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42nd Chinese Control Conference, CCC 2023

作者： Song, Kun Li, Danyun Zhao, Dashuang School of Automation China University of Gcoscicnccs 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

ISBN: (纸本)9789887581543

For hybrid energy storage systems in DC microgrids, a droop control consisting of virtual capacitors and virtual resistors can decompose power into high-frequency components and low-frequency components, then assign them to batteries and supercapacitors to respond respectively. However, aiming at the service life of the energy storage system, this paper considers the characteristics and key parameters of the hybrid energy storage structure and proposes an adaptive drooping comprehensive control strategy considering the SOC of the energy storage unit given the shortcomings of power distribution within the current hybrid energy storage. According to the self-regulation capacity of each energy storage unit, it is sorted and constrained, and protected by using SOC, which ensures the economy and safety of the system while ensuring power distribution. The traditional droop control and adaptive droop control are simulated to verify the effectiveness of the proposed control strategy. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Electric power distribution

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Anomaly Detection and Alarm Limit Design for In-Hole Bit Bounce Based on Interval Augmented Mahalanobis Distance 4th

Anomaly Detection and Alarm Limit Design for In-Hole Bit B...

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Proceedings of the 4th International Conference on Neural Computing for advanced Applications, NCAA 2023

作者： Hu, Bin Hu, Wenkai Zhang, Peng Cao, Weihua 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

ISBN: (纸本)9789819958467

Timely and accurate anomaly detection is of great importance for the safe operation of the drilling process. To detect bit bounce during the drilling process, this paper proposes a method based on interval augmentation Mahalanobis distance. The method first selects process variables that are closely related to bit bounce through mechanism analysis;secondly, data augmentation is performed on the selected data;then, the Mahalanobis distance statistic of normal data is calculated and its distribution threshold is designed using the Kernel Density Estimation method;finally, the Mahalanobis distance is calculated for the augmented online data and compared to the threshold to determine if a bit bounce is present. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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A Novel intelligent Method for Extracting the Multi-Domain Features of Ground Penetrating Radar Data

A Novel Intelligent Method for Extracting the Multi-Domain F...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Run Gan, Chao Cao, Weihua 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

ISBN: (纸本)9798350303759

Ground Penetrating Radar (GPR) features are vital for accurately predicting the formation environment in the tunnel engineering. In this paper, a novel intelligent method for extracting the multi-domain (time domain, time frequency domain, and spatial domain) features of ground penetrating radar data is proposed. Firstly, the GPR exploration data is read to obtain the relative amplitude matrix. Secondly, multi-domain features are obtained using the following methods. The relative amplitude matrix of GPR is averaged by rows to obtain the time domain feature called the average relative amplitude(ARA). The S-transform is used to extract the time frequency domain feature called the average maximum weight frequency(AMWF) of the electromagnetic waves. And the events of the GPR images are highlighted by using Gaussian filtering and edge detection, and the spatial domain feature called the maximum event length(M EL) is obtained through contour detection. Finally, the three extracted multi-domain features are stored in the GPR feature database. Compared simulation results verify the effectiveness of the proposed method. © 2023 IEEE.

关键词： Matrix algebra

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Semantic Segmentation of Historical Landslide Based on Improved U-Net 42

Semantic Segmentation of Historical Landslide Based on Impro...

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42nd Chinese Control Conference, CCC 2023

作者： Qu, Hanqi Lu, Chengda Pan, Yutong Zeng, Yuxuan 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

ISBN: (纸本)9789887581543

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. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Landslides

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Stability Analysis of Discrete Networked systems with Network Induced Delay and Malicious Packet Dropout Based on a Matrix Transformation Method 42

Stability Analysis of Discrete Networked Systems with Networ...

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42nd Chinese Control Conference, CCC 2023

作者： Zhou, Meng-Ying Chen, Wen-Hu Zhang, Chuan-Ke Hou, Ya-Xin Xie, Ke-You 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

ISBN: (纸本)9789887581543

This paper is concerned with the stability of discrete-time networked control systems with network induced delay and malicious packet dropout. Firstly, network induced delay and malicious packet dropout are analyzed, and the data packet dropout is converted into the change rate of time delay. Secondly, the functional of time delay and change rate of time delay is constructed, and some summation terms are generated when calculating the functional forward difference. Moreover, the auxiliary-function-based summation inequality and reciprocally convex matrix inequality are used to estimate the resulting summation terms. Then, a less conservative stability criterion for discrete networked systems with network delay and data packet loss is established. Finally, the validity of the proposed stability criterion is illustrated by a numerical example. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Time delay

