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A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model 被引量:1
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作者 Xiongzhuo Zhu Dali Gao +1 位作者 Chong Yang Chunjie Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2023年第5期151-161,共11页
The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring f... The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable. 展开更多
关键词 Chemical processes Principal component analysis gaussian mixture model Process monitoring ENSEMBLE Process control
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Robust Frequency Estimation Under Additive Symmetric α-Stable Gaussian Mixture Noise
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作者 Peng Wang Yulu Tian +1 位作者 Bolong Men Hailong Song 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期83-95,共13页
Here the estimating problem of a single sinusoidal signal in the additive symmetricα-stable Gaussian(ASαSG)noise is investigated.The ASαSG noise here is expressed as the additive of a Gaussian noise and a symmetric... Here the estimating problem of a single sinusoidal signal in the additive symmetricα-stable Gaussian(ASαSG)noise is investigated.The ASαSG noise here is expressed as the additive of a Gaussian noise and a symmetricα-stable distributed variable.As the probability density function(PDF)of the ASαSG is complicated,traditional estimators cannot provide optimum estimates.Based on the Metropolis-Hastings(M-H)sampling scheme,a robust frequency estimator is proposed for ASαSG noise.Moreover,to accelerate the convergence rate of the developed algorithm,a new criterion of reconstructing the proposal covar-iance is derived,whose main idea is updating the proposal variance using several previous samples drawn in each iteration.The approximation PDF of the ASαSG noise,which is referred to the weighted sum of a Voigt function and a Gaussian PDF,is also employed to reduce the computational complexity.The computer simulations show that the performance of our method is better than the maximum likelihood and the lp-norm estimators. 展开更多
关键词 Additive symmetricα-stable gaussian mixture metropolis-hastings algorithm robust frequency estimation probability density function approximation
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Soft Image Segmentation Based on the Mixture of Gaussians and the Phase-Transition Theory
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作者 Celia A. Z. Barcelos Yunmei Chen Fuhua Chen 《Applied Mathematics》 2014年第18期2888-2898,共11页
In this paper, we propose a new soft multi-phase segmentation model where it is assumed that the pixel intensities are distributed as a Gaussian mixture. The model is formulated as a minimization problem through the u... In this paper, we propose a new soft multi-phase segmentation model where it is assumed that the pixel intensities are distributed as a Gaussian mixture. The model is formulated as a minimization problem through the use of the maximum likelihood estimator and phase-transition theory. The mixture coefficients, which are estimated using a spatially varying mean and variance procedure, are used for image segmentation. The experimental results indicate the effectiveness of the method. 展开更多
关键词 Image SEGMENTATION VARIATIONAL Model gaussian mixturE
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Cascaded projection of Gaussian mixture model for emotion recognition in speech and ECG signals 被引量:1
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作者 黄程韦 吴迪 +5 位作者 张晓俊 肖仲喆 许宜申 季晶晶 陶智 赵力 《Journal of Southeast University(English Edition)》 EI CAS 2015年第3期320-326,共7页
A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are... A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios. 展开更多
关键词 gaussian mixture model emotion recognition sample adaptation emotion inducing
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Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management 被引量:1
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作者 孙璐 张惠民 +3 位作者 高荣 顾文钧 徐冰 陈鲤梁 《Journal of Southeast University(English Edition)》 EI CAS 2011年第2期174-179,共6页
Based on Gaussian mixture models(GMM), speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM ... Based on Gaussian mixture models(GMM), speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization (E-M) algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc. 展开更多
关键词 traffic flow patterns gaussian mixture model level of service data mining cluster analysis CLASSIFIER
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Adaptive moving target detection algorithm based on Gaussian mixture model 被引量:1
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作者 杨欣 刘加 +1 位作者 费树岷 周大可 《Journal of Southeast University(English Edition)》 EI CAS 2013年第4期379-383,共5页
In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions ... In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image's segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes. 展开更多
关键词 moving target detection gaussian mixture model background subtraction adaptive method
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EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION 被引量:3
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作者 蔡维玲 丁军娣 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第4期266-274,共9页
A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec-... A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model (GMM) and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results. 展开更多
关键词 pattern recognition image processing image segmentation gaussian mixture model (GMM) expectation maximization (EM)
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Gaussian mixture model clustering with completed likelihood minimum message length criterion 被引量:1
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作者 曾洪 卢伟 宋爱国 《Journal of Southeast University(English Edition)》 EI CAS 2013年第1期43-47,共5页
