• 대한원격탐사학회지
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• 제16권4호
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• pp.315-325
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• 2000

Observations were made to validate ocean color algorithms in the Ulleung Basin, East Sea in May 2000. Small scale and meso-scale surveys were conducted for the validation of ocean color products (nLw: normalized water-leaving radiance and chlorophyll concentration). There were discrepancies between SeaWiFS and in situ nLw showing the current aerosol models of standard SeaWiFS processing software are less than adequate (Gordon and Wang, 1994). Applying the standard SeaWiFS in-water algorithm resulted in an overestimation of chlorophyll concentration. This is because that CDOM absorption was higher than the estimated chlorophyll absorption. TSS concentration was also high. Therefore, the study region deviated from Case 1 waters. The source of these materials seems to be the entrainment of coastal water by the Tsushima Warm Current. Study of the bio-optical properties in other season is desirable.

• 한국심리학회지 : 문화 및 사회문제
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• 제30권1호
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• pp.55-79
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• 2024

본 연구는 현재까지의 국내 사이코패시 평가도구 타당화 연구의 결과를 종합하여 한국형 사이코패시에 대한 탐색적 연구를 수행하는 데 목적이 있다. 국내 사이코패시 척도 타당화 연구는 협소한 연구방법론, 남성 중심의 연구, 부적절한 요인구조 적용의 문제를 가진다. 또한, 구성타당도 연구에서 원척도의 요인구조와 차이가 있음이 확인되었으나 한국형 사이코패시에 대한 개념적 논의가 부족한 실정이다. 따라서 본 연구에서는 사이코패시 평가도구 타당화 연구를 한 국내 논문 16편과 각 원척도의 요인구조를 다룬 해외 논문 9편을 비교하였다. 각 연구에서 도출된 요인구조, 요인 별 할당된 문항, 탈락된 문항을 비교하여 국내 사이코패시가 가지는 특성을 탐색하였다. 결과적으로 한국형 사이코패시는 물질만능주의, 마키아벨리즘, 반사회성과 충동성 간 구분된 인식을 가지는 것으로 나타났다. 본 연구는 국내의 모든 사이코패시 타당화 연구 결과를 종합하였다는 데에 의의를 가지며 한국형 사이코패시에 대한 개념적 토대를 제시한다.

• 정보처리학회논문지D
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• 제8D권2호
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• pp.175-180
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• 2001

본 논문은 미래의 소프크웨어 공장 수나 고장시간 예측 정확성을 얻기 위해, 뉴로-피지 시스템을 이용할 경우 최적의 검증 데이터 할당 비율에 대한 연구이다. 훈련 데이터가 주어졌을 때, 과소 적합과 과잉 적합을 회피하면서 최적의 일반화 능력을 얻기 취해 Early Stopping 방법이 일반적으로 사용되고 있다. 그러나 훈련과 검증 데이터로 얼마나 많은 데이터를 할당갈 것인가는 시행착오법을 이용해 경험적으로 해를 구해야만 하며, 과다한 시간이 소요된다. 최적의 검증 데이터 양을 구하기 위해 규칙 수를 증가시키면서 다양한 검증 데이터 양을 할당하였다. 실험결과 최소의 검증 데이터로도 좋은 예측 능력을 보였다. 이 결과는 뉴로-퍼지 시스템을 소프트웨어 신뢰성 분야에 적용시 실질직언 지침을 제공할 수 있는 것이다.

• 디지털콘텐츠학회 논문지
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• 제18권3호
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• pp.525-534
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• 2017

열린 집합 인식 방법론은 테스트 데이터의 클래스를 학습 시에 모두 파악할 수 없는 경우에 대한 인식 방법론이다. 따라서 열린 집합 인식 방법론은 분류와 유효성 검증의 절차를 필요로 한다. 이러한 연구는 얼굴 인식 모듈의 상용화를 위해 필수적이지만 지금까지 국내에서 연구 결과들이 거의 발표되지 않았다. 우리는 두 개의 검증 단계를 가지는 열린 집합 얼굴 인식 방법론을 제안한다. 첫 번째 단계에서는 학습 클래스 외에 더미 클래스들을 설정하고 희소표현 기반 분류를 수행한다. 이 때 테스트 데이터가 더미 클래스로 분류되면 무효 데이터로 판별하고, 유효한 클래스로 분류되면 다음 유효성 검증 단계로 넘어간다. 두 번째 단계에서 제안하는 네 가지 특징을 추출하고, 확률분포에 기반을 둔 판별함수를 통해 유효성 검증을 수행한다. 우리는 실험을 통해 열린 집합 인식 방법론의 시뮬레이션 방법을 제안하였고 제안하는 방법론의 성능을 제시하고, 희소기반 분류 방식에서 널리 사용되는 SCI 지표를 이용한 유효성 테스트보다 높은 성능을 보임을 입증할 수 있었다.

