• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.4
• /
• pp.69-75
• /
• 2019

The frequency of natural disasters and the scale of damage are increasing due to the abnormal weather phenomenon that occurs worldwide. Especially, damage caused by natural disasters in coastal areas around the world such as Earthquake in Japan, Hurricane Katrina in the United States, and Typhoon Maemi in Korea are huge. If we can predict the damage scale in response to disasters, we can respond quickly and reduce damage. In this study, we developed damage prediction functions for Wind waves caused by sea breezes and waves during various natural disasters. The disaster report (1991 ~ 2017) has collected the history of storm and typhoon damage in coastal areas in Korea, and the amount of damage has been converted as of 2017 to reflect inflation. In addition, data on marine weather factors were collected in the event of storm and typhoon damage. Regression analysis was performed through collected data, Finally, predictive function of the sea turbulent damage by the sea area in 74 regions of the country were developed. It is deemed that preliminary damage prediction can be possible through the wind damage prediction function developed and is expected to be utilized to improve laws and systems related to disaster statistics.

• Korean Journal of Psychosomatic Medicine
• /
• v.27 no.1
• /
• pp.69-76
• /
• 2019

Objectives : The purpose of this study was to examine the prevalence and clinical characteristics of nonsuicidal self-injury (NSSI), and its association with suicide attempts among clinically depressed adolescents in Korea. Methods : In total, 113 depressed adolescents aged 12-18 years in South Korea were enrolled in this study. We assessed sociodemographic and clinical characteristics including suicidality and non-suicidal self-injury (NSSI) using various self-reported scales and semi-structured interview for diagnosis of psychiatric disorders. Demographic and clinical characteristics of the subjects were compared between NSSI and non-NSSI groups. We examined significant predictors of suicide attempts using logistic regression analysis. Results : Among 113 depressed participants, 48 (42.1%) adolescents were classified into the NSSI group. In the NSSI group, there were significantly more females, showed higher depression, higher state-anxiety, and more suicide ideation. The most predictive factors of suicide attempts were history of NSSI, observed suicide/NSSI behaviors of their family or friends, and total state anxiety score. Conclusions : NSSI is more common problem among clinically depressed adolescents and history of NSSI is a significant predictor of present suicide attempts. To include the assessment of NSSI for clinically depressed adolescent may be crucial for intervention programs for high risk adolescents of suicide in Korea.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.14 no.4
• /
• pp.161-178
• /
• 2019

The purpose of this study is to analyze the effect of characteristics of the online shopping mall website and the service quality factors required on the online service on the reliability and repurchase intention. Through this, the key customer response service and operation management strategy can be proposed for the growth of kidult online shopping mall which is continuously spreading. Also, implications for development direction can be suggested. For this purpose, the ES-QUAL and E-Rec-S-QUAL factors proposed by Parasuraman et al.(2005) were set as independent variables, as well as professionalism, diversity, playfulness, and personalization, and website reliability as a mediating factor, and repurchase intention as a dependent variable. For the analysis, a structured questionnaires survey was conducted to 200 domestic online shopping mall users. The main results are as follows. First, the professionalism, personalization in the characteristics, and service quality factors had positive effects on website reliability. Second, playfulness, personalization in the characteristics, and service quality factors had positive effects on repurchase intention. Third, website reliability had a positive effect on repurchase intention. Fourth, website reliability was found to mediate the relationship between predictive (independent) variables and dependent variables. The above results show that if a professional online shopping mall is equipped with online service quality and establish personalization through enhancing the professionalism, the online shopping mall can form a website reliability and expect to build continuous relationship with customers. In addition, implications can be suggested for future customer response service and operation management strategies.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.155-170
• /
• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• The Journal of Korean Academy of Prosthodontics
• /
• v.57 no.1
• /
• pp.66-74
• /
• 2019

Excessive wear causes many complications when untreated, so that accurate diagnosis, analysis and predictive treatment plan should be made, and through evaluation of vertical dimension and stepwise treatment, a stable inter-arch relationship can be set. For the long-term success of implant treatment, ideal position and angle of implant is important, and its importance increases especially in multiple implant cases. Therefore, thorough diagnosis and planning, accurate surgery and prosthodontic procedures are significant. In this case, a 68-year-old male patient with a loss of vertical dimension due to multiple tooth loss and overall tooth wear was planned with systematic analyses from the pre-treatment stage to rehabilitate vertical dimension. Full-mouth fixed rehabilitation with computer tomography guided implant surgery was performed to the newly set vertical dimension and attained satisfactory outcomes both functionally and esthetically.

• The Korean Journal of Applied Statistics
• /
• v.34 no.5
• /
• pp.745-760
• /
• 2021

Prediction of a stock price has been a subject of interest for a long time in financial markets, and thus, many studies have been conducted in various directions. As the efficient market hypothesis introduced in the 1970s acquired supports, it came to be the majority opinion that it was impossible to predict stock prices. However, recent advances in predictive models have led to new attempts to predict the future prices. Here, we summarize past studies on the price prediction by evaluation measures, and predict the direction of stock prices of Samsung Electronics, LG Chem, and NAVER by applying various machine learning models. In addition to widely used technical indicator variables, accounting indicators such as Price Earning Ratio and Price Book-value Ratio and outputs of the hidden Markov Model are used as predictors. From the results of our analysis, we conclude that no models show significantly better accuracy and it is not possible to predict the direction of stock prices with models used. Considering that the models with extra predictors show relatively high test accuracy, we may expect the possibility of a meaningful improvement in prediction accuracy if proper variables that reflect the opinions and sentiments of investors would be utilized.

