• 수산해양기술연구
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• 제54권3호
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• pp.217-223
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• 2018

Overfishing capacity has become a global issue due to over-exploitation of fisheries resources, which result from excessive fishing intensity since the 1980s. In the case of Korea, the fishing effort has been quantified and used as an quantified index of fishing intensity. Fisheries resources of coastal fisheries in the Korean waters of the East Sea tend to decrease productivity due to deterioration in the quality of ecosystem, which result from the excessive overfishing activities according to the development of fishing gear and engine performance of vessels. In order to manage sustainable and reasonable fisheries resources, it is important to understand the fluctuation of biomass and predict the future biomass. Therefore, in this study, we forecasted biomass in the Korean waters of the East Sea for the next two decades (2017~2036) according to the changes in fishing intensity using four fishing effort scenarios; $f_{current}$, $f_{PY}$, $0.5{\times}f_{current}$ and $1.5{\times}f_{current}$. For forecasting biomass in the Korean waters of the East Sea, parameters such as exploitable carrying capacity (ECC), intrinsic rate of natural increase (r) and catchability (q) estimated by maximum entropy (ME) model was utilized and logistic function was used. In addition, coefficient of variation (CV) by the Jackknife re-sampling method was used for estimation of coefficient of variation about exploitable carrying capacity ($CV_{ECC}$). As a result, future biomass can be fluctuated below the $B_{PY}$ level when the current level of fishing effort in 2016 maintains. The results of this study are expected to be utilized as useful data to suggest direction of establishment of fisheries resources management plan for sustainable use of fisheries resources in the future.

• 한국정보시스템학회지:정보시스템연구
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• 제26권3호
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• pp.63-90
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• 2017

Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.

• 한국산학기술학회논문지
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• 제20권7호
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• pp.562-570
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• 2019

본 연구는 한국 청소년의 저체중에 영향을 미치는 요인을 확인하기 위하여 제 13차 청소년건강행태온라인조사 자료를 활용하여 시행된 이차 자료 분석 연구이다. 연구 대상자는 중학교 1학년부터 고등학교 3학년까지 청소년 48,242명이었다. 측정변수는 인구사회학적 특성, 신체활동 특성, 식이 특성 및 정신건강 특성을 조사하였다. 수집된 자료는 로지스틱 회귀분석으로 분석하였다. 본 연구 결과, 전체 청소년 중 5.9%가 저체중이었다. 우리나라 청소년의 저체중에 영향을 미치는 요인은 성별, 학교유형, 학업성적, 경제수준, 신체활동, 체중조절 노력, 라면 섭취, 과자 섭취, 스트레스 및 주관적 수면 충족률이었다. 한국 청소년의 저체중은 여성인 경우, 남녀공학인 경우, 학업 성적과 경제수준이 낮은 경우, 신체활동을 하지 않는 경우, 체중 증가를 노력하는 경우, 라면이나 과자를 섭취하는 경우, 스트레스가 있는 경우 및 주관적 수면 충족률이 충분한 경우에 증가하였다. 본 연구결과를 바탕으로 저체중 청소년이 적정 체중을 유지할 수 있는 방안을 모색하는 것이 요구된다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권16호
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• pp.6811-6817
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• 2014

