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2020 2nd International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2020

作者： Mo, Dai Li Yu, Zhi Hua Cai, Yun Fei Liu, Rui School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

This paper presents a novel method of distinguishing signal, the way expected to reduce the nuisance alarm rate since the high nuisance alarm rate will restrict the capability of phase-sensitive optical time-domain reflection technology. The proposed method includes two parts: wavelet positioning mutation to obtain the perturbation area and Pearson correlation algorithm to directly convert the intensity of the perturbation into a useful amplitude. This technique avoids the use of irrelevant data in these differential signals and provides a simple and feasible new approach for distinguishing signal and optimizing the positioning speed of f-OTDR systems. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Time domain analysis

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Current-Induced Magnetic Switching in an L1_(0) FePt Single Layer with Large Perpendicular Anisotropy Through Spin–Orbit Torque

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Engineering 2022年第5期8卷 55-61页

作者： Kaifeng Dong Chao Sun Laizhe Zhu Yiyi Jiao Ying Tao Xin Hu Ruofan Li Shuai Zhang Zhe Guo Shijiang Luo Xiaofei Yang Shaoping Li Long You School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of GeosciencesWuhan 430074China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China China Resources Microelectronics Limited Shanghai 200072China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518000China Wuhan National High Magnetic Field Center Huazhong University of Science and TechnologyWuhan 430074China

In this study,current-induced partial magnetization-based switching was realized through the spin–orbit torque(SOT)in single-layer L1_(0) FePt with a perpendicular anisotropy(K_(u⊥))of 1.19×10^(7) erg·cm^(-3)(1 erg·cm^(-3)=0.1 J·m^(-3)),and its corresponding SOT efficiency(βDL)was 8×10^(-6) Oe·(A·cm^(-2))^(-1)(1 Oe=79.57747 A·m^(-1)),which is several times higher than that of the traditional Ta/CoFeB/MgO structure reported in past *** SOT in the FePt films originated from the structural inversion asymmetry in the FePt films since the dislocations and defects were inhomogeneously distributed within the ***,the FePt grown on MgO with a granular structure had a larger effective SOT field and effi-ciency than that grown on SrTiO_(3)(STO)with a continuous *** SOT efficiency was found to be considerably dependent on not only the sputtering temperature-induced chemical ordering but also the lattice mismatch-induced evolution of the *** findings can provide a useful means of efficiently electrically controlling a magnetic bit that is highly thermally stable via SOT.

关键词： L1_(0)FePt SOT Inversion asymmetry Magnetic switching Perpendicular anisotropy

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Clustering single-cell RNA sequencing data by deep learning algorithm 9

Clustering single-cell RNA sequencing data by deep learning ...

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9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021

作者： Bai, Litai Zhu, Yuan Yi, Ming 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 China Wuhan China

ISBN: (纸本)9780738132020

The development of single-cell RNA sequencing (scRNA-seq) technology provides a good opportunity to study cell heterogeneity and diversity. Especially, clustering is an important step in scRNA-seq analysis. With the advance of technology, many scRNA-seq data are available, which develop a lot of clustering methods. However, the existing methods usually employ the gene expression data, ignoring the related information between genes and the structure information in data. Therefore, we propose a new method (NDMgcn) to reconstruct the gene expression data based on the association of gene network, and cluster the data by Variational Autoencoder (V AE) and Graph Convolutional Network (GCN). The V AE learns low-dimensional information and the GCN learns structural information. The experimental results indicate that NDMgcn outperforms other popular algorithms in terms of NMI and ARI metrics. It provides a new insight for clustering scRNA-seq data from the network perspective. © 2021 IEEE.

关键词： RNA

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Bayesian Exponential Regularized Tensor Based Approach for Sparse Geomagnetic Data Completion

Bayesian Exponential Regularized Tensor Based Approach for S...

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

作者： Guoyu Li Junchi Bin Huan Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan China School of Engineering University of British Columbia Okanagan BC Canada

Geomagnetic data is vital for predicting earthquakes and magnetic storms. In this regard, a new Bayesian exponential regularized tensor completion framework for sparse geomagnetic data, i.e. BERTC, is proposed to address this problem in the study. First, the spatiotemporal geomagnetic data is reshaped into a 3D tensor with days and hours that features random missing elements. Second, a Gibbs sampling algorithm is developed to achieve probabilistic inference on matrices' factors and corresponding parameters in this model. Thus, the sparse tensor can be gradually optimized to fill the missing entries during iterations. Third, an exponential regularizer is proposed to reduce oscillations before and after iterations to enhance imputation quality further. Finally, the derived factor matrices are aggregated from Gibbs sampling to complete the sparse tensor. Numerical geomagnetic datasets from 13 cities are employed, and extensive comparison experiments are conducted to evaluate the imputation performance of the BERTC. The results show the superiority of the proposed BERTC compared to the state-of-the-art methods in terms of imputation accuracy, with an approximate improvement of the imputation accuracy as no less than 20%.

