• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.29-42
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• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• Journal of Animal Science and Technology
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• v.56 no.4
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• pp.15.1-15.10
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• 2014

Background: Morphologically classifying embryos is important for numerous laboratory techniques, which range from basic methods to methods for assisted reproduction. However, the standard method currently used for classification is subjective and depends on an embryologist's prior training. Thus, our work was aimed at developing software to classify morphological quality for blastocysts based on digital images. Methods: The developed methodology is suitable for the assistance of the embryologist on the task of analyzing blastocysts. The software uses artificial neural network techniques as a machine learning technique. These networks analyze both visual variables extracted from an image and biological features for an embryo. Results: After the training process the final accuracy of the system using this method was 95%. To aid the end-users in operating this system, we developed a graphical user interface that can be used to produce a quality assessment based on a previously trained artificial neural network. Conclusions: This process has a high potential for applicability because it can be adapted to additional species with greater economic appeal (human beings and cattle). Based on an objective assessment (without personal bias from the embryologist) and with high reproducibility between samples or different clinics and laboratories, this method will facilitate such classification in the future as an alternative practice for assessing embryo morphologies.

• Korean Journal of Biological Psychiatry
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• v.8 no.1
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• pp.53-61
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• 2001

The authors, in this paper, addressed a variety of problems and difficulties which Korean psychiatrists should cope with. The surprising development of neurosciences, splitting of neuropsychiatry into neurology and psychiatry, easygoing attitude of psychiatrists, changes in the delivery system of health care and ill-balanced education of psychiatry were listed as causes of or contributors to them. Social bias to psychiatry and regulations from outside are also considered as contributors. Psychiatric education, including medical school, residency training, continuing medical education and psychiatric textbooks, need to be changed in order to enlarge the boundary of psychiatry. Reestablishment of identity of psychiatry and psychiatrist is unavoidable, considering far-reaching new knowledge of neuroscience and gradually invisible borderzone between neurology and psychiatry. The other ways worth while to consider are : the expansion of psychiatrists' activities, development of medical behavioral science to a clinical specialty, creation of new psychiatric subspecialties, and additional training of psychiatric residencies in the primary medical care.

• Journal of Korean Academy of Nursing
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• v.43 no.3
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• pp.420-430
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• 2013

Purpose: The aim of this study was to review the literature to determine whether intensive pelvic floor muscle training during pregnancy and after delivery could prevent urinary and fecal incontinence. Methods: Randomized controlled trials (RCT) of low-risk obstetric populations who had done Kegel exercise during pregnancy and after delivery met the inclusion criteria. Articles published between 1966 and 2012 from periodicals indexed in Ovid Medline, Embase, Scopus, KoreaMed, NDSL and other databases were selected, using the following keywords: 'Kegel, pelvic floor exercise'. The Cochrane's Risk of Bias was applied to assess the internal validity of the RCT. Fourteen selected studies were analyzed by meta-analysis using RevMan 5.1. Results: Fourteen RCTs with high methodological quality, involving 6,454 women were included. They indicated that Kegel exercise significantly reduced the development of urinary and fecal incontinence from pregnancy to postpartum. Also, there was low clinical heterogeneity. Conclusion: There is some evidence that for antenatal and postnatal women, Kegel exercise can prevent urinary and fecal incontinence. Therefore, a priority task is to develop standardized Kegel exercise programs for Korean pregnant and postpartum women and make efficient use of these programs.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Labour Economics
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• v.33 no.2
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• pp.53-89
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• 2010

Curricula relevant to labor market needs are often designed with the goals of individual employment and regional development at the forefront. This study provided information on regional scope by investigating the extent and determinants of the geographic mobility of vocational high school graduates and the effects of that mobility on first-job wage rates. Geographic mobility was defined as being employed in other provinces in which vocational schools were located. About 38% of graduates were employed in other provinces. Geographic mobility was positively related to gender and human capital such as health, course of study, vocational certificate, and job training. Mobility led to higher wage rates even after controlling for sample selection bias. The implication is that vocational high school curricula which focus excessively on provincial concerns may weaken a workforce's effectiveness.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.107-112
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• 2015

This paper introduces an evolutionary nonlinear regression based compensation technique for the short-range prediction of wind speed using AWS(Automatic Weather Station) data. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS wind forecast guidance. Also FCM(Fuzzy C-Means) clustering is adopted to mitigate bias of wind speed data. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days prediction of wind speed in South Korean regions. This method is then compared to the UM model and has shown superior results. Data for 2007-2009, 2011 is used for training, and 2012 is used for testing.

• Journal of IKEEE
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• v.23 no.4
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• pp.1134-1139
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• 2019

One of the most important applications of computer vision algorithms is the detection of threat objects in x-ray security images. However, in the practical setting, this task is complicated by two properties inherent to the dataset, namely, the problem of class imbalance and visual complexity. In our previous work, we resolved the class imbalance problem by using a GAN-based anomaly detection to balance out the bias induced by training a classification model on a non-practical dataset. In this paper, we propose a new method to alleviate the visual complexity problem by using a KNN-based automatic cropping algorithm to remove distracting and irrelevant information from the x-ray images. We use the cropped images as inputs to our current model. Empirical results show substantial improvement to our model, e.g. about 3% in the practical dataset, thus further outperforming previous approaches, which is very critical for security-based applications.

• The Journal of Korean Obstetrics and Gynecology
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• v.33 no.4
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• pp.1-22
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• 2020

Objectives: The purpose of this review is to evaluate the efficacy of moxibustion for stress urinary incontinence (SUI) in women. Methods: For relevant randomized controlled trials (RCTs), we searched the following databases from their inception to September 1, 2020: The Cochrane Library, PubMed, EMBASE, Chinese National Knowledge Infrastructure Database (CNKI), Koreanstudies Information Service System (KISS), Research Information Sharing Service (RISS), and National Digital Science Library (NDSL). The key search terms were 'stress urinary incontinence' and 'moxibustion'. Data extraction and assessment of risk of bias were conducted by two authors independently. Results: A total of 11 RCTs were finally included in this systematic review. In all studies, moxibustion treatment was applied as an adjuvant therapy to the conventional treatment, and the most common conventional treatment was pelvic floor muscle training (PFMT). The treatment group (conventional treatment plus moxibustion) showed statistically more significant effect than the control group (conventional treatment only) in various evaluation indicators including urinary incontinence frequency, 1 hour urine pad test, quality of life, the clinical efficacy rate, and pelvic muscle strength. Conclusions: In this study, we investigated the efficacy of moxibustion as an adjuvant therapy for female SUI patients. Further studies are needed to supplement the safety of moxibustion and the evaluation of moxibustion dose.