In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaus...In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. This step replaces the resampling stage needed by most particle filters and relieves the effect caused by sample impoverishment. A nonlinear tracking problem shows that this new approach outperforms other related particle filters.展开更多
The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It au...The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.展开更多
The potency of Al3Zr and Al3Nb as grain refiners for Al alloys was investigated from a crystallographic point of view using the edge-to-edge matching （E2EM） model. The results show that both Al3Zr and Al3Nb have sma...The potency of Al3Zr and Al3Nb as grain refiners for Al alloys was investigated from a crystallographic point of view using the edge-to-edge matching （E2EM） model. The results show that both Al3Zr and Al3Nb have small values of interatomic spacing misfit and interplanar spacing mismatch with respect to Al. Furthermore, energetically favourable orientation relationships predicted by the model exist between Al and each of these two intermetallic phases. In the light of the edge-to-edge matching model predictions, it is suggested that both Al3Zr and Al3Nb are potent heterogeneous nucleation refiners for Al grains from the crystallographic point of view. The present crystallographic analysis provides a more reasonable explanation for the significant grain refinement obtained in the peritectic Al-Zr and Al-Nb alloys and also provides fresh insight into the understanding of the grain refinement mechanism of Al alloys.展开更多
An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and fore...An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate(the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.展开更多
Among the different available wind sources, i.e. in situ measurements, numeric weather models, the retrieval of wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can ...Among the different available wind sources, i.e. in situ measurements, numeric weather models, the retrieval of wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give high wind resolution cells. For this purpose, one can find two principal approaches: via electromagnetic (EM) models and empirical (EP) models. In both approaches, the Geophysical Model Functions (GMFs) are used to describe the relation of radar scattering, wind speed, and the geometry of observations. By knowing radar scattering and geometric parameters, it is possible to invert the GMFs to retrieve wind speed. It is very interesting to compare wind speed estimated by the EM models, general descriptions of radar scattering from sea surface, to the one estimated by the EP models, specific descriptions for the inverse problem. Based on the comparisons, some ideas are proposed to improve the performance of the EM models for wind speed retrieval.展开更多
We develop a new formulation of the integral equation(IE)method for three-dimensional(3D)electromagnetic(EM)field computation in large-scale models with multiple inhomogeneous domains.This problem arises in many pract...We develop a new formulation of the integral equation(IE)method for three-dimensional(3D)electromagnetic(EM)field computation in large-scale models with multiple inhomogeneous domains.This problem arises in many practical applications including modeling the EM fields within the complex geoelectrical structures in geophysical exploration.In geophysical applications,it is difficult to describe an earth structure using the horizontally layered background conductivity model,which is required for the efficient implementation of the conventional IE approach.As a result,a large domain of interest with anomalous conductivity distribution needs to be discretized,which complicates the computations.The new method allows us to consider multiple inhomogeneous domains,where the conductivity distribution is different from that of the background,and to use independent discretizations for different domains.This reduces dramatically the computational resources required for largescale modeling.In addition,using this method,we can analyze the response of each domain separately without an inappropriate use of the superposition principle for the EM field calculations.The method was carefully tested for the modeling the marine controlled-source electromagnetic(MCSEM)fields for complex geoelectric structures with multiple inhomogeneous domains,such as a seafloor with the rough bathymetry,salt domes,and reservoirs.We have also used this technique to investigate the return induction effects from regional geoelectrical structures,e.g.,seafloor bathymetry and salt domes,which can distort the EM response from the geophysical exploration target.展开更多
A new dry deposition velocity pattern (NDDVP) for the study of region-scale dry deposition processes is developed. The mean ratio between NDDVP and 1022 experimental data of dry deposi- tion velocity V_d is 1. 06±...A new dry deposition velocity pattern (NDDVP) for the study of region-scale dry deposition processes is developed. The mean ratio between NDDVP and 1022 experimental data of dry deposi- tion velocity V_d is 1. 06±0.82. The result shows that NDDVP is well consistent with experimental data. Practical cases are forecasted by the high resolution regional acid deposition model (EM3) with both NDDVP and old V_d pattern. The maximum ratio between the central concentrations for SO4 can reach 2.4 only due to different V_d patterns. 3-D distributions of species concentrations and dry depositions forecasted by NDDVP are better than those by the old V_d pattern.展开更多
In injection moulding production,the tuning of the process parameters is a challenging job,which relies heavily on the experience of skilled operators.In this paper,taking into consideration operator assessment during...In injection moulding production,the tuning of the process parameters is a challenging job,which relies heavily on the experience of skilled operators.In this paper,taking into consideration operator assessment during moulding trials,a novel intelligent model for automated tuning of process parameters is proposed.This consists of case based reasoning (CBR),empirical model (EM),and fuzzy logic (FL) methods.CBR and EM are used to imitate recall and intuitive thoughts of skilled operators,respectively,while FL is adopted to simulate the skilled operator optimization thoughts.First,CBR is used to set up the initial process parameters.If CBR fails,EM is employed to calculate the initial parameters.Next,a moulding trial is performed using the initial parameters.Then FL is adopted to optimize these parameters and correct defects repeatedly until the moulded part is found to be satisfactory.Based on the above methodologies,intelligent software was developed and embedded in the controller of an injection moulding machine.Experimental results show that the intelligent software can be effectively used in practical production,and it greatly reduces the dependence on the experience of the operators.展开更多
基金Sponsored by the National Security Major Basic Research Project of China(Grant No.973 -61334)
文摘In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. This step replaces the resampling stage needed by most particle filters and relieves the effect caused by sample impoverishment. A nonlinear tracking problem shows that this new approach outperforms other related particle filters.
