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STABC-IR:An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

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Chinese Journal of Aeronautics 2023年第3期36卷 316-334页

作者： Siyuan WANG Gang WANG Qiang FU Yafei SONG Jiayi LIU Sheng HE Air Defense and Antimissile School Air Force Engineering UniversityXi’an 710051China

The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making *** current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of *** address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and conditional random field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in ***,a temporal network based on Bi GRU is constructed to achieve the temporal ***,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing ***,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each *** experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition ***-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.

关键词： Bidirectional gated recurrent network conditional random field Intention recognition Intention transformation Situation cognition Space-time attention mechanism

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Horizontal convergence reconstruction in the longitudinal direction for shield tunnels based on conditional random field

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Underground Space 2023年第3期10卷 118-136页

作者： Jingkang Shi Fei Wang Hongwei Huang Dongming Zhang Department of Geotechnical Engineering Tongji UniversityShanghai 200092China Shanghai Institute of Disaster Prevention and Relief Tongji UniversityShanghai 200092China Key Laboratory of Geotechnical and Underground Engineering of Minister of Education Tongji UniversityShanghai 200092China

Tunnel horizontal convergence monitoring is essential to ensure the operation ***,only a few representative tunnel sec-tions are chosen for monitoring due to the cost *** is difficult to capture the horizontal convergence of each tunnel ring with limited *** with this difficulty,the paper proposes a horizontal convergence reconstruction method based on the measurements of deployed *** tunnel horizontal convergence along the longitudinal direction is seen as a one-dimensional sta-tionary and ergodic random *** reconstruction problem is then transformed into the generation of conditional random *** Carlo simulation is adopted to generate possible realizations and the mean of realizations is considered as the maximum likeli-hood *** analysis proves the effectiveness of the proposed reconstruction *** proposed method is proved to be applicable in reconstructing the time-variant horizontal convergence and is verified by the monitoring results of the shield tunnel of Shanghai Metro Line *** effect of sensor numbers is parametrically studied,and an optimal sensor placement scheme is *** sensors placed at the deformation drastically changed location can significantly improve the performance of the proposed method.

关键词： Structural performance reconstruction Tunnel convergence monitoring conditional random field Optimal sensor placement

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Characterizing large-scale weak interlayer shear zones using conditional random field theory

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第10期15卷 2611-2625页

作者： Gang Han Chuanqing Zhang Hemant Kumar Singh Rongfei Liu Guan Chen Shuling Huang Hui Zhou Yuting Zhang Key Laboratory of Geotechnical Mechanics and Engineering of the Ministry of Water Resources Changjiang River Scientific Research InstituteWuhanChina State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanChina University of Chinese Academy of Sciences BeijingChina Department of Petroleum Engineering and Geoengineering Rajiv Gandhi Institute of Petroleum TechnologyJaisAmethi229304India State Key Laboratory of Water Resources and Hydropower Engineering Science Institute of Engineering Risk and Disaster PreventionWuhan UniversityWuhan430072China Institute for Risk and Reliability Leibniz Universität HannoverHannover30167Germany

The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground ***,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is *** study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental *** analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical *** Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower *** results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local ***,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.

关键词： Interlayer shear weakness zone Baihetan hydropower station conditional random field Kriging interpolation technique Activation analysis

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Improving conditional random field model for prediction of protein-RNA residue-base contacts

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Frontiers of Electrical and Electronic Engineering in China 2018年第2期6卷 155-162页

作者： Morihiro Hayashida Noriyuki Okada Mayumi Kamada Hitoshi Koyano Department of Electrical Engineering and Computer Science National Institute of Technology Matsue College Shimane 690- 8518 Japan Graduate School of Medicine Kyoto University Kyoto 606-8507 Japan Riken Quantitative Biology Center Hyogo 650-0047 Japan

Background： For understanding biological cellular systems, it is important to analyze interactions between protein residues and RNA bases. A method based on conditional random fields （CRFs） was developed for predicting contacts between residues and bases, which receives multiple sequence alignments for given protein and RNA sequences, respectively, and learns the model with many parameters involved in relationships between neighboring residue-base pairs by maximizing the pseudo likelihood function. Methods： In this paper, we proposed a novel CRF-based model with more complicated dependency relationships between random variables than the previous model, but which takes less parameters for the sake of avoidance of overfitting to training data. Results： We performed cross-validation experiments for evaluating the proposed model, and took the average of AUC （area under receiver operating characteristic curve） scores. The result suggests that the proposed CRF-based model without using Ll-norm regularization （lasso） outperforms the existing model with and without the lasso under several input observations to CRFs. Conclusions： We proposed a novel stochastic model for predicting protein-RNA residue-base contacts, and improved the prediction accuracy in terms of the AUC score. It implies that more dependency relationships in a CRF could be controlled by less parameters.

