A Stochastic Multivariate Validation Method for Dynamic Systems

2016 ◽  
Author(s):  
Jun Lu ◽  
Zhenfei Zhan ◽  
Pan Wang ◽  
Yudong Fang ◽  
Junqi Yang
Keyword(s):  
Dynamic Model ◽  
Model Validation ◽  
Dynamic Systems ◽  
Probability Model ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
New Approach ◽  
Processing Ability ◽  
Highly Correlated

As computer models become more powerful and popular, the complexity of input and output data raises new computational challenges. One of the key difficulties for model validation is to evaluate the quality of a computer model with multivariate, highly correlated and non-normal data, the direct application of traditional validation approaches does not appear to be suitable. This paper proposes a stochastic method to validate the dynamic systems. Firstly, a dimension reduction utilizing kernel principal component analysis (KPCA) is used to improve the computational efficiency. A probability model is then established by non-parametric kernel density estimation (KDE) method, and differences between the test data and simulation results are finally extracted to further comparative validation. This new approach resolves some critical drawbacks of the previous methods and improves the processing ability to nonlinear problem to validation the dynamic model. The proposed method and process are successfully illustrated through a real-world vehicle dynamic system example. The results demonstrate that the method of incorporate with KPCA and KDE is an effective approach to solve the dynamic model validation problem.

An Enhanced Bayesian Based Model Validation Method for Dynamic Systems

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Zhenfei Zhan ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Yinghong Peng
Keyword(s):  
Dynamic System ◽  
Hypothesis Testing ◽  
Model Validation ◽  
Dynamic Systems ◽  
Computational Models ◽  
Principal Component ◽  
Functional Responses ◽  
New Approach ◽  
Validation Method ◽  
Critical Issues

Validation of computational models with multiple correlated functional responses requires the consideration of multivariate data correlation, uncertainty quantification and propagation, and objective robust metrics. This paper presents an enhanced Bayesian based model validation method together with probabilistic principal component analysis (PPCA) to address these critical issues. The PPCA is employed to handle multivariate correlation and to reduce the dimension of the multivariate functional responses. The Bayesian interval hypothesis testing is used to quantitatively assess the quality of a multivariate dynamic system. The differences between the test data and computer-aided engineering (CAE) results are extracted for dimension reduction through PPCA, and then Bayesian interval hypothesis testing is performed on the reduced difference data to assess the model validity. In addition, physics-based threshold is defined and transformed to the PPCA space for Bayesian interval hypothesis testing. This new approach resolves some critical drawbacks of the previous methods and adds some desirable properties of a model validation metric for dynamic systems, such as symmetry. Several sets of analytical examples and a dynamic system with multiple functional responses are used to demonstrate this new approach.

Model Validation of Dynamic Engineering Models Under Uncertainty

2016 ◽  
Author(s):  
Zequn Wang ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Saeed Barbat ◽  
Wei Chen
Keyword(s):  
Dynamic Model ◽  
Model Validation ◽  
Dynamic Systems ◽  
Time Series Data ◽  
Integral Transform ◽  
Random Coefficients ◽  
Series Data ◽  
Input Space ◽  
Eigen Analysis ◽  
Engineering Models

Validating dynamic engineering models is critically important in practical applications by assessing the agreement between simulation results and experimental observations. Though significant progresses have been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments, which may stem from computer model instability, imperfection in material fabrication and manufacturing process, and variations in experimental conditions. In addition, it is challenging to validate a dynamic model aggregately over both the time domain and a model input space with data at multiple validation sites. To overcome these difficulties, this paper presents an area-based metric to systemically handle uncertainty and validate computational models for dynamic systems over an input space by simultaneously integrating the information from multiple validation sites. To manage the complexity associated with a high-dimensional data space, Eigen analysis is performed for the time series data from simulations at each validation site to extract the important features. A truncated Karhunen-Loève (KL) expansion is then constructed to represent the responses of dynamic systems, resulting in a set of uncorrelated random coefficients with unit variance. With the development of a hierarchical data fusion strategy, probability integral transform is then employed to pool all the resulting random coefficients from multiple validation sites across the input space into a single aggregated metric. The dynamic model is thus validated by calculating the cumulative area difference of the cumulative density functions. The proposed model validation metric for dynamic systems is illustrated with a mathematical example, a supported beam problem with stochastic loads, and real data from the vehicle occupant restraint system.

scholarly journals Research and Application of a Rolling Gap Prediction Model in Continuous Casting

