Using 20 years （1993-2012） of merged data recorded by contemporary multi-altimeter missions, a variety of sea-level variability modes are recovered in the South China Sea employing three- dimensional harmonic extrac...Using 20 years （1993-2012） of merged data recorded by contemporary multi-altimeter missions, a variety of sea-level variability modes are recovered in the South China Sea employing three- dimensional harmonic extraction. In terms of the long-term variation, the South China Sea is estimated to have a rising sea-level linear trend of 5.39 mm/a over these 20 years. Among the modes extracted, the seven most statistically significant periodic or quasi-periodic modes are identified as principal modes. The geographical distributions of the magnitudes and phases of the modes are displayed. In terms of intra- annual and annual regimes, two principal modes with strict semiannual and annual periods are found, with the annual variability having the largest amplitudes among the seven modes. For interannual and decadal regimes, five principal modes at approximately 18, 21, 23, 28, and 112 months are found with the most mode- active region being to the east of Vietnam. For the phase distributions, a series of amphidromes are observed as twins, termed ＂amphidrome twins＂, comprising rotating dipole systems. The stability of periodic modes is investigated employing joint spatiotemporal analysis of latitude/longitude sections. Results show that all periodic modes are robust, revealing the richness and complexity of sea-level modes in the South China Sea.展开更多
For the accurate extraction of cavity decay time, a selection of data points is supplemented to the weighted least square method. We derive the expected precision, accuracy and computation cost of this improved method...For the accurate extraction of cavity decay time, a selection of data points is supplemented to the weighted least square method. We derive the expected precision, accuracy and computation cost of this improved method, and examine these performances by simulation. By comparing this method with the nonlinear least square fitting （NLSF） method and the linear regression of the sum （LRS） method in derivations and simulations, we find that this method can achieve the same or even better precision, comparable accuracy, and lower computation cost. We test this method by experimental decay signals. The results are in agreement with the ones obtained from the nonlinear least square fitting method.展开更多
In response to the lack of reliable physical parameters in the process simulation of the butadiene extraction,a large amount of phase equilibrium data were collected in the context of the actual process of butadiene p...In response to the lack of reliable physical parameters in the process simulation of the butadiene extraction,a large amount of phase equilibrium data were collected in the context of the actual process of butadiene production by acetonitrile.The accuracy of five prediction methods,UNIFAC(UNIQUAC Functional-group Activity Coefficients),UNIFAC-LL,UNIFAC-LBY,UNIFAC-DMD and COSMO-RS,applied to the butadiene extraction process was verified using partial phase equilibrium data.The results showed that the UNIFAC-DMD method had the highest accuracy in predicting phase equilibrium data for the missing system.COSMO-RS-predicted multiple systems showed good accuracy,and a large number of missing phase equilibrium data were estimated using the UNIFAC-DMD method and COSMO-RS method.The predicted phase equilibrium data were checked for consistency.The NRTL-RK(non-Random Two Liquid-Redlich-Kwong Equation of State)and UNIQUAC thermodynamic models were used to correlate the phase equilibrium data.Industrial device simulations were used to verify the accuracy of the thermodynamic model applied to the butadiene extraction process.The simulation results showed that the average deviations of the simulated results using the correlated thermodynamic model from the actual values were less than 2%compared to that using the commercial simulation software,Aspen Plus and its database.The average deviation was much smaller than that of the simulations using the Aspen Plus database(>10%),indicating that the obtained phase equilibrium data are highly accurate and reliable.The best phase equilibrium data and thermodynamic model parameters for butadiene extraction are provided.This improves the accuracy and reliability of the design,optimization and control of the process,and provides a basis and guarantee for developing a more environmentally friendly and economical butadiene extraction process.展开更多
Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized charact...Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.展开更多
