Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-f...Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-forms,which leverage sparse coding(SC),have gained significant attention in the geosciences due to their ability to achieve high TF resolution.However,the iterative approaches typically employed in sparse TF transforms are computationally intensive,making them impractical for real seismic data analysis.To address this issue,we propose an interpretable convolutional sparse coding(CSC)network to achieve high TF resolution.The proposed model is generated based on the traditional short-time Fourier transform(STFT)transform and a modified UNet,named ULISTANet.In this design,we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm(LISTA)blocks,a specialized form of CSC.The LISTA block,which evolves from the traditional iterative shrinkage thresholding algorithm(ISTA),is optimized for extracting sparse features more effectively.Furthermore,we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet.Finally,the proposed method’s performance is subsequently validated using both synthetic and field data,demonstrating its potential for enhanced seismic data analysis.展开更多
Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional...Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
随着全球供应链的日益复杂化和不确定性增加,提升供应链韧性成为我国面临的重要挑战。本文基于Web of Science数据库和知网数据库,结合可视化分析方法,对2013—2024年国内外供应链韧性领域相关文献进行对比分析,研究结果表明:(1)国内研...随着全球供应链的日益复杂化和不确定性增加,提升供应链韧性成为我国面临的重要挑战。本文基于Web of Science数据库和知网数据库,结合可视化分析方法,对2013—2024年国内外供应链韧性领域相关文献进行对比分析,研究结果表明:(1)国内研究起步晚于国外,且发文量少于国外。国外整体合作密切程度强于国内,国内、国外均未形成核心作者群。(2)国内相关研究主要集中在技术创新对供应链韧性的影响、供应链韧性战略以及供应链韧性评价等方面;国外相关研究主要集中在供应链韧性内涵、供应链韧性作用机制、供应链韧性评估模型等方面。(3)国内研究演进脉络分为两个阶段,国外研究演进脉络分为三个阶段。(4)在研究前沿方面,国内现阶段聚焦数字化方面,反映了产业升级需求;国外现阶段侧重于数字化与地缘政治方面。展开更多
The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improv...The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.展开更多
本文基于知识图谱理论,选取1986—2024年中国知网(CNKI)收录的1072篇中文文献为样本,并辅以Web of Science核心合集中的英文文献,运用CiteSpace和VOSviewer软件,从发文数量、研究机构、载文期刊、关键词突现、时间演进与聚类等维度,对...本文基于知识图谱理论,选取1986—2024年中国知网(CNKI)收录的1072篇中文文献为样本,并辅以Web of Science核心合集中的英文文献,运用CiteSpace和VOSviewer软件,从发文数量、研究机构、载文期刊、关键词突现、时间演进与聚类等维度,对制造业转型研究的演化脉络和热点主题进行系统梳理。研究发现:制造业转型相关文献数量持续增长,呈现出显著的多学科交叉特征;研究主题经历了从早期的价值链优化,逐步拓展至数字化、绿色化、智能化与服务化等多个路径;近年来,研究重点进一步聚焦于企业绩效、人才培养与国际竞争力等议题。整体来看,制造业转型正由要素驱动向数字技术主导、多路径融合演进。本文研究成果可为理论拓展与政策制定提供系统参考。展开更多
We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to ...We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to examine the connections between various data during the COVID-19 pandemic.We consider two characteristics:time and frequency.Based on Diebold and Yilmaz’s(Int J Forecast 28:57-66,2012)technique,our findings indicate that comparable data have a substantially stronger correlation(regarding return)than volatility.Per Baruník and Křehlík’(J Financ Econ 16:271-296,2018)approach,interconnectedness among returns(volatilities)reduces(increases)as one moves from the short to the long term.A moving window analysis reveals a sudden increase in correlation,both in volatility and return,during the COVID-19 pandemic.In the context of wavelet coherence analysis,we observe a strong interconnection between data corresponding to the COVID-19 outbreak.The only exceptions are the behavior of Bitcoin and Ethereum.Specifically,Bitcoin combinations with other data exhibit a distinct behavior.The period precisely coincides with the COVID-19 pandemic.Evidently,volatility spillover has a long-lasting impact;policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks(e.g.,the COVID-19 pandemic)and simultaneously reduce its side effects.展开更多
