• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.175-180
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• 2001

This paper presents the optimal fraction of validation set to obtain a prediction accuracy of software failure count or failure time in the future by a neuro-fuzzy system. Given a fixed amount of training data, the most popular effective approach to avoiding underfitting and overfitting is early stopping, and hence getting optimal generalization. But there is unresolved practical issues : How many data do you assign to the training and validation set\ulcorner Rules of thumb abound, the solution is acquired by trial-and-error and we spend long time in this method. For the sake of optimal fraction of validation set, the variant specific fraction for the validation set be provided. It shows that minimal fraction of the validation data set is sufficient to achieve good next-step prediction. This result can be considered as a practical guideline in a prediction of software reliability by neuro-fuzzy system.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.525-534
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• 2017

The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method

• 한국노년학
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• v.28 no.4
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• pp.1023-1039
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• 2008

The purpose of this study was to develop a group validation therapy(V/T) which could be implemented for the elderly with dementia in nursing home, and to evaluate the effectiveness of the program on cognition, ADL(Activity of Daily Living), depression, problematic behavior and QOL(Quality of Life). Subjects were recruited from 4 nursing homes in D city. The sample comprised forty elderly with dementia, capable of verbal communication. Each twenty were in an experimental and control groups. However, four elders with dementia dropped out in experimental and control groups due to personal affairs. Experimental group completed twelve consecutive group validation therapy sessions that combined centering, asking factual questions, rephrasing, identifying and using the preferred sense, asking the extreme, imagining the opposite, reminiscing, touching, maintaining eye contact and a caring tone of voice, observing, matching and expressing the emotion with emotion, using ambiguity, linking behavior with a basic human need, using music and mirroring techniques. Following the intervention, experimental group experienced a significant improvement in cognition, ADL, depression, and QOL. But it is a nonsignificant in problematic behavior on statistically. As a result, a group validation therapy should be applied as an effective and practical psychosocial intervention for the elderly with dementia.

• Korean Journal of Radiology
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• v.24 no.7
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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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• v.23 no.3
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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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• v.21 no.4
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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.

• Journal of Nutrition and Health
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• v.31 no.2
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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.

• Korean Journal of Remote Sensing
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• v.38 no.4
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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.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.189-199
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• 2012

PURPOSES: The purpose of this study is to propose a new methodology for developing statistical collision models and to show the validation results of the methodology. METHODS: A new modeling method of introducing variables into the model one by one in a multiplicative form is suggested. A method for choosing explanatory variables to be introduced into the model is explained. A method for determining functional forms for each explanatory variable is introduced as well as a parameter estimating procedure. A model selection method is also dealt with. Finally, the validation results is provided to demonstrate the efficacy of the final models developed using the method suggested in this study. RESULTS: According to the results of the validation for the total and injury collisions, the predictive powers of the models developed using the method suggested in this study were better than those of generalized linear models for the same data. CONCLUSIONS: Using the methodology suggested in this study, we could develop better statistical collision models having better predictive powers. This was because the methodology enabled us to find the relationships between dependant variable and each explanatory variable individually and to find the functional forms for the relationships which can be more likely non-linear.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1-10
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• 2021

This study examines the dynamic pattern of the exchange rate volatilities of the ASEAN-5 currencies from January 2006 to August 2020. The exchange rates applied in this study comprise bilateral and effective exchange rates in order to investigate the influence of the US dollar on the stability of the ASEAN-5 currencies. Since a volatility model employed in this study is a natural cubic spline volatility model, the Monte Carlo simulation is consequently conducted to determine an appropriate criterion to select a number of quantile knots for this model. The simulation results reveal that, among four candidate criteria, Generalized Cross-Validation is a suitable criterion for modeling the ASEAN-5 exchange rate volatilities. The estimated volatilities showed the inconstant dynamic patterns reflecting the uncertain exchange rate risk arising in international transactions. The bilateral exchange rate volatilities of the ASEAN-5 currencies to the US dollar are more variable than their corresponding effective exchange rate volatilities, indicating the influence of the US dollar on the stability of the ASEAN-5 currencies. The findings of this study suggest that the natural cubic spline volatility model with the quantile knots selected by Generalized Cross-Validation is practical and can be used to examine the dynamic patterns of the financial volatility.