The fight against fraud and trafficking is a fundamental mission of customs. The conditions for carrying out this mission depend both on the evolution of economic issues and on the behaviour of the actors in charge of...The fight against fraud and trafficking is a fundamental mission of customs. The conditions for carrying out this mission depend both on the evolution of economic issues and on the behaviour of the actors in charge of its implementation. As part of the customs clearance process, customs are nowadays confronted with an increasing volume of goods in connection with the development of international trade. Automated risk management is therefore required to limit intrusive control. In this article, we propose an unsupervised classification method to extract knowledge rules from a database of customs offences in order to identify abnormal behaviour resulting from customs control. The idea is to apply the Apriori principle on the basis of frequent grounds on a database relating to customs offences in customs procedures to uncover potential rules of association between a customs operation and an offence for the purpose of extracting knowledge governing the occurrence of fraud. This mass of often heterogeneous and complex data thus generates new needs that knowledge extraction methods must be able to meet. The assessment of infringements inevitably requires a proper identification of the risks. It is an original approach based on data mining or data mining to build association rules in two steps: first, search for frequent patterns (support >= minimum support) then from the frequent patterns, produce association rules (Trust >= Minimum Trust). The simulations carried out highlighted three main association rules: forecasting rules, targeting rules and neutral rules with the introduction of a third indicator of rule relevance which is the Lift measure. Confidence in the first two rules has been set at least 50%.展开更多
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 le...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 methods.The 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 second.The 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 TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.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 criteria.The 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 cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part 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.展开更多
为了快速、高效、自动地提取湖泊水域岸线,测定岸线位置和属性的动态,实现岸线的动态提取和监测,本研究依托GEE云计算平台,创新性地采用JRC(joint research centre)全球地表水体数据集,基于水体淹没频率计算模型构建岸线提取算法。选取...为了快速、高效、自动地提取湖泊水域岸线,测定岸线位置和属性的动态,实现岸线的动态提取和监测,本研究依托GEE云计算平台,创新性地采用JRC(joint research centre)全球地表水体数据集,基于水体淹没频率计算模型构建岸线提取算法。选取太湖为研究区,与阈值法(NDWI)、边缘检测法(sobel)和目视解译结果进行对比,研究结果表明:JRC的湖泊水域岸线提取结果精度较高(误差在16%以内,精度在20 m以内),拟合效果较好;Sobel与实际岸线的偏移量较大;NDWI与目视解译的误差最大。基于JRC的方法提取的岸线与实际较为吻合,能够简单、快速、自动地提取湖泊水域岸线,实现岸线的动态提取和监测,为全球大尺度范围的岸线相关研究提供基础。展开更多
基金Supported by the National Natural Science Foundation of China(Nos.41331172,U1406404)the National High Technology Research and Development Program of China(863 Program)(No.2013AA09A505)
文摘Using 20 years （1993-2012） of merged data recorded by contemporary multi-altimeter missions, a variety of sea-level variability modes are recovered in the South China Sea employing three- dimensional harmonic extraction. In terms of the long-term variation, the South China Sea is estimated to have a rising sea-level linear trend of 5.39 mm/a over these 20 years. Among the modes extracted, the seven most statistically significant periodic or quasi-periodic modes are identified as principal modes. The geographical distributions of the magnitudes and phases of the modes are displayed. In terms of intra- annual and annual regimes, two principal modes with strict semiannual and annual periods are found, with the annual variability having the largest amplitudes among the seven modes. For interannual and decadal regimes, five principal modes at approximately 18, 21, 23, 28, and 112 months are found with the most mode- active region being to the east of Vietnam. For the phase distributions, a series of amphidromes are observed as twins, termed ＂amphidrome twins＂, comprising rotating dipole systems. The stability of periodic modes is investigated employing joint spatiotemporal analysis of latitude/longitude sections. Results show that all periodic modes are robust, revealing the richness and complexity of sea-level modes in the South China Sea.
基金supported by the Preeminent Youth Fund of Sichuan Province,China(Grant No.2012JQ0012)the National Natural Science Foundation of China(Grant Nos.11173008,10974202,and 60978049)the National Key Scientific and Research Equipment Development Project of China(Grant No.ZDYZ2013-2)
文摘For the accurate extraction of cavity decay time, a selection of data points is supplemented to the weighted least square method. We derive the expected precision, accuracy and computation cost of this improved method, and examine these performances by simulation. By comparing this method with the nonlinear least square fitting （NLSF） method and the linear regression of the sum （LRS） method in derivations and simulations, we find that this method can achieve the same or even better precision, comparable accuracy, and lower computation cost. We test this method by experimental decay signals. The results are in agreement with the ones obtained from the nonlinear least square fitting method.