Clarifying the mechanisms that control the evolution of territorial space patterns is essential for regulating and optimizing the geographical structure and processes related to sustainable development.Using the Guang...Clarifying the mechanisms that control the evolution of territorial space patterns is essential for regulating and optimizing the geographical structure and processes related to sustainable development.Using the Guangdong and Guangxi sections of the Pearl River Basin as examples,the transfer-matrix method and standard deviation ellipse model were applied to characterize the evolution of territorial space patterns from 1990 to 2020.A trend surface analysis and the Theil index were used to analyze regional differences in the evolution process,and geodetectors were used to identify the underlying mechanisms of the changes.There were three key results.(1)In these critical areas of the Pearl River Basin,agricultural and ecological spaces have rapidly declined due to urban expansion,with transfers between these spaces dominating the evolution of territorial space patterns.Spatial pattern changes in the Guangdong section were more intense than in the Guangxi section.(2)Regional differences in urban space have decreased,whereas differences in agricultural and ecological spaces have intensified.Driven by socio-economic growth,the cross-regional transfers of territorial space have created a“high in the east,while low in the west”inter-regional difference,and a“high in the south,while low in the north”intra-regional difference shaped by natural conditions.The regional differences in space patterns were greater in Guangdong than in Guangxi.(3)The evolution of watershed territorial space patterns resulted from scale changes,locational shifts,structural reorganizations,and directional changes driven by multiple factors.Natural environment,social life,economic development,and policy factors played foundational,leading,key driving,and guiding roles,respectively.Additionally,the regional differences in the evolution of watershed territorial space patterns originated from the differential transmission of the influence of various factors affecting spatial evolution.Enhancing urban space efficiency,restructuring agricultural space,and optimizing ecological space are key strategies for building a complementary and synergistic territorial space pattern in the basin.展开更多
基金supported by the National Natural Science Foundation of China under Grant 42474139the Key Research and Development Program of Shaanxi under Grant 2024GX-YBXM-067.
文摘Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-forms,which leverage sparse coding(SC),have gained significant attention in the geosciences due to their ability to achieve high TF resolution.However,the iterative approaches typically employed in sparse TF transforms are computationally intensive,making them impractical for real seismic data analysis.To address this issue,we propose an interpretable convolutional sparse coding(CSC)network to achieve high TF resolution.The proposed model is generated based on the traditional short-time Fourier transform(STFT)transform and a modified UNet,named ULISTANet.In this design,we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm(LISTA)blocks,a specialized form of CSC.The LISTA block,which evolves from the traditional iterative shrinkage thresholding algorithm(ISTA),is optimized for extracting sparse features more effectively.Furthermore,we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet.Finally,the proposed method’s performance is subsequently validated using both synthetic and field data,demonstrating its potential for enhanced seismic data analysis.
基金Aeronautical Science Foundation of China (20071551016)
文摘Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
文摘随着全球供应链的日益复杂化和不确定性增加,提升供应链韧性成为我国面临的重要挑战。本文基于Web of Science数据库和知网数据库,结合可视化分析方法,对2013—2024年国内外供应链韧性领域相关文献进行对比分析,研究结果表明:(1)国内研究起步晚于国外,且发文量少于国外。国外整体合作密切程度强于国内,国内、国外均未形成核心作者群。(2)国内相关研究主要集中在技术创新对供应链韧性的影响、供应链韧性战略以及供应链韧性评价等方面;国外相关研究主要集中在供应链韧性内涵、供应链韧性作用机制、供应链韧性评估模型等方面。(3)国内研究演进脉络分为两个阶段,国外研究演进脉络分为三个阶段。(4)在研究前沿方面,国内现阶段聚焦数字化方面,反映了产业升级需求;国外现阶段侧重于数字化与地缘政治方面。
基金Supported by the National Science Foundation of China(42055402)。
文摘The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.