基金Supported by the National Natural Science Foundation of China under Grant No 60972106the China Postdoctoral Science Foundation under Grant No 2014M561053+1 种基金the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108the Hebei Province Natural Science Foundation under Grant No E2016202341
文摘The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.
基金the Australian Research Council for funding supportthe support of China Scholarship Council
文摘The potency of Al3Zr and Al3Nb as grain refiners for Al alloys was investigated from a crystallographic point of view using the edge-to-edge matching （E2EM） model. The results show that both Al3Zr and Al3Nb have small values of interatomic spacing misfit and interplanar spacing mismatch with respect to Al. Furthermore, energetically favourable orientation relationships predicted by the model exist between Al and each of these two intermetallic phases. In the light of the edge-to-edge matching model predictions, it is suggested that both Al3Zr and Al3Nb are potent heterogeneous nucleation refiners for Al grains from the crystallographic point of view. The present crystallographic analysis provides a more reasonable explanation for the significant grain refinement obtained in the peritectic Al-Zr and Al-Nb alloys and also provides fresh insight into the understanding of the grain refinement mechanism of Al alloys.
基金Project(50805023)supported by the National Natural Science Foundation of ChinaProject(BA2010093)supported by the Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements,ChinaProject(2008144)supported by the Hexa-type Elites Peak Program of Jiangsu Province,China
文摘An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate(the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.
文摘Among the different available wind sources, i.e. in situ measurements, numeric weather models, the retrieval of wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give high wind resolution cells. For this purpose, one can find two principal approaches: via electromagnetic (EM) models and empirical (EP) models. In both approaches, the Geophysical Model Functions (GMFs) are used to describe the relation of radar scattering, wind speed, and the geometry of observations. By knowing radar scattering and geometric parameters, it is possible to invert the GMFs to retrieve wind speed. It is very interesting to compare wind speed estimated by the EM models, general descriptions of radar scattering from sea surface, to the one estimated by the EP models, specific descriptions for the inverse problem. Based on the comparisons, some ideas are proposed to improve the performance of the EM models for wind speed retrieval.
文摘We develop a new formulation of the integral equation(IE)method for three-dimensional(3D)electromagnetic(EM)field computation in large-scale models with multiple inhomogeneous domains.This problem arises in many practical applications including modeling the EM fields within the complex geoelectrical structures in geophysical exploration.In geophysical applications,it is difficult to describe an earth structure using the horizontally layered background conductivity model,which is required for the efficient implementation of the conventional IE approach.As a result,a large domain of interest with anomalous conductivity distribution needs to be discretized,which complicates the computations.The new method allows us to consider multiple inhomogeneous domains,where the conductivity distribution is different from that of the background,and to use independent discretizations for different domains.This reduces dramatically the computational resources required for largescale modeling.In addition,using this method,we can analyze the response of each domain separately without an inappropriate use of the superposition principle for the EM field calculations.The method was carefully tested for the modeling the marine controlled-source electromagnetic(MCSEM)fields for complex geoelectric structures with multiple inhomogeneous domains,such as a seafloor with the rough bathymetry,salt domes,and reservoirs.We have also used this technique to investigate the return induction effects from regional geoelectrical structures,e.g.,seafloor bathymetry and salt domes,which can distort the EM response from the geophysical exploration target.
基金The study is supported by the National Natural Science Foundation of China,LASG and LAPC in IAP, CAS
文摘A new dry deposition velocity pattern (NDDVP) for the study of region-scale dry deposition processes is developed. The mean ratio between NDDVP and 1022 experimental data of dry deposi- tion velocity V_d is 1. 06±0.82. The result shows that NDDVP is well consistent with experimental data. Practical cases are forecasted by the high resolution regional acid deposition model (EM3) with both NDDVP and old V_d pattern. The maximum ratio between the central concentrations for SO4 can reach 2.4 only due to different V_d patterns. 3-D distributions of species concentrations and dry depositions forecasted by NDDVP are better than those by the old V_d pattern.
基金Project supported by the National Natural Science Foundation of China (Nos.50905162 and 51005151)the Open Foundation of State Key Laboratory of Material Processing and Die & Mould Technology (No. 2010-P01),China
文摘In injection moulding production,the tuning of the process parameters is a challenging job,which relies heavily on the experience of skilled operators.In this paper,taking into consideration operator assessment during moulding trials,a novel intelligent model for automated tuning of process parameters is proposed.This consists of case based reasoning (CBR),empirical model (EM),and fuzzy logic (FL) methods.CBR and EM are used to imitate recall and intuitive thoughts of skilled operators,respectively,while FL is adopted to simulate the skilled operator optimization thoughts.First,CBR is used to set up the initial process parameters.If CBR fails,EM is employed to calculate the initial parameters.Next,a moulding trial is performed using the initial parameters.Then FL is adopted to optimize these parameters and correct defects repeatedly until the moulded part is found to be satisfactory.Based on the above methodologies,intelligent software was developed and embedded in the controller of an injection moulding machine.Experimental results show that the intelligent software can be effectively used in practical production,and it greatly reduces the dependence on the experience of the operators.
