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이영섭,서경훈,박인기,Young Seob Lee,MD,Kyung Hoon Seo,MD,In Ki Park,MD 대한안과학회 2011 대한안과학회지 Vol.52 No.10
Purpose: To report a case of unilateral mydriasis after accidental exposure to an Angel’s trumpet leaf. Case summary: A 53-year-old woman visited the Eye Department complaining of blurred vision and difficulty in light adaptation in the left eye of 1-week duration. The best corrected visual acuity was 20/20 in both eyes and other ophthalmological findings were considered normal except for dilation of the left pupil and a decrease in light reflex. The patient was initially unaware of any cause of her eye problems, but when asked specifically, the patient remembered an Angel’s trumpet leaf brushing against her left eye when she carried the plants. There were no other previous medical or drug histories, thus the Angel’s trumpet was considered as the cause for the unilateral mydriasis. The patient’s progress was followed for a week, her symptoms improved and the pupil size and reflex returned to normal. Conclusions: The cultivation of Angel’s trumpet has become increasingly popular in Korea in recent years. The present case emphasizes the importance of an accurate and detailed history regarding specific contact history with plants like Angel’s trumpet in otherwise healthy patients affected by unilateral mydriasis. J Korean Ophthalmol Soc 2011;52(10):1259-1261
군산풍력발전단지의 풍력발전량 단기예측모형 비교에 관한 연구
이영섭,김진,장문석,김현구,Lee, Yung-Seop,Kim, Jin,Jang, Moon-Seok,Kim, Hyun-Goo 한국데이터정보과학회 2013 한국데이터정보과학회지 Vol.24 No.3
최근 신재생에너지와 대체에너지의 필요성이 증가함에 따라 환경오염과 온실효과를 초래하지 않는 풍력에너지 개발에 많은 연구와 투자가 이루어지고 있다. 풍력에너지는 무공해 에너지이며 자원양이 무한대이고 바람이 부는 곳이라면 어디에서든지 전력생산이 가능하다. 그러나 풍력에너지는 바람에 크게 의존하며 불규칙적인 특성이 있어 효율적인 풍력발전이 어렵다는 단점이 있다. 이러한 이유로 풍력발전에 있어서 정확한 풍력발전량 예측은 매우 중요한 요소이다. 본 연구에서는 이러한 풍력발전량의 효율적인 예측을 위해 군산 풍력단지의 자료를 이용해 시계열모형인 ARMA모형과 데이터 마이닝 기법 중 신경망모형을 사용하여 풍력발전량을 예측하고 비교분석 하였다. 그 결과 신경망모형 적합결과가 ARMA모형 적합결과 보다 더 좋은 예측력을 나타내었다. As the needs for alternative energy and renewable energy increase, there has been a lot of investment in developing wind energy, which does not cause air pollution nor the greenhouse gas effect. Wind energy is an environment friendly energy that is unlimited in its resources and is possible to be produced wherever the wind blows. However, since wind energy heavily relies on wind that has unreliable characteristics, it may be difficult to have efficient energy transmissions. For this reason, an important factor in wind energy forecasting is the estimation of available wind power. In this study, Gunsan wind farm data was used to compare ARMA model to neural network model to analyze for more accurate prediction of wind power generation. As a result, the neural network model was better than the ARMA model in the accuracy of the wind power predictions.