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Zhe Hu, Wenkai Li, Yupeng Wan, Xiongbo Cao, Weihua China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9798350337754

Effective identification of faults or abnormal conditions can help operators make corrective decisions and plan equipment maintenance. Sequence matching and cluster analysis are important methods to distinguish different faults. Most existing sequence matching methods mainly focus on alarm event sequences, which reflect the amplitude change characteristics of process data. However, due to the complexity of the equipment and the coupling between variables, alarm event sequences caused by different faults may still assemble each other in a certain extent, which makes it difficult to distinguish faults based on alarms only. To solve this problem, this paper proposes a sequence similarity analysis method combining both alarm and trend events. A qualitative trend representation method is proposed to extract trend changes as trend events. A feature event fusion method is proposed to generate a hybrid sequence to distinguish different fault sequences. The proposed method is evaluated based on data generated by the Tennessee Eastman process model. © 2023 IEEE.

关键词： Cluster analysis

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Realizing submeter spatial resolution for Raman distributed fiber-optic sensing using a chaotic asymmetric paired-pulse correlation-enhanced scheme

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Photonics Research 2024年第10期12卷 2365-2375页

作者： BOWEN FAN JIAN LI ZIJIA CHENG XIAOHUI XUE MINGJIANG ZHANG College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control Systems(Ministry of Education and Shanxi Province) Taiyuan University of TechnologyTaiyuan 030024China College of Physics Taiyuan University of TechnologyTaiyuan 030024China

The sensing spatial resolution and signal-to-noise ratio(SNR)of Raman distributed optical fiber sensors are limited by the pulse width and weak Raman scattering ***,the sensing spatial resolution cannot exceed the order of meters at several kilometers sensing *** break through this physical bottleneck,a novel,to our knowledge,Raman scattering model based on paired-pulse sensing is *** fundamental origins of the observed limited spatial resolution of conventional schemes are analyzed,and a chaotic asymmetric paired-pulse correlation-enhanced scheme for Raman distributed fiber-optic sensing is proposed and experimentally *** proposed scheme uses a chaotic asymmetric paired-pulse as the sensing signal and extracts the light intensity information of each data point of the sensing fiber,which carries the random undulation characteristics of chaotic time series,based on the time-domain differential reconstruction *** scheme overcomes the pulse width limitation of spatial resolution via correlation and demodulation,enhances the correlation characteristics between the temperature-modulated Raman scattered light field and detection signal,and improves the ***,a sensing performance of 10 km,a spatial resolution of 30 cm,and an SNR of 6.67 dB are realized in the *** scheme provides a new research idea for a high-performance Raman distributed optical fiber sensing system.

关键词： fiber pulse scattering

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Evaluation of Landslide Susceptibility Based on SVM with GWO Algorithm 42

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

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42nd Chinese Control Conference, CCC 2023

作者： Zeng, Yuxuan Lu, Chengda Pan, Yutong Qu, Hanqi 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

ISBN: (纸本)9789887581543

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. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Landslides

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Displacement Prediction of Landslide Using SARIMA Model Based on Seasonal Characteristic 42

Displacement Prediction of Landslide Using SARIMA Model Base...

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42nd Chinese Control Conference, CCC 2023

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

ISBN: (纸本)9789887581543

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. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Variational mode decomposition

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Trend Features Extraction of Apparent Resistivity in Tunnel Engineering

Trend Features Extraction of Apparent Resistivity in Tunnel ...

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2023 China Automation Congress, CAC 2023

作者： Zhou, Kai Cao, Weihua Gan, Chao Li, Yupeng 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

ISBN: (纸本)9798350303759

Troublesome incidents like sudden water inflows increase the risk of collapse accidents in tunnel excavation. In this study, a data-driven underground water prediction method is proposed based on trend features extracted from apparent resistivity. A novel framework is developed for extracting trend features from the contour lines of apparent resistivity. These trend features are subsequently integrated with numerical features from the resistivity matrix for classification. The effectiveness of the proposed method is demonstrated by apparent resistivity data from real tunnel engineering. The result indicates that the classification accuracy of the proposed method outperforms the method without feature extraction. © 2023 IEEE.

关键词： Feature extraction

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