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Frequency Domain-Based Low-Rank Approximation for Magnetic Anomaly Detection

Frequency Domain-Based Low-Rank Approximation for Magnetic A...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Yumeng Lv Congyu Liao Huan Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan China Department of Radiology Stanford University CA USA

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Current magnetic anomaly detection (MAD) methods prioritize signal-to-noise ratio (SNR) over signal features and edge information, leading to signal distortion. To address this issue, a new MAD approach utilizes structured lowrank approximation and block singular value decomposition based on the spatial frequency domain, dubbed FL-BSVD is proposed. First, the low-rankness structure of the magnetic anomaly signal is obtained through 2D Discrete Fourier Transform (2D-DFT) and structured Hankel transformation. Then, block singular value decomposition is applied to the Hankel matrix to reduce noise while preserving more signal edge features and enhancing detection accuracy. Finally, a field experiment comparing FL-BSVD with four commonly used methods is conducted. The experiment confirms that FL-BSVD can effectively recover magnetic anomaly signal features and edge information in a strong noisy environment.

关键词： Image edge detection Frequency-domain analysis Noise reduction Magnetic domains Feature extraction Noise measurement Magnetic anomaly detection Signal to noise ratio Singular value decomposition Periodic structures

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An accurate tuning model of spintronic memristor with different switching thresholds by logic judgment of domain wall motion 41

An accurate tuning model of spintronic memristor with differ...

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第41届中国控制会议

作者： Kun Wang Yajuan Hui Zhong Yang Xiangshui Miao 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 Wuhan National Lab for Optoelectronics Huazhong University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Spintronic memristor possessing extraordinary advantages such as high-speed,low-power,highly scalable,and capable of constructing dynamic complex systems,which is a potential candidate for the next-generation memory *** many excellent models of spintronic memristor have been proposed,nonlinear model is not suitable for application analysis because of its complex *** this paper,an accurate tuning model of spintronic memristor with different switching thresholds by logic judgment of domain-wall motion is *** switching thresholds of current density are introduced to drive the domain-wall movement by judging the position of state value and real-time current density,which solves the general problems of boundary ***,by the simulation and verification via PSpice modeling,an accurate regulation of the resistance by voltage excitation is *** results pave the way for the applications of diversified circuit design for spintronic memristor.

关键词： Spintronic memristor Different switching thresholds SPICE model

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Blast Furnace Gas Utilization Rate Prediction Model based on Northern Goshawk Optimization and Long Short-Term Memory in Massive Data Set

Blast Furnace Gas Utilization Rate Prediction Model based on...

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Industrial Cyber-Physical systems (ICPS)

作者： Yue Zhou Weihua Cao Zhuofu Zhang Yan Yuan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Affected by the global New Crown Pneumonia epidemic, energy prices in the global market continue to rise. The high quality production and energy saving of steel companies have become an important task. The blast furnace is the front-end core of the steel manufacturing process. The blast furnace gas utilization rate can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level of the blast furnace. The blast furnace Gas Utilization Rate (GUR) can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level. Most of the existing studies are based on data-driven models. The shallow neural network model is selected. But blast furnace iron making has complex uncertainty. Massive data samples under industrial information technology need to be processed. The robustness and generalization ability of the prediction model are not satisfactory. To address the above problems, this paper proposes a prediction model based on NGO-LSTM regression. The model parameter searchs can be intelligently. It achieves high-precision prediction results for massive data samples. Firstly, the selection of feature parameters is completed by maximal information coefficient. Secondly, the strong coupling of blast furnace ground needs to fully predict the relationship between the characteristic parameters. The Long Short-Term Memory (LSTM) neural network with certain memory capability is selected. And multiple parameters of this neural network model are optimized by the Northern Goshawk Optimization (NGO) algorithm. An NGO-LSTM regression prediction model is established. In this paper, experiments are carried out using actual production data. The experimental results show that the proposed method can accurately predict the blast furnace gas utilization rate. This can provide a reference for improving blast furnace product quality, reducing costs and increasing efficiency.

