• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.645-660
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• 2001

In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

• Journal of the Chungcheong Mathematical Society
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• v.29 no.1
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• pp.59-71
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• 2016

To obtain more accurate approximation of the true images in the deblurring problems, the weighted global generalized cross validation(GCV) function to the inverse problem with multiple right-hand sides is suggested as an efficient way to determine the regularization parameter. We analyze the experimental results for many test problems and was able to obtain the globally useful range of the weight when the preconditioned global conjugate gradient linear least squares(Gl-CGLS) method with the weighted global GCV function is applied.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.137-141
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• 1994

The purpose of this paper is to examine what is meant by a ralid measure of health. Guyatt, Kirshner and Jaeschke propose that health tests should be designed so as to have one of several kinds of validity: 'longitudinal construct validity' for those which are used for longitudinal research designs, and 'cross-sectional construct validity' for those which are used for cross-sectional designs. Williams and Naylor argue that this approach to test classification and validation confuses what a test purports to measure with the purpose for which it is used, and that some tests have multiple uses. A review of the meanings of validity in the psychologica test literature shows that both sets of authors use the term validity in an idiosyncratic way. Although the use of a test (evaluated by content validity) should not be conflated with whether the test actually measures a specified construct (evaluated by construct validity);' if health is actually made up of several constructs (as suggested in Hyland's interactional model) then there may be an association between types of construct and types of purpose. Evidence is reviewed that people make several, independent judgements about their health: cognitive perceptions of health problems are likely to be more sensitive to change in a longitudinal research design. whereas emotional evaluations of health provide less bias in cross-sectional designs. Thus. a classification of health measures in terms of the purpose of the test may parallel a classification in terms of what tests purport to measure.

• ETRI Journal
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• v.46 no.2
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.14 no.2
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• pp.150-155
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• 2014

The measurement of $17{\alpha}$-hydroxyprogesterone ($17{\alpha}$-OHP) in a dried blood spot on filter paper is an important for screening of congenital adrenal hyperplasia (CAH). Since high levels of $17{\alpha}$-OHP are frequently observed in premature infants without congenital adrenal hyperplasia, we evaluated cuts-off based on birth weight and performed validation. Birth weight and $17{\alpha}$-OHP concentration data of 292,204 newborn screening subjects in Greencross labopratories were analyzed. The cut-off values based on birth weight were newly evaluated and validated with the original data. The mean $17{\alpha}$-OHP concentration were 7.25 ng/mL in very low birth weight (VLBW) group, 4.02 ng/mL in low birth weight (LBW) group, 2.53 g/mL in normal birth weight (NBW) group, and 2.24 ng/mL in heavy birth weight (HBW) group. The cut-offs for CAH were decided as follows: 21.12 ng/mL for VLBW and LBW groups and 11.14 ng/mL for NBW and HBW groups. When applied new cut-offs for original data, positive rates in VLBW and LBW groups were decreased and positive rates in NBW and HBW groups were increased. The cut-offs based on birth weight should be used in the screening for CAH. We believe that our new cut-off reduce the false positive rate and false negative rate and our experience for cut-off set up and validation will be helpful for other laboratories doing newborn screening test.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.669-680
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• 2012

Noise removal of document images is a necessary step during preprocessing to recognize characters effectively because it has influences greatly on processing speed and performance for character recognition. We have considered using the spatial filters such as traditional mean filters and Gaussian filters, and wavelet transformed based methods for noise deduction in natural images. However, these methods are not effective for the noise removal of document images. In this paper, we present noise removal of document images using support vector regression. The proposed approach consists of two steps which are SVR training step and SVR test step. We construct an optimal prediction model using grid search with cross-validation in SVR training step, and then apply it to noisy images to remove noises in test step. We evaluate our SVR based method both quantitatively and qualitatively for noise removal in Korean, English and Chinese character documents, and compare it to some existing methods. Experimental results indicate that the proposed method is more effective and can get satisfactory removal results.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• The Korean Journal of Pain
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• v.34 no.4
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• pp.501-508
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• 2021

Background: Yellow flags are psychosocial factors shown to be indicative of long-term chronicity and disability. The purpose of the study was to evaluate the psychometric properties of the Turkish Yellow Flag Questionnaire (YFQ) in patients with chronic musculoskeletal pain (CMP). Methods: The cross-cultural adaptation was conducted with translation and back-translation of the original version. Reliability (internal consistency and test-retest) was examined for 231 patients with CMP. Construct validity was assessed by correlating the YFQ with the Hospital Anxiety and Depression Scale (HADS), Orebro Musculoskeletal Pain Questionnaire (OMPQ), and Tampa Kinesiophobia Scale (TKS). Factorial validity was examined with both exploratory and confirmatory factorial analysis. Results: The YFQ showed excellent test/retest reliability with an Intraclass correlation coefficient of 0.82. The internal consistency was moderate (Cronbach's alpha of 0.797). As a result of the exploratory factor analysis, there were 7 domains compatible with the original version. As a result of confirmatory factor analysis, the seven-factor structure of YFQ was confirmed. There was a statistically significant correlation between YFQ-total score and OMPQ (r = 0.57, P < 0.001), HADS-anxiety (r = 0.32, P < 0.001), HADS-depression (r = 0.44, P < 0.001), and TKS (r = 0.37, P < 0.001). Conclusions: This study's results provide considerable evidence that the Turkish version of the YFQ has appropriate psychometric properties, including test-retest reliability, internal consistency, construct validity and factorial validity. It can be used for evaluating psychosocial impact in patients with CMP.

• 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 Pharmaceutical Investigation
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• v.39 no.4
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• pp.263-267
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• 2009

The purpose of this study is to evaluate the efficacy of stipulated cleaning process, and the prohibition of cross-contamination and microbiological contamination, which inadequate cleaning in multi-production could occur, through cleaning validation of multi-purpose facility used to produce five biopharmaceutical products as sterile injection. After production of five biopharmaceutical products such as hGH, rhGCSF, rhEPO, rhFSH and rhIFN using vial filling machine, the cleaning validation such as residual analysis of active ingredients or human serum albumin, measurement of total organic carbon (TOC), residual analysis of detergent and microbiological contamination were carried out. In the case of rhGH and rhGCSF clean validations, drug residues were not detected. Furthermore, in the case of rhEPO, rhFSH and rhIFN clean validations, human serum albumin residues were not detected. At TOC (total organic carbon) analysis, all clean validations gave the TOC of about average 137.93%, not more than 150% of acceptance criteria. At sodium analysis for the checking of residues of cleaning agent, sodium residues were not detected. In sterility test, they showed no microbiological contamination of bacteria and fungi. Thus, this cleaning validation was determined as successful in protection of cross-contamination and induction of safety in multi-purpose facility.