Background: Breast cancer risk prediction models are widely used in clinical practice. They should be useful in identifying high risk women for screening in limited-resource countries. However, previous models showed poor performance in derived and validated settings. Therefore, we aimed to develop and validate a breast cancer risk prediction model for Thai women. Materials and Methods: This cross-sectional study consisted of derived and validation phases. Data collected at Ramathibodi and other two hospitals were used for deriving and externally validating models, respectively. Multiple logistic regression was applied to construct the model. Calibration and discrimination performances were assessed using the observed/expected ratio and concordance statistic (C-statistic), respectively. A bootstrap with 200 repetitions was applied for internal validation. Results: Age, menopausal status, body mass index, and use of oral contraceptives were significantly associated with breast cancer and were included in the model. Observed/expected ratio and C-statistic were 1.00 (95% CI: 0.82, 1.21) and 0.651 (95% CI: 0.595, 0.707), respectively. Internal validation showed good performance with a bias of 0.010 (95% CI: 0.002, 0.018) and C-statistic of 0.646(95% CI: 0.642, 0.650). The observed/expected ratio and C-statistic from external validation were 0.97 (95% CI: 0.68, 1.35) and 0.609 (95% CI: 0.511, 0.706), respectively. Risk scores were created and was stratified as low (0-0.86), low-intermediate (0.87-1.14), intermediate-high (1.15-1.52), and high-risk (1.53-3.40) groups. Conclusions: A Thai breast cancer risk prediction model was created with good calibration and fair discrimination performance. Risk stratification should aid to prioritize high risk women to receive an organized breast cancer screening program in Thailand and other limited-resource countries.

• 한국산학기술학회논문지
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• 제9권5호
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• pp.1460-1466
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• 2008

본 연구는 코스닥기업이 발표하는 회계정보가 유용하다면, 부도발생, 자본전액잠식, 거래실적부진, 감사의견 거절, 감사의견부적정으로 정의된 도산기업과 정상기업 간의 "회계정보는 어떠한 차이가 있는지" 재무지표를 기준으로 검증하는 것이 본 연구의 목적이다. 표본기업은 $2000{\sim}2007$년 도산한 코스닥기업 45개와 자산규모를 기준으로 대응하여 선정한 45개의 정상기업으로 구성하였으며, 같은 방법으로 모형확인을 위한 검증용 표본 30개 기업을 선정하였다. 실증분석 결과 도산 5년 전부터 본 연구에 사용된 17개의 재무지표 중 안전성에 관련된 변수들이 도산 및 정상기업에서 많은 유의한 차이를 보였다. 확인표본을 이용한 분류정확도는 도산 5년 전 76.7%, 도산 4년 전 76.7%, 도산 3년 전 65.0%, 도산 2년 전 76.7%, 도산 1년 전 88.3%로 나타났으며, 이는 정상기업에서 도산기업으로 서서히 진행되었음을 알 수 있는 것으로, 도산 5년 전부터 추정표본 83.3%, 확인표본 76.7%의 분류정확도를 보여 재무지표를 통한 회계정보의 유용성을 확인하였다.

• 대한방사선기술학회지:방사선기술과학
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• 제23권1호
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• pp.49-54
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• 2000

It is important to identify modifiable risk factors for breast cancer, because the breast cancer is one of the major causs of mortality among women. Some reported that obesity is a risk factor for breast cancer, but the results are not constant. Many risk factors are related to the duration of estrogenic stimulation of the breast. In general, early menarche and late menopause are positive risk factors. Human breast cancer has different characteristics according to the status of menopause(premenopause and postmenopause). In premenopausal women, about 60% of circulating estrogen is from the ovaries in the form of estradiol, and the remaining 40% is estrogen formed primarily in the adipose(fat) tissue via aromatization of androstenedion from the adrenal glands. After menopause this adipose cell production of estrone is the main source of estrogens and the level of estrone is maintained approximately at premenopausal levels. This study was undertaken to determine the role of body size and body mass index by status of menopause in development of breast cancer using retrospective case/control study. From March 1991 to February 1997 at the Wonkwang University Hospital, the breast cancer cases(n=72) and controls(n=86) were selected. By statistical analysis method, regression analysis, paired T-test and multiple logistic regression were done to estimate the influenced factors same as height, weight, BMI, age at menarche and age at menopause. The following results were obtained : 1. In premenopausal women, age at menarche was showed comparatively high correlation coefficients and BMI was described prominently highly in postmenopause. 2. At the results of multiple regression analysis, age at monarch, BMI and weight were showed as significant variables. In this method, critical factor ($R^2$) was 0.054. 3. Paired samples T-test was undertaken to test mean difference between two groups of cases and controls. The result of test performance showed a significant difference. 4. In comparison with women whose weight less than 50 kg, the ORs for the upper 5th group was 1.82(95% confidence interval). The heaviest women had a higher risk(OR=1.14, 95% confidence interval $1.12{\sim}1.31$, p=0.005). Higher body mass index was significantly associated with increased risk of premenopausal breast cancer (OR=1.01, 95% confidence interval $1.08{\sim}l.18$, p=0.05).

