Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard ...Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.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
Design and operation optimization of centrifugal compressor are always based on an accurate prediction model, however, due to the short time operation and lack of data information, it is difficult to get an accurate p...Design and operation optimization of centrifugal compressor are always based on an accurate prediction model, however, due to the short time operation and lack of data information, it is difficult to get an accurate prediction model of a new centrifugal compressor in time. This paper applies an improved fast model migration method(FMM method) to develop the model of the new centrifugal compressor. The method adapts a Gaussian Process(GP) model from an old centrifugal compressor to fit a new and similar centrifugal compressor, and the adaptation is conducted by a scale-bias adjustment migration technology. In order to obtain the better estimated parameters of migration, Bayesian method, which takes the prior knowledge into consideration, is used in the sequential experiment. The approach is validated by a specific simulation bench. The results indicate that the applied approach can achieve a better prediction precision with fewer data of the new centrifugal compressor compared to pure GP method, and can model the new centrifugal compressor rapidly.展开更多
A single-channel electroencephalography(EEG)device,despite being widely accepted due to convenience,ease of deployment and suitability for use in complex environments,typically poses a great challenge for reactive bra...A single-channel electroencephalography(EEG)device,despite being widely accepted due to convenience,ease of deployment and suitability for use in complex environments,typically poses a great challenge for reactive brain-computer interface(BCI)applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles.In this study,a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal.The proposed decoder is constructed based on Gaussian Process model(GPM)which is a nonparametric Bayesian approach that has the advantages of being able to operate on small datasets and providing measurements of uncertainty on predictions.To evaluate the effectiveness of the proposed method,the GPM is compared against other competitive techniques which include k-Nearest Neighbors,linear discriminant analysis,support vector machine,ensemble learning and neural network.Results demonstrate that a significant improvement can be achieved via the GPM approach with average accuracy reaching over 96%and mean absolute error of no greater than 0.8 cm/s.In addition,the analysis reveals that while the performances of other existing methods deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks,the proposed GPM exhibits consistent performance across all stimuli considered,thereby manifesting its generalization capability and making it a more suitable option for dynamic commands with a single-channel EEG-controlled actuator.展开更多
The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation proce...The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.展开更多
Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters fo...Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.展开更多
Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increase...Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.展开更多
Based on the accurate analysis of cucumber disease images, the low level feature of images was effectively extracted, and Gaussian Mixture Model （GMM） for 8 common cucumber diseases was built. The parameters of GMM ...Based on the accurate analysis of cucumber disease images, the low level feature of images was effectively extracted, and Gaussian Mixture Model （GMM） for 8 common cucumber diseases was built. The parameters of GMM were estimated by the algorithm of expectation maximum （EM） to accurately charac- terize the feature distribution of 8 cucumber diseases, thus increased the correct identification of cucumber diseases and accurate grasping of damage conditions, and provided basis for achievement of real-time and accurate prediction of cucumber diseases.展开更多
Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussi...Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.展开更多
The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly pr...The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly predict due to the complicated relationships between the chemical composition and process(like quenching temperature(Qr)).A Gaussian process regression model in machine learning was developed to predict V_(RA),and the model accuracy was further improved by introducing a metallurgical parameter of martensite fraction(fo)to accurately predict V_(RA) in Q&P steels.The developed machine learning model combined with Bayesian global optimization can serve as another selection strategy for the quenching temperature,and this strategy is very effcient as it found the"optimum"Qr with the maximum V_(RA) using only seven consecutive iterations.The benchmark experiment also reveals that the developed machine learning model predicts V_(RA) more accurately than the popular constrained carbon equilibrium thermodynamic model,even better than a thermo-kinetic quenching-partitioning-tempering-local equilibrium model.展开更多
We obtain the H?lder continuity and joint H?lder continuity in space and time for the random field solution to the parabolic Anderson equation ■ in d-dimensional space, where ■ is a mean zero Gaussian noise with tem...We obtain the H?lder continuity and joint H?lder continuity in space and time for the random field solution to the parabolic Anderson equation ■ in d-dimensional space, where ■ is a mean zero Gaussian noise with temporal covariance γ0 and spatial covariance given by a spectral density μ(ξ). We assume that ■ and ■ , where αi, i = 1, · · ·, d(or α) can take negative value.展开更多
Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by link...Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.展开更多
Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the...Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.展开更多
基金Supported by the National High Technology Research and Development Program of China(2014AA041803)the National Natural Science Foundation of China(61320106009)
文摘Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.
