• Journal of Korean Neurosurgical Society
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• v.65 no.3
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• pp.469-478
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• 2022

Objective : Cranial base chordomas are rare, but their treatment is challenging. Tumor recurrence is still common despite improvements in microsurgical techniques and postoperative radiotherapy. We retrospectively analyzed the course of treatment, overall survival, and recurrence/progression of chordomas over the past 10 years. Methods : We retrospectively reviewed 50 patients who underwent surgery at Tianjin Huanhu Hospital between 2010 and 2020 and were pathologically diagnosed with chordomas. Tumor resection was performed within the maximum safe range in all patients; the extent of resection was evaluated by imaging; and the incidence of complications, recurrence or progression, and overall survival were assessed. Results : Fifty patients were divided into the low-risk group (LRG) and high-risk group (HRG) based on the cranial chordoma grading system (CCGS). The Karnofsky Performance Scale scores and gross total resection rate of the LRG were significantly higher than those of the HRG (p<0.05). The incidence of complications and mortality in the LRG were lower than those of HRG. The analysis of cumulative survival and cumulative recurrence free survival/progression free survival (RFS/PFS) showed no statistical differences in the extent of resection for survival, recurrence, or progression. Univariate and multivariate analyses showed that Ki-67 was significantly associated with tumor recurrence and was an independent hazard factor (p=0.02). Conclusion : The CCGS can help neurosurgeons anticipate surgical outcomes. Pathological results are important in evaluating the possibility of tumor recurrence, and postoperative radiotherapy improves overall survival and RFS/PFS.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.629-641
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• 2024

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.

• East Asian Journal of Business Economics (EAJBE)
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• v.12 no.3
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• pp.25-35
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• 2024

Purpose: The purpose of this paper is to investigate the consumption behavior of consumers more closely and efficient purchasing channels of domestic e-commerce buyers by specifically analyzing the relationship between demographics and channels that are used first to acquire purchasing information during e-commerce. Research design, data and methodology: Korean Media Panel Survey performs 17 cities and provinces nationwide, and 4,537 panel households (all household members aged 6 or older) are surveyed, and a household visit interview is conducted. Correspondence analysis, one of the popular multivariate techniques, is exploited to explore the association between priority channel for acquisition of information and demographics. Results: The findings show that the considered demographics are closely associated with the priority channel for acquisition of purchase information. In particular, 'searching for portal sites' are closely relevant to 'more than 5 million won', 'device & machine control assembly', 'graduate school or higher', and '40-59', while 'searching for online open market' is linked with college graduate and '20-39'. Conclusions: The substantial contribution of this work is that by analyzing the association between demographics and priority channel for acquisition of purchase information in e-commerce in South Korea, we can discern the segmentation standard factor for e-commerce market and advance the subdivided market.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.403-410
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• 2004

Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.522-526
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• 2007

Since newly emerged disease, the Severe Acute Respiratory Syndrome (SARS), spread from Asia to North America and Europe rapidly in 2003, many researchers have tried to determine where the virus came from. In the phylogenetic point of view, SARS virus has been known to be one of the genus Coronavirus, but, the overall conservation of SARS virus sequence was not highly similar to that of known coronaviruses. The natural reservoirs of SARS-CoV are not clearly determined, yet. In the present study, the genomic sequences of SARS-CoV were analyzed by bioinformatics techniques such as multiple sequence alignment and phylogenetic analysis methods as well multivariate statistical analysis. All the calculating processes, including calculations of the relative synonymous codon usage (RSCU) and other genomic parameters using 30,305 coding sequences from the two genera, Coronavirus, and Lentivirus, and one family, Orthomyxoviridae, were performed on SMP cluster in KISTI, Supercomputing Center. As a result, SARS_CoV showed very similar RSCU patterns with feline coronavirus on the both axes of the correspondence analysis, and this result showed more agreeable results with serological results for SARS_CoV than that of phylogenetic result itself. In addition, SARS_CoV, human immunodeficiency virus, and influenza A virus commonly showed the very low RSCU differences among each synonymous codon group, and this low RSCU bias might provide some advantages for them to be transmitted from other species into human beings more successfully. Large-scale genomic analysis using bioinformatics techniques may be useful in genetic epidemiology field effectively.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1733-1741
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• 2020

This study analyzed the effect of meteorological factors on the concentration of PM10 (particulate matter 10) in the atmosphere and the variation of rainwater quality using multivariate statistical analysis. The concentration of PM10 in the atmosphere was continuously measured during eleven precipitation events with a custom-built PM sensor node. A total of 183 rainwater samples were analyzed for pH, EC (electrical conductivity), and water-soluble cations (Na+, Mg2+, K+, Ca2+, NH4+) and anions (Cl-, NO3-, SO42-). The data has been analyzed using two multivariate statistical techniques (principal component analysis, PCA, and Pearson correlation analysis) to identify relationships among PM10 concentrations in the atmosphere, meteorological factors, and rainwater quality factors. When the rainfall intensity was relatively strong (> 5 mm/h, rainfall type 1), the PM10 concentration in the atmosphere showed a negative correlation (r = -0.55, p < 0.05) with cumulative rainfall. The PM10 concentration increased the concentration of water-soluble ions (r = 0.25) and EC (r = 0.4), and decreased the pH (r = -0.7) of rainwater samples. However, for rainfall type 2 (< 5 mm/h), there was no negative correlation between the PM10 concentration in the atmosphere and cumulative rainfall and no statistically significant correlation between the PM10 concentration in the atmosphere and rainwater quality.

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

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1245-1245
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145-154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1152-1152
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145 154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.