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

Semantic Segmentation of Historical Landslide Based on Impro...

引用

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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A Novel Hybrid Model for Online Prediction of Rate of Penetration (ROP) in Drilling Process

A Novel Hybrid Model for Online Prediction of Rate of Penetr...

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2023 China Automation Congress, CAC 2023

作者： Wang, Yao 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 Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9798350303759

In this paper, a novel hybrid model is proposed for online prediction of rate of penetration (ROP) in drilling process, which including two parts (online data pre-processing and online hybrid modeling). In the first part, threshold filtering and Savitzky Golay (SG) filtering are both employed to enhance the quality of drilling data considering the expert experience and data characteristics. In the next part, a novel hybrid model with error compensation is established, which is combined the Bingham sub-model and gradient boosting decision tree (GBDT) sub-model. To better capture the dynamic changes of ROP, the hybrid model is updated with moving window strategy. Finally, compared simulation results with well-known ROP prediction models indicate the efficiency of the hybrid model. © 2023 IEEE.

关键词： Error compensation

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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...

引用

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 Semi-Supervised Root Cause Identification Method for Alarm Floods via Graph-Based Manifold Regularization 43

A Semi-Supervised Root Cause Identification Method for Alarm...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Ning Hu, Wenkai Liu, Zhe Wang, Fei 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 Petrochemical Company Limited Wuhan430082 China

ISBN: (纸本)9789887581581

An alarm flood is a situation where the alarm rate is too high and exceeds the operator's management ability. The occurrence and propagation of an alarm flood cause difficulties for the operators to respond to critical alarms in time, which may make the situation further deteriorate into an industrial accident. In this paper, a semi-supervised method is proposed for root cause identification of alarm floods via graph-based manifold regularization. A vector representation method for industrial alarm event data is presented based on word embedding for alarm textual encoding. A semi-supervised method is proposed to identify root causes of alarm floods based on both labeled and unlabeled alarm flood sequences. The effectiveness of the proposed method is demonstrated by a case study with data generated by a public industrial simulation model. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Semi-supervised learning

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An Autoencoder with a Memory Module for Video Anomaly Detection 36

An Autoencoder with a Memory Module for Video Anomaly Detect...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Tang, Wei Feng, Yunjian Li, Jun Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

ISBN: (纸本)9781665437127

With the rise of deep convolutional neural networks (CNNs), considerable attention has been paid to video anomaly detection (VAD). Autoencoders are a popular type of framework for VAD, and many existing VAD methods are based on it. However, these methods take an assumption of closed-world VAD, i.e., do not comprehensively consider the diversity of normal patterns. Besides, a competent CNN allows the autoencoder to reconstruct or predict abnormal video frames proficiently, resulting in missing anomalies. To mitigate these drawbacks, we propose an Autoencoder with a Memory Module (AMM) to realize video anomaly detection by predicting video frames. AMM consists of three modules: An encoder, a decoder, and a memory module. First, the consecutive frames are fed into the encoder to yield latent spatial features. Then, the features are utilized to retrieve corresponding memory items in the memory module to generate memory mapping features. Finally, the memory mapping features are adopted in the decoder for predicting the next frame. To match the queries against memory items accurately, we propose a memory triplet loss, which takes into account both size and angle discrepancies between the queries and memory items. At the training stage, AMM utilizes the memory triplet loss, a prediction loss, and multi-scale structure similarity measure. Moreover, the modes of retrieving and updating memory items are ameliorated by a scaled dot product model, which can alleviate vanishing gradient problems to a certain extent. Extensive experiments are conducted on three benchmark public datasets, and the results demonstrate the superior performance of AMM. © 2021 IEEE.

关键词： Forecasting

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A Pipeline Leak Detection Method Based on MGD-KLD and On-Delay Timer 42

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

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42nd Chinese control Conference, CCC 2023

作者： He, Jiaqi Yuan, Yan Hu, Wenkai 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)9789887581543

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

关键词： Leak detection

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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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An Informer Based Alarm Early Prediction Method over Consecutive Alarm Monitoring Periods 22

An Informer Based Alarm Early Prediction Method over Consecu...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Jiang, Wenbin Hu, Wenkai Liu, Zhe Wang, Fei School of Automation China University of Geosciences Wuhan430074 China Petrochemical Company Limited Wuhan430082 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)9798331527471

Alarm systems serve as the first layer of protection for modern process industries to monitor industrial processes and ensure operational safety. However, the presence of alarm floods is common in alarm systems and may distract operators from critical alarms. To provide decision supports to operators during alarm flood situations, alarm prediction becomes a potential effective solution. The difficulty of alarm prediction lies in how to achieve high prediction accuracy over long-term consecutive alarm monitoring periods, including situations with either high or low alarm rates. Consequently, this paper presents an industrial alarm prediction method for both alarm flood periods and non-alarm flood periods. Specifically, a strategy of combining fixed-length and fixed-time sliding windows is designed. Further, an alarm prediction model based on Informer is proposed for alarm prediction under different alarm rates. A case study is presented to prove the validity of the proposed alarm prediction method. © 2024 IEEE.

关键词： Alarm systems

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Imbalance control with stratified voltage of modularized CC-to-CV converter for auto and seamless module switching 11

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

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11th International Conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Cai, Jie Fang, Zhijian Xia, Yuangeng Song, Xiaoxian School of Automation China University of Geosciences 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)9788957083505

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. © 2023 The Korean Institute of Power Electronics.

关键词： Efficiency

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Dynamic Event-Triggered Synchronization control for complex Dynamical Networks 42

Dynamic Event-Triggered Synchronization Control for Complex ...

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42nd Chinese control Conference, CCC 2023

作者： Yang, Fuxiang Wan, Xiongbo 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 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. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： complex networks

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