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Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年第11期12卷 17791-17806页

作者： Hongjian Zeng Yonghua Xiong Jinhua She Anjun Yu 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 School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Operation Management Department Jiangxi Ganyue Expressway Company Ltd. Nanchang Jiangxi China

Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers’ mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms.

关键词： Sensors Costs Time factors Data collection Crowdsensing Evolutionary computation Data integrity Optimization Inference algorithms Mobile computing

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An Error Correcting Output Code Algorithm Based on Hierarchical Clustering for Lithology Identification

An Error Correcting Output Code Algorithm Based on Hierarchi...

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第43届中国控制会议

作者： Ao Li Xi Chen School of Rail Transit Hunan University of Technology Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems.Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Lithology identification serves as a crucial foundation for the precise exploration and safe exploitation of geological resources. However, challenges arise from the inherent imbalance and overlap present in logging data, making the accurate identification of lithology a complex task. Addressing these challenges effectively, this paper introduces a novel algorithm,the Error Correcting Output Code algorithm based on Hierarchical Clustering(HC-ECOC). The proposed algorithm initially formulates a coding matrix using distance and overlap measures between class clusters. Subsequently, it extends the length of the coding matrix by considering both the accuracy of the integrated classifier and the diversity among base classifiers. To evaluate the efficacy of the proposed method, comparisons were conducted with alternative algorithms using UCI datasets and an authentic lithology dataset. The results unequivocally demonstrate the superior performance of our algorithm.

关键词： Lithology Identification Error Correcting Output Code Hierarchical Clustering

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Smart Infrared Temperature Measurement system Based On Elastic Compute Service Platform

Smart Infrared Temperature Measurement System Based On Elast...

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2021 china automation Congress, CAC 2021

作者： Li, Yongbo Yuan, Li 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

ISBN: (纸本)9781665426473

In the fight against the novel coronavirus, this paper designs a smart infrared temperature measurement system based on the Elastic Compute Service platform. In the perceptual layer part of the Internet of Things(IoT), the system uses infrared temperature sensors to quickly collect the temperature of the forehead or arm of the human body and Uses the MCU to automatically transmits the measured data to the Elastic Compute Service through the WiFi module. In the network layer part of the Internet of Things,the data written into the database of the Elastic Compute Service through the program deployed on the Elastic Compute Service. In the application layer of the Internet of Things, the remote management terminal monitors the collected body temperature data in real-time and provides quick warnings. Finally, the User can log in to the applet on his mobile phone to quickly and easily obtain personal information. © 2021 IEEE

关键词： Internet of things

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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer

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Computers in Biology and Medicine 2024年 179卷 108771-108771页

作者： Wang, Wenqing He, Ji Liu, Han School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relationships, and due to direct aggregation of the extracted features, they introduce unnecessary implicit noise into the fused images. To solve the above problems, this paper proposes a novel dual-branch hybrid fusion network called EMOST for medical image fusion that combines a convolutional neural network (CNN) and a transformer. First, to extract more comprehensive feature information, an effective feature extraction module is proposed, which consists of an efficient dense block (EDB), an attention module (AM), a multiscale convolution block (MCB), and three sparse transformer blocks (STB). Meanwhile, a lightweight efficient model (EMO) is used in the feature extraction module to exploit the efficiency of the CNN with the dynamic modeling capability of the transformer. Additionally, the STB is incorporated to adaptively maintain the most useful self-attention values and remove as much redundant noise as possible by developing the top-k selection operator. Moreover, a novel feature fusion rule is designed to efficiently integrate the features. Experiments are conducted on four types of multimodal medical images. The proposed method shows higher performance than the art-of-the-state methods in terms of quantitative and qualitative evaluations. The code of the proposed method is available at https://***/XUTauto/EMOST. © 2024 Elsevier Ltd

关键词： Feature extraction

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Nash Equilibrium Estimation of Non-Cooperative Games with Time-Varying Delay

Nash Equilibrium Estimation of Non-Cooperative Games with Ti...

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Chinese automation Congress (CAC)

作者： Ziqi Liu Chao Zhai China University of Geosciences Wuhan Campus School of Automation 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 China

Time delay has great impacts on the stability and the reliability and real-time of the communication of multi-agent systems. In multi-agent communication network, due to network congestion, transmission distance and other factors, there are various communication delays. In this paper, we study the deviation of convergence value after adding time-varying delays under gradient descent method, and the upper bound related to delay time is estimated. This upper bound can be used to analyze the magnitude of deviation under different time delays and minimize the loss caused by delays, and provide more explicit information for system optimization and resource allocation. Numerical simulation is conducted to verify the proposed approach.

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Predefined-Time Synchronization of Inertial Memristive Neural Networks

Predefined-Time Synchronization of Inertial Memristive Neura...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Zeng, Kan Wang, Leimin School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781665412872

This article investigates the predefined-time synchronization (PTS) of inertial memristive neural networks (IMNNs). First, we reduce the order of the system and design an effective controller for the error system. Furthermore, several new criteria for PTS of IMNNs are obtained and a user-defined settling time is provided. Lastly, the effectiveness of the results is proved by simulation experiment. © 2021 IEEE.

关键词： Synchronization

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