• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.563-582
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• 2021

This paper deals with developing a general class of accelerated sequential procedures and obtaining the associated second-order approximations for the expected sample size and 'regret' (difference between the risks of the proposed accelerated sequential procedure and the optimum fixed sample size procedure) function. We establish that the estimation problems based on various lifetime distributions can be tackled with the help of the proposed class of accelerated sequential procedures. Extensive simulation analysis is presented in support of the accuracy of our proposed methodology using the Pareto distribution and a real data set on carbon fibers is also analyzed to demonstrate the practical utility. We also provide the brief details of some other inferential problems which can be seen as the applications of the proposed class of accelerated sequential procedures.

• Journal of Biosystems Engineering
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• v.40 no.2
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• pp.124-136
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• 2015

Purpose: Grain quality is a general concept that covers many characteristics, ranging from physical to biochemical and physiochemical properties. Rice aging during storage is currently a challenge in the rice industry, and is a complicated process involving changes in all of the above properties. Spectroscopic techniques can be used to obtain information on the quality of rice samples in a non-destructive manner. Methods: The objective of this review was to highlight the factors that contribute to rice quality and aging, and to describe various spectroscopic modalities, particularly vibrational and hyperspectral imaging, for the assessment of rice quality. Results: Starch and protein are the main components of the rice endosperm, and are therefore key factors contributing to eating and cooking quality. While the overall starch, protein, and lipid content in the rice grain remains essentially unchanged during storage, structural changes do occur. These changes affect pasting and gel properties, and ultimately the flavor of cooked rice. In addition, grain quality is significantly affected by growing and environmental conditions, such as water availability, temperature, fertilizer application, and salinity stress. These properties can be evaluated using spectroscopic techniques, and rice samples can be discriminated by using multivariate statistical analysis methods. Conclusion: Hyperspectral imaging and vibrational spectroscopy techniques have good potential for determining rice quality properties in a non-invasive manner, i.e., not requiring the introduction of instruments into the rice grain.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.517-528
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• 2018

We provide R scripts to detect outliers in multivariate data and visualization. Detecting outliers is provided using three approaches 1) Robust Mahalanobis distance, 2) High Dimensional data, 3) density-based approach methods. We use the following techniques to visualize detected potential outliers 1) multidimensional scaling (MDS) and minimal spanning tree (MST) with k-means clustering, 2) MDS with fviz cluster, 3) principal component analysis (PCA) with fviz cluster. For real data sets, we use MLB pitching data including Ryu, Hyun-jin in 2013 and 2014. The developed R scripts can be downloaded at "http://www.knou.ac.kr/~sskim/ddpoutlier.html" (R scripts and also R package can be downloaded here).

• Economic and Environmental Geology
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• v.36 no.3
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• pp.201-212
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• 2003

Recently, the ability of earthquake detection in the Kyungsang Basin of southeastern Korean Peninsula is greatly improved since seismic stations including seismic network of KIGAM(Korea Institute of Geoscience and Mineral Resources) have been significantly increased. However, a large number of signals from explosions are recorded because of frequent medium to large chemical explosions. The discrimination between natural earthquakes and explosions in the Basin has become an important issue. High frequency local records from 43 earthquakes and 43 explosions with comparable magnitude are selected to establish a reliable discrimination technique in the Basin. Several discrimination techniques in spectral domain using spectral amplitude ratios among Pg, Sg, and Lg waves are widely examined with tile selected data. Among them the Pg/Lg spectral ratio method is appeared to be a good discrimination technique to improve the discrimination power. Multivariate discriminant analysis is also applied to the Pg/Lg spectral ratios. The discrimination power of the Pg/Lg ratios for distance corrected three component record compared to uncorrected vertical component one shows distinct improvement. In the frequency band 4 to 14 Hz, Pg/Lg spectral ratio for distance corrected three component record provides discrimination power with a total misclassification probability of only 0.89%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.40-45
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• 2018

Intelligent monitoring and diagnosis of production processes based on multivariate statistical methods has been one of important tasks for safety and quality issues. This is due to the fact that faults and unexpected events may have serious impacts on the operation of processes. This study proposes a diagnostic scheme based on effective representation of process measurement data and is evaluated using simulation process data. The effects of utilizing a preprocessing step and nonlinear statistical methods are also tested using fifteen faults of the simulation process. Results show that the proposed scheme produced more reliable results and outperformed other tested schemes with none of the filtering step and nonlinear methods. The proposed scheme is expected to be robust to process noises and easy to develop due to the lack of required rigorous mathematical process models or expert knowledge.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2203-2207
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• 2015