• 한국노년학
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• 제28권4호
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• pp.1023-1039
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• 2008

본 연구는 집단인정치료가 치매노인에게 미치는 영향에 대해 알아보고자 하였으며 구체적으로는 집단인정치료가 치매노인의 인지기능, 일상생활활동, 문제행동, 우울 및 치매노인의 삶의 질에 미치는 영향에 대해 알아보았다. 이를 위하여 대상자인 치매노인 40명을 실험집단 및 통제집단으로 각각 20명씩 무작위 배정하였고, 두 집단의 동질성 검사와 사전검사 후 집단인정치료를 실시하였다. 이후 사후 검사를 실시하였으며 36명의 최종 결과를 얻게 되었다. 본 연구의 분석 결과, 통계적으로 집단인정치료는 치매노인의 인지기능의 향상, 일상생활활동 증가, 우울 감소, 삶의 질 향상에 유의미하였으나 문제행동 감소에는 유의미하지 않았다. 결론적으로 집단인정치료는 치매노인의 인지기능 향상뿐만 아니라 일상생활활동을 증진시키고 우울을 감소시켰으며, 삶의 질을 높이는데 효과적이었다. 그러므로 향후 집단인정치료는 치매노인을 돕는데 효율적인 심리사회적인 치료방법으로 실무에서 충분히 활용할 수 있을 것으로 기대된다.

• Korean Journal of Radiology
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• 제24권7호
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• pp.626-639
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• 2023

Objective: To investigate the association of clinical, pathologic, and magnetic resonance imaging (MRI) variables with progressive disease (PD) during neoadjuvant chemotherapy (NAC) and distant metastasis-free survival (DMFS) in patients with triple-negative breast cancer (TNBC). Materials and Methods: This single-center retrospective study included 252 women with TNBC who underwent NAC between 2010 and 2019. Clinical, pathologic, and treatment data were collected. Two radiologists analyzed the pre-NAC MRI. After random allocation to the development and validation sets in a 2:1 ratio, we developed models to predict PD and DMFS using logistic regression and Cox proportional hazard regression, respectively, and validated them. Results: Among the 252 patients (age, 48.3 ± 10.7 years; 168 in the development set; 84 in the validation set), PD was occurred in 17 patients and 9 patients in the development and validation sets, respectively. In the clinical-pathologic-MRI model, the metaplastic histology (odds ratio [OR], 8.0; P = 0.032), Ki-67 index (OR, 1.02; P = 0.044), and subcutaneous edema (OR, 30.6; P = 0.004) were independently associated with PD in the development set. The clinical-pathologic-MRI model showed a higher area under the receiver-operating characteristic curve (AUC) than the clinical-pathologic model (AUC: 0.69 vs. 0.54; P = 0.017) for predicting PD in the validation set. Distant metastases occurred in 49 patients and 18 patients in the development and validation sets, respectively. Residual disease in both the breast and lymph nodes (hazard ratio [HR], 6.0; P = 0.005) and the presence of lymphovascular invasion (HR, 3.3; P < 0.001) were independently associated with DMFS. The model consisting of these pathologic variables showed a Harrell's C-index of 0.86 in the validation set. Conclusion: The clinical-pathologic-MRI model, which considered subcutaneous edema observed using MRI, performed better than the clinical-pathologic model for predicting PD. However, MRI did not independently contribute to the prediction of DMFS.

• Fisheries and Aquatic Sciences
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• 제23권3호
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• pp.9.1-9.6
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• 2020

Background: Fucosterol is a compound commonly found in algae that has various biological activities. The purpose of this study was to develop a high-performance liquid chromatography (HPLC) validation method for fucosterol and to compare the fucosterol contents of 11 algal species from Ulleungdo, Korea. Method: In this study, we successfully isolated and identified fucosterol from a 70% EtOH extract of Sargassum miyabei, and subsequently conducted specificity, linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, and precision analyses for development of an HPLC validation method. Fucosterol contents were compared using the established HPLC validation conditions. Results: We successfully isolated fucosterol from a 70% EtOH extract of S. miyabei and identified it based on spectroscopic analysis. On the basis of HPLC validation using the fucosterol isolated from S. miyabei, we confirmed specificity (8.5 min), linearity (R² = 0.9998), LOD (3.20 ㎍ mL^-1), LOQ (9.77 ㎍ mL^-1), accuracy (intra-day and inter-day variation, 90-110%), and precision (RSD, 1.07%). Fucosterol contents in the 11 assessed algal species ranged from 0.22 to 81.67 mg g^-1, with the highest content being recorded in a 70% EtOH extract of Desmarestia tabacoides (81.67 mg g^-1), followed by that of Agarum clathratum (78.70 mg g^-1). Conclusions: The results indicate that 70% EtOH extracts of D. tabacoides and A. clathratum containing fucosterol with various effects can be potential alternative sources of fucosterol.

• Computers and Concrete
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• 제21권4호
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• 대한원격탐사학회지
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• 제38권4호
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• pp.395-409
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• 2022

The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.

• Journal of Nutrition and Health
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• 제31권2호
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• pp.220-230
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• 1998

The purpose of this study was to develop and validate the Food frequency questionnaire (FFQ) for dietary studies of Koreans. One hundred and five food items for the Food frequency questionnaire were selected based on information of frequently consumed foods from National Nutrition Survey Reports and on raw data from a dietary survey on diabetic patients. Frequency of consumption was determined through nine categories ranging from more than three times a day to almost never to indicate how often the specified amount of each food item was consumed during the past month. Three portion sizes were given for each food item(small, medium or large) with respect to a stated medium portion. Seventy-three healthy women served for the validation study. They completed both the FFQ and a 3-day diet record. The FFQ estimate of mean nutrient intake was higher by 10-20% than that of the 3-days diet record and the Spearman correlation coefficients between the two methods ranged from 0.26 to 0.59 . The degree of agreement was from 36% to 38% when nutrients intake assessed by the FFQ and 3day diet record were classified within the same quintile. On the whole , the result of this study seemed to be in good agreement with other studies. Therefore the FFQ developed in this study is considered to be a reliable tool in assessing the dietary habits of Korean.