Metals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 380 ◽  
Author(s):  
Zhufeng Lei ◽  
Wenbin Su
Keyword(s):  
Continuous Casting ◽  
Process Parameters ◽  
Principal Component ◽  
Casting Process ◽  
Support Vector ◽  
New Approach ◽  
Value Prediction ◽  
Svm Model ◽  
Roll Gap

Control of the roll gap of the caster segment is one of the key parameters for ensuring the quality of a slab in continuous casting. In order to improve the precision and timeliness of the roll gap value control, we proposed a rolling gap value prediction (RGVP) method based on the continuous casting process parameters. The process parameters collected from the continuous casting production site were first dimension-reduced using principal component analysis (PCA); 15 process parameters were chosen for reduction. Second, a support vector machine (SVM) model using particle swarm optimization (PSO) was proposed to optimize the parameters and perform roll gap prediction. The experimental results and practical application of the models has indicated that the method proposed in this paper provides a new approach for the prediction of roll gap value.

Forecasting tourism demand with KPCA-based web search indexes

2020 ◽  
pp. 135481661989857 ◽  
Author(s):  
Gang Xie ◽  
Xin Li ◽  
Yatong Qian ◽  
Shouyang Wang
Keyword(s):  
Principal Component Analysis ◽  
Empirical Study ◽  
Web Search ◽  
Demand Forecasting ◽  
Principal Component ◽  
Real Data ◽  
Kernel Principal Component Analysis ◽  
Tourism Demand ◽  
Search Query ◽  
Processing Ability

Search query data (SQD) can be helpful in predicting tourism demand by generating web search indexes. However, valuable nonlinear information in SQD may be neglected by researchers. To effectively capture the nonlinear information, we used kernel principal component analysis (KPCA) to extract web search indexes from SQD. Then, several models with KPCA-based web search indexes were developed for tourism demand forecasting. An empirical study was conducted with collected SQD and real data of tourist arrivals at Hong Kong. The results suggest that models with KPCA-based web search indexes are more accurate than other models because of the nonlinear data processing ability of the KPCA and demonstrate that KPCA-based web search indexes can be excellent predictors for tourism demand forecasting.

scholarly journals Quality Assessment of Different Species and Differently Prepared Slices of Zedoray Rhizome by High-Performance Liquid Chromatography and Colorimeter with the Aid of Chemometrics

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Fei Sun ◽  
Xiaolu Yang ◽  
Fei Liu ◽  
Ying Zhang ◽  
Shumei Wang ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
High Performance ◽  
Pearson Correlation ◽  
Principal Component ◽  
New Approach ◽  
Colour Parameters ◽  
Pearson Correlation Analysis ◽  
Chemometric Tools

In this study, high-performance liquid chromatography (HPLC) and colorimeter were applied to evaluate the quality of different species and differently prepared slices of Zedoray Rhizome samples with the aid of chemometric tools. Fifty batches of Zedoray Rhizome samples from different species and forty-two batches of Zedoray Rhizome samples from differently prepared slices were collected. The quantitative method was developed using HPLC to simultaneously determine the contents of twelve chemical ingredients in Zedoray Rhizome. The colour parameters L, a, and b were measured by a colorimeter. Then, the collected data were analyzed by the principal component analysis and Pearson correlation analysis. The results showed that the proposed method was capable of accurately determining the contents of the twelve chemical ingredients and the colour parameters for the collected samples. There was a dramatic difference in the contents of the chemical ingredients and in the colour parameters among different species and differently prepared slices of Zedoray Rhizome samples. This study reveals that combining HPLC, colorimeter, and chemometric tools can provide a new approach to comprehensively evaluate the quality of Zedoray Rhizome samples.

scholarly journals Hyperspectral Image Classification Based on Two-Stage Subspace Projection

2018 ◽  
Vol 10 (10) ◽  
pp. 1565 ◽  
Author(s):  
Xiaoyan Li ◽  
Lefei Zhang ◽  
Jane You
Keyword(s):  
Hyperspectral Image ◽  
Small Neighborhood ◽  
Principal Component ◽  
Feature Space ◽  
Kernel Principal Component Analysis ◽  
Subspace Projection ◽  
Two Stage ◽  
Discrimination Information ◽  
Highly Correlated ◽  
Low Dimensional

Hyperspectral image (HSI) classification is a widely used application to provide important information of land covers. Each pixel of an HSI has hundreds of spectral bands, which are often considered as features. However, some features are highly correlated and nonlinear. To address these problems, we propose a new discrimination analysis framework for HSI classification based on the Two-stage Subspace Projection (TwoSP) in this paper. First, the proposed framework projects the original feature data into a higher-dimensional feature subspace by exploiting the kernel principal component analysis (KPCA). Then, a novel discrimination-information based locality preserving projection (DLPP) method is applied to the preceding KPCA feature data. Finally, an optimal low-dimensional feature space is constructed for the subsequent HSI classification. The main contributions of the proposed TwoSP method are twofold: (1) the discrimination information is utilized to minimize the within-class distance in a small neighborhood, and (2) the subspace found by TwoSP separates the samples more than they would be if DLPP was directly applied to the original HSI data. Experimental results on two real-world HSI datasets demonstrate the effectiveness of the proposed TwoSP method in terms of classification accuracy.