An improved Gaussian mixture model (GMM)- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the ... An improved Gaussian mixture model (GMM)- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization (EM) algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results. 展开更多
关键词 gaussian mixture model non-gaussian distribution model selection expectation-maximization algorithm completed likelihood minimum message length criterion
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Noise-improved information transmission in a nonlinear threshold array for Gaussian mixture noise 被引量:1
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作者 王友国 吴乐南 《Journal of Southeast University(English Edition)》 EI CAS 2006年第1期31-34,共4页
To discuss further the dependence of stochastic resonance on signals, nonlinear systems and noise, especially on noise, the binary input signal buried in Gaussian mixture noise through a nonlinear threshold array is s... To discuss further the dependence of stochastic resonance on signals, nonlinear systems and noise, especially on noise, the binary input signal buried in Gaussian mixture noise through a nonlinear threshold array is studied based on mutual information. It is obtained that Gaussian mixture noise can improve the information transmission through the array. Both stochastic resonance (SR) and suprathreshold stochastic resonance (SSR) can be observed in the single threshold system and in the threshold array. The parameters in noise distribution affect the occurrence of SR and SSR. The efficacy of information transmission can be significantly enhanced as the number of threshold devices in the array increases. These results show further the dependence of SR and SSR on the noise distribution, and also extend the applicability of SR and SSR in information transmission. 展开更多
关键词 stochastic resonance suprathreshold stochastic resonance mutual information gaussian mixture noise
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A Block-Adaptive Blind Separation Algorithm for Post-Nonlinear Mixture of Sub- and Super-Gaussian Signals
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作者 陈阳 杨绿溪 何振亚 《Journal of Southeast University(English Edition)》 EI CAS 2000年第2期1-9,共9页
The problem of blind separation of signals in post nonlinear mixture is addressed in this paper. The post nonlinear mixture is formed by a component wise nonlinear distortion after the linear mixture. Hence a nonlin... The problem of blind separation of signals in post nonlinear mixture is addressed in this paper. The post nonlinear mixture is formed by a component wise nonlinear distortion after the linear mixture. Hence a nonlinear adjusting part placed in front of the linear separation structure is needed to compensate for the distortion in separating such signals. The learning rules for the post nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for blind separation of post nonlinearly mixed sub and super Gaussian signals is proposed based on some previous work. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub and super Gaussian probability models during learning according to a stability condition and operates in a block adaptive manner. The effectiveness of the algorithm is verified by experiments on simulated and real world signals. 展开更多
关键词 blind separation neural networks nonlinear mixture sub and super gaussian
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An efficiency algorithm on Gaussian mixture UKF for BDS/INS navigation system 被引量:7
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作者 Qing Dai Lifen Sui +2 位作者 Lingxuan Wang Tian Zeng Yuan Tian 《Geodesy and Geodynamics》 2018年第2期169-174,共6页
To further improve the performance of UKF(Unscented Kalman Filter) algorithm used in BDS/SINS(BeiDou Navigation Satellite System/Strap down Inertial Navigation System), an improved GM-UKF(Gaussian Mixture Unscented Ka... To further improve the performance of UKF(Unscented Kalman Filter) algorithm used in BDS/SINS(BeiDou Navigation Satellite System/Strap down Inertial Navigation System), an improved GM-UKF(Gaussian Mixture Unscented Kalman Filter) considering non-Gaussian distribution is discussed in this paper. This new algorithm using SVD(Singular Value Decomposition) is proposed to alternative covariance square root calculation in UKF sigma point production. And to end the rapidly increasing number of Gaussian distributions, PDF(Probability Density Function) re-approximation is conducted. In principle this efficiency algorithm proposed here can achieve higher computational speed compared with traditional GM-UKF. And simulation experiment result show that, compared with UKF and GM-UKF algorithm, new algorithm implemented in BDS/SINS tightly integrated navigation system is suitable for handling nonlinear/non-Gaussian integrated navigation position calculation, for its lower computational complexity with high accuracy. 展开更多
关键词 gaussian mixture UKF Singular Value Decomposition Integrated navigation Time complexity
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Adaptive learning algorithm based on mixture Gaussian background 被引量:9
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作者 Zha Yufei Bi Duyan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第2期369-376,共8页
The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are... The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are inferred based on the maximum likelihood rule. Secondly, the forgetting factor and learning rate factor are redefined, and their still more general formulations are obtained by analyzing their practical functions. Lastly, the convergence of the proposed algorithm is proved to enable the estimation converge to a local maximum of the data likelihood function according to the stochastic approximation theory. The experiments show that the proposed learning algorithm excels the formers both in converging rate and accuracy. 展开更多
关键词 mixture gaussian model Background model Learning algorithm.
