• Conservation Science in Museum
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• v.13
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• pp.37-44
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• 2012

White crystals on 46 costumes excavated from Shim Su-Ryun(1534 - 1589)'s tomb were examined their characterization and distribution. In 36 of such samples, white crystals with different shape and hardness were found. The formation of crystals did not correlated with a kind and use of textiles. However, crystals were found in the back side than the front of costume, specially around the marks of shrouding dead body. White crystals from 7 textiles were investigated by EPMA, XRD, or FT-IR. The composition of white crystal was analysed by EPMA and the structure characterization of crystals was used by X-ray diffraction. FT-IR spectroscopy was applied to check if non-crystalline compounds were also present. Mg and P were detected as the main element of white crystals and these compounds were identified a struvite and newberyite, the inorganic mineral magnesium ammonium phosphates. Struvite precipitation are influenced by many factors including concentration of Mg²⁺, NH₄⁺, and PO₄^3- ions, pH, and temperatures. It is assumed that magnesium, phosphorous, ammonia, a base material of struvite comes from decomposition product of human body. Tomb covered with lime, a unique triple-structure in Joseon period offering the basic condition, an anaerobe in a coffin, and high magnesium concentration of outer coffin with lime can be inferred as important factor for precipitation of crystals.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.134-138
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• 2006

Depression is the most common and widespread mood disorder. About 20% of the population might suffer a major, incapacitating episode of depression during their lifetime. This disorder can be classified into two types: major depressive disorders and bipolar disorder. Since pharmaceutical treatments are different according to types of depression disorders, correct and fast classification is quite critical for depression patients. Yet, classical statistical method, such as minnesota multiphasic personality inventory (MMPI), have some difficulties in applying to depression patients, because the patients suffer from concentration. We used electroencephalogram (EEG) analysis method fer classification of depression. We extracted nonlinearity of information flows between channels and estimated approximate entropy (ApEn) for the EEG at each channel. Using these attributes, we applied two types of data mining classification methods: decision tree and possibilistic support vector machines (PSVM). We found that decision tree showed 85.19% accuracy and PSVM exhibited 77.78% accuracy for classification of depression, 30 patients with major depressive disorder and 24 patients having bipolar disorder.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.723-737
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• 2017

In order to decide the national technical standards policy for national policy/market economy activities, the people in charge commonly make policy decisions based on the current technology level/concentration/utilization by means of major common technology field analysis using patent data. One possible source of such patent data is the domestic mobile carriers through the Korea Intellectual Property Rights Information System (KIPRIS) of the Korean Intellectual Property Office (KIPO). Using this system, we collected 20,294 patents and 152 International Patent Classification (IPC) types and confirmed KTs (9,738 cases / 47.98%), which perform relatively high technology retention activities compared to other mobile carriers through the KIPRIS of KIPO. Based on these data, we performed three analyses (SNA, PCA, ARIMA) and extracted 30 IPC types from the SNA and 4 IPC types from the PCA. Based on the above analysis results, we confirmed that 4 IPC (H04W, H04B, G06Q, H04L) types are the major common technology field of the domestic mobile carriers. Finally, the number of 4 IPC (H04W, H04B, G06Q, H04L) forecast averages of the ARIMA forecast result is lower than the number of existing time series patent data averages.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.123-138
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• 2022

Korea has been huge investments in its port system, annually upgrading its infrastructure to turn the ports into Asian hub port. However, while Busan port is ranked fifth globally for container throughput, Other Korean ports are ranked much lower. This article applies Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI) to evaluate selected major Korean seaports' operational efficiency and productivity from 2010 to 2018. It further integrates Principal Component Analysis (PCA) into DEA, with the PCA-DEA combined model strengthening the basic DEA results, as the discriminatory power weakens when the variable number exceeds the number of Decision Making Units(DMU). Meanwhile, MPI is applied to measure the seaports' productivity over the years. The analyses generate efficiency and productivity rankings for Korean seaports. The results show that except for Gwangyang and Ulsan port, none of the selected seaports is currently efficient enough in their operations. The study also indicates that technological progress has led to impactful changes in the productivity of Korean seaports.