基金supported by the National Natural Science Foundation of China(22178190)。
文摘In response to the lack of reliable physical parameters in the process simulation of the butadiene extraction,a large amount of phase equilibrium data were collected in the context of the actual process of butadiene production by acetonitrile.The accuracy of five prediction methods,UNIFAC(UNIQUAC Functional-group Activity Coefficients),UNIFAC-LL,UNIFAC-LBY,UNIFAC-DMD and COSMO-RS,applied to the butadiene extraction process was verified using partial phase equilibrium data.The results showed that the UNIFAC-DMD method had the highest accuracy in predicting phase equilibrium data for the missing system.COSMO-RS-predicted multiple systems showed good accuracy,and a large number of missing phase equilibrium data were estimated using the UNIFAC-DMD method and COSMO-RS method.The predicted phase equilibrium data were checked for consistency.The NRTL-RK(non-Random Two Liquid-Redlich-Kwong Equation of State)and UNIQUAC thermodynamic models were used to correlate the phase equilibrium data.Industrial device simulations were used to verify the accuracy of the thermodynamic model applied to the butadiene extraction process.The simulation results showed that the average deviations of the simulated results using the correlated thermodynamic model from the actual values were less than 2%compared to that using the commercial simulation software,Aspen Plus and its database.The average deviation was much smaller than that of the simulations using the Aspen Plus database(>10%),indicating that the obtained phase equilibrium data are highly accurate and reliable.The best phase equilibrium data and thermodynamic model parameters for butadiene extraction are provided.This improves the accuracy and reliability of the design,optimization and control of the process,and provides a basis and guarantee for developing a more environmentally friendly and economical butadiene extraction process.
基金funded by National Natural Science Foundation of China(Grant Nos.42272333,42277147).
文摘Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.
文摘The fight against fraud and trafficking is a fundamental mission of customs. The conditions for carrying out this mission depend both on the evolution of economic issues and on the behaviour of the actors in charge of its implementation. As part of the customs clearance process, customs are nowadays confronted with an increasing volume of goods in connection with the development of international trade. Automated risk management is therefore required to limit intrusive control. In this article, we propose an unsupervised classification method to extract knowledge rules from a database of customs offences in order to identify abnormal behaviour resulting from customs control. The idea is to apply the Apriori principle on the basis of frequent grounds on a database relating to customs offences in customs procedures to uncover potential rules of association between a customs operation and an offence for the purpose of extracting knowledge governing the occurrence of fraud. This mass of often heterogeneous and complex data thus generates new needs that knowledge extraction methods must be able to meet. The assessment of infringements inevitably requires a proper identification of the risks. It is an original approach based on data mining or data mining to build association rules in two steps: first, search for frequent patterns (support >= minimum support) then from the frequent patterns, produce association rules (Trust >= Minimum Trust). The simulations carried out highlighted three main association rules: forecasting rules, targeting rules and neutral rules with the introduction of a third indicator of rule relevance which is the Lift measure. Confidence in the first two rules has been set at least 50%.
基金supported by the National Key R&D Program of China(Grant No.2018YFB1702504)the National Natural Science Foundation of China(Grant Nos.52179121,51879284)+3 种基金the State Key Laboratory of Simulations and Regulation of Water Cycle in River Basin,China(Grant No.SKL2022ZD05)the IWHR Research&Development Support Program,China(Grant No.GE0145B012021)the Natural Science Foundation of Shaanxi Province,China(Grant No.2021JLM-50)the National Key R&D Program of China(Grant No.2022YFE0200400).
文摘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 methods.The 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 second.The 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 TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.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 criteria.The 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 cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part 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.
文摘为了快速、高效、自动地提取湖泊水域岸线,测定岸线位置和属性的动态,实现岸线的动态提取和监测,本研究依托GEE云计算平台,创新性地采用JRC(joint research centre)全球地表水体数据集,基于水体淹没频率计算模型构建岸线提取算法。选取太湖为研究区,与阈值法(NDWI)、边缘检测法(sobel)和目视解译结果进行对比,研究结果表明:JRC的湖泊水域岸线提取结果精度较高(误差在16%以内,精度在20 m以内),拟合效果较好;Sobel与实际岸线的偏移量较大;NDWI与目视解译的误差最大。基于JRC的方法提取的岸线与实际较为吻合,能够简单、快速、自动地提取湖泊水域岸线,实现岸线的动态提取和监测,为全球大尺度范围的岸线相关研究提供基础。