关键词： Energy consumption Neural networks Production Predictive models Blast furnaces Prediction algorithms Robustness

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Predicting microbe-disease associations via attention guided graph convolutional networks 4

Predicting microbe-disease associations via attention guided...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Gong, Shiyun Zhu, Yuan School of Automation China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Lumo Road Hongshan District Hubei Wuhan430074 China School of Mathematics and Physics China University of Geosciences Lumo Road Wuhan430074 China

ISBN: (纸本)9781510682443

It is important to predict microbe-disease associations, as it helps to understand the cause of diseases episodes, the prevention of diseases, among other roles. Traditionally, the study of microbe-disease association is mainly based on laboratory purified culture for research and observation, which is highly scientific but has some limitations, such as some microbes needing to be cultured in a laboratory environment and costs heavily. This study proposes an attention guided graph convolutional network d approach (AGGCN) to predict possible potential associations between microbes and diseases. In the first place, AGGCN constructs four different similarity networks of microbes and diseases, which are combined with association networks to form a microbe-disease heterogeneous network. Subsequently, it is used as an input to the graph convolutional neural network to obtain each node embedding of this heterogeneous network. In this study, a three-layer network is used, so three different types of node embeddings can be obtained, and these three different types of node embedding representations are integrated via the multichannel graph attention mechanism to obtain the final node embeddings. Finally, the node embeddings obtained above are used for association prediction of microbes-diseases in heterogeneous networks. Experimentally verified, the AUC of AGGCN is 0.8972, and the AUPR is 0.5387. Comparative experimental results indicated that AGGCN shows better than the other five mainstream methods. It provides a new insight to explore network potential feature representation and predict associations between microbe and disease. © 2024 SPIE.

关键词： Graph neural networks

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A Reinforcement Learning Approach for Robust Restoration of Generators in Power System

A Reinforcement Learning Approach for Robust Restoration of ...

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2022 Chinese Automation Congress, CAC 2022

作者： Li, Huimiao Yang, Xiaozhou Zhai, Chao Hubei Key Laboratory of Advanced Control and Intelligent Automation Fo Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Future Resilient Systems Singapore-ETH Centre Create Campus Singapore

ISBN: (纸本)9781665465335

In the past decades, cascading blackouts have caused serious damages to power systems and affected the normal operation of society, so it is crucial to quickly restore the damaged power system to normal state. In this paper, a reinforcement learning (RL) approach is developed to achieve the robust restoration of generators in power systems. Firstly, the performance recovery of damaged power system is modeled as markov decision process, and constraints such as line voltage and active power output of generators are considered. Secondly, a RL algorithm is proposed to search the optimal control strategy for generator units recovery. Based on the proposed restoration approach, Q-learning algorithm is employed to obtain the optimal strategy for power supply of generaors in power grids. Finally, numerical simulations are carried out on IEEE 9-bus system under different scenarios of external disturbances and certainties. The simulation results demonstrate the effectiveness and feasibility of the proposed approach. © 2022 IEEE.

关键词： Reinforcement learning

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Fixed-/Preassigned-Time Stabilization of Memristive Neural Networks with Discontinuous Activations

Fixed-/Preassigned-Time Stabilization of Memristive Neural N...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Li, Haoyu Wang, Leimin School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China

ISBN: (纸本)9781665412872

In this paper, the fixed-time stabilization (FTS) and preassigned-time stabilization (PTS) problems for memristive neural networks (MNNs) with discontinuous activation functions (DAF) is investigated by designing a unified controller. In addition, less conservative stabilization criteria are derived to ensure the FTS and PTS of MNNs. Besides, a more accurate estimate of settling time is obtained based on Lyapunov stability theory. Finally, a numerical simulation is illustrated to verify the validity of theoretical results. © 2021 IEEE.

关键词： Stabilization

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Fixed-Time and Preassigned-Time Synchronization of Delayed Inertial Neural Networks 36

Fixed-Time and Preassigned-Time Synchronization of Delayed I...

