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中华医学杂志(英文版) 2024年第7期137卷 871-873页

作者： Hu Zhenfa su kai Miao Huihui Gao Xue Xue Fushan Tian ming Department of Anesthesiology Beijing Friendship Hospital Capital Medical University Beijing 100050 China Beijing Shijitan Hospital Beijing 100038

Intelligent geochemical interpretation of mass chromatograms:Based on convolution neural network

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Petroleum Science 2024年第2期21卷 752-764页

作者： kai-ming su Jun-Gang Lu Jian Yu Zi-Xing Lu Shi-Jia Chen Hubei Key Laboratory of Petroleum Geochemistry and Environment Wuhan430100HubeiChina College of Resources and Environment Yangtze UniversityWuhan430100HubeiChina School of Geoscience and Technology Southwest Petroleum UniversityChengdu610500SichuanChina Changqing Oilfield Company PetroChinaXi©an710021ShaanxiChina

Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity determination.However,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this method.To overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,China.In this way,the mass chromatogram can be automatically interpreted.This research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(i.e.MI and PMI)that represent the organic matter thermal maturity and parent material type,respectively.subsequently,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,respectively.The significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.

关键词： Organic geochemistry Biomarker Mass chromatographic analysis Automated interpretation Convolution neural network Machine learning

On the Strong Approximation for a Simple Reentrant Line in Light Traffic Under First-buffer First-served Service Discipline

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Acta Mathematicae Applicatae Sinica 2024年第3期40卷 823-839页

作者： kai-ming YANG Yong-jiang GUO School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China

For a 2-station and 3-class reentrant line under first-buffer first-served(FBFS)service discipline in light traffic,we firstly construct the strong approximations for performance measures including the queue length,workload,busy time and idle time processes.Based on the obtained strong approximations,we use a strong approximation method to find all the law of the iterated logarithms(LILs)for the above four performance measures,which are expressed as some functions of system parameters:means and variances of interarrival and service times,and characterize the fluctuations around their fluid approximations.

关键词： reentrant line queueing network FBFS service discipline strong approximation LIL Brownian motion

Research on a multi‑dimensional image information fusion algorithm based on NSCT transform

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Frontiers of Optoelectronics 2024年第1期17卷 29-44页

作者： Yuxiang su Xi Liang Danhua Cao Zhenyu Yang Yuanlong Peng ming Zhao School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China State Grid Information and Telecommunication Branch Beijing 100761China

Traditional inspection cameras determine targets and detect defects by capturing images of their light intensity,but in complex environments,the accuracy of inspection may decrease.Information based on polarization of light can characterize various features of a material,such as the roughness,texture,and refractive index,thus improving classification and recognition of targets.This paper uses a method based on noise template threshold matching to denoise and preprocess polarized images.It also reports on design of an image fusion algorithm,based on NSCT transform,to fuse light intensity images and polarized images.The results show that the fused image improves both subjective and objective evaluation indicators,relative to the source image,and can better preserve edge information and help to improve the accuracy of target recognition.This study provides a reference for the comprehensive application of multi-dimensional optical information in power inspection.

关键词： Power inspection Object detection Polarization imaging Image fusion Image denoising

Variability Analysis for a Two-station Queueing Network in Heavy Traffic with Arrival Processes Driven by Queues

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Acta Mathematicae Applicatae Sinica 2024年第2期40卷 445-466页

作者： Jian CAO Yong-jiang GUO kai-ming YANG School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China

The law of the iterated logarithm(LIL)for the performance measures of a two-station queueing network with arrivals modulated by independent queues is developed by a strong approximation method.For convenience,two arrival processes modulated by queues comprise the external system,all others are belong to the internal system.It is well known that the exogenous arrival has a great influence on the asymptotic variability of performance measures in queues.For the considered queueing network in heavy traffic,we get all the LILs for the queue length,workload,busy time,idle time and departure processes,and present them by some simple functions of the primitive data.The LILs tell us some interesting insights,such as,the LILs of busy and idle times are zero and they reflect a small variability around their fluid approximations,the LIL of departure has nothing to do with the arrival process,both of the two phenomena well explain the service station’s situation of being busy all the time.The external system shows us a distinguishing effect on the performance measures:an underloaded(overloaded,critically loaded)external system affects the internal system through its arrival(departure,arrival and departure together).In addition,we also get the strong approximation of the network as an auxiliary result.

