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检索条件"机构=Hubei Key Labortory of Advanced Control and Intelligent Automation for Complex System"

共 1179 条记录，以下是131-140 订阅

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Energy-Aware Coverage control of Multi-Agent systems with control Barrier Function 42

Energy-Aware Coverage Control of Multi-Agent Systems with Co...

引用

42nd Chinese control Conference, CCC 2023

作者： Qu, Chengkai Zhai, Chao 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)9789887581543

Geo-hazards have become one of the main disasters endangering the safety of people's lives and property in the world. In order to improve the early warning of disasters, a persistent monitoring method of multi-agent systems is proposed in this work. To ensure that the agent's energy is never exhausted, the set invariance constraint is included in the optimization problem. The goal is to minimize the difference between the actual control input of the robot and the nominal control input corresponding to the task to be performed. Moreover, the control barrier function (CBF) is used to transform the forward invariance of a subset of the robot state space into a control input constraint. The coverage control method in an uncertain environment is verified by numerical simulation. This work provides new insights into effective monitoring and early warning of geo-hazards. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Multi agent systems

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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...

引用

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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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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Optimizing Bounding Box Regression in complex Backgrounds

Optimizing Bounding Box Regression in Complex Backgrounds

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2024 China automation Congress, CAC 2024

作者： Chen, Jun Zhang, Haiyan Yu, Anjun Wang, Yiwei 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 Wuhan430074 China Jiangxi Ganyue Expressway Co.Ltd. Nanchang China School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350368604

In the context of electrical power operation sites, the automated and accurate detection of whether workers are properly equipped with safety gear, such as safety clothing, helmets, and safety ropes for high-altitude operations, is crucial for ensuring the safety of personnel. Traditional object detection models often struggle to accurately identify these critical pieces of safety equipment in complex environments, due to variable object sizes, overlapping objects, and substantial background noise. To specifically address these challenges, we propose a novel loss function-Enhanced Alignment Intersection over Union (EAIoU) loss. This loss function is designed to enhance the model’s ability to discriminate between closely spaced and overlapping safety gear in real-time, significantly improving robustness against scale variations and complex backgrounds. In experiments based on the YOLOv8s architecture, our model achieved a mean Average Precision (mAP) of 92.1% on a dataset specific to electrical power operations, markedly outperforming traditional IoU loss functions. Additionally, EAIoU demonstrated improved performance on the COCO dataset, indicating its generalization capability across various complex scenarios. This research not only enhances the accuracy and real-time performance of safety equipment detection at electric power operation sites but also opens new avenues for advanced object detection technology in complex, multi-object environments. © 2024 IEEE.

关键词： Safety devices

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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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Event-Triggered control of Networked system Considering Denial of Service Attack 42

Event-Triggered Control of Networked System Considering Deni...

引用

42nd Chinese control Conference, CCC 2023

作者： Zhang, Ting-Wei Fan, Yu-Long Zhang, Chuan-Ke School of Automation Chinese 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 of Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581543

This paper presents an improved stability criterion and controller design scheme condition for a networked control system under denial of service (DoS) attack. Firstly, the DoS attack interval is divided into attack interval and no attack interval, therefore, a switching-like event-triggered control can be established to reduce the waste of network resources and improve network efficiency. Then, the studied system is transformed into a time-delay system, and an improved stability criterion and controller design method are established by using Lyapunov-Krasovskii functional (LKF). Finally, the effectiveness of the proposed method is verified by a simulation example. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Networked control systems

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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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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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Dynamic Event-Triggered Synchronization control for complex Dynamical Networks 42

Dynamic Event-Triggered Synchronization Control for Complex ...

引用

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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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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