关键词： protein-RNA interaction residue-base contact conditional random field

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Accurate prediction of protein dihedral angles through conditional random field

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Frontiers in Biology 2013年第3期8卷 353-361页

作者： Shesheng ZHANG Shengping JIN Bin XUE Department of Statistics Wuhan University of Technology Wuhan 430070 China Department of Molecular Medicine College of Medicine University of South Florida Tampa FL 33612 USA

Identifying local conformational changes induced by subtle differences on amino acid sequences is critical in exploring the functional variations of the proteins. In this study, we designed a computational scheme to predict the dihedral angle variations for different amino acid sequences by using conditional random field. This computational tool achieved an accuracy of 87% and 84% in 10-fold cross validation in a large data set for φ and ψ, respectively. The prediction accuracies of φand ψ are positively correlated to each other for most of the 20 types of amino acids. Helical amino acids can achieve higher prediction accuracy in general, while amino acids in beet sheet have higher accuracy at specific angular regions. The prediction accuracy of φ is negatively correlated with amino acid flexibility represented by Vihinen Index. The prediction accuracy of φ can also be negatively correlated with angle distribution dispersion.

关键词： conditional random field flexibility angle distribution dispersion

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Fast Chinese syntactic parsing method based on conditional random fields

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Journal of Beijing Institute of Technology 2015年第4期24卷 519-525页

作者：韩磊罗森林陈倩柔潘丽敏 School of Information and Electronics Beijing Institute of Technology

A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses the bottom-up to connect the recognized phrase nodes to construct the syn- tactic tree. On the basis of Beijing forest studio Chinese tagged corpus, two experiments are de- signed to select the training parameters and verify the validity of the method. The result shows that the method costs 78. 98 ms and 4. 63 ms to train and test a Chinese sentence of 17. 9 words. The method is a new way to parse the phrase structure grammar for Chinese, and has good generalization ability and fast speed.

关键词： phrase structure grammar syntactic tree syntactic parsing conditional random field

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Information networks fusion based on multi-task coordination

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Frontiers of Computer Science 2021年第4期15卷 121-132页

作者： Dong LI Derong SHEN Yue KOU Tiezheng NIE School of Computer Science&Engineering Northeastern UniversityShenyang 110004China School of Information Liaoning UniversityShenyang 110036China

Information networks provide a powerful representation of entities and the relationships between *** networks fusion is a technique for information fusion that jointly reasons about entities,links and relations in the presence of various ***,existing methods for information networks fusion tend to rely on a single task which might not get enough evidence for *** order to solve this issue,in this paper,we present a novel model called MC-INFM(information networks fusion model based on multi-task coordination).Different from traditional models,MC-INFM casts the fusion problem as a probabilistic inference problem,and collectively performs multiple tasks(including entity resolution,link prediction and relation matching)to infer the final result of ***,we define the intra-features and the inter-features respectively and model them as factor graphs,which can provide abundant evidence to ***,we use conditional random field(CRF)to learn the weight of each feature and infer the results of these tasks simultaneously by performing the maximum probabilistic *** demonstrate the effectiveness of our proposed model.

关键词： information networks fusion multi-task coordination conditional random field inference

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Mapping mountain glaciers using an improved U-Net model with cSE

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International Journal of Digital Earth 2022年第1期15卷 463-477页

作者： Suzheng Tian Yusen Dong Ruyi Feng Dong Liang Lizhe Wang School of Computer Science China University of GeosciencesWuhanPeople’s Republic of China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences Chinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China

Global warming is melting *** in mountain glaciers have a tremendous impact on human *** identification and extraction of glaciers from satellite images are ***,when studying glaciers,materials surrounding the glacier have high spectral similarity to glaciers and are easily misclassified in the identification ***,in this study of glacier extraction,we used an improved U-Net model(a channel-attention U-Net)to map *** model was trained on Landsat 8 Operational Land Imager(OLI)data and a Shuttle Radar Topography Mission(SRTM)digital elevation model(DEM),and was tested on glaciers in the Pamir *** results show that the channel-attention U-Net identifies glaciers with relatively high accuracy compared to U-Net and *** obtained results were fine-tuned by the conditional random field model,effectively reducing background misidentification.

