• 정보보호학회논문지
• /
• 제13권1호
• /
• pp.139-146
• /
• 2003

본 논문은 과포화 다변수 방정식을 이용하여 LILI-128 스트림 암호를 분석한다. LILI-128 암호$^{[8]}$ 는 128비트 키를 가진 선형귀환 쉬프트 레지스터 기반의 스트림 암호로 구조를 살펴보면 크게 “CLOCK CONTROL” 부분과 “DATA GENERATION” 부분으로 나뉘어진다. 분석 방법은 “DATA CENERATION” 부분에 사용되는 함수 \ulcorne $r^{d}$ 의 대수적 차수가 높지 못하다는 성질을 이용한다. 간략히 설명하면 차수(K)가 6차인 다변수 방정식을 많이 얻을 수 있고, 이를 7차 (D)의 다변수 방정식으로 확장하여 주어진 변수보다 많은 연립방정식을 얻어 그 해를 구하는 XL 알고리즘을 통해 전수조사보다 빠르게 키정보를 찾을 수 있다. 결과 중 가장 좋은 것은 출력 키수열 2$^{26.3}$비트를 가지고 2$^{110.7}$ CPU 시간을 통해 128비트 키정보를 얻는 것이다.다.

• 산업경영시스템학회지
• /
• 제31권3호
• /
• pp.125-130
• /
• 2008

이 논문은 자동차 차체 조립과정에서, 품질관리의 일환으로써, 비접촉 자동측정시스템을 이용하여 검사해야 하는 수많은 비독립적인 검사점을 다변량분산분석과 주성분분석을 이용하여 효율적으로 검사점을 감소시키는 방법을 설명하고 있다. 이 연구의 목적은 다변량분산분석, 주성분 분석의 개념과 이러한 기법들을 산업체 제조분야에서 응용하는 방법을 설명하여 독자의 사례 응용 이해를 돕는데 있으며, 또한 특히 주성분분석을 이용하여 수 많은 비독립적인 검사점을 어떻게 유효하게 줄여나가는지를 보여주고자 한다. 독자의 이해를 돕기 위하여 위와 같은 절차를 순서대로 설명하였으며, 실제 자동차 조립공장에서 발생하는 사례를 수치 예를 들어 설명하였다.

• Journal of information and communication convergence engineering
• /
• 제9권4호
• /
• pp.475-482
• /
• 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.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2006년도 한국컴퓨터종합학술대회 논문집 Vol.33 No.1 (A)
• /
• pp.4-6
• /
• 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.

• 한국품질경영학회:학술대회논문집
• /
• 한국품질경영학회 2004년도 품질경영모델을 통한 가치 창출
• /
• pp.655-673
• /
• 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
• /
• 제52권6호
• /
• pp.509-521
• /
• 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.

• 대한인간공학회지
• /
• 제25권2호
• /
• pp.107-118
• /
• 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.

• 지역사회간호학회지
• /
• 제28권3호
• /
• pp.302-312
• /
• 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
• /
• 제14권12호
• /
• pp.7133-7136
• /
• 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.

• 정보처리학회논문지D
• /
• 제12D권2호
• /
• pp.211-218
• /
• 2005

본 연구는 금융기관에서의 고객신용평가를 위한 최적의 데이터마이닝 모형을 제안한다. 이를 위해 할부금융시장에서의 고객정보 및 할부진행 과정에 대한 세부 내역을 바탕으로 다계층 퍼셉트론(Multi-Layered Perceptrons:MLP)과 다변량 판별분석(Multivariate Discrimination Analysis : MDA), 그리고 의사결정나무(Decision Tree)를 적용하여 각각의 개별모형을 도출하고 이론 유전자 알고리즘을 이용하여 통합한 최종 모형을 구해 그 결과론 각 단일모형과 비교${\cdot}$분석하였다. 그 견과 유전자 알고리즘을 통해 결합한 통합모형의 성능이 가장 우수한 것으로 나타났다. 이에 본 연구는 기존에 진행되었던 개변모형에 대한 검증은 물론, 단순히 여러 개의 모형을 비교${\cdot}$분석하여 우월한 모형을 평가하는 기존 방법론 상의 한계를 극복하기 위해 각각의 개별모형을 유전자 알고리즘을 통해 통합모형으로 구축하는 하나의 방법론을 제시하였다는데 그 의의가 있다.