• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.1
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• pp.139-146
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• 2003

In this paper we demonstrate a cryptanalysis of the stream cipher LILI-128. Our approach to analysis on LILI-128 is to solve an overdefined system of multivariate equations. The LILI-128 keystream generato $r^{[8]}$ is a LFSR-based synchronous stream cipher with 128 bit key. This cipher consists of two parts, “CLOCK CONTROL”, pan and “DATA GENERATION”, part. We focus on the “DATA GENERATION”part. This part uses the function $f_d$. that satisfies the third order of correlation immunity, high nonlinearity and balancedness. But, this function does not have highly nonlinear order(i.e. high degree in its algebraic normal form). We use this property of the function $f_d$. We reduced the problem of recovering the secret key of LILI-128 to the problem of solving a largely overdefined system of multivariate equations of degree K=6. In our best version of the XL-based cryptanalysis we have the parameter D=7. Our fastest cryptanalysis of LILI-128 requires $2^{110.7}$ CPU clocks. This complexity can be achieved using only $2^{26.3}$ keystream bits.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.125-130
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• 2008

Multivariate analysis is a rapidly expanding approach to data analysis. One specific technique in multivariate analysis is Principal Component Analysis (PCA). PCA is a statistical technique that linearly transform a given set of variables into a new set of composite variables. These new variables are orthogonal to each other and capture most of the information in the original variables. PCA is used to reduce the number of control points to be checked by measurement system. Therefore, the structure of the data set is simplified significantly It is also shown that eigenvectors obtained by conducting principal component analysis on the basis of the covariance matrix can be used to physically interpret the pattern of relative deformation for the points. This case study reveals the twisting deformation pattern of the underbody which is the largest mode of the total variation.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.475-482
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• 2011

In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.4-6
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• 2006

Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metaboiic shift data, and yeast sporulation data, shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.655-673
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• 2004

This paper is concerned with analysing the bankruptcy prediction power of three models: Multivariate Discriminant Analysis(MDA ), Logit Analysis, Neural Network. The after-crisis bankrupted companies were limited to the research data and the listed companies belonging to manufacturing industry was limited to the research data so as to improve prediction accuracy and validity of the model. In order to assure meaningful bankruptcy prediction, training data and testing data were not extracted within the corresponding period. The result is that prediction accuracy of neural network model is more excellent than that of logit analysis and MDA model when considering that execution of testing data was followed by execution of training data.

• Journal of Periodontal and Implant Science
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• v.52 no.6
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• pp.509-521
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• 2022

Purpose: Systemic health has a profound effect on dental treatment. The aim of this study was to evaluate peri-implant bone loss and health screening data to discover factors that may influence peri-implant diseases. Methods: This study analyzed the panoramic X-rays of patients undergoing health screenings at the Health Promotion Center at Seoul St. Mary's Hospital in 2018, to investigate the relationship between laboratory test results and dental data. The patients' physical data, such as height, weight, blood pressure, hematological and urine analysis data, smoking habits, number of remaining teeth, alveolar bone level, number of implants, and degree of bone loss around the implant, were analyzed for correlations. Their associations with glycated hemoglobin, glucose, blood urea nitrogen (BUN), creatinine, and severity of periodontitis were evaluated using univariate and multivariate regression analysis. Results: In total, 2,264 patients opted in for dental health examinations, of whom 752 (33.2%) had undergone dental implant treatment. These 752 patients had a total of 2,658 implants, and 129 (17.1%) had 1 or more implants with peri-implant bone loss of 2 mm or more. The number of these implants was 204 (7%). Body mass index and smoking were not correlated with peri-implant bone loss. Stepwise multivariate regression analysis revealed that the severity of periodontal bone loss (moderate bone loss: odds ratio [OR], 3.154; 95% confidence interval [CI], 1.175-8.475 and severe bone loss: OR, 7.751; 95% CI, 3.003-20) and BUN (OR, 1.082; 95% CI, 1.027-1.141) showed statistically significant predictive value. The severity of periodontitis showed greater predictive value than the biochemical parameters of blood glucose, renal function, and liver function. Conclusions: The results of this study showed that periodontal bone loss was a predictor of peri-implant bone loss, suggesting that periodontal disease should be controlled before dental treatment. Diligent maintenance care is recommended for patients with moderate to severe periodontal bone loss.