• Journal of Korean Neurosurgical Society
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• 제64권6호
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• pp.950-956
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• 2021

Objective : Psoas and masseter muscles are known markers of sarcopenia. However, the relative superiority of either muscle as a marker is unclear. Therefore, this study analyzed the two muscles in patients with a prognosis of traumatic brain injury (TBI). Methods : Patients with TBI visiting a regional trauma center between January 2017 and December 2018 were selected, and their medical records were reviewed. TBI patients with an abbreviated injury score (AIS) of 4 or 5 were selected. Patients with an AIS of 4 or 5 at the chest, abdomen, and extremity were excluded. Patients with a hospital stay of 1 to 2 days were excluded. Both muscle areas were measured based on the initial computed tomography. The psoas muscle index (PMI) and the masseter muscle index (MMI) were calculated by dividing both muscle areas by height in meters squared (cm²/m²). These muscle parameters along with other medical information were used to analyze mortality and the Glasgow outcome scale (GOS). Results : A total of 179 patients, including 147 males (82.1%), were analyzed statistically. The mean patient age was 58.0 years. The mortality rate was 16.8% (30 patients). The mean GOS score was 3.7. Analysis was performed to identify the parameters associated with mortality, which was a qualitative study outcome. The psoas muscle area (16.9 vs. 14.4 cm², p=0.028) and PMI (5.9 vs. 5.1 cm²/m², p=0.004) showed statistical differences between the groups. The PMI was also statistically significant as a risk factor for mortality in logistic regression analysis (p=0.023; odds ratio, 0.715; 95% confidence interval, 0.535-0.954). Quantitative analyses were performed with the GOS scores. Bivariate correlation analysis showed a statistically significant correlation between PMI and GOS scores (correlation coefficient, 0.168; p=0.003). PMI (p=0.004, variation inflation factor 1.001) was significant in multiple regression analysis. The masseter muscle area and MMI did not show significance in the study. Conclusion : Larger PMI was associated with statistically significant improved survival and GOS scores, indicating its performance as a superior prognostic marker. Further analyses involving a larger number of patients, additional parameters, and more precise settings would yield a better understanding of sarcopenia and TBI.

• 한국빅데이터학회지
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• 제3권2호
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• pp.71-78
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• 2018

본 연구는 산불 발생 예측 모형의 정확도를 높이기 위해 머신러닝 기법을 적용한 연구이다. 산불 피해면적이 가장 큰 강원도를 중심으로 2003년부터 2016년까지 총 14년의 산불 자료를 이용하였다. 기상자료의 오차를 줄이기 위해 강원도를 9개의 구역으로 나누어 각 구역 관측소의 기상자료를 이용하였다. 9개의 구역으로 나누어 각 구역의 산불 예측 모형을 만들게 되면 산불이 발생한 날(majority)과 산불이 발생하지 않은 날(minority)의 비율 차이가 큰 불균형 문제가 발생한다. 불균형 문제에서는 모델의 성능이 떨어지는 현상이 발생할 수 있다. 이를 해결하기 위해 여러 샘플링 방법을 적용하였다. 또한 모델의 정확도를 높이기 위해 캐나다 산불 기상 지수(FWI)의 5가지 지수를 파생변수로 사용하였다. 모델링 방법은 통계적 방법인 로지스틱 회귀분석 방법과 머신러닝 방법인 random forest와 xgboost 방법을 사용하였다. 각 구역의 최종모델의 선택기준을 정확도, 민감도, 특이도를 고려하여 정했으며, 9개 구역의 예측 결과는 산불이 발생한 104건 중 80건의 발생 예측에 성공하였으며 산불이 발생하지 않은 9758건 중 7426건의 발생하지 않음을 예측했다. 전체의 정확도는 76.1%였다.