• Journal of the Korean Wood Science and Technology
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• v.35 no.1
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• pp.36-43
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• 2007

Elemental analysis, SEM-EDX, X-ray diffraction (XRD) and IR analysis were adopted to examine the quality of charred woods excavated from the underground of the Daewoongjeon Hall of Youngguksa Temple, Youngdong-gun, Chungbuk, Korea. A large amount of calcium was detected in SEM-EDX analysis. The analyses of chemical elements suggested that completely charred wood was carbonized at about $500^{\circ}C$. The XRD results indicated the destruction of cellulose crystalline region. The IR analysis exhibited that thermal degradation of wood component was different depending upon the carbonization temperature. It can be suggested from the results that PEG with different molecular weights should be used for the conservation of excavated charred woods.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.152-152
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• 2023

하천의 건강성을 평가하기 위해 일반적으로 수생태계 건강성 지표(TDI, BMI, FAI, HRI, RVI)가 사용되고 있다. 이 지표는 5가지 등급으로 구분하여 매우 좋음(A), 좋음(B), 보통(C), 나쁨(D), 매우나쁨(E)으로 구분된다. 하지만, 하천의 건강성 관점에서 수질, 토지이용, 지리적 특성, 경관지수와의 상관성을 바탕으로 어떤 영향을 미치는지에 대한 연구가 필요하다. 본 연구에서는 하천의 수생태계 건강성에 영향을 미치는 환경적 인자들과의 관계성을 분석하여 수생태계 건강성이 '좋음'에 해당되는 하천으로 분류하고자 한다. 이를 통해 환경적 인자들의 임계값을 산출하여 하천 관리에 대한 구체적인 우선순위 설정 방안을 제안하고자 한다. 낙동강대권역을 대상으로 수질, 토지이용, 지리적 특성, 경관지수의 여러 변수 중 수생태계 건강성과의 관계에서 대표성을 나타낼 수 있는 환경적 인자를 선정하기 위하여 정준상관분석(CCA)을 수행하였다. 또한 모델 기반의 클러스터 분석을 활용하여 소권역별로 수생태계 건강성이 '좋음'에 해당할 확률을 파악하고, 여기에 해당하는 소권역에 대하여 각각의 환경적 인자에 대한 임계값을 정량적으로 평가하였다. 본 연구에서는 하천의 환경 인자들과의 관계를 분석하여 수생태계 건강성을 평가하고 하천 관리에 대한 구체적인 우선순위를 파악하는 방법을 제안한다. 주성분 분석 및 모델 기반 클러스터 분석을 사용하여 각 소권역에 대한 환경 인자의 임계값을 평가하고, 정책 결정자들이 하천의 건강성을 유지하고 개선할 수 있는 정보를 제공할 수 있다.

• The Korean Journal of Financial Management
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• v.16 no.1
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• pp.283-309
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• 1999

본 연구에서는 최근에 투입과 산출관계가 복잡한 비영리기관의 운영효율성을 평가하는데 많이 적용되고 있는 DEA분식기법을 이용하여 국내일반은행의 생산성을 상대적 개념에서 측정하고, 은행감독원에 의한 경영평가와 금융감독위원회의 은행퇴출평가 결과를 비교하여 봄으로서 평가결과가 타당성이 있는지 여부를 알아보았다. 금융감독위원회의 평가는 수익성, 건전성, 유동성, 내부유보 등 비율분석에 의한 평가와 BIS비율 등을 고려하여 평가해 왔기 때문에 다산출물 대 다투입물이 상호작용하는 것을 무시하고 있다는 것으로 볼 수 있다. 그리고 은행감독원의 평가와 DEA기법에 의한 경영효율성 간에는 상당한 차이가 있는 것으로 드러났다. 본 연구 분석결과로는 1995년도 효율성 값이 하위 5개 은행은 국민은행, 충북은행, 광주은행, 경남은행, 평화은행이며, 1996년도 효율성 값이 하위 5개 은행은 광주은행, 제주은행, 동남은행, 전북은행, 국민은행, 1997년도의 효율성 값이 하위 5개 은행은 대동은행, 광주은행, 충청은행, 충북은행, 전북은행으로 분석되었다. 퇴출은행 5개중 1996년도에 동남은행, 1997년도에 대동은행, 충청은행만이 효율성 값이 하위 5위안에 포함되어 본 연구의 분석결과와 다소 상이한 결과가 분석되었다. 그리고 DEA 분석모형에 의하면 비율분석에서는 점검할 수 없는 은행의 상대적 경영효율성을 분석한 결과가 금융당국의 평가결과와 다소 일치한 점도 있으나 운영효율성 측면에서 퇴출대상은행이 효율성이 높은 은행으로 평가되어 금융감독위원회가 결정한 퇴출대상은행과 DEA 분석결과와는 상이한 결과가 도출되었다.

