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Chinese automation Congress (CAC)

作者： Bo-Wen Liu Da Xu Yong He 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

ISBN: (纸本)9781665426480

The global coronavirus disease (COVID-19) has brought great challenges to the power systems due to its limitations on social, economic and productive activities. This paper proposes a short-term load forecasting method during COVID-19 pandemic based on copula theory and eXtreme Gradient Boosting (XGBoost). In this method, the coupling relationship among the cross-domain meteorological, public health, and mobility time-series data are fully analyzed based on copula theory, which is used for the short-term power load forecasting based on multi-factor fusion XGBoost algorithm. The proposed method has been fully evaluated and benchmarked on available cross-domain open-access United States data to demonstrate its effectiveness and superiority on short-term load forecasting of COVID-19.

关键词： COVID-19 Support vector machines Economics Load forecasting Pandemics Human factors Social factors

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Survival prediction of LGG patients based on deep learning of MRI data and genomic data integration

Survival prediction of LGG patients based on deep learning o...

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Chinese automation Congress (CAC)

作者： Lingzhi Sun Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (纸本)9781665426480

Low-grade gliomas (LGG) is the most common primary intracranial tumor, with high incidence rate, high recurrence rate, high mortality rate and low cure rate. Therefore, it is necessary to predict the survival of LGG patients in diagnosis. Considering the complementarity of information, the new proposed algorithm in this study is designed to integrate Magnetic Resonance Image (MRI) data and gene expression data using deep learning method to predict the Disease Specific Survival (DSS) of LGG patients. Firstly, MRI data of 44 patients is screened from TCIA database, and then the corresponding gene expression data of 44 patients is searched from TCGA database. Then, 724 image feature data extracted from MRI data are filtered and extracted by deep learning method, and DSS tags are used to train the model; deep learning method is used to extract 20530 features of gene expression data, and DSS tags are also used for training. As a contrast, the deep learning method is used to integrate the two features to train the model. Experiments are evaluated on MRI data, gene expression data and the integration data of MRI and gene expression data, respectively. The results show that by using the integration of MRI data and gene expression data performs better than using single data in terms of the time-dependent receiver operating characteristic(ROC) and the area under the curve (AUC) of the ROC curve criteria.

关键词： Deep learning Training Databases Magnetic resonance imaging Feature extraction Data models Gene expression

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Incomplete Multi-view Clustering for Single cell RNA Sequencing Data

Incomplete Multi-view Clustering for Single cell RNA Sequenc...

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Chinese automation Congress (CAC)

作者： Tijian Zhu Yuan Zhu Chuan-Ke Zhang 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

ISBN: (纸本)9781665426480

With the rapid development of sequencing technology, researchers can obtain a large number of single cell RNA sequencing (scRNA-seq) data which is useful for analysis of cell fate decision and growth process at individual cell resolution. But due to the limitations of sequencing technology, the data acquired has dropouts which may affect the results of down-steam analysis. Therefore, many algorithms have been proposed to impute the data before clustering, here in, imputation and clustering are considered as two separate processing stage. In this paper, we adopt a clustering algorithm—Incomplete Multiple Kernel k-means Clustering with Mutual Kernel Completion (MKKM-IK-MKC) to analyze scRNA-seq data. It unifies imputation and clustering into a process. Comparing with some existing "two stage" (imputation +clustering) algorithms, the experimental results on five scRNA-seq datasets from various species demonstrate the effective performance of our new proposed method.

关键词： Dimensionality reduction Sequential analysis automation RNA Clustering algorithms Optimization methods Iterative methods

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Disturbance Rejection Based on Equivalent-Input-Disturbance Approach Using High-Order Filter

Disturbance Rejection Based on Equivalent-Input-Disturbance ...

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International Conference on Mechatronics (ICM)

作者： Qicheng Mei Jinhua She Zhentao Liu Yonghua Xiong Wangyong He Danyun Li Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9781728144429

ISBN: (纸本)9781728144436

This paper presents an improved equivalent-input-disturbance (EID) approach to deal with exogenous disturbances. In this approach, an EID estimator that contains a high-order filter is used to estimate and compensate for the disturbances. The system design is based on the results of stability analysis. Simulation results of a position control system demonstrate the validity of the approach and its superiority over the conventional one.

关键词： Mechatronics Simulation Position control Transfer functions Observers Stability analysis System analysis and design

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A new result for $H_{\infty}$ performance analysis of sampled-data systems via looped-functionals

A new result for $H_{\infty}$ performance analysis of sample...

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Chinese control Conference (CCC)

作者： Du Xiong Wen-Hu Chen Yong-He Lao Yu-Long Fan Chuan-Ke Zhang School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan P. R. China

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper is concerned with $H_{\infty}$ performance analysis of aperiodic sampled-data systems by employing the input delay approach. Firstly, a novel augmented looped-functional (ALF) is presented under the double-side looped-functional technique, which considers coupled relationships among state vectors more fully than the current ones. Then, by using the Bessel-Legendre inequality to estimate the derivative of the ALF, a stability criterion is derived in the form of linear matrix inequalities. Finally, a numerical example demonstrates the effectiveness and the advantages of the proposed criterion by comparing it with the existing ones.