• 대한원격탐사학회지
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• 제36권5_3호
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• pp.1109-1123
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• 2020

국토 대부분이 산림으로 구성되어 있는 대한민국은 매 년 많은 산불이 발생한다. 산불은 토양의 전단강도를 약화시켜 산사태에 취약한 토양층을 만들기도 하고, 수목의 복구가능여부에 따라 다른 계획 설립이 필요하기 때문에 산불피해면적 뿐만 아니라 피해강도에 대한 파악도 중요하다. 위성 원격탐사를 통한 산불피해강도 추정 연구가 많이 수행되어 왔으나, NDVI(Normalized Difference Vegetation Index)와 NBR(Normalized Burn Ratio) 등과 같은 단일 인자의 시계열 변화만을 이용하여 피해강도를 파악하기에는 한계가 있다. 본 연구에서는 Sentinel-1A SAR-C (Synthetic Aperture Radar-C)와 Sentinel-2A MSI(Multi Spectral Instrument)센서의 자료를 이용하여 기계학습방법을 통한 산불 피해강도 탐지 모델들을 제시하였다. 2017년 5월 삼척, 2019년 4월 강릉·동해, 2019년 4월 고성·속초 총 세개의 산불사례를 이용하여 RF(Random forest), LR(Logistic regression), SVM(Support Vector Machine)기계학습 모델을 구축하였다. 연구결과, random forest 모델이 82.3%의 총정확도로 가장 높은 성능을 보여주었다. 모델의 범용성 및 학습자료 민감도 확인을 위해 사례교차검증도 추가 시행하였는데, 그 결과 사례들의 시기적 차이에 의한 식생활력 및 재생도의 차이에 민감도가 높음을 확인하였다. 이는 추후 다양한 시공간적 사례를 추가할 시 개선이 될 것으로 보인다.

• Journal of Preventive Medicine and Public Health
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• 제30권2호
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• pp.342-355
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• 1997

This research was carried out to determine the performance rate of health related practices, to measure the agreement between morbidity by doctor's diagnosis and morbidity by subject' self-reported and the degree of association between health related practices and morbidity rate by doctor's diagnosis, to identify their effects on morbidity among rural area populations. The data were gathered by volunteer residents (over the age of 20) of Haman Myeon, Haman Gun, Kyeongsangnam Do in Korea, from June 10, 1993 to June 12, 1993 (369 male and 516 female). Face to face interview, lab, chest P-A, EKG and physical examination were completed. Descriptive statistics, agreement analysis and multiple logistic regression procedures were employed for analyses. The results of the study were summarized as follows : 1) Age adjusted morbidity rates by doctor's diagnosis and self-reported were 38.5% (male:37.3%, female:36.5%), 26.4% (male:33.3%, female:27.5%), respectively. Kappa coefficient between morbidity by doctor's diagnosis and morbidity by self-reported was 0.21 (male:0.21, female:0.22). 2) The frequency of disease by doctor's diagnosis was as follows: hypertension(15.3%), gastritis (9.6%), diabetes mellitus (8.5%), live. disease (8.1%), and degenerative arthritis (6.2%) in the study population. 3) Order of health practice performance rate was as follows: Males-normal body weight (62.1%), non-heavy alcohol consumption (57.5%), 7-8 hours of sleeping (50.1%), non-smoking (21.7%), and exercise (19.8%). Females- non-heavy alcohol consumption (97.3%), non-smoking (84.7%), normal body weight (57.8%), 7-8 hours of sleeping (45.0%), and exercise (9.9%). 4) There was no significant relationship between health related practice and morbidity except exercise among health related practices. 5) Health related practice index which was recategorized by high, medium, and low had effects on the probability of developing morbidity.