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

作者： Zeng, Kan Wang, Leimin Cheng, Jiajun China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation Wuhan430074 China

ISBN: (纸本)9781665437127

This paper investigates the fixed-Time synchronization (FXTS) and the preassigned-Time synchronization (PATS) of inertial neural networks (INNs) by improved control strategies. Firstly, using the order-reduce method, a proper Lyapunov function is constructed and a FXTS controller is designed. Then, some novel conditions for TXTS of INNs are derived and a more precise estimate for the settling time are provided. Moreover, depending on the FXTS, another PATS controller is proposed, with which the PATS of INNs is also achieved. Finally, the correctness and validity of the FXTS and PATS results are illustrated by a numerical example. © 2021 IEEE.

关键词： Lyapunov functions

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Model Predictive control of Decomposition Furnace Based on Equivalent-Input-Disturbances

Model Predictive Control of Decomposition Furnace Based on E...

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Chinese Automation Congress (CAC)

作者： WeiKang Wu Gang Yu Wangyong He Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geoscience Wuhan China Huaxin Cement Technology Management(Wuhan) Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the cement production process, the decomposer outlet temperature control faces challenges such as large time delay and multiple disturbances. A general control system cannot handle time delay and disturbances effectively at the same time. Therefore, in this paper, we consider the problem of disturbances in time-delay systems and suppress the disturbances present in the outlet temperature control of the decomposer by combining equivalent-input-disturbance (EID) and Model Predictive control (MPC), where EID can estimate the disturbances and apply them to the input channels of the system, which do not require any information about the disturbances. Finally simulations are carried out in the catabolizer system and the results indicate that this system is able to suppress the perturbations effectively in time delay systems.

关键词： Delay effects Perturbation methods Optimal control Production Predictive models Aerospace electronics Mathematical models Temperature control State estimation Predictive control

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Auto-Regressive with Exogenous Input Modelling Based Model Predictive control of Cement Grate Cooler

Auto-Regressive with Exogenous Input Modelling Based Model P...

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Chinese Automation Congress (CAC)

作者： Tian Zhang Gang Yu Wangyong He Renjie Gui Weikang Wu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geoscience Wuhan China Huaxin Cement Technology Management(Wuhan) Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the cement production process, the speed of grate coolers directly affects the residence time and cooling effect of cement clinker. Its effective control is of great significance to ensuring cement quality. But the adjustment of grate speed is a complex process, involving moderate cooling air, uniform clinker thickness, regulated output and other constraints. In this study, an Auto-Regressive with exogenous input (ARX) grate cooler prediction model is developed using a data-driven approach based on a company's cement production line, a model predictive control (MPC) approach is employed for the purpose of optimising the speed of the grate cooler based on the ARX model. The results demonstrate that the method of MPC exhibits greater stability than the method of PID, and that MPC technology has good efficacy and superiority in the adjustment of grate speed.

关键词： Kilns Cooling Cement industry Velocity control Production Switches Predictive models control systems Stability analysis Predictive control

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Research on Eigenvalue of Excitation Signal in Nondestructive Testing of Power Transformer Core 1

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18th Annual Conference of China Electrotechnical Society, ACCES 2023

作者： Qu, Yinghui Jin, Fang Dong, Kaifeng Cai, Shengwei Gong, Yujia Fan, Fan 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 China Electric Power Research Institute Co. Ltd. Wuhan430074 China

ISBN: (数字)9789819714285

ISBN: (纸本)9789819714278

Transformer is the most critical equipment in the power system. In recent years, secondary silicon steel sheets have been used in the manufacture of transformer cores, which has caused hidden dangers in the quality of transformers. Currently, the main testing method for transformer core materials is disassembly testing, and there is no effective non-destructive testing method. Because magnetic Buckhausen noise (MBN) signals can reflect the internal defects of ferromagnetic components, this paper uses MBN testing methods to conduct non-destructive testing and analysis the side of transformer core silicon steel sheets. In the study of transformer core MBN signals, it was found that the waveform, frequency, amplitude and other parameters of the excitation signal can affect the eigenvalues of the transformer core MBN signal. The effects of excitation signal waveform, frequency, and amplitude on the characteristic values of MBN signals on the side of transformer core silicon steel sheet were experimentally studied. The research results show that when the excitation signal is a square wave with an amplitude of about 5V, excellent MBN signals can be excited. © Beijing Paike Culture Commu. Co., Ltd. 2024.

关键词： Nondestructive examination

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