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Chinese Control Conference (CCC)

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

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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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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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Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables

arXiv

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arXiv 2024年

作者： Gong, Haisong Xu, Weizhi Wu, Shu Liu, Qiang Wang, Liang Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China ByteDance Inc

Fact checking aims to predict claim veracity by reasoning over multiple evidence pieces. It usually involves evidence retrieval and veracity reasoning. In this paper, we focus on the latter, reasoning over unstructured text and structured table information. Previous works have primarily relied on fine-tuning pretrained language models or training homogeneous-graph-based models. Despite their effectiveness, we argue that they fail to explore the rich semantic information underlying the evidence with different structures. To address this, we propose a novel word-level Heterogeneous-graph-based model for Fact Checking over unstructured and structured information, namely HeterFC. Our approach leverages a heterogeneous evidence graph, with words as nodes and thoughtfully designed edges representing different evidence properties. We perform information propagation via a relational graph neural network, facilitating interactions between claims and evidence. An attention-based method is utilized to integrate information, combined with a language model for generating predictions. We introduce a multitask loss function to account for potential inaccuracies in evidence retrieval. Comprehensive experiments on the large fact checking dataset FEVEROUS demonstrate the effectiveness of HeterFC. Code will be released at: https://***/Deno-V/HeterFC. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Prediction of Silicon Content in Molten Iron of Blast Furnace Based on Multi-dimensional Sequential Characteristic Reconstruction

Prediction of Silicon Content in Molten Iron of Blast Furnac...

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Industrial Cyber-Physical systems (ICPS)

作者： Zhuofu Zhang Weihua Cao Jie Hu Qifu 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

Molten iron is the primary output of blast furnace production. The content of silicon in molten iron clearly correlates with blast furnace temperature. However, due to the intricate conditions of blast furnace production, the silicon content in molten iron is nonlinear and unstable. Therefore, this paper adopts variational mode decomposition (VMD) to decompose and extract the feature information of the real silicon content data of LY Steel in March 2022, then uses Grey Wolf optimization (GWO) algorithm to optimize the parameters of the support vector regression (SVR) prediction model, and takes the decomposed data as model input for experimental verification. By comparing the predicted results with the real historical data of blast furnace production, it is found that the degree of fit is about 94.2%, which offers a new idea for the prediction of silicon content.

关键词： Support vector machines Production Predictive models Blast furnaces Prediction algorithms Feature extraction Silicon

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Network Spreading from Network Dimension

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Physical Review Letters 2024年第23期132卷 237401-237401页

作者： Jack Murdoch Moore Michael Small Gang Yan Huijie Yang Changgui Gu Haiying Wang MOE Key Laboratory of Advanced Micro-Structured Materials and School of Physical Science and Engineering Tongji University Shanghai 200092 People’s Republic of China National Key Laboratory of Autonomous Intelligent Unmanned Systems MOE Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai 200092 People’s Republic of China Complex Systems Group Department of Mathematics and Statistics University of Western Australia Crawley 6009 Western Australia Australia Mineral Resources CSIRO Kensington 6151 Western Australia Australia Business School University of Shanghai for Science and Technology 334 Jungong Road Shanghai 200093 People’s Republic of China

Continuous-state network spreading models provide critical numerical and analytic insights into transmission processes in epidemiology, rumor propagation, knowledge dissemination, and many other areas. Most of these models reflect only local features such as adjacency, degree, and transitivity, so can exhibit substantial error in the presence of global correlations typical of empirical networks. Here, we propose mitigating this limitation via a network property ideally suited to capturing spreading. This is the network correlation dimension, which characterizes how the number of nodes within range of a source typically scales with distance. Applying the approach to susceptible-infected-recovered processes leads to a spreading model which, for a wide range of networks and epidemic parameters, can provide more accurate predictions of the early stages of a spreading process than important established models of substantially higher complexity. In addition, the proposed model leads to a basic reproduction number that provides information about the final state not available from popular established models.

关键词： Spreading complex networks Real world networks Spreading models

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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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Template Matching and Trend Feature Analysis-Based Data Pre-Processing Method for Seismic Wave Detection

Template Matching and Trend Feature Analysis-Based Data Pre-...

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Chinese Automation Congress (CAC)

作者： Zheng Xu 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

In this paper, a template matching and trend feature analysis-based data pre-processing method for seismic wave detection is proposed with two stages. In the first stage, it involves extracting the rock physical parameters from seismic wave detection results using OCR (Optical Character Recognition) method, and extracting the original rock physical parameters from the raw rock property table using keyword matching method. Using the rock physical parameters as a template, a template matching approach is employed to eliminate abnormal values from the original rock physical parameters. In the next stage, a technique is proposed to extract trend features of rock physical parameters for conducting advanced geological forecasting, which considered the expertise of experts in interpreting seismic wave detection data. Finally, the effectiveness of the proposed method is verified by the compared simulation results.

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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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