary algorithm for cooperative shovel scheduling in open-pit mining

Reinforcement learning-enhanced dynamic multi-objective evol...

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2024 China automation Congress, CAC 2024

作者： Zhang, Zihao Li, Changhe Yang, Shengxiang 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 Wuhan430074 China School of Artificial Intelligence Anhui University of Science & Technology Hefei232001 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350368604

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm. © 2024 IEEE.

关键词： Open pit mining

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Distributed Active Disturbance Rejection control of Multiple Mobile Robots for Online Workload Partition

Distributed Active Disturbance Rejection Control of Multiple...

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2023 China automation Congress, CAC 2023

作者： Li, Xin Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350303759

Considering the actual multi-agent coverage process, the motion trajectories are seriously affected by disturbances and noise. In this paper, a cooperative control method based on active disturbance rejection controller (ADRC) for multiple mecanum-wheeled robots (MWMR) is proposed to improve its robustness. In particular, to suppress the total disturbances during the running of each robot and reduce large computation and observation cost of high order ADRC, a reduced-order cascaded ADRC (RC-ADRC) is proposed. Moreover, distributed controller is proposed to track the expected trajectories of multiple mecanum-wheeled robots under disturbances. Compared with the proposed RC-ADRC based cooperative control scheme and PID based cooperative control one, the favorable motion trajectory tracking performance is obtained. Simulation results verify the superiority of the method. © 2023 IEEE.

关键词： controllers

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A Reinforcement Learning Based Dynamic Multi-Objective Constrained Evolutionary Algorithm for Open-Pit Mine Truck Scheduling

A Reinforcement Learning Based Dynamic Multi-Objective Const...

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2023 China automation Congress, CAC 2023

作者： Qiu, Junxiang Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 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 Wuhan430074 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350303759

Aiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods. © 2023 IEEE.

关键词： Evolutionary algorithms

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Photovoltaic Data Cleaning Method Based on DBSCAN Clustering, Quartile Algorithm and Pearson Correlation Coefficient Interpolation Method 6

Photovoltaic Data Cleaning Method Based on DBSCAN Clustering...

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6th International Conference on Energy, Electrical and Power Engineering, CEEPE 2023

作者： Wang, Biyuan Deng, Xingye Chen, Taowei Li, Yidong Huizhou Power Supply Bureau Guangdong Power Grid Co Guangdong China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798350348279

The proportion of photovoltaic power generation in the global energy structure is increasing year by year. Because of its volatility and periodicity, grid-connected photovoltaic challenges the safe and stable operation of power grid. One of the ways to solve this problem is data mining. Data mining needs high-quality data support, and data cleaning is one of the important means to improve data quality. Aiming at the problem of poor quality of original PV data, this paper analyzes two typical outliers of PV data and proposes a PV output data cleaning method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, quartile algorithm and Pearson correlation coefficient interpolation method. Comparison of DBSCAN clustering algorithm combined with the quartile method of outlier identification with the quartile method alone outlier identification, the effectiveness of the method proposed in this paper is verified. Then using the method proposed in this paper for outlier identification based on Pearson correlation coefficient interpolation and cubic spline interpolation to verify the data filling effect, the results show that Pearson correlation coefficient interpolation is superior to cubic spline interpolation. Finally, two sets of data with different data cleaning methods are substituted into the Long short-term memory (LSTM) Model to verify accuracy of the cleaning method proposed in this paper. © 2023 IEEE.

关键词： Data mining

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Adaptive Backstepping Sliding Mode control for Underwater Glider Trajectory Tracking Based on Nonlinear Disturbance Observer

Adaptive Backstepping Sliding Mode Control for Underwater Gl...

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2023 China automation Congress, CAC 2023

作者： Li, Zhenjia Cao, Weihua Gan, Chao Wen, Mingming 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 Guangzhou Marine Geological Survey Guangzhou510000 China

ISBN: (纸本)9798350303759

An adaptive backstepping dynamic surface sliding mode controller based on nonlinear disturbance observer (NDOABSMC) is designed to track zigzag motion of underwater glider (UG). Firstly, a nonlinear disturbance observer is devised to observe ocean current. Then, the backstepping sliding mode control is used to devise motion attitude controller of UG to ensure that UG can track the target trajectory quickly. Additionally, the stability of UG's control system is analyzed. In the end, the proposed control strategy is compared with other approaches. © 2023 IEEE.

关键词： Backstepping

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