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Bounded Tracking of Uncertain Multiagent systems with Time-Delays and Multiplicative Noises

Bounded Tracking of Uncertain Multiagent Systems with Time-D...

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Industrial Cyber-Physical systems (ICPS)

作者： Guoliu Chen Xiaofeng Zong 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

This paper focuses on the bounded tracking control of general linear multi-agent systems(MASs), considering the effects of inevitable communication time-delays, measurement noises, and uncertain disturbances in practical applications. Firstly, the bounded tracking control problem of uncertain MASs under multiplicative noises is transformed into the boundedness problem of stochastic differential delay equations. Then, the upper bound of agent tracking is calculated by means of linear variation, variation of constants formula and stochastic analysis theory, and sufficient conditions are given for the system to achieve the bounded tracking control.

关键词： Sufficient conditions Upper bound Uncertainty Measurement uncertainty Stochastic processes Cyber-physical systems Mathematical models

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Asynchronous Fault Detection for Fuzzy systems Based on Dynamic Event-Triggered Mechanism

Asynchronous Fault Detection for Fuzzy Systems Based on Dyna...

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Chinese Control Conference (CCC)

作者： Yuxi Wang Xiongbo Wan 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

This article investigates the asynchronous fault detection (FD) problem for fuzzy systems with event-triggered mechanism (ETM). A new dynamic ETM (DETM) is adopted to further reduce the waste of network resources. Considering the impact of asynchronous premise variables brought by ETM, a design criterion for fuzzy FD filter (FDF) is derived. A reasonable residual evaluation function is constructed and an appropriate threshold is set. To ensure the error dynamics be asymptotically stable with a prescribed $H_{\infty}$ performance, we construct a new Lyapunov function that contains an internal dynamic variable in the ETM. A sufficient condition satisfying the proposed performance index is derived. Finally, we provide a numerical simulation to verify the effectiveness of the proposed asynchronous FD strategy under dynamic event-triggered (ET) communication.

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13th International Conference on Bioinformatics and Biomedical technology, ICBBT 2021

作者： Liu, Chenliang Zhu, Yuan Zhang, Houwang 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 China

ISBN: (纸本)9781450389655

Single cell RNA sequencing (scRNA-seq) technology can study gene expression in single cell resolution and solve cell heterogeneity that cannot be solved by the traditional RNA sequencing (Bulk RNA-seq) technology. It provides a powerful analytical tool for in-depth study of the immune, regulatory, replication, and other activities of cells and explanations of life activities rules. However, single cell sequencing experiments are hindered by several technical issues, which cause output scRNA-seq with a high percentage of zeros (dropout events), impacting the reliability of downstream analyses. Therefore, a great number of computational methods have been proposed to address the increased sparsity observed in scRNA-seq data. In this paper, motivated by the image denoising method, we propose an imputation algorithm CSI based on the similarity between cells. The cell similarity is considered as the weight to average the gene expression values to impute the missing values in the data. CSI achieves better accuracy than other the 7 publicly notable scRNA-seq imputation methods on some real scRNA-seq data, especially gains better performance in some downstream analysis such as cluster analysis and pseudo trajectory inference. © 2021 ACM.

关键词： RNA

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Robust Finite-Time Stabilization of Stochastic Parabolic PDE systems via Non-Fragile Spatial Sampled-Data Control

Robust Finite-Time Stabilization of Stochastic Parabolic PDE...

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IEEE Conference on Decision and Control

作者： Xiaofang Hu Feida Song Leimin Wang 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

This paper addresses the robust finite-time stabi-lization (FTS) issue for stochastic parabolic PDE systems via non-fragile spatial sampled-data control scheme. First, a class of distributed parameter systems characterized by the delayed stochastic parabolic partial differential equation is developed for analyzing the effects of stochastic disturbance, structural uncertainty, and discrete delay on the system performance. Then, a non-fragile spatial sampled-data control scheme is established by setting sampling points in the spatial domain, which effectively saves communication resources and ensures that the closed-loop system maintains good performance when the controller is perturbed. Moreover, based on the partial differential equation theory, stochastic analysis approach, and the extended Wirtinger's inequality technique, several criteria are provided to ensure the robust FTS of stochastic parabolic PDE systems in the mean square sense. Lastly, a numerical example is provided to verify the feasibility of the suggested stabilization criteria and control scheme.

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Semantic Segmentation of Historical Landslide Based on Improved U-Net

Semantic Segmentation of Historical Landslide Based on Impro...

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Chinese Control Conference (CCC)

作者： Hanqi Qu Chengda Lu Yutong Pan Yuxuan Zeng Min Wu 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

Landslide disasters are extremely destructive. Accurate identification of landslides plays an important role in disaster assessment, loss control and post-disaster reconstruction. This paper proposes a semantic segmentation landslide identification method based on improved U-Net. The deep convolution neural network and jump connection method is used for end-to-end semantic segmentation to achieve deep feature extraction and fusion of different receptive fields, thus enriching feature information. SENet modules are adopted to enhance the ability of the model to extract important features, so as to further improve the accuracy of model recognition. Extensive experiments show that our improved U-Net achieves better performance than the original algorithm on our landslide datasets. The results of Iou are improved by 4.12% which demonstrates our work is of great significance for the research of landslide area identification. Finally, the model is deployed to the web and applied to the geological hazard intelligent monitoring system to realize the landslide identification task.

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Displacement Prediction of Landslide Using SARIMA Model Based on Seasonal Characteristic

Displacement Prediction of Landslide Using SARIMA Model Base...

