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Model-data-driven seismic inversion method based on small sample data 认领 引用 被引量：4
1
作者 LIU Jinshui SUN Yuhang LIU Yang 《Petroleum Exploration and Development》 SCIE 2022年第5期1046-1055,共10页
As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this prob... As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data. 展开更多
关键词 small sample data space-variant objective function model-data-driven neural network seismic AVO inversion thin interbedded sandstone identification Paleocene Lishui sag
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Physically-consistent-WGAN based small sample fault diagnosis for industrial processes 认领 引用 被引量：1
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作者 Siyu Tang Hongbo Shi +2 位作者 Bing Song Yang Tao Shuai Tan 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第2期163-174,共12页
In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversa... In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversarial networks for smallsample data has achieved a wide range of applications. However, the current generative adversarial networks applied in industrial processes do not impose realistic physical constraints on the generation of data, resulting in the generation of data that do not have realistic physical consistency. To address this problem, this paper proposes a physical consistency-based WGAN, designs a loss function containing physical constraints for industrial processes, and validates the effectiveness of the method using a common dataset in the field of industrial process fault diagnosis. The experimental results show that the proposed method not only makes the generated data consistent with the physical constraints of the industrial process, but also has better fault diagnosis performance than the existing GAN-based methods. 展开更多
关键词 Chemical processes Fault diagnosis Physical consistency Generative adversarial networks Small sample data
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Failure rate analysis and maintenance plan optimization method for civil aircraft parts based on data fusion 认领 引用 被引量：2
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作者 Kang CAO Yongjie ZHANG Jianfei FENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第1期306-324,共19页
In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.... In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.This study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information.Through a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the algorithm.Building upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization.The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency ratio.This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs.The findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis. 展开更多
关键词 Small sample data Data fusion Failure rate Maintenance planning Aircraft parts
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A New Economy Forecasting Method Based on Data Barycentre Forecasting Method 认领 引用
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作者 Jilin Zhang Qun Zhang 《Chinese Business Review》 2005年第5期25-28,共4页
A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting ... A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting of economy volume and could greatly decrease the errors of the fitting and forecasting results. Moreover, the strict hypothetical conditions in least squares method were not necessary in the method presented in this paper, which overcame the shortcomings of least squares method and expanded the application of data barycentre method. Application to the steel consumption volume forecasting was presented in this paper. It was shown that the result of fitting and forecasting was satisfactory. From the comparison between data barycentre forecasting method and least squares method, we could conclude that the fitting and forecasting results using data barycentre method were more stable than those of using least squares regression forecasting method, and the computation of data barycentre forecasting method was simpler than that of least squares method. As a result, the data barycentre method was convenient to use in technical economy. 展开更多
关键词 data barycentre method parameter estimation small sample steel forecasting
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Structural Reliability Analysis Based on Support Vector Machine and Dual Neural Network Direct Integration Method 认领 引用 被引量：1
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作者 NIE Xiaobo LI Haibin 《Journal of Donghua University(English Edition)》 CAS 2021年第1期51-56,共6页
Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DN... Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis. 展开更多
关键词 support vector machine(SVM) neural network direct integration method structural reliability small sample data performance function
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Bayesian Method Reliability of Flight Simulator 认领 引用
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作者 WANG Li XIONG Jing 《International English Education Research》 2017年第1期76-78,共3页
This paper introduces the basic viewpoints and characteristics of Bayesian statistics. Which provides a theoretical basis for solving the problem of small sample of flight simulator using Bayesian method. A series of ... This paper introduces the basic viewpoints and characteristics of Bayesian statistics. Which provides a theoretical basis for solving the problem of small sample of flight simulator using Bayesian method. A series of formulas were derived to establish the Bayesian reliability modeling and evaluation model for flight simulation equipment. The two key problems of Bayesian method were pointed out as follows： obtaining the prior distribution of WeibuU parameter, calculating the parameter a posterior distribution and parameter estimation without analytic solution, and proposing the corresponding solution scheme. 展开更多
关键词 Small sample data Flight simulation equipment Reliability modeling Bayesian method Weibull parameter
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A comparative study on shipping accident damagecompensation prediction based on SVR and a BP neuralnetwork 认领 引用
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作者 Jianhai He Jianxia Zhang 《Transportation Safety and Environment》 EI CSCD 2025年第4期15-25,共11页
To address the challenges of institutional gaps in liability regimes,small sample data,and nonlinear regression in shipping accident damage compensation assessment,this study proposes Support Vector Regression(SVR)and... To address the challenges of institutional gaps in liability regimes,small sample data,and nonlinear regression in shipping accident damage compensation assessment,this study proposes Support Vector Regression(SVR)and Backpropagation(BP)neural network models and compares their performance in small sample scenarios.Using data from 17 typical shipping accidents(between 1991 and 2020)and seven key risk factors(e.g.,leakage volume and gross tonnage),the models were built and optimized.The results indicate that SVR outperforms BP neural networks when evaluated using the root mean squared error and mean absolute error metrics.SVR’s superior generalizability via structural risk minimization and kernel mapping makes it more effective for small sample nonlinear regression.Conversely,BP neural networks suffer from local optima trapping and parameter sensitivity,leading to lower prediction accuracy.This study offers a data-driven solution for rapid shipping accident compensation assessment and references for small sample machine learning applications. 展开更多
关键词 shipping accident damage compensation support vector regression(SVR) BP neural network small sample data damage compensation assessment
A comprehensive performance evaluation framework of complex products based on a fuzzy AHP and DS theory 认领 引用 被引量：2
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作者 Yuhong Li Guanghong Gong Ni Li 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2016年第3期181-198,共18页
With the development of computer technique,performance evaluation of complex products is playing an increasingly critical role in ensuring product quality and improving development process.An extensible comprehensive ... With the development of computer technique,performance evaluation of complex products is playing an increasingly critical role in ensuring product quality and improving development process.An extensible comprehensive performance evaluation framework with the integration of effective group decision-making algorithms could be a supporting tool to achieve an efficient evaluation process and reduce comprehensive evaluation dif-ficulty.This paper aims to provide a evaluation framework with friendly interactive operation and extensive expansibility,which adopts a multi-expert evaluation approach based on fuzzy,analytical hierarchy process(FAHP)and Dempstere–Shafer(DS)theory(FADS)in order to consider experts’relative importance degree.In addition,an extensible evaluation process and related auxiliary functions are implemented in the framework,including the establishment of an assessment index system,integration and calls of multiple types of testing data preprocessing methods and index assessment methods suitable for small sample data,graphical result display and data analysis,etc.Finally,performance evaluation cases of two models of airborne radar anti-jamming are presented to verify the feasibility and expansibility of our assessment framework.The group decision-making method shows its effectiveness compared with the experimental evaluation results by the FAHP researched method. 展开更多
关键词 Evaluation framework small sample data analytical hierarchy process fuzzy Dempstere-Shafer theory.
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