Multivariate micropollutants analysis in marine waters

1995 ◽  
Vol 32 (9-10) ◽  
pp. 341-348
Author(s):  
V. Librando ◽  
G. Magazzù ◽  
A. Puglisi
Keyword(s):  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Environmental Chemistry ◽  
Sampling Site ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Micropollutants ◽  
Suspended Material ◽  
The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

scholarly journals Exploratory Monitoring of the Quality and Authenticity of Commercial Honey in Ecuador

Foods ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 105 ◽  
Author(s):  
Lorena Salvador ◽  
Michelle Guijarro ◽  
Daniela Rubio ◽  
Bolívar Aucatoma ◽  
Tanya Guillén ◽  
...  
Keyword(s):  
Honey Bees ◽  
Reducing Sugars ◽  
Conductivity Measurement ◽  
Principal Component ◽  
Economic Importance ◽  
Critical Problem ◽  
Spectroscopic Analyses ◽  
Chemical Threats ◽  
Raman And Ir Spectroscopy

Honey is one of the oldest sweetening foods and has economic importance, making this product attractive to adulteration with cheap sugars. This can cause a critical problem in the honey industry and a possible health risk. The present work has the aim of evaluating the authenticity of honey commercialized in two different provinces of Ecuador (Pichincha and Loja) by performing physicochemical and spectroscopic analyses. For this study 25 samples were collected from different places and markets and characterized by water, sucrose, reducing sugars and electric conductivity measurement. Also, their Raman and Infrared (IR) spectra were recorded and analysed using a Principal Component Analysis (PCA) in order to verify the quality of the honeys. In addition, a screening of several pesticides was performed in order to verify possible chemical threats to human health and honey bees. It was found that 8 samples have a deviation from the Standard established parameters. Two of them have a high difference in the content of sucrose and reducing sugars, which are located deviated from all the other samples in the PCA of the applied vibrational spectroscopy (IR/Raman), shaping two clear clusters. The results show that Raman and IR spectroscopy is appropriate techniques for the quality control of honey and correlates well with the physicochemical analyses.

scholarly journals Suitability Analysis of 17 Probiotic Type Strains of Lactic Acid Bacteria as Starter for Kimchi Fermentation

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1435
Author(s):  
Hee Seo ◽  
Jae-Han Bae ◽  
Gayun Kim ◽  
Seul-Ah Kim ◽  
Byung Hee Ryu ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Lactic Acid Bacteria ◽  
Growth Rates ◽  
Principal Component ◽  
Fermented Foods ◽  
Suitability Analysis ◽  
Moderate Growth ◽  
Health Promoting ◽  
Highly Correlated ◽  
Type Strains

The use of probiotic starters can improve the sensory and health-promoting properties of fermented foods. This study aimed to evaluate the suitability of probiotic lactic acid bacteria (LAB) as a starter for kimchi fermentation. Seventeen probiotic type strains were tested for their growth rates, volatile aroma compounds, metabolites, and sensory characteristics of kimchi, and their characteristics were compared to those of Leuconostoc (Le.) mesenteroides DRC 1506, a commercial kimchi starter. Among the tested strains, Limosilactobacillus fermentum, Limosilactobacillus reuteri, Lacticaseibacillus rhamnosus, Lacticaseibacillus paracasei, and Ligilactobacillus salivarius exhibited high or moderate growth rates in simulated kimchi juice (SKJ) at 37 °C and 15 °C. When these five strains were inoculated in kimchi and metabolite profiles were analyzed during fermentation using GC/MS and 1H-NMR, data from the principal component analysis (PCA) showed that L. fermentum and L. reuteri were highly correlated with Le. mesenteroides in concentrations of sugar, mannitol, lactate, acetate, and total volatile compounds. Sensory test results also indicated that these three strains showed similar sensory preferences. In conclusion, L. fermentum and L. reuteri can be considered potential candidates as probiotic starters or cocultures to develop health-promoting kimchi products.

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