Utilizing the material of monthly means of the three primary kinetic energy modes over the whote globe at 500 hPa during the nine years of 1980-1988, both the rapid seasonal changes and the interannual variability in ...Utilizing the material of monthly means of the three primary kinetic energy modes over the whote globe at 500 hPa during the nine years of 1980-1988, both the rapid seasonal changes and the interannual variability in tie general circulation in terms of the energy modes have been investigated, with special attention paid to the unusual year 1983, Two main results are obtained. One, there are remarkable seasonal rapid changes over the Northern Hemisphere, occurring ganerally in April and October. The other, among the nine years of 1980-1988, 1983 is the only one with unusual energy modes and remarkably abnormal seasonal changes.展开更多
Analysis is done of five-year low-pass filtered data by a five-layer low-order global spectral model, indicating that although any non-seasonal external forcing is not considered in the model atmosphere,monthly-scale ...Analysis is done of five-year low-pass filtered data by a five-layer low-order global spectral model, indicating that although any non-seasonal external forcing is not considered in the model atmosphere,monthly-scale anomaly takes place which is of remarkable seasonality and interannual variability.Analysis also shows that for the same seasonal external forcing the model atmosphere can exhibit two climatic states,similar in the departure pattern but opposite in sign, indicating that the anomaly is but the manifestation of the adverse states, which supports the theory of multi-equilibria proposed by Charney and Devore(1979) once again.Finally, the source for the low-frequency oscillation of the global atmosphere is found to be the convective heat source / sink inside the tropical atmosphere as discussed before in our study.Therefore, the key approach to the exploration of atmospheric steady low-frequency oscillation and the associated climatic effect lies in the examination of the distribution of convective heat sources / sinks and the variation in the tropical atmosphere.展开更多
In this paper, the global well-posedness of the three-dimensional incompressible Navier-Stokes equations with a linear damping for a class of large initial data slowly varying in two directions are proved by means of ...In this paper, the global well-posedness of the three-dimensional incompressible Navier-Stokes equations with a linear damping for a class of large initial data slowly varying in two directions are proved by means of a simpler approach.展开更多
Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI constr...Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.展开更多
In this paper,the geoecological information-modeling system(GIMS)is described as possible improvement of the Big Data approach.The main GIMS function is the use of algorithms and models that capture the fundamental pr...In this paper,the geoecological information-modeling system(GIMS)is described as possible improvement of the Big Data approach.The main GIMS function is the use of algorithms and models that capture the fundamental processes controlling the evolution of the climate-nature-society(CNSS)system.The GIMS structure includes 24 blocks that realize a series of models and algorithms for global big data processing and analysis.The CNSS global model is the basic block of the GIMS.The operational tools of GIMS are demonstrated by examining several scenarios associated with the reconstruction of forest areas.It is shown that significant impacts on forests can lead to global climate variations on a large scale.展开更多
基金This work is supported by the Doctorial Program Foundation of the Institution of Higher Education.
文摘Utilizing the material of monthly means of the three primary kinetic energy modes over the whote globe at 500 hPa during the nine years of 1980-1988, both the rapid seasonal changes and the interannual variability in tie general circulation in terms of the energy modes have been investigated, with special attention paid to the unusual year 1983, Two main results are obtained. One, there are remarkable seasonal rapid changes over the Northern Hemisphere, occurring ganerally in April and October. The other, among the nine years of 1980-1988, 1983 is the only one with unusual energy modes and remarkably abnormal seasonal changes.
文摘Analysis is done of five-year low-pass filtered data by a five-layer low-order global spectral model, indicating that although any non-seasonal external forcing is not considered in the model atmosphere,monthly-scale anomaly takes place which is of remarkable seasonality and interannual variability.Analysis also shows that for the same seasonal external forcing the model atmosphere can exhibit two climatic states,similar in the departure pattern but opposite in sign, indicating that the anomaly is but the manifestation of the adverse states, which supports the theory of multi-equilibria proposed by Charney and Devore(1979) once again.Finally, the source for the low-frequency oscillation of the global atmosphere is found to be the convective heat source / sink inside the tropical atmosphere as discussed before in our study.Therefore, the key approach to the exploration of atmospheric steady low-frequency oscillation and the associated climatic effect lies in the examination of the distribution of convective heat sources / sinks and the variation in the tropical atmosphere.
基金supported by the National Natural Science Foundation of China(Nos.11471215,11031001,11121101,11626156)Shanghai Leading Academic Discipline Project(No.XTKX2012)+1 种基金the Key Laboratory of Mathematics for Nonlinear Sciences(Fudan University)the Ministry of Education of China,Shanghai Key Laboratory for Contemporary Applied Mathematics,School of Mathematical Sciences,Fudan University and 111 Program of MOE,China(No.B08018)
文摘In this paper, the global well-posedness of the three-dimensional incompressible Navier-Stokes equations with a linear damping for a class of large initial data slowly varying in two directions are proved by means of a simpler approach.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB3400700)the China Academy of Railway Sciences Corporation Limited within the major issues of the fund(Grant No.2021YJ212)+1 种基金the National Natural Science Foundation of China(Grant Nos.12072188,12121002)the Natural Science Foundation of Shanghai(Grant No.20ZR1425200)。
文摘Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.
基金This study was partly supported by the Russian Fund for Basic Researches[Project No.16-01-000213-a].
文摘In this paper,the geoecological information-modeling system(GIMS)is described as possible improvement of the Big Data approach.The main GIMS function is the use of algorithms and models that capture the fundamental processes controlling the evolution of the climate-nature-society(CNSS)system.The GIMS structure includes 24 blocks that realize a series of models and algorithms for global big data processing and analysis.The CNSS global model is the basic block of the GIMS.The operational tools of GIMS are demonstrated by examining several scenarios associated with the reconstruction of forest areas.It is shown that significant impacts on forests can lead to global climate variations on a large scale.