• Journal of Korea Technology Innovation Society
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• v.17 no.3
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• pp.449-466
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• 2014

There have been various researches on the relationship between a company's R&D investment and the outcome from innovation. However, these studies failed to effectively analyze the decision-making process followed by companies in relation to knowledge production. Especially, in analyzing the patent of companies, the Poisson model has been commonly used, but its limitations have been pointed out. In recent years, many studies have adopted negative binomial models, but they still pose limitations in analyzing the selection process. This paper proposed a hurdle negative binomial model to effectively reflect the company's decision embedded within patent information and conduct an empirical analysis on a survey of businesses' activities. In particular, the study analyzed the selection process of companies in determining the number of patents. As a result of estimation, the presence of over-dispersion was identified. In addition, the Wald-test confirmed that setting up of hurdles was valid, and there was a difference between the results of hurdle models and those of general negative binomial settings.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.172-172
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• 2022

분산계수는 하천에서 오염물질의 혼합능을 파악할 수 있는 대표적인 인자이다. 특히 하수처리장 방류수 혼합예측과 같이 횡 방향 혼합에 대한 예측이 중요한 경우, 하천의 지형적, 수리학적 특성을 고려한 2차원 횡 분산계수의 결정이 필요하다. 2차원 횡 분산계수의 결정을 위해 기존 연구에서는 추적자실험결과로부터 경험식을 만들어 횡 분산계수 산정에 사용해왔다. 회귀분석을 통한 경험식 산정을 위해서는 충분한 데이터가 필요하지만, 2차원 추적자 실험 건수가 충분치 않아 신뢰성 높은 경험식 산정이 어려운 상황이다. 따라서 본 연구에서는 SMOTE기법을 이용하여 횡분산계수 실험데이터를 증폭시켜 이로부터 횡 분산계수 경험식을 산정하고자 한다. 또한 다중선형회귀분석을 통해 도출된 경험식의 한계를 보완하기 위해 다양한 머신러닝 기법을 적용하고, 횡 분산계수 산정에 적합한 머신러닝 기법을 제안하고자 한다. 기존 추적자실험 데이터로부터 하폭 대 수심비, 유속 대 마찰유속비, 횡 분산계수 데이터 셋을 수집하였으며, SMOTE 알고리즘의 적용을 통해 회귀분석과 머신러닝 기법 적용에 필요한 데이터그룹을 생성했다. 새롭게 생성된 데이터 셋을 포함하여 다중선형회귀분석을 통해 횡 분산계수 경험식을 결정하였으며, 새로 제안한 경험식과 기존 경험식에 대한 정확도를 비교했다. 또한 다중선형회귀분석을 통해 결정된 경험식은 횡 분산계수 예측범위에 한계를 보였기 때문에 머신러닝기법을 적용하여 다중선형회귀분석에 대한 예측성능을 평가했다. 이를 위해 머신러닝 기법으로서 서포트 벡터 머신 회귀(SVR), K근접이웃 회귀(KNN-R), 랜덤 포레스트 회귀(RFR)를 활용했다. 세 가지 머신러닝 기법을 통해 도출된 횡 분산계수와 경험식으로부터 결정된 횡 분산계수를 비교하여 예측 성능을 비교했다. 이를 통해 제한된 실험데이터 셋으로부터 2차원 횡 분산계수 산정을 위한 데이터 전처리 기법 및 횡 분산계수 산정에 적합한 머신러닝 절차와 최적 학습기법을 도출했다.