• Journal of the Korean Wood Science and Technology
• /
• v.49 no.3
• /
• pp.213-225
• /
• 2021

In this paper, we investigated the carbonization characteristics of lignin hydrochar prepared by hydrothermal carbonization and established a model for predicting the carbonization degree using near-infrared spectroscopy and partial least squares regression. The carbon content of the hydrothermally carbonized lignin at the temperature of 200 ℃ was higher by approximately 3 wt% than that of the untreated sample, and the carbon content tended to gradually increase as the heating time increased. Hydrothermal carbonization made lignin more carbon-intensive and more homogeneous by eliminating the microparticles. The discriminant and predictive models using near-infrared spectroscopy and partial least squares regression approppriately determined whether hydrothermal carbonization has been applied and predicted the carbon content of hydrothermal carbonized lignin with high accuracy. In this study, we confirmed that we can quickly and nondestructively predict the carbonization characteristics of lignin hydrochar manufactured by hydrothermal carbonization using a partial least squares regression model combined with near-infrared spectroscopy.

• Mycobiology
• /
• v.49 no.4
• /
• pp.406-420
• /
• 2021

Gloeostereum incarnatum has edible and medicinal value and was first cultivated and domesticated in China. We sequenced the G. incarnatum monokaryotic strain GiC-126 on an Illumina HiSeq X Ten system and obtained a 34.52-Mb genome assembly sequence that encoded 16,895 predicted genes. We combined the GiC-126 genome with the published genome of G. incarnatum strain CCMJ2665 to construct a genetic linkage map (GiC-126 genome) that had 10 linkage groups (LGs), and the 15 assembly sequences of CCMJ2665 were integrated into 8 LGs. We identified 1912 simple sequence repeat (SSR) loci and detected 700 genes containing 768 SSRs in the genome; 65 and 100 of them were annotated with gene ontology (GO) terms and KEGG pathways, respectively. Carbohydrate-active enzymes (CAZymes) were identified in 20 fungal genomes and annotated; among them, 144 CAZymes were annotated in the GiC-126 genome. The A mating-type locus (MAT-A) of G. incarnatum was located on scaffold885 at 38.9 cM of LG1 and was flanked by two homeodomain (HD1) genes, mip and beta-fg. Fourteen segregation distortion markers were detected in the genetic linkage map, all of which were skewed toward the parent GiC-126. They formed three segregation distortion regions (SDR1-SDR3), and 22 predictive genes were found in scaffold1920 where three segregation distortion markers were located in SDR1. In this study, we corrected and updated the genomic information of G. incarnatum. Our results will provide a theoretical basis for fine gene mapping, functional gene cloning, and genetic breeding the follow-up of G. incarnatum.

• The Korean Journal of Applied Statistics
• /
• v.33 no.6
• /
• pp.791-804
• /
• 2020

Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.

• Journal of The Korean Society of Clinical Toxicology
• /
• v.18 no.2
• /
• pp.66-77
• /
• 2020

Purpose: The aims of the present study were twofold. First, the research investigated the effect of an individual's risk factors and the prevalence of psychotropic drugs on QTc prolongation, TdP (torsades de pointes), and death. Second, the study compared the risk scoring systems (the Mayo Pro-QT risk score and the Tisadale risk score) on QTc prolongation. Methods: The medical records of intoxicated patients who visited the emergency department between March 2010 and February 2019 were reviewed retrospectively. Among 733 patients, the present study included 426 psychotropic drug-intoxicated patients. The patients were categorized according to the QTc value. The known risk factors of QTc prolongation were examined, and the Mayo Pro-QT risk score and the Tisadale risk score were calculated. The analysis was performed using multiple logistic regression, Spearman correlation, and ROC (receiver operating characteristic). Results: The numbers in the mild to moderate group (male: 470≤QTc<500 ms, female: 480≤QTc<500 ms) and severe group (QTc≥500 ms or increase of QTc at least 60ms from baseline, both sex) were 68 and 95, respectively. TdP did not occur, and the only cause of death was aspiration pneumonia. The statically significant risk factors were multidrug intoxications of TCA (tricyclic antidepressant), atypical antipsychotics, an atypical antidepressant, panic disorder, and hypokalemia. The Tisadale risk score was larger than the Mayo Pro-QT risk score. Conclusion: Multiple psychotropic drugs intoxication (TCA, an atypical antidepressant, and atypical antipsychotics), panic disorder, and hypokalemia have been proven to be the main risk factors of QTc prolongation, which require enhanced attention. The present study showed that the Tisadale score had a stronger correlation and predictive accuracy for QTc prolongation than the Mayo Pro-QT score. As a result, the Tisadale risk score is a crucial assessment tool for psychotropic drug-intoxicated patients in a clinical setting.