• Journal of Korean Society for Atmospheric Environment
• /
• v.14 no.6
• /
• pp.631-642
• /
• 1998

Fall-out particles were collected by the modified British deposit gauges at 35 sampling sites in Suwon area from January to November, 1996. Twenty chemical species (Al. Ba, Cd, Cr, K, Pb, Sb, Zn, Cu, Fe, Ni, V, F-, Cl-, NO3-, 5042-, Na+, NH4+, Mg2+, and Ca2+) were analyzed by AAS and If. The purposes of this study were to estimate qualitatively various emission sources of the fell-out particle by applying multivariate statistical techniques such as factor analysis, multiple regression analysis, and discriminant analysis. During the study, outlier sites were determined by a z-score method. Cl-, Na+, Mg2+, and SO42- were highly correlated due to their common marine related source. Wind speed was the most influential factor for the deposition fluxes of the particle itself and all the chemical species as well. When applying the factor analysis, 8 source patterns were qualitatively obtained, such as marine source, soil source, oil burning source, Cr related source, tire source, Cd related source, agriculture source, and F- related source. As a result of the multiple regression analysis, we could suggest that some chemical compounds may possibly exist in the form of CaSO4, NaN03, NaCl, MgC12, (NH4)2SO4, NaF, and CaCl2 in the fall-out particles. Finally, spatial and seasonal classification study performed by a discriminant analysis showed th.at SO42-, Ca2+, Cl-, and Fe were dominant in the group of spatial pattern; however, SO42-, Cl-, Al, and V were in the group of seasonal pattern.

• Journal of the Korean Vacuum Society
• /
• v.15 no.4
• /
• pp.338-346
• /
• 2006

In semiconductor process, it is so hard to predict an exact failure point of the vacuum pump due to its harsh operation conditions and nonlinear properties, which may causes many problems, such as production of inferior goods or waste of unnecessary materials. Therefore it is very urgent and serious problem to develop diagnostic models which can monitor the operation conditions appropriately and recognize the failure point exactly, indicating when to replace the vacuum pump. In this study, many influencing factors are totally considered and eventually the monitoring model using multivariate statistical methods is suggested. The pivotal algorithms are Multiway Principal Component Analysis(MPCA), Dynamic Time Warping Algorithm(DTW Algorithm), etc.

• The Korean Journal of Applied Statistics
• /
• v.30 no.6
• /
• pp.889-899
• /
• 2017

Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.

• Journal of Korean Society of Forest Science
• /
• v.19 no.1
• /
• pp.49-54
• /
• 1973

Surveys of stock volume on steep and vast expanse of mountains, involves various difficulties. And it is extremely uneconomical in forest management point of view, to spend lots of time and man power for surveying such tree volume as the value is much cheaper in comparison with volume and weight. Therefore, a stand volume table estimate easely stock volume per hectare basis from aerial photographs was prepared and correlations to stand volume among factors affecting tree volumation, were studied. Data were 114 places selected from planted Korean white pine, Pinus koreiensis Sieb. et Zucc. stands in Kwangnung Experiment Forest and were computed and analysed by the means of the quantification in the multi-variate statistical analysis. Electronic Data Processing System was applied for data processing at Korean Instiute of Science and Technology. Coefficients of multiple correlations of stand volume table was ranged 0.85~0.88.

• Structural Engineering and Mechanics
• /
• v.28 no.2
• /
• pp.129-152
• /
• 2008

This paper considers the solution of the stochastic differential equations (SDEs) with random operator and/or random excitation using the spectral SFEM. The random system parameters (involved in the operator) and the random excitations are modeled as second order stochastic processes defined only by their means and covariance functions. All random fields dealt with in this paper are continuous and do not have known explicit forms dependent on the spatial dimension. This fact makes the usage of the finite element (FE) analysis be difficult. Relying on the spectral properties of the covariance function, the Karhunen-Loeve expansion is used to represent these processes to overcome this difficulty. Then, a spectral approximation for the stochastic response (solution) of the SDE is obtained based on the implementation of the concept of generalized inverse defined by the Neumann expansion. This leads to an explicit expression for the solution process as a multivariate polynomial functional of a set of uncorrelated random variables that enables us to compute the statistical moments of the solution vector. To check the validity of this method, two applications are introduced which are, randomly loaded simply supported reinforced concrete beam and reinforced concrete cantilever beam with random bending rigidity. Finally, a more general application, randomly loaded simply supported reinforced concrete beam with random bending rigidity, is presented to illustrate the method.