关键词： two-station queueing network the law of the iterated logarithm(LIL) strong approximation fluid approximation

Pre-MASLD:Should it be defined separately?

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Hepatobiliary & Pancreatic Diseases International 2024年第1期23卷 1-3页

作者： Hang-kai Huang You-ming Li Cheng-Fu Xu Department of Gastroenterology Zhejiang Provincial Clinical Research Center for Digestive Diseasesthe First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China

Nonalcoholic fatty liver disease(NAFLD)is one of the most common chronic liver diseases,yet it has not received sufficient attention[1].Over the past two decades,the overall global prevalence of NAFLD has risen from 25%to 37%[2].The disease burden of NAFLD is fueled by the epidemic of obesity and diabetes[3].It is estimated that more than half of patients with diabetes have NAFLD.

关键词： NAFLD liver diseases

Research on Carbon Emission for Preventive Maintenance of Wind Turbine Gearbox Based on Stochastic Differential Equation

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Energy Engineering 2024年第4期121卷 973-986页

作者： Hongsheng su Lixia Dong Xiaoying Yu kai Liu School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

Time based maintenance(TBM)and condition based maintenance(CBM)are widely applied in many large wind farms to optimize the maintenance issues of wind turbine gearboxes,however,these maintenance strategies do not take into account environmental benefits during full life cycle such as carbon emissions issues.Hence,this article proposes a carbon emissions computing model for preventive maintenance activities of wind turbine gearboxes to solve the issue.Based on the change of the gearbox state during operation and the influence of external random factors on the gearbox state,a stochastic differential equation model(SDE)and corresponding carbon emission model are established,wherein SDE is applied to model the evolution of the device state,whereas carbon emission is used to implement carbon emissions computing.The simulation results indicate that the proposed preventive maintenance cannot ensure reliable operation of wind turbine gearboxes but reduce carbon emissions during their lifespan.Compared with TBM,CBM minimizes unit carbon emissions without influencing reliable operation,making it an effective maintenance method.

关键词： Stochastic differential equation(SDE) condition-based maintenance(CBM) carbon emissions

Effect of drying-wetting cycles on pore characteristics and mechanical properties of enzyme-induced carbonate precipitation-reinforced sea sand

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 291-302页

作者： ming Huang kai Xu Zijian Liu Chaoshui Xu mingjuan Cui College of Civil Engineering Fuzhou UniversityFuzhou350108China Xiamen Rail Transit Construction and Development Group Co. Ltd.Xiamen361000China School of Civil Environment and Mining EngineeringThe University of AdelaideAdelaide5005Australia

Enzyme-induced carbonate precipitation(EICP)is an emanating,eco-friendly and potentially sound technique that has presented promise in various geotechnical applications.However,the durability and microscopic characteristics of EICP-treated specimens against the impact of drying-wetting(D-W)cycles is under-explored yet.This study investigates the evolution of mechanical behavior and pore charac-teristics of EICP-treated sea sand subjected to D-W cycles.The uniaxial compressive strength(UCS)tests,synchrotron radiation micro-computed tomography(micro-CT),and three-dimensional(3D)recon-struction of CT images were performed to study the multiscale evolution characteristics of EICP-reinforced sea sand under the effect of D-W cycles.The potential correlations between microstructure characteristics and macro-mechanical property deterioration were investigated using gray relational analysis(GRA).Results showed that the UCS of EICP-treated specimens decreases by 63.7% after 15 D-W cycles.The proportion of mesopores gradually decreases whereas the proportion of macropores in-creases due to the exfoliated calcium carbonate with increasing number of D-W cycles.The micro-structure in EICP-reinforced sea sand was gradually disintegrated,resulting in increasing pore size and development of pore shape from ellipsoidal to columnar and branched.The gray relational degree suggested that the weight loss rate and UCS deterioration were attributed to the development of branched pores with a size of 100-1000 m m under the action of D-W cycles.Overall,the results in this study provide a useful guidancee for the long-term stability and evolution characteristics of EICP-reinforced sea sand under D-W weathering conditions.

