On the dynamics of persistent states and their secular trends in the waveguides of the Southern Hemisphere troposphere     

Terence J. O’Kane  James S. Risbey  Didier P. Monselesan  Illia Horenko  Christian L. E. Franzke 《Climate Dynamics》2016,46(11-12):3567-3597

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New soil thermal stabilization systems for building fundaments in permafrost regions     

Illia P. Rilo  Grigory M. Dolgikh  Vladimir F. Vlasov 《寒旱区科学》2013,5(4):0387-0392

This paper describes new building construction methods that utilize soil thermal stabilization regimes to compensate for negative environmental warming and anthropogenic factors that impair fundament stability. Based on long-standing research, the Fundamentstroyarkos Company (FSA) of Tyumen, Russia has developed four primary seasonally active cooling devices (SCDs) that maintain soil in the frozen state, which are now extensively used on oil and gas facilities located in cold regions of Russia. This paper reports on the testing and validation of these SCDs in experimental conditions. On this basis, designs and technologies for building bases and foundations on permafrost with use of soil thermal stabilization systems, using carbon dioxide as the heat-transfer agent, were developed.  相似文献   

On clustering of non-stationary meteorological time series     

Illia Horenko 《Dynamics of Atmospheres and Oceans》2010,49(2-3):164-187

A method for clustering of multidimensional non-stationary meteorological time series is presented. The approach is based on optimization of the regularized averaged clustering functional describing the quality of data representation in terms of several regression models and a metastable hidden process switching between them. Proposed numerical clustering algorithm is based on application of the finite element method (FEM) to the problem of non-stationary time series analysis. The main advantage of the presented algorithm compared to Hidden Markov Models (HMMs) and to finite mixture models is that no a priori assumptions about the probability model for the hidden and observed processes (e.g., Markovianity or stationarity) are necessary for the proposed method. Another attractive numerical feature of the discussed algorithm is the possibility to choose the optimal number of metastable clusters and a natural opportunity to control the fuzziness of the resulting decomposition a posteriory, based on the statistical distinguishability of the resulting persistent cluster states. The resulting FEM-K-trends algorithm is compared with some standard fuzzy clustering methods on toy model examples and on analysis of multidimensional historical temperature data locally in Europe and on the global temperature data set.  相似文献