망막정맥폐쇄 환자에서 유리체내 베바시주맙 주입술의 2년 임상 결과
이영섭,김무상,유승영,곽형우,Young Seob Lee,Moo Sang Kim,Seung Young Yu,Hyung Woo Kwak 대한안과학회 2011 대한안과학회지 Vol.52 No.9
Purpose: The authors evaluated the 2-year clinical results of intravitreal bevacizumab injection in retinal vein occlusion (RVO). Methods: Thirty-two eyes of 32 patients treated with an intravitreal bevacizumab injection of 1.25 mg (0.05 ml) for RVO (branch RVO: 22 eyes, central RVO: 10 eyes), repeated 3 times at a 6-week interval and were available for a follow-up period of at least 2 years were retrospectively reviewed. Best-corrected visual acuity (BCVA) before treatment and 6, 12, and 24 months after 3 serial injections, was recorded. The optical coherence tomography (OCT) results were analyzed to identify prognostic factors for recurrent macular edema that needed reinjection. Results: Two years after the treatment, mean BCVA was significantly improved (<em>p</em> = 0.000). Out of 32 eyes, 16 (branch RVO: 8 eyes; central RVO: 8 eyes) needed reinjection because of recurrent macular edema. In central RVO, a significantly high reinjection rate was shown in serous retinal detachment (SRD) compared with cystoid macular edema (CME) as identified in OCT findings (<em>p</em> = 0.049). Additionally, in branch RVO, a high reinjection rate was shown in SRD, although statistically not significant (<em>p</em> = 0.375). Conclusions: In patients with RVO, a significant visual improvement was maintained for at least 2 years after intravitreal bevacizumab injection. Based on OCT results, SRD showed a high reinjection rate compared with CME in CRVO. J Korean Ophthalmol Soc 2011;52(9):1039-1047
데이터 마이닝에서 배깅, 부스팅, SVM 분류 알고리즘 비교 분석
이영섭,오현정,김미경,Lee Yung-Seop,Oh Hyun-Joung,Kim Mee-Kyung 한국통계학회 2005 응용통계연구 Vol.18 No.2
데이터 마이닝에서 데이터를 효율적으로 분류하고자 할 때 많이 사용하고 있는 알고리즘을 실제 자료에 적용시켜 분류성능을 비교하였다. 분류자 생성기법으로는 의사결정나무기법 중의 하나인 CART, 배깅과 부스팅 알고리즘을 CART 모형에 결합한 분류자, 그리고 SVM 분류자를 비교하였다. CART는 결과 해석이 쉬운 장점을 가지고 있지만 데이터에 따라 생성된 분류자가 다양하여 불안정하다는 단점을 가지고 있다. 따라서 이러한 CART의 단점을 보완한 배깅 또는 부스팅 알고리즘과의 결합을 통해 분류자를 생성하고 그 성능에 대해 평가하였다. 또한 최근 들어 분류성능을 인정받고 있는 SVM의 분류성능과도 비교?평가하였다. 각 기법에 의한 분류 결과를 가지고 의사결정나무를 형성하여 자료가 가지는 데이터의 특성에 따른 분류 성능을 알아보았다. 그 결과 데이터의 결측치가 없고 관측값의 수가 적은 경우는 SVM의 분류성능이 뛰어남을 알 수 있었고, 관측값의 수가 많을 때에는 부스팅 알고리즘의 분류성능이 뛰어났으며, 데이터의 결측치가 존재하는 경우는 배깅의 분류성능이 뛰어남을 알 수 있었다. The goal of this paper is to compare classification performances and to find a better classifier based on the characteristics of data. The compared methods are CART with two ensemble algorithms, bagging or boosting and SVM. In the empirical study of twenty-eight data sets, we found that SVM has smaller error rate than the other methods in most of data sets. When comparing bagging, boosting and SVM based on the characteristics of data, SVM algorithm is suitable to the data with small numbers of observation and no missing values. On the other hand, boosting algorithm is suitable to the data with number of observation and bagging algorithm is suitable to the data with missing values.
A Study of Combined Splitting Rules in Regression Trees
이영섭,Lee, Yung-Seop 한국데이터정보과학회 2002 한국데이터정보과학회지 Vol.13 No.1
Regression trees, a technique in data mining, are constructed by splitting function-a independent variable and its threshold. Lee (2002) considered one-sided purity (OSP) and one-sided extreme (OSE) splitting criteria for finding a interesting node as early as possible. But these methods cannot be crossed each other in the same tree. They are just concentrated on OSP or OSE separately in advance. In this paper, a new splitting method, which is the combination and extension of OSP and OSE, is proposed. By these combined criteria, we can select the nodes by considering both pure and extreme in the same tree. These criteria are not the generalized one of the previous criteria but another option depending on the circumstance.