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Chinese Journal of Electronics 2021年第5期30卷 815-823页

作者： WANG Chao ZOU Quan Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Hainan Key Laboratory for Computational Science and Application Hainan Normal University

Several Coronaviruses(CoVs) are epidemic pathogens that cause severe respiratory syndrome and are associated with significant morbidity and mortality. In this paper, a machine learning method was developed for predicting the risk of human infection posed by CoVs as an early warning system. The proposed Spike-SVM(Support vector machine) model achieved an accuracy of 97.36% for Human-infective CoV(HCoV) and Nonhuman-infective CoV(Non-HCoV) classification. The top informative features that discriminate HCoVs and Non-HCoVs were identified. Spike-SVM is anticipated to be a useful bioinformatics tool for predicting the infection risk posed by CoVs to humans.

关键词： Spike protein NonHCoVs genetics microorganisms Support vector machine mortality Spike-SVM model Human-infective CoV severe respiratory syndrome machine learning method biology computing predicting Human-infective coronavirus diseases proteins HCoV human infection differentiating Nonhuman-infective CoV infection risk CoVs discriminate HCoVs learning (artificial intelligence) bioinformatics support vector machines informative features significant morbidity molecular biophysics early warning system epidemic pathogens Coronaviruses

Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method

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Earth and Planetary Physics 2023年第3期7卷 407-413页

作者： Mao Zhang GaoPeng Lu HaiLiang Huang ZhengWei Cheng YaZhou Chen Steven A.Cummer JiaYi Zheng JiuHou Lei School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China National Key Laboratory on Electromagnetic Environment Effects Army Engineering UniversityShijiazhuang CampusShijiazhuang 050003China Electrical and Computer Engineering Department Duke UniversityDurhamNC 27708USA

Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long *** this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning *** differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),***,the automatic method is capable of recognizing higher harmonic tweek *** evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.

关键词： machine learning method tweek atmospherics reflection height D-region ionosphere

Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第5期14卷 1609-1625页

作者： Zhengheng Xu Hadi Khabbaz Behzad Fatahi Di Wu School of Civil and Environmental Engineering University of Technology SydneySydneyAustralia

An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional(3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are *** proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.

关键词： Intelligent compaction machine learning method Finite element modelling Acceleration response

Feedback on a shared big dataset for intelligent TBM PartⅠ:Feature extraction and machine learning methods

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Underground Space 2023年第4期11卷 1-25页

作者： Jian-Bin Li Zu-Yu Chen Xu Li Liu-Jie Jing Yun-Pei Zhangf Hao-Han Xiao Shuang-Jing Wang Wen-Kun Yang Lei-Jie Wu Peng-Yu Li Hai-Bo Li Min Yao Li-Tao Fan China Railway Group Co.Ltd. Beijing 100089China Department of Geotechnical Engineering China Institute of Water Resources and Hydropower ResearchBeijing 100038China Key Laboratory of Urban Underground Engineering Ministry of EducationBeijing Jiaotong UniversityBeijing 100044China China Railway Engineering Equipment Group Co. Ltd.ZhengzhouHenan 450016China School of Civil Engineering Southeast UniversityNanjing 211189China Xi’an University of Technology Xi’an 710048China

This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine learning *** big dataset was col-lected during the Yinsong water diversion project construction in China,covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one *** research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of *** review com-prises two *** I is concerned with the data processing,feature extraction,and machine learning methods applied by the *** review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified,requiring further studies to achieve commonly accepted *** techniques for cleaning and amending the raw data adopted by the contributors were summarized,indicating some highlights such as the importance of sufficiently high fre-quency of data acquisition(higher than 1 second),classification and standardization for the data preprocessing process,and the appro-priate selections of features in a boring *** review finds that both supervised and unsupervised machine learning methods have been utilized by various *** ensemble and deep learning methods have found wide *** I highlights the impor-tant features of the individual methods applied by the contributors,including the structures of the algorithm,selection of hyperparam-eters,and model validation approaches.

关键词： Big data machine learning method TBM construction Data extraction machine learning contest

Malware Detection Algorithm Based on the Attention Mechanism and ResNet

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Chinese Journal of Electronics 2020年第6期29卷 1054-1060页

作者： WANG Lele WANG Binqiang ZHAO Peipei LIU Ruyi LIU Jiangang MIAO Qiguang Institute of Information Technology Information Engineering University School of Computer Science and Technology Xidian University Nanjing Institute of Information Technology

The cost of misclassifying a malware program as normal is often higher than that of misclassifying a normal program as ***,how to improve the detection accuracy of malware programs is a very important *** paper proposes a deep learning malware program detection algorithm based on attention *** 2 Vec model is used to map the Application programming interface(API) into word vectors,and all word vectors of each sample are arranged into a matrix with the same *** this basis,residual network is used to extract features of *** features are input into the attention mechanism to learn the similarity between ***,the features are weighted with the similarity to obtain the new features with better *** new features and the original features are added element by element to obtain the sample features more suitable for ***,samples are classified by *** show that the classification effect of the proposed method is better than that of the traditional machine learning method.

