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检索条件"机构=Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan"

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Photovoltaic abnormal data cleaning based on fuzzy clustering-quartile algorithm

Photovoltaic abnormal data cleaning based on fuzzy clusterin...

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Industrial Cyber-Physical systems (ICPS)

作者： Yidong Li Danyun Li 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 irradiance-power curve is an important basis for examining the operating status of photovoltaic power stations. In the actual operation process, sensor failure, abnormal communication and equipment damage will bring a large number of abnormal values to the output data of photovoltaic power plants. It will have a significant impact on a variety of applications based on photovoltaic output data. This paper analyzes the typical outliers on the irradiance-power curve and proposes a photovoltaic output data cleaning method based on fuzzy clustering algorithm and quartile algorithm. By comparing with the quartile method, it is proved that this method can effectively identify abnormal data when there are a large number of outliers in the photovoltaic output data.

关键词： Photovoltaic systems Training Clustering algorithms Prediction algorithms Cleaning Filling Data models

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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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Multi-Agent Coverage control with High-order Voronoi Partitions in Non-convex Environments

Multi-Agent Coverage Control with High-order Voronoi Partiti...

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Chinese control Conference (CCC)

作者： Yuming Wu Chao Zhai 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

At present, most research on the coverage of multi-agent systems is based on Euclidean distance. This does not consider the existence of obstacles and has great limitations in the application. In this paper, a kind of coverage control problem based on high-order geodesic Voronoi partition is practically investigated. It allows multiple agents to monitor an area with obstacles to achieve the monitoring of the overall environment. As a result, the geodesic distance is introduced as a metric form. Based on the geodesic distance, point-by-point scanning on the layer is taken to achieve high-order Voronoi diagram division. The coverage algorithm can be implemented in a distributed manner through the exchange of location information with each other, and the Lloyd algorithm is added to realize the movement of the sensor toward the optimal position.

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Undersampling Ensemble and Deep Learning - Based Landslide Susceptibility Mapping Method for Geological Hazard Warning

Undersampling Ensemble and Deep Learning - Based Landslide S...

引用

Chinese automation Congress (CAC)

作者： Yulong Fang Chao Gan 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

Landslide is the most frequent geological hazard. Landslide susceptibility mapping (LSM) can be used to predict the possibility of landslide occurring at a certain location. In this paper, an undersampling ensemble and deep learning - based landslide susceptibility mapping method for geological hazard warning is proposed. The Zigui to Badong section of the Three Gorges Reservoir is taken as the study area. Firstly, the correlation analysis of fourteen landslide influencing factors is carried out and two unimportant factors are eliminated. Then, an EasyEnsemble - one dimensional convolutional neural network (EE-1DCNN) model is constructed with the remaining twelve factors as inputs. Finally, the proposed EE-1DCNN model is compared with two well-known methods on test data, and a landslide susceptibility map of the study area is obtained based on the EE-1DCNN model. As the experimental result shows, the proposed EE-1DCNN model achieves superior AUC, accuracy and recall of 0.909, 91.7% and 85.1%. The applicability of the proposed method is proved.

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

Reinforcement Learning with Reward Shaping and Hybrid Explor...

引用

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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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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Chinese automation Congress (CAC)

作者： Run Zhang Chao Gan 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

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.

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Model Predictive Load Frequency control for Power systems Under New Event-triggered Mechanism†

Model Predictive Load Frequency Control for Power Systems Un...

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Chinese control Conference (CCC)

作者： Ziqian Wang Fan Wei 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 article, we pay attention to event-based model predictive control (MPC) for load frequency control of multi-area power system. Considering the practical issues, the inputs are subject to hard constraints. A novel dynamic event-triggered mechanism (DETM) which contains an additive internal dynamic variable and an adjusting variable is designed to reduce data transmission burden. The MPC problem is expressed as a “min-max“ optimisation problem. By considering the effects of load disturbances and the DETM, we give the design approach for the controller which integrates H 2 and $H$ ∞ performance indexes through an auxiliary optimization problem. A simulation example is provided to verify the effectiveness of the proposed algorithm.

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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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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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