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Improved pruning algorithm for Gaussian mixture probability hypothesis density filter 被引量:7
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作者 NIE Yongfang ZHANG Tao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第2期229-235,共7页
With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved ... With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones. 展开更多
关键词 gaussian mixture probability hypothesis density(GM-PHD) filter pruning algorithm proximity targets clutter rate
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Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and Logistic Regression 被引量:3
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作者 LIU Wenbin ZHONG Xin +2 位作者 LEE Jay LIAO Linxia ZHOU Min 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第5期879-884,共6页
The currently prevalent machine performance degradation assessment techniques involve estimating a machine's current condition based upon the recognition of indications of failure features,which entail complete data ... The currently prevalent machine performance degradation assessment techniques involve estimating a machine's current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition(WPD) is used to extract features from raw signals,principal component analysis(PCA) is utilized to reduce feature dimensions,and Gaussian mixture model(GMM) is then applied to approximate the feature space distributions.Single-channel confidence value(SCV) is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value(MCV),which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression(LR) and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment. 展开更多
关键词 performance degradation assessment gaussian mixture model logistic regression proactive maintenance sensor fusion
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Color-texture segmentation using JSEG based on Gaussian mixture modeling 被引量:4
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作者 Wang Yuzhong Yang Jie Zhou Yue 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2006年第1期24-29,共6页
An improved approach for J-value segmentation (JSEG) is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift ... An improved approach for J-value segmentation (JSEG) is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift (AMS) based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling (GMM) of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust. 展开更多
关键词 color image segmentation JSEG adaptive mean shift based dustering gaussian mixture modeling soft J-value.
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RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation 被引量:9
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作者 Debiao Meng Shiyuan Yang +3 位作者 Tao Lin Jiapeng Wang Hengfei Yang Zhiyuan Lv 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第8期553-568,共16页
Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex en... Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system design.The Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in RBMDO.However,the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution,which significantly limits its application.In this study,the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above problem.It is integrated with the Collaborative Optimization(CO)method to solve RBMDO problems.Furthermore,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are proposed.Finally,an engineering example is given to show the application of the GMM-SOMVSA-CO method. 展开更多
关键词 Uncertain factors reliability-based multidisciplinary design optimization saddlepoint approximate gaussian mixture model collaborative optimization
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Research on Initialization on EM Algorithm Based on Gaussian Mixture Model 被引量:4
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作者 Ye Li Yiyan Chen 《Journal of Applied Mathematics and Physics》 2018年第1期11-17,共7页
The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effectiv... The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effective algorithm to estimate the finite mixture model parameters. However, EM algorithm can not guarantee to find the global optimal solution, and often easy to fall into local optimal solution, so it is sensitive to the determination of initial value to iteration. Traditional EM algorithm select the initial value at random, we propose an improved method of selection of initial value. First, we use the k-nearest-neighbor method to delete outliers. Second, use the k-means to initialize the EM algorithm. Compare this method with the original random initial value method, numerical experiments show that the parameter estimation effect of the initialization of the EM algorithm is significantly better than the effect of the original EM algorithm. 展开更多
关键词 EM ALGORITHM gaussian mixturE Model K-Nearest NEIGHBOR K-MEANS ALGORITHM INITIALIZATION
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Multimodal process monitoring based on transition-constrained Gaussian mixture model 被引量:4
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作者 Shutian Chen Qingchao Jiang Xuefeng Yan 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2020年第12期3070-3078,共9页
Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challengi... Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challenging.Most multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical application.This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring.This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data.This process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode overlap.Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap. 展开更多
关键词 Multimodal process monitoring gaussian mixture model State transition matrix Process control Process systems Systems engineering
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Automatic Delineation of Lung Parenchyma Based on Multilevel Thresholding and Gaussian Mixture Modelling 被引量:2
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作者 S.Gopalakrishnan A.Kandaswamy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2018年第2期141-152,共12页
Delineation of the lung parenchyma in the thoracic Computed Tomography(CT)is an important processing step for most of the pulmonary image analysis such as lung volume extraction,lung nodule detection and pulmonary ves... Delineation of the lung parenchyma in the thoracic Computed Tomography(CT)is an important processing step for most of the pulmonary image analysis such as lung volume extraction,lung nodule detection and pulmonary vessel segmentation.An automatic method for accurate delineation of lung parenchyma in thoracic Computed Tomography images is presented in this paper.The proposed method involves a segmentation phase followed by a lung boundary correction technique.The tissues in the thoracic Computed Tomography can be represented by a number of Gaussians.We propose a histogram utilized Adaptive Multilevel Thresholding(AMT)for estimating the total number of Gaussians and their initial parameters.The parameters of Gaussian components are updated by Expectation Maximization(EM)algorithm.The segmented lung parenchyma from the Gaussian Mixture model(GMM)undergoes an Adaptive Morphological Filtering(AMF)to reduce the boundary errors.The proposed method has been tested on 70 diseased and 119 normal lung images from 28 cases obtained from Lung Image Database Consortium(LIDC).The performance of the proposed system has been validated. 展开更多
关键词 Lung PARENCHYMA DELINEATION THORACIC COMPUTED tomography MULTILEVEL THRESHOLDING gaussian mixture model Adaptive Morphological Filtering
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Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes 被引量:9
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作者 Congli Mei Yong Su +2 位作者 Guohai Liu Yuhan Ding Zhiling Liao 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2017年第1期116-122,共7页
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
关键词 Dynamic modeling Process systems Instrumentation gaussian mixture regression Fermentation processes
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