Background: Breast conservative surgery (BCS) followed by radiotherapy is the standard approach in management of stage I-II breast cancer. Several factors can affect cosmetic outcomes. The aim of this study was to evaluate the cosmetic results of BCS and influencing factors in the Iranian Breast Cancer Research Center. Materials and Methods: Patients who had undergone BCS were included. Photographs were taken of both breasts of the patients in three aspects and were evaluated by three specialists. The cosmetic scores were calculated based on a standard questionnaire. The data were analyzed using univariate and multivariate regression for relationships between cosmetic scores and clinical data. Results: A total number of 103 patients were included in the study. Mean age and BMI of the patients were $46.8{\pm}8.9$ and $28.1{\pm}3.9$, respectively. Breast cup sizes C and D accounted for 74.7% of the study group. The mean cosmetic score obtained from three referees was 5.72+2.06, consisting of 35.9% excellent-good, 35% moderate, and 29.1% unsatisfactory results. Patient BMI, volume of the resected tissue and breast cup size (D) showed significant correlation with the cosmetic score. On multivariate regression analysis, cosmetic score and BMI (p=0.022,) as well as breast cup size (p=0.040), remained significant. Conclusions: Immediate or delayed symmetrization of the breasts is suggested during breast conservative surgery, meanwhile performing oncoplastic techniques to improve the results significantly. Also it is suggested to discuss anticipation of less satisfactory results with patients having higher BMI and large breast cup size.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.667-675
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• 2018

When a financial time series consists of daily (closing) returns, traditional volatility models such as autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) are useful to figure out daily volatilities. With high frequency returns in a day, one may adopt various multivariate GARCH techniques (MGARCH) (Tsay, Multivariate Time Series Analysis With R and Financial Application, John Wiley, 2014) to obtain intraday volatilities as long as the high frequency is moderate. When it comes to the ultra high frequency (UHF) case (e.g., one minute prices are available everyday), a new model needs to be developed to suit UHF time series in order to figure out continuous time intraday-volatilities. Aue et al. (Journal of Time Series Analysis, 38, 3-21; 2017) proposed functional GARCH (fGARCH) to analyze functional volatilities based on UHF data. This article introduces fGARCH to the readers and illustrates how to estimate fGARCH equations using UHF data of KOSPI and Hyundai motor company.

• Economic and Environmental Geology
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• v.53 no.4
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• pp.363-381
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• 2020

The polymetallic Co, Ni, Cu, As, Au, and Ag deposits of Bou Azzer East are located in the western part of the Bou Azzer inlier in the Central Anti Atlas, Morocco. Six stages of emplacement of the mineralization have been identified. Precious metals (native gold and electrum) are present in all stages of this deposit except the early nickeliferous stage. From the Statistical analysis of the Co, As, Ni, Au, and Ag contents of a set of 501 samples, shows that the Pearson correlation coefficient between As-Co elements (0.966) is the highest followed by that of the Au-Ag couple (0.506). Principal component analysis (PCA) and hierarchical ascending classification (HAC) of the grades show, that Ni is associated with the pair (As-Co) and Cu is rather related to the pair (Au-Ag). The kriging maps show that the highest values of the Co, As and Ni appear in the contact of the serpentinite with other facies, as for those of Au and Ag, in addition to anomalous zones concordant with those of Co, Ni and As, they show anomalies at the extreme South and North of the study area. The development of the anomalous Au and Ag zones is mainly along the N40-50°E and N145°E directions.

• Journal of Soil and Groundwater Environment
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• v.23 no.3
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• pp.27-42
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• 2018

It is important to identify the contaminant sources and to evaluate the fate and transport of heavy metals to crops in agricultural lands. This study was conducted to evaluate metal sources and its transfer and accumulation to crop in agricultural soils. Pollution indices were calculated and multivariate analysis was performed to identify metal sources. To evaluate transfer and accumulation of metals to crops, the contents of phytoavailable metals were evaluated by using single extraction method and the correlation between metal content and soil properties was analyzed. Also the BCF was quantitatively evaluated for investigating the metal transition to each crop grown in the research area. As a result, Cr, Ni, and Co were expected to be mainly derived from geologic factors due to weathering of certain parent rocks. The content of nickel in soils of the research area was slightly higher than that of the concern level criteria based on total concentration, but the amount transferred and accumulated in the crops was actually low. Understanding the contamination characteristics by investigating the pollution sources of heavy metals and its transfer and accumulation to crops through various evaluation techniques could provide important information for proper management of the agricultural land.