关键词： normal program API detection accuracy deep learning malware program invasive software malware detection algorithm attention mechanism word2Vec model Document processing techniques sample features application programming interface text analysis application program interfaces Natural language processing Data security word vectors feature extraction General utility programs machine learning method learning (artificial intelligence) pattern classification Knowledge engineering techniques

Multi-Distributed Speech Emotion Recognition Based on Mel Frequency Cepstogram and Parameter Transfer

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Chinese Journal of Electronics 2022年第1期31卷 155-167页

作者： LIN Long TAN Liang School of Computer Science Sichuan Normal University Institute of Computing Technology Chinese Academy of Sciences

Speech emotion recognition(SER) is the use of speech signals to estimate the state of emotion. At present, machine learning is one of the main research methods of SER, the test and training data S of traditional machine learning all have the same distribution and feature space, but the data of speech is accessed from different environments and devices, with different distribution characteristics in real life. Thus, the traditional machine learning method is applied to the poor performance of SER. This paper proposes a multi-distributed SER method based on Mel frequency cepstogram(MFCC) and parameter transfer. The method is based on single-layer long short-term memory(LSTM), pre-trained inceptionv3 network and multi-distribution corpus. The speech pre-processed MFCC is taken as the input of single-layer LSTM, and input to the pre-trained inception-v3 *** features are extracted through the pre-trained inception-v3 model. Then the features are sent to the newly defined the fully connected layer and classification layer,let the parameters of the fully connected layer be finetuned, finally get the classification result. The experiment proves that the method can effectively complete the classification of multi-distribution speech emotions and is more effective than the traditional machine learning framework of SER.

关键词： multidistribution corpus fully connected layer feature extraction speech signals machine learning method pre-trained inception-v3 model multidistributed speech emotion recognition pre-trained inception-v3 network feature space recurrent neural nets Mel frequency cepstogram parameter transfer single-layer LSTM speech pre-processed MFCC learning (artificial intelligence) speech recognition emotion recognition multidistributed SER method multidistribution speech emotions

Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact

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Advances in Atmospheric Sciences 2021年第3期38卷 400-412页

作者： Yihe FANG Haishan CHEN Yi LIN Chunyu ZHAO Yitong LIN Fang ZHOU Institute of Atmospheric Environment China Meteorological AdministrationShenyang 110016China Regional Climate Center of Shenyang Liaoning Province Meteorological AdministrationShenyang 110016China Key Opening Laboratory for Northeast China Cold Vortex Research China Meteorological AdministrationShenyang 110016China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/International Joint Research Laboratory on Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjing 210044China Liaoning Provincial Meteorological Service Center Shenyang 110016China Climate Change Research Center Institute of Atmospheric Physicsand Nansen-Zhu International

The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in *** on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively ***,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path *** rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the *** obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the *** included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.

关键词： northeastern China early summer Northeast China Cold Vortex classification of activity paths machine learning method k-means clustering high-pressure blocking

System reliability-based robust design of deep foundation pit considering multiple failure modes

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Geoscience Frontiers 2024年第2期15卷 169-182页

作者： Li Hong Xiangyu Wang Wengang Zhang Yongqin Li Runhong Zhang Chunxia Chen School of Civil Engineering Chongqing UniversityChongqing 400045China School of Design and the Built Environment Curtin UniversityBentley 6102Western AustraliaAustralia Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing UniversityChongqing 400045China Institute of Smart City and Intelligent Transportation Southwest Jiaotong UniversityChengdu 610097China China Southwest Geotechnical Investigation&Design Institute Co. LtdChengdu 610052SichuanChina

Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical ***,for deep foundation pit,evaluating the system safety of retaining structures and finding cost-effective design points are main *** address this,this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou,*** proposed method included two parts:system reliability model and robust design *** Propagation Neural Network(BPNN)is used to fit limit state functions and conduct efficient reliability *** common source random variable(CSRV)model are used to evaluate correlation between failure modes and determine the system ***,based on the system reliability model,a robust design method is *** method aims to find cost-effective design *** solve this problem,the third generation non-dominated genetic algorithm(NSGA-III)is *** efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III *** proposed method has a good performance in locating the balanced design point between safety and construction ***,the proposed method can provide design points with reasonable stiffness distribution.

关键词： System reliability machine learning method Non-dominated sorting genetic algorithm Robust design Multiple objective optimization models

Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction

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Chinese Journal of Electronics 2021年第5期30卷 824-832页

作者： ZHANG Xueying GUO Yuling LI Fenglian WEI Xin HU Fengyun HUI Haisheng JIA Wenhui College of Information and Computer Taiyuan University of Technology Department of Neurology Shanxi Province People's Hospital

Carotid artery stenosis is a serious medical condition that can lead to stroke. Using machine learning method to construct classifier model, carotid artery stenosis can be diagnosed with transcranial doppler data. We propose an improved fuzzy support vector machine model to predict carotid artery stenosis, with the maximum geometric mean as the optimization *** fuzzy membership function is obtained by combining information entropy with the normalized class-center distance. Experimental results showed that the proposed model was superior to the benchmark models in sensitivity and geometric mean criteria.

关键词： blood vessels machine learning method fuzzy set theory improved fuzzy support vector machine model pattern classification geometric mean maximum FSVMI model maximum geometric mean classifier model support vector machines carotid artery stenosis risk prediction geometric mean criteria learning (artificial intelligence) entropy

The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer

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Advances in Atmospheric Sciences 2023年第1期40卷 62-77页

作者： Yitong LIN Yihe FANG Chunyu ZHAO Zhiqiang GONG Siqi YANG Yiqiu YU Regional Climate Center of Shenyang Liaoning Provincial Meteorological AdministrationShenyang 110016China Key Opening Laboratory for Northeast China Cold Vortex Research China Meteorological AdministrationShenyang 110016China College of Physics and Electronic Engineering Changshu Institute of TechnologyChangshu 215500China Laboratory for Climate Studies National Climate Research CenterChina Meteorological AdministrationBeijing 100081China Inner Mongolia Climate Center Hohhot 010051China

The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine *** relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are *** sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical *** results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of *** the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second *** third type is atypical and is not analyzed in *** tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the *** contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.

关键词： machine learning method Northeast China cold vortex path classification Indian Ocean sea surface temperature Arctic sea ice model sensitivity test