关键词： Enzyme-induced carbonate precipitation(EICP) Plant-based urease Drying-wetting(D-W)cycles Microstructure

Geometric error measuring,modeling,and compensation for CNC machine tools:A review

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Chinese Journal of Aeronautics 2024年第2期37卷 163-198页

作者： Zhao ZHANG Feng JIANG ming LUO Baohai WU Dinghua ZHANG kai TANG Engineering Research Center of Advanced Manufacturing Technology for Aero Engine(Northwestern Polytechnical University) Ministry of EducationXi’an 710072China Key Laboratory of High Performance Manufacturing for Aero Engine(Northwestern Polytechnical University) Ministry of Industry and Information TechnologyXi’an 710072China Department of Mechanical and Aerospace Engineering Hong Kong University of Science and TechnologyHong KongChina

Geometric error,mainly due to imperfect geometry and dimensions of machine components,is one of the major error sources of machine tools.Considering that geometric error has significant effects on the machining quality of manufactured parts,it has been a popular topic for academic and industrial research for many years.A great deal of research work has been carried out since the 1970s for solving the problem and improving the machining accuracy.Researchers have studied how to measure,detect,model,identify,reduce,and compensate the geometric errors.This paper presents a thorough review of the latest research activities and gives an overview of the state of the art in understanding changes in machine tool performance due to geometric errors.Recent advances in measuring the geometrical errors of machine tools are summarized,and different kinds of error identification methods of translational axes and rotation axes are illustrated respectively.Besides,volumetric geometric error modeling,tracing,and compensation techniques for five-axis machine tools are emphatically introduced.Finally,research challenges in order to improve the volumetric accuracy of machine tools are also highlighted.

关键词： Error compensation Error identification Error measurement Error modeling Geometric error Machine tools

An approximate analytical model for unconventional reservoir considering variable matrix blocks and simultaneous matrix depletion

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Petroleum Science 2024年第1期21卷 352-365页

作者： kai-Xuan Qiu Jia Li Dong Feng Shi-ming Wei Gang Lei Jiangmen Laboratory of Carbon Science and Technology Jiangmen529100GuangdongChina The Hong Kong University of Science and Technology(Guangzhou) Guangzhou511400GuangdongChina China University of Geosciences(Beijing) Beijing100083China China University of Petroleum(Beijing) Beijing102249China China University of Geosciences(Wuhan) Wuhan430074HubeiChina Shenzhen Research Institute China University of GeosciencesShenzhen518063GuangdongChina

In regard to unconventional oil reservoirs,the transient dual-porosity and triple-porosity models have been adopted to describe the fluid flow in the complex fracture network.It has been proven to cause inaccurate production evaluations because of the absence of matrix-macrofracture communication.In addition,most of the existing models are solved analytically based on Laplace transform and numerical inversion.Hence,an approximate analytical solution is derived directly in real-time space considering variable matrix blocks and simultaneous matrix depletion.To simplify the derivation,the simultaneous matrix depletion is divided into two parts:one part feeding the macrofractures and the other part feeding the microfractures.Then,a series of partial differential equations(PDEs)describing the transient flow and boundary conditions are constructed and solved analytically by integration.Finally,a relationship between oil rate and production time in real-time space is obtained.The new model is verified against classical analytical models.When the microfracture system and matrix-macrofracture communication is neglected,the result of the new model agrees with those obtained with the dual-porosity and triple-porosity model,respectively.Certainly,the new model also has an excellent agreement with the numerical model.The model is then applied to two actual tight oil wells completed in western Canada sedimentary basin.After identifying the flow regime,the solution suitably matches the field production data,and the model parameters are determined.Through these output parameters,we can accurately forecast the production and even estimate the petrophysical properties.

关键词： Analytical solution Unconventional reservoir Variable matrix Simultaneous flow