• Journal of Ginseng Research
• /
• v.44 no.6
• /
• pp.775-783
• /
• 2020

Background: The reports about valuable oligosaccharides in ginseng are quite limited. There is an urgent need to develop a practical procedure to detect and analyze ginseng oligosaccharides. Methods: The oligosaccharide extracts from ginseng were permethylated by solid-phase methylation method and then were analyzed by ultra-high-performance liquid chromatography-Q-Orbitrap/MS. The sequence, linkage, and configuration information of oligosaccharides were determined by using accurate m/z value and tandem mass information. Several standard references were used to further confirm the identification. The oligosaccharide composition in white ginseng and red ginseng was compared using a multivariate statistical analysis method. Results: The nonreducing oligosaccharide erlose among 12 oligosaccharides identified was reported for the first time in ginseng. In the comparison of the oligosaccharide extracts from white ginseng and red ginseng, a clear separation was observed in the partial least squares-discriminate analysis score plot, indicating the sugar differences in these two kinds of ginseng samples. The glycans with variable importance in the projection value large than 1.0 were considered to contribute most to the classification. The contents of oligosaccharides in red ginseng were lower than those in white ginseng, and the contents of maltose, maltotriose, maltotetraose, maltopentaose, maltohexaose, maltoheptaose, maltooctaose, maltononaose, sucrose, and erlose decreased significantly (p < 0.05) in red ginseng. Conclusion: A solid-phase methylation method combined with liquid chromatography-tandem mass spectrometry was successfully applied to analyze the oligosaccharides in ginseng extracts, which provides the possibility for holistic evaluation of ginseng oligosaccharides. The comparison of oligosaccharide composition of white ginseng and red ginseng could help understand the differences in pharmacological activities between these two kinds of ginseng samples from the perspective of glycans.

• The Korean Journal of Applied Statistics
• /
• v.37 no.3
• /
• pp.283-295
• /
• 2024

Many studies have been conducted to properly handle nonresponse. Recently, many nonresponse imputation methods have been developed and practically used. Most imputation methods assume MCAR (missing completely at random) or MAR (missing at random). On the contrary, there are relatively few studies on imputation under the assumption of MNAR (missing not at random) or NN (nonignorable nonresponse) that are affected by the study variable. The MNAR causes Bias and reduces the accuracy of imputation whenever response probability is not properly estimated. Lee and Shin (2022) proposed a nonresponse imputation method that can be applied to nonignorable nonresponse assuming homoscedasticity in super-population model. In this paper we propose an generalized version of the imputation method proposed by Lee and Shin (2022) to improve the accuracy of estimation by removing the Bias caused by MNAR under heteroscedasticity. In addition, the superiority of the proposed method is confirmed through simulation studies.

• Korean Chemical Engineering Research
• /
• v.46 no.2
• /
• pp.233-247
• /
• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• Journal of Broadcast Engineering
• /
• v.25 no.2
• /
• pp.208-217
• /
• 2020

The classification of plant diseases by images captured by a camera sensor has been studied over past decades. A method that has gained much interest is to use image segmentation, from which statistical features are derived and analyzed by machine learning. Recently, deep learning has been adopted in this area. However, image segmentation is still a difficult task to achieve stable performance due to a variety of environmental variations. The end-to-end learning in neural network has a demerit that train images may be different from real images acquired in outdoor fields. To solve these problems, we propose superpixel-based disease classification method using end-to-end CNN (convolutional neural network) learning. Based on experiments performed on PlantVillage apple images, the classification accuracy is 98.29% and 92.43% for full-image and superpixel. As well, the multivariate F1-score is (0.98, 0.93). Therefore we validate that the method of using superpixel is comparable to that of full-image.

• The Korean Journal of Applied Statistics
• /
• v.22 no.4
• /
• pp.745-758
• /
• 2009

Cluster analysis is the automated search for groups of related observations in a data set. To group the observations into clusters many techniques has been proposed, and a variety measures aimed at validating the results of a cluster analysis have been suggested. In this paper, we compare complete linkage, Ward's method, K-means and model-based clustering and compute validity measures such as connectivity, Dunn Index and silhouette with simulated data from multivariate distributions. We also select a clustering algorithm and determine the number of clusters of Korean consumers based on Korean consumers' palatability scores for Hanwoo bull in BBQ cooking method.