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Chinese Control Conference (CCC)

作者： Ruoxi Wu Chengda Lu Wanke Yu Min Wu Xuzhi Lai 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

Landslide displacement prediction is an important and indispensable part of landslide monitoring and warning. The change of the displacement is always considered being related to inducing factors, which are aimed at improving accuracy of the predicted model. However, the seasonal characteristic of the displacement, which has not been carefully analyzed, reveals the law of inducing factors. In order to gain a deeper understanding of characteristics, the Baijiabao landslide is taken as an example. The variational mode decomposition (VMD) method, which can extract effective information well, is introduced to decompose the displacement. Introducing the seasonal parameters, the seasonal autoregressive integrated moving average (SARIMA) model is established to predict the displacement subseries. Finally, accumulative displacement prediction values are obtained by superimposing the predicted subseries. With higher accuracy and lower error, the VMD-SARIMA model proves a better option in application compared with VMD-ARIMA, SARIMA and ARIMA models.

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A Feature Difference Network for Defect Detection on the Surface with Complex Pattern of Ceramic Tile

A Feature Difference Network for Defect Detection on the Sur...

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Chinese Control Conference (CCC)

作者： Jianru Xiong Jianqi An Xing-ao Wang Huimin Ou Xin Chen 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

The surface defects of ceramic tile greatly affect the service life of ceramic tile. At present, many detection methods of ceramic tile surface defects are mostly used for ceramic tiles with monochrome background or simple texture. However, many tiles with complex and irregular surface patterns are used in practical applications, but many methods cannot effectively detect surface defects in such tiles. This paper presents a double input feature difference network structure to overcome the limitation. First, a double input channel is constructed to extract features from the template image and the defect image respectively. Next, a method of feature difference is performed at different depths to suppress the background interference and prevent misclassification between different defect categories. Then a parameter-free attention module is embedded in the backbone to improve the ability of feature extraction. Experimental results show that this model effectively improves the mean average accuracy of 8.3% and the recall rate of 11.7%.

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Evaluation of Landslide Susceptibility Based on SVM with GWO Algorithm

Evaluation of Landslide Susceptibility Based on SVM with GWO...

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Chinese Control Conference (CCC)

作者： Yuxuan Zeng Chengda Lu Yutong Pan Hanqi Qu Min Wu 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

Since landslide is one of the most universal natural disasters in China, the study of regional landslide susceptibility evaluation is important to protect people's lives and property. This paper analyzes the geospatial characteristics of the Zigui-Badong section in the Three Gorges. By Pearson correlation analysis methodselects, nine impact factors of landslide susceptibility are extracted from the aspects of topography and geomorphology, geological environment, and hydrological conditions, used to establish the evaluation index system of landslide susceptibility. On the above data basis, the paper applies a support vector machine (SVM) model and an SVM model for gray wolf optimization (GWO) to the susceptibility evaluation of landslides, and product landslide susceptibility index maps according to the results. The research area is divided into four regions by jenks method on the map: high-risk, medium-risk, low-risk, and very low-risk areas. Applying the accuracy, confusion matrix, and receiver operating characteristic (ROC) curve to evaluate the model, The prediction accuracy of the GWO-SVM model and the SVM model is 88.55 % and 82.82 % respectively, the comparison proves that the GWO-SVM model is much more accurate, which can provide a reference for the study of regional landslide susceptibility.

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Prediction of Silicon Content in Molten Iron of Blast Furnace Based on Multi-dimensional Sequential Characteristic Reconstruction

Prediction of Silicon Content in Molten Iron of Blast Furnac...

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Industrial Cyber-Physical systems (ICPS)

作者： Zhuofu Zhang Weihua Cao Jie Hu Qifu Chen Min Wu 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

Molten iron is the primary output of blast furnace production. The content of silicon in molten iron clearly correlates with blast furnace temperature. However, due to the intricate conditions of blast furnace production, the silicon content in molten iron is nonlinear and unstable. Therefore, this paper adopts variational mode decomposition (VMD) to decompose and extract the feature information of the real silicon content data of LY Steel in March 2022, then uses Grey Wolf optimization (GWO) algorithm to optimize the parameters of the support vector regression (SVR) prediction model, and takes the decomposed data as model input for experimental verification. By comparing the predicted results with the real historical data of blast furnace production, it is found that the degree of fit is about 94.2%, which offers a new idea for the prediction of silicon content.

关键词： Support vector machines Production Predictive models Blast furnaces Prediction algorithms Feature extraction Silicon

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Events-Based Model Predictive Control for Polytopic Uncertain systems Under False Data Injection Attacks

Events-Based Model Predictive Control for Polytopic Uncertai...

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Industrial Cyber-Physical systems (ICPS)

作者： Fan Wei Chaoling Zhangabc Xiongbo Wan 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

In this paper, the events-based model predictive control (MPC) problem is studied for systems under false data injection (FDI) attacks. A time-varying event-triggered mechanism (ETM) is proposed to manage measurement data packet releases and a static ETM is used to reduce the influence of the FDI attacks on the controller. By using the properties of the defined robust positive invariant set, a solvable auxiliary optimization problem (OP) is proposed to design the controller. The recursive feasibility of the auxiliary OP and the input-to-state stability of the closed-loop system are guaranteed. The validity of the developed ETMs-based anti-attack MPC algorithm is shown by an example.

关键词： Uncertain systems Predictive models Prediction algorithms Stability analysis Data models Numerical models Closed loop systems

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