文摘本文基于知识图谱理论,选取1986—2024年中国知网(CNKI)收录的1072篇中文文献为样本,并辅以Web of Science核心合集中的英文文献,运用CiteSpace和VOSviewer软件,从发文数量、研究机构、载文期刊、关键词突现、时间演进与聚类等维度,对制造业转型研究的演化脉络和热点主题进行系统梳理。研究发现:制造业转型相关文献数量持续增长,呈现出显著的多学科交叉特征;研究主题经历了从早期的价值链优化,逐步拓展至数字化、绿色化、智能化与服务化等多个路径;近年来,研究重点进一步聚焦于企业绩效、人才培养与国际竞争力等议题。整体来看,制造业转型正由要素驱动向数字技术主导、多路径融合演进。本文研究成果可为理论拓展与政策制定提供系统参考。
文摘We used daily return series for three pairs of datasets from the crude oil markets(WTI and Brent),stock indices(the Dow Jones Industrial Average and S&P 500),and benchmark cryptocurrencies(Bitcoin and Ethereum)to examine the connections between various data during the COVID-19 pandemic.We consider two characteristics:time and frequency.Based on Diebold and Yilmaz’s(Int J Forecast 28:57-66,2012)technique,our findings indicate that comparable data have a substantially stronger correlation(regarding return)than volatility.Per Baruník and Křehlík’(J Financ Econ 16:271-296,2018)approach,interconnectedness among returns(volatilities)reduces(increases)as one moves from the short to the long term.A moving window analysis reveals a sudden increase in correlation,both in volatility and return,during the COVID-19 pandemic.In the context of wavelet coherence analysis,we observe a strong interconnection between data corresponding to the COVID-19 outbreak.The only exceptions are the behavior of Bitcoin and Ethereum.Specifically,Bitcoin combinations with other data exhibit a distinct behavior.The period precisely coincides with the COVID-19 pandemic.Evidently,volatility spillover has a long-lasting impact;policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks(e.g.,the COVID-19 pandemic)and simultaneously reduce its side effects.
基金National Social Science Foundation Program,No.22VRC163National Natural Science Foundation of China,No.42061043+1 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province,No.KYCX24_1008Innovation Project of Guangxi Graduate Education,No.YCSW2024473。
文摘Clarifying the mechanisms that control the evolution of territorial space patterns is essential for regulating and optimizing the geographical structure and processes related to sustainable development.Using the Guangdong and Guangxi sections of the Pearl River Basin as examples,the transfer-matrix method and standard deviation ellipse model were applied to characterize the evolution of territorial space patterns from 1990 to 2020.A trend surface analysis and the Theil index were used to analyze regional differences in the evolution process,and geodetectors were used to identify the underlying mechanisms of the changes.There were three key results.(1)In these critical areas of the Pearl River Basin,agricultural and ecological spaces have rapidly declined due to urban expansion,with transfers between these spaces dominating the evolution of territorial space patterns.Spatial pattern changes in the Guangdong section were more intense than in the Guangxi section.(2)Regional differences in urban space have decreased,whereas differences in agricultural and ecological spaces have intensified.Driven by socio-economic growth,the cross-regional transfers of territorial space have created a“high in the east,while low in the west”inter-regional difference,and a“high in the south,while low in the north”intra-regional difference shaped by natural conditions.The regional differences in space patterns were greater in Guangdong than in Guangxi.(3)The evolution of watershed territorial space patterns resulted from scale changes,locational shifts,structural reorganizations,and directional changes driven by multiple factors.Natural environment,social life,economic development,and policy factors played foundational,leading,key driving,and guiding roles,respectively.Additionally,the regional differences in the evolution of watershed territorial space patterns originated from the differential transmission of the influence of various factors affecting spatial evolution.Enhancing urban space efficiency,restructuring agricultural space,and optimizing ecological space are key strategies for building a complementary and synergistic territorial space pattern in the basin.