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supported by the Fundamental Research Funds for the Central Universities(Grant No.2015QNA65)
文摘Design and operation optimization of centrifugal compressor are always based on an accurate prediction model, however, due to the short time operation and lack of data information, it is difficult to get an accurate prediction model of a new centrifugal compressor in time. This paper applies an improved fast model migration method(FMM method) to develop the model of the new centrifugal compressor. The method adapts a Gaussian Process(GP) model from an old centrifugal compressor to fit a new and similar centrifugal compressor, and the adaptation is conducted by a scale-bias adjustment migration technology. In order to obtain the better estimated parameters of migration, Bayesian method, which takes the prior knowledge into consideration, is used in the sequential experiment. The approach is validated by a specific simulation bench. The results indicate that the applied approach can achieve a better prediction precision with fewer data of the new centrifugal compressor compared to pure GP method, and can model the new centrifugal compressor rapidly.
基金This work was supported by the Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code:FRGS/1/2021/TK0/USM/02/18.
文摘A single-channel electroencephalography(EEG)device,despite being widely accepted due to convenience,ease of deployment and suitability for use in complex environments,typically poses a great challenge for reactive brain-computer interface(BCI)applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles.In this study,a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal.The proposed decoder is constructed based on Gaussian Process model(GPM)which is a nonparametric Bayesian approach that has the advantages of being able to operate on small datasets and providing measurements of uncertainty on predictions.To evaluate the effectiveness of the proposed method,the GPM is compared against other competitive techniques which include k-Nearest Neighbors,linear discriminant analysis,support vector machine,ensemble learning and neural network.Results demonstrate that a significant improvement can be achieved via the GPM approach with average accuracy reaching over 96%and mean absolute error of no greater than 0.8 cm/s.In addition,the analysis reveals that while the performances of other existing methods deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks,the proposed GPM exhibits consistent performance across all stimuli considered,thereby manifesting its generalization capability and making it a more suitable option for dynamic commands with a single-channel EEG-controlled actuator.
基金Supported by the Natural Science Foundation of Jiangsu Province of China(BK20130531)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD[2011]6)Jiangsu Government Scholarship
文摘The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.
基金Supported by the National High Technology Research and Development Program of China (863 Program,No.2006AA010102)
文摘Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.
基金supported by the National Natural Science Foundation of China(No.61627810)。
文摘Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.
基金Supported by National Natural Science Foundation of China ( 60903066,0985244)Natural Science Foundation of Beijing City ( 4102049)+1 种基金 New Teacher Fund of Ministry of Education ( 20090009120006) Basic Scientific Research Expenses of Central College ( 20100008030)~~
文摘Based on the accurate analysis of cucumber disease images, the low level feature of images was effectively extracted, and Gaussian Mixture Model （GMM） for 8 common cucumber diseases was built. The parameters of GMM were estimated by the algorithm of expectation maximum （EM） to accurately charac- terize the feature distribution of 8 cucumber diseases, thus increased the correct identification of cucumber diseases and accurate grasping of damage conditions, and provided basis for achievement of real-time and accurate prediction of cucumber diseases.
基金Supported by the National Natural Science Foundation of China(61273167)
文摘Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.
基金The authors acknowledge financial support from the National Natural Science Foundation of China(Grant Nos.51771114 and 51371117).
文摘The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly predict due to the complicated relationships between the chemical composition and process(like quenching temperature(Qr)).A Gaussian process regression model in machine learning was developed to predict V_(RA),and the model accuracy was further improved by introducing a metallurgical parameter of martensite fraction(fo)to accurately predict V_(RA) in Q&P steels.The developed machine learning model combined with Bayesian global optimization can serve as another selection strategy for the quenching temperature,and this strategy is very effcient as it found the"optimum"Qr with the maximum V_(RA) using only seven consecutive iterations.The benchmark experiment also reveals that the developed machine learning model predicts V_(RA) more accurately than the popular constrained carbon equilibrium thermodynamic model,even better than a thermo-kinetic quenching-partitioning-tempering-local equilibrium model.
基金supported by an NSERC granta startup fund of University of Albertasupported by Martin Hairer’s Leverhulme Trust leadership award
文摘We obtain the H?lder continuity and joint H?lder continuity in space and time for the random field solution to the parabolic Anderson equation ■ in d-dimensional space, where ■ is a mean zero Gaussian noise with temporal covariance γ0 and spatial covariance given by a spectral density μ(ξ). We assume that ■ and ■ , where αi, i = 1, · · ·, d(or α) can take negative value.
文摘Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.
基金National Natural Science Foundation of China(No.61374140)the Youth Foundation of National Natural Science Foundation of China(No.61403072)
文摘Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.