• Journal of the Ergonomics Society of Korea
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• v.25 no.2
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• pp.107-118
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• 2006

Recently, Korean's 3-D foot data have been extensively collected through 5th national anthropometric survey known as 'Size Korea'. In this study, Korean foot shape was investigated and subsequently classified, based on the existing standard for foot shaping. This study analyzed and categorized Korean foot shapes through the following methods. Although the data used in this study were limited to those of Korean adults, major factors affecting the foot shape were deduced and then categorically grouped by the multivariate statistical analysis. For those whose age ranged from 14 to 70, major factors affecting the foot shape for the male were related to foot breadth, ankle thickness, 1st toe shape, malleolus height, heel to top of the foot length, the ratio between toe-side and heel-side and 5th toe shape. For the female, the ball of foot height was added to the above factors. From the factors extracted, the Korean foot shape was categorized into three groups for the male and four groups for the female. They were the ladder type, the inverted triangle type and the square type. For the female, the triangular type was added to the three types. These findings will serve as useful information for the footwear production industry in Korea.

• Research in Community and Public Health Nursing
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• v.28 no.3
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• pp.302-312
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• 2017

Purpose: The purpose of this study was to identify associated factors of chewing discomfort among community-dwelling elderly. Methods: The study was cross-sectional design and secondary data analysis using the 6th Korea National Health and Nutrition Examination Survey. Among the total of 7,550 participants, data was analyzed with 1,126 adults aged 65 years and over. Chewing discomfort was assessed by the perceived chewing discomfort. Multivariate logistic regression analysis was used to find the associated factors of chewing discomfort. Results: Along with 61.7% of the participants reported having chewing discomfort, 85.2% reported to perceive poor oral health and 35.0% had oral pain. In multivariate logistic regression, perceived oral health (OR 3.22, 95% CI 2.24~4.63), oral pain (OR 2.46, 95% CI 1.76~3.43), activity limitation (OR 1.71, 95% CI 1.05~2.80), teeth requiring treatment (OR 1.61, 95% CI 1.14~2.26), number of remaining teeth (OR 1.60, 95% CI 1.22~2.10) and educational level (OR 1.56, 95% CI 1.15~2.12) were the significant predictors of chewing discomfort. Conclusion: The prevalence in chewing discomfort was high in elderly Koreans and various factors were associated with chewing discomfort. To improve chewing ability, it is suggested that the national level of policies offer strategical oral health programs in this population.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7133-7136
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• 2013

Background: We studied Surveillance, Epidemiology and End Results (SEER) breast cancer data of Georgia USA to analyze the impact of socio-economic factors on the disparity of breast cancer treatment outcome. Materials and Methods: This study explored socio-economic, staging and treatment factors that were available in the SEER database for breast cancer from Georgia registry diagnosed in 2004-2009. An area under the receiver operating characteristic curve (ROC) was computed for each predictor to measure its discriminatory power. The best biological predictors were selected to be analyzed with socio-economic factors. Survival analysis, Kolmogorov-Smirnov 2-sample tests and Cox proportional hazard modeling were used for univariate and multivariate analyses of time to breast cancer specific survival data. Results: There were 34,671 patients included in this study, 99.3% being females with breast cancer. This study identified race and education attainment of county of residence as predictors of poor outcome. On multivariate analysis, these socio-economic factors remained independently prognostic. Overall, race and education status of the place of residence predicted up to 10% decrease in cause specific survival at 5 years. Conclusions: Socio-economic factors are important determinants of breast cancer outcome and ensuring access to breast cancer treatment may eliminate disparities.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.211-218
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• 2005

This study presents an integrated data mining model for the credit evaluation of the customers of a capital company. Based on customer information and financing processes in capital market, we derived individual models from multi-layered perceptrons(MLP), multivariate discrimination analysis(MDA), and decision tree. Further, the results from the existing models were compared with the results from the integrated model using genetic algorithm. The integrated model presented by this study turned out to be superior to the existing models. This study contributes not only to verifying the existing individual models but also to overcoming the limitations of the existing approaches.