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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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Stability Analysis of a New Constant Current to Constant Voltage Power Supply System

Stability Analysis of a New Constant Current to Constant Vol...

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Chinese control Conference (CCC)

作者： Xiaoxian Song Zhijian Fang Yuangeng Xia Jie Cai Zihao Wei 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

A high-reliability constant current to constant voltage power supply system has the advantages of small volume of switching power supply, high power density, high efficiency was proposed. This paper use two controllers to control the shunt regulator(SR) circuit and single-end flyback converter part, and separate the two parts for small signal modeling and give the parameters to stabilize the closed loop. The state space average modeling idea was used to solve the state equations for the modes of the converters in a switching cycle. In order to ensure the stability of cascade system, this paper collaborative optimization of hardware filter parameters and the appropriate PI parameter design. The experimentals verify the correctness of our theory, and the system has good stability under closed-loop conditions.

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Prediction and scheduling for blast furnace gas generation based on time series feature extraction

Prediction and scheduling for blast furnace gas generation b...

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Industrial Cyber-Physical systems (ICPS)

作者： Huihang Li Jie Hu Qingfeng Yang Luefeng 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

Due to the significant time lag and under-regulation, predicting the blast furnace gas generation and formulating its scheduling strategy is complex. This paper proposes a blast furnace gas generation prediction method based on time series feature extraction and designs a blast furnace gas scheduling strategy based on the prediction results. Firstly, Pearson correlation analysis is used to identify the parameters that have a significant correlation with the blast furnace gas generation, and the selected parameters are decomposed into several intrinsic mode components with different frequency characteristics using the complete ensemble empirical mode decomposition; Then, the principal component analysis method is used to extract the principal components of several intrinsic modal components, and these principal components are employed as the inputs of long short-term memory neural network to predict the blast furnace gas generation; Finally, according to the prediction results designs the scheduling strategy of blast furnace gas. The experiment and contrast experiments are carried out with the industrial field data, and experimental results illustrate that the proposed method is correct and effective.

关键词： Job shop scheduling Correlation Time series analysis Neural networks Predictive models Blast furnaces Feature extraction

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Imbalance control with stratified voltage of modularized CC-to-CV converter for auto and seamless module switching

Imbalance control with stratified voltage of modularized CC-...

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International Conference on Power Electronics (ICPE)

作者： Jie Cai Zhijian Fang Yuangeng Xia Xiaoxian Song 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

Constant current (CC) based power distribution is widely used in the submarine power supply grid for its robustness against cable impedance and short circuit faults. An input-series-output-parallel (ISOP) modularized CC-to-CV converter is be used to provide constant voltage (CV) for the submarine instruments. In this paper, an imbalance control with stratified voltage is proposed for the modularized CC-to-CV converter by switching modules to adjust the power. The power of each power module is decided by the output voltage realizing auto and seamless module switching. Specially, only one module is regulated to adjust the power, other modules are out of control working either in full power or in standby, improving the efficiency for light power conditions. The modeling and analysis of the modularized CC-to-CV converter is also presented, as well as the proposed the control method. Finally, a prototype is built to verify the proposed method.

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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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A Pipeline Leak Detection Method Based on MGD-KLD and On-Delay Timer

A Pipeline Leak Detection Method Based on MGD-KLD and On-Del...

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Chinese control Conference (CCC)

作者： Jiaqi He Yan Yuan Wenkai Hu 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 Engineering Research Center of Intelligent Technology for Geo Exploration Ministry of Education Wuhan China

Leaks in natural gas pipelines can cause very serious safety accidents, and timely detection and remedial action can greatly reduce the losses. In recent years, pipeline leak detection has received extensive studies. Most methods use pressure sensors or acoustic sensors to detect pipelines, but there are certain limitations on the usage scenarios and detection time delays. On this basis, this paper selects maglev vibration detector to detect the vibration signal of pipelines. The difficulty lies in that, sudden changes in vibration signals due to external disturbances, may lead to false alarms. Therefore, this paper proposes a pipeline leak detection method using Multivariate Gaussian Distribution based Kullback-Leibler Divergence (MGD-KLD) and on-delay timer to reduce false alarms during the detection process. In this paper, by constructing a simulated pipeline platform for leak experiments and applying the above method to process the experimental data, the false alarm rate of pipeline leak detection can be effectively reduced.

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Dynamic Event-Triggered Synchronization control for Complex Dynamical Networks

Dynamic Event-Triggered Synchronization Control for Complex ...

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Chinese control Conference (CCC)

作者： Fuxiang Yang 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

This paper explores the finite-time synchronization of a class of discrete-time nonlinear singularly perturbed complex networks using a dynamic event-triggered mechanism (DETM). The DETM is designed to optimize packet transmission, aiming to conserve network resources. By constructing a Lyapunov function considering singularly perturbed parameters (SPPs) and DETM information, a sufficient condition for the dynamics of synchronization error system to be finite-time stable is given. The parameters of the synchronization controller can be determined by solving a set of matrix inequalities. The effectiveness of the proposed controller is demonstrated through a numerical example.

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Reinforcement Learning with Reward Shaping and Hybrid Exploration in Sparse Reward Scenes

Reinforcement Learning with Reward Shaping and Hybrid Explor...

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Industrial Cyber-Physical systems (ICPS)

作者： Yulong Yang Weihua Cao Linwei Guo Chao Gan 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

High precision modeling in industrial systems is difficult and costly. Model-free intelligent control methods, represented by reinforcement learning, have been applied in industrial systems broadly. The hard evaluated of production states and the low value density of processing data causes sparse rewards, which lead to an insufficient performance of reinforcement learning. To overcome the difficulty of reinforcement learning in sparse reward scenes, a reinforcement learning method with reward shaping and hybrid exploration is proposed. By perfecting the rewards distribution in the state space of environment, the reward shaping can make the state-value estimation of reinforcement learning more accurate. By improving the rewards distribution in time dimension, the hybrid exploration can make the iteration of reinforcement learning more efficient and more stable. Finally, the effectiveness of the proposed method is verified by simulations.

关键词： Estimation Reinforcement learning Production Cyber-physical systems Stability analysis Intelligent control Convergence

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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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Cooperative Diving of Multi-AUV System with Self-triggered Mechanism

Cooperative Diving of Multi-AUV System with Self-triggered M...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Zhang, Junjie Yang, Jingxin Bian, Yougang Cui, Qingjia Qin, Xiaohui Hu, Manjiang Shen, Da Gao, Hongbo Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China University of Science and Technology of China The Department of Automation Hefei China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

A self-triggered distributed model predictive control (DMPC) approach is proposed in this paper for the cooperative diving of a multi-autonomous underwater vehicle (AUV) system. In order to realize cooperative diving of the multi-AUV system, we design a basic DMPC mechanism with a stability condition which is easy to be realized. Then, a self-triggering mechanism is introduced into the basic DMPC approach which saves communication and computation resources. In the self-triggered mechanism, the maximum time instant that is inside the terminal region decides the triggering interval. The triggering interval is decided after solving the optimization problem, therefore helps reduce the computation burden at each control loop. Lastly, numerical examples are provided to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Autonomous underwater vehicles

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