Currently, most of MT （magnetotelluric） data are still collected on sparse survey lines and interpreted using 2D inversion methods because of the field work cost, the work area environment, and so on. However, there...Currently, most of MT （magnetotelluric） data are still collected on sparse survey lines and interpreted using 2D inversion methods because of the field work cost, the work area environment, and so on. However, there are some 2D interpretation limitations of the MT data from 3D geoelectrical structures which always leads to wrong geological interpretations. In this paper, we used the 3D inversion method to interpret the MT sparse lines data. In model testing, the sparse lines data are the MT full information data generated from a test model and processed using the 3D conjugate gradients inversion code. The inversion results show that this inversion method is reasonable and effective. Meanwhile, we prove that for inversion results with different element parameters, the results by joint inversion of both the impedance tensor data and the tipper data are more accurate and closer to the test model.展开更多
In order to solve the problem of the invalidation of thermal parameters andoptimal running, we present an efficient soft sensor approach based on sparse online Gaussianprocesses( GP), which is based on a combination o...In order to solve the problem of the invalidation of thermal parameters andoptimal running, we present an efficient soft sensor approach based on sparse online Gaussianprocesses( GP), which is based on a combination of a Bayesian online algorithm together with asequential construction of a relevant subsample of the data to specify the prediction of the GPmodel. By an appealing parameterization and projection techniques that use the reproducing kernelHubert space (RKHS) norm, recursions for the effective parameters and a sparse Gaussianapproximation of the posterior process are obtained. The sparse representation of Gaussian processesmakes the GP-based soft sensor practical in a large dataset and real-time application. And theproposed thermalparameter soft sensor is of importance for the economical running of the powerplant.展开更多
A new dynamic path planning method in high dimensional workspace, radial based probabilistic roadmap motion (RBPRM) planning method, is presented. Different from general probabilistic roadmap motion planning methods, ...A new dynamic path planning method in high dimensional workspace, radial based probabilistic roadmap motion (RBPRM) planning method, is presented. Different from general probabilistic roadmap motion planning methods, it uses straight lines as long as possible to construct a path graph, so the final path obtained from the graph is relatively shorter and straighter. Experimental results show the efficiency of the algorithm in finding shorter paths in sparse environment.展开更多
基金supported by the National Hi-Tech Research and Development Program of China (863 Program) (No. 2007AA09Z310)National Natural Science Foundation of China (No. 40677037, 40774029, 41004028)+1 种基金Fundamental Research Funds for the Central Universities (No. 2010ZY53) Program for New Century Excellent Talents in University (NCET)
文摘Currently, most of MT （magnetotelluric） data are still collected on sparse survey lines and interpreted using 2D inversion methods because of the field work cost, the work area environment, and so on. However, there are some 2D interpretation limitations of the MT data from 3D geoelectrical structures which always leads to wrong geological interpretations. In this paper, we used the 3D inversion method to interpret the MT sparse lines data. In model testing, the sparse lines data are the MT full information data generated from a test model and processed using the 3D conjugate gradients inversion code. The inversion results show that this inversion method is reasonable and effective. Meanwhile, we prove that for inversion results with different element parameters, the results by joint inversion of both the impedance tensor data and the tipper data are more accurate and closer to the test model.
文摘In order to solve the problem of the invalidation of thermal parameters andoptimal running, we present an efficient soft sensor approach based on sparse online Gaussianprocesses( GP), which is based on a combination of a Bayesian online algorithm together with asequential construction of a relevant subsample of the data to specify the prediction of the GPmodel. By an appealing parameterization and projection techniques that use the reproducing kernelHubert space (RKHS) norm, recursions for the effective parameters and a sparse Gaussianapproximation of the posterior process are obtained. The sparse representation of Gaussian processesmakes the GP-based soft sensor practical in a large dataset and real-time application. And theproposed thermalparameter soft sensor is of importance for the economical running of the powerplant.
文摘A new dynamic path planning method in high dimensional workspace, radial based probabilistic roadmap motion (RBPRM) planning method, is presented. Different from general probabilistic roadmap motion planning methods, it uses straight lines as long as possible to construct a path graph, so the final path obtained from the graph is relatively shorter and straighter. Experimental results show the efficiency of the algorithm in finding shorter paths in sparse environment.