关键词： U-Net channel-attention mechanism conditional random field glacier extraction Pamir Plateau

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Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

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Genomics, Proteomics & Bioinformatics 2020年第1期18卷 52-64页

作者： Fuyi Li Andre Leier Quanzhong Liu Yanan Wang Dongxu Xiang Tatsuya Akutsu Geoffrey IWebb A.Ian Smith Tatiana Marquez-Lago Jian Li Jiangning Song Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash UniversityMelbourneVIC 3800Australia Monash Centre for Data Science Faculty of Information TechnologyMonash UniversityMelbourneVIC 3800Australia School of Medicine University of Alabama at BirminghamBirminghamAL 35233USA College of Information Engineering Northwest A&F UniversityYangling 712100China Bioinformatics Center Institute for Chemical ResearchKyoto UniversityUjiKyoto 611-0011Japan ARC Centre of Excellence in Advanced Molecular Imaging Monash UniversityMelbourneVIC 3800Australia Biomedicine Discovery Institute and Department of Microbiology Monash UniversityMelbourneVIC 3800Australia

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate ***-controlled proteolysis plays a key role in the degradation and recycling of proteins,which is essential for various physiological ***,solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases,as well as for therapeutic target identification and pharmaceutical ***,there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural *** this study,we present Procleave,a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural *** features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method,which turned out to be critical for the performance of *** optimal approximations of all structural parameter values were encoded in a conditional random field(CRF)computational framework,alongside sequence and chemical group-based ***,we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent *** is capable of correctly identifying most cleavage sites in the case ***,when applied to the human structural proteome encompassing 17,628 protein structures,Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different *** is implemented as a webserver and is freely accessible at http://***/.

关键词： Protease Cleavage site prediction Machine learning conditional random field Structural determinants

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Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

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Computers, Materials & Continua 2022年第9期72卷 5793-5806页

作者： Noreen Mushtaq Arfat Ahmad Khan Faizan Ahmed Khan Muhammad Junaid Ali Malik Muhammad Ali Shahid Chitapong Wechtaisong Peerapong Uthansakul COMSATS University Islamabad Islamabad Campus45550Pakistan Suranaree University of Technology Nakhon Ratchasima30000Thailand COMSATS University Islamabad Lahore Campus54000Pakistan Virtual University of Pakistan Islamabad Campus45550Pakistan COMSATS University Islamabad Vehari Campus61100Pakistan

Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain *** detection of brain tumor increases the survival rate and helps in reducing the fatality *** imaging modalities have been used for diagnosing by expert radiologists,and Medical Resonance Image(MRI)is considered a better option for detecting brain tumors as MRI is a non-invasive technique and provides better visualization of the brain *** of the challenging issues is to identify the tumorous region from the MRI scans *** segmentation is performed by medical experts,which is a time-consuming task and got chances of *** overcome this issue,automatic segmentation is performed for quick and accurate *** proposed approach is to capture inter-slice information and reduce the *** learning-based brain tumor segmentation techniques proved best among available segmentation ***,deep learning may miss some preliminary info while using MRI images during *** MRI volumes are volumetric,3D U-Net-based models are used but *** of multiple 2D U-Net predictions in axial,sagittal,and coronal views help to capture inter-slice *** approach may reduce the system ***,the conditional random fields(CRF)reduce the predictions’false positives and improve the segmentation *** model is applied to Brain Tumor Segmentation(BraTS)2019 dataset,and cross-validation is performed to check the accuracy of *** proposed approach achieves Dice Similarity Score(DSC)of 0.77 on Enhancing Tumor(ET),0.90 on Whole Tumor(WT),and 0.84 on Tumor Core(TC)with reduced Hausdorff Distance(HD)of 3.05 on ET,5.12 on WT and 3.89 on TC.

关键词： Brain tumor deep learning detection conditional random field segmentation

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