关键词： Linear systems Stability criteria Sampled data systems control systems Performance analysis Delays Linear matrix inequalities

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An improved ant colony optimization for path planning with multiple UAVs

An improved ant colony optimization for path planning with m...

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International Conference on Mechatronics (ICM)

作者： Jing Li Yonghua Xiong Jinhua She Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9781728144429

ISBN: (纸本)9781728144436

As exploiting unmanned aerial vehicles (UAVs) as mobile elements is a new research trend recently, approximation algorithms to solve path planning problems for UAVs are promising approaches. This paper present a solution for the problem of minimum mission time to cover a set of target points in the surveillance area with multiple UAVs. In this methodology, we propose an improved ant colony optimization (ACO) combining ACO with greedy strategy. The main purpose is to find the optimal number of UAVs and to plan the paths of the minimum mission time. Simulation results demonstrate the validity and the superiority of the proposed algorithm.

关键词： Ant colony optimization Mechatronics Surveillance Simulation Market research Path planning Unmanned aerial vehicles

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A Multi-feature Extraction-based Image Identification Method for Rock Debris in The Drilling Process

A Multi-feature Extraction-based Image Identification Method...

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Chinese automation Congress (CAC)

作者： Pengju Xu Chao Gan Luzhao Wang Weihua Cao School of Automation China University of Geosciences 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 No.3 Exploration Institute of Geology and Mineral Resources of Shandong Province Yantai China

In this paper, a multi-feature extraction-based image identification method for rock debris in the drilling process is proposed, involving three main parts (trainable feature extractor, strong feature extraction, and classification). In trainable feature extractor, abstract features are obtained by extracting the full connection layer of Convolutional Neural Network (CNN). In strong feature extraction, the method uses Gray-Level Co-occurrence Matrix (GLCM) and Color Coherence Vector (CCV) to get the strong feature. In classification, the extracted abstract features and strong features are concatenated and fed into the Support Vector Machine (SVM). Comparison results with two well-known methods indicated the effectiveness of the proposed method.

关键词： Support vector machines Drilling Measurement Image color analysis Neural networks Feature extraction Rocks

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Locating,Mapping and Motion Planning of Smart Wheelchair

Locating,Mapping and Motion Planning of Smart Wheelchair

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第40届中国控制会议

作者： Mengyang Yao Yi Liu Chi Xu School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Smart wheelchair plays an important role in rehabilitation training and daily movement of people with high mobility *** ensure that the smart wheelchair can operate stably in an unfamiliar environment,it is necessary to solve the problems of locating,mapping and motion *** the complex indoor environment of rehabilitation training and the wheelchair model,we propose a new smart wheelchair system *** on Simultaneous Localization and Mapping（SLAM） technology,location and mapping can be obtained through the Rao-Blackwellized Particle Filters（RBPF） *** integrate the sensor data into the proposal distribution to solve the particle degradation problem of RBPF and use selective resampling to improve the algorithm *** to the wheelchair’s own location and the surrounding environment map,global path planning and local real-time path planning are used to complete the motion *** show that the smart wheelchair system we proposed can operate robustly and quickly in a complex indoor environment.

关键词： Smart wheelchair Rehabilitation training SLAM Motion planning

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota 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 School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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Nonlinear Cooperative Output Regulation with Input Delay Compensation

arXiv

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arXiv 2024年

作者： Zheng, Shiqi Ahn, Choon Ki Jiang, Xiaowei Yan, Huaicheng Shi, Peng The School of Automation China University of Geosciences Wuhan430079 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Shaoyang Institute of Advanced Manufacturing Technology Shaoyang422000 China The School of Electrical Engineering Korea University Seoul136-701 Korea Republic of The Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology Shanghai200237 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia

This paper investigates the cooperative output regulation (COR) of nonlinear multi-agent systems (MASs) with long input delay based on periodic event-triggered mechanism. Compared with other mechanisms, periodic event-triggered control can automatically guarantee a Zeno-free behavior and avoid the continuous monitoring of triggered conditions. First, a new periodic event-triggered distributed observer, which is based on the fully asynchronous communication data, is proposed to estimate the leader information. Second, a new distributed predictor feedback control method is proposed for the considered nonlinear MASs with input delay. By coordinate transformation, the MASs are mapped into new coupled ODE-PDE target systems with some disturbance-like terms. Then, we show that the COR problem is solvable. At last, to further save the communication resource, a periodic event-triggered mechanism is considered in the sensor-to-controller transmission in every agent. A new periodic event-triggered filter is proposed to deal with the periodic event-triggered feedback data. The MASs with input delay are mapped into coupled ODE-PDE target systems with sampled data information. Then, Lyapunov-Krasovskii functions are constructed to demonstrate the exponential stability of the MASs. Simulations verify the validity of the proposed results. © 2024, CC BY-NC-ND.

关键词： Feedback control

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