• Geomechanics and Engineering
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• v.20 no.2
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• Speech Sciences
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• v.10 no.2
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• pp.7-25
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• 2003

Predicting the right prosodic elements is a key factor in improving the quality of synthesized speech. Prosodic elements include break, pitch, duration and loudness. Pitch, which is realized by Fundamental Frequency (F0), is the most important element relating to the quality of the synthesized speech. However, the previous method for predicting the F0 appears to reveal some problems. If voiced and unvoiced sounds are not correctly classified, it results in wrong prediction of pitch, wrong unit of triphone in synthesizing the voiced and unvoiced sounds, and the sound of click or vibration. This kind of feature is usual in the case of the transformation from the voiced sound to the unvoiced sound or from the unvoiced sound to the voiced sound. Such problem is not resolved by the method of grammar, and it much influences the synthesized sound. Therefore, to steadily acquire the correct value of pitch, in this paper we propose a new model for predicting and classifying the voiced and unvoiced sounds using the CART tool.

• Korean Journal of Social Welfare
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• v.42
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• pp.254-289
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• 2000

This study conducted a step-wise discriminant analysis to determine which variables play key roles in distinguishing among 3 mutually exclusive delinquent groups - disclosed delinquency group, closed delinquency group, and no delinquency group - and to 'develop a procedure for predicting group membership for new cases whose group membership is undetermined. Variables included in this study were collected by the review of previous empirical studies, which were identified as related to juvenile delinquency. The variables can be classified as character-related variables, family-related variables, school-related variables, peer-related variables, and socioeconomic variables. A sample of 693 youths were employed to conduct the statistical analysis. Out of 35 possible predictors of juvenile delinquency 14 variables were included in the pool of predicting variables. This study used a hold-out sample (n=300) to test if the linear discriminant function classify cases correctly into one of 3 groups. The percentage of cases classified correctly by the discriminant function was turned out to be acceptable in the area of social sciences. Discussions and suggestions were provided in terms of prevention and intervention for juvenile delinquency with focus on the 14 predictor variables.

• Journal of the Korean Society of School Health
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• v.36 no.3
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• Atmosphere
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• v.17 no.2
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• pp.135-145
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• 2007

We developed a simple regression model for predicting the intesity change (central pressure) of major tropical cyclones (TCs) for 24 hours after landfall using 51 TC cases which landed over the Korean Peninsula for 1951-2004. Clusters 1 and 4 with a relatively strong intensity of TC after landfall classified by Choi and Kim (2007) are used to develop a statistical model for the prediction of TC intensity change. Predicting parameters (falling constants) in the regression models $(P_t=P_0+alnt)$ are 6.46 and 10.11 for clusters 1 and 4, respectively. It might be mentioned that there is some feasibility in employing a simple regression model developed in this study for TC intensity change after landfall for operational purpose of TC forecasting compared with RSMC-Tokyo best-track in both TC cases of Clusters 1 and 4 and Ewiniar (0603) case, but the room for improvement of model still remains for further study.

• The Research Journal of the Costume Culture
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• v.12 no.6 s.53
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• pp.1031-1044
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• 2004

The purpose of this study were to investigate the relationships between consumer's value and sportswear's benefit sought/attribute evaluation, and to examine how sportswear's benefit sought were influenced by consumers' value and demographic variables. Subjects were 468 males and females in their teens to 30's in Seoul. Consumer's value was classified into four dimensions by using factors analysis: materialism, achievement orientation, traditionalism, and other consciousness. The value of materialism and achievement orientation had positive relations with brand orientation, fashion, appearance attraction, enterprise image, and brand advertisement of sportswear in both male and female. Achievement orientation had, in case of the male, positive relations with economics, practicality, fabrics, and quality of sportswear. In case of the female, traditionalism had positive relations with economics and practicality, and other consciousness value had positive relations with brand advertisement. Materialism was the most important in predicting brand orientation, followed by social class in both male and female. Age was the most important in predicting practicality, followed by achievement orientation in case of the male. Materialism(-) was, in case of the female, the most important in predicting practicality, followed by traditionalism, achievement orientation, and age.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.4989-4992
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• 2013

Aim: To compare p16INK4a immunocytochemistry with the HPV polymerase chain reaction in predicting high grade cervical squamous intraepithelial lesions. Materials and Methods: This diagnostic case-control study was conducted from January 2010 until December 2010. We obtained 30 samples, classified according to the degree of cervical intraepithelial neoplasia (CIN): 11 samples for CIN 1, 9 samples for CIN 2, and 10 samples for CIN 3. HPV PCR, p16INK4a immunocytochemistry, and histopathological examination were performed on all samples. Statistical analysis was conducted using SPSS 20.0. Results: In predicting CIN 2-3, we found p16INK4a to have similar specificity and positive predictive value as HPV PCR (95%, 97.2% vs 96.7%), but better sensitivity (87.5% vs 72.5%) and negative predictive value (82.1% vs 67.6%). The most prevalent types of high-risk HPV in our study were HPV 33, 35, 58, 52, and 16. Conclusions: p16INK4a has better diagnostic values than HPV PCR and may be incorporated in the triage of ASCUS and LSIL to replace HPV PCR. Genotype distribution of HPV differs in each region, providing a challenge to develop HPV vaccines based on the epidemiology of HPV in that particular region.

• Asia Pacific Journal of Corpus Research
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• v.2 no.1
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• pp.35-45
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• 2021

With the wide spread of the Common European Framework of Reference (CEFR) scales, many studies attempt to apply them in routine teaching and rater training, while more evidence regarding criterial features at different CEFR levels are still urgently needed. The current study aims to explore complexity features that distinguish and predict CEFR proficiency levels in oral performance. Using a quantitative/corpus-based approach, this research analyzed lexical and syntactic complexity features over 80 transcriptions (includes A1, A2, B1 CEFR levels, and native speakers), based on an interview test, Standard Speaking Test (SST). ANOVA and correlation analysis were conducted to exclude insignificant complexity indices before the discriminant analysis. In the result, distinctive differences in complexity between CEFR speaking levels were observed, and with a combination of six major complexity features as predictors, 78.8% of the oral transcriptions were classified into the appropriate CEFR proficiency levels. It further confirms the possibility of predicting CEFR level of L2 learners based on their objective linguistic features. This study can be helpful as an empirical reference in language pedagogy, especially for L2 learners' self-assessment and teachers' prediction of students' proficiency levels. Also, it offers implications for the validation of the rating criteria, and improvement of rating system.

• Korean Journal of Human Ecology
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• v.22 no.5
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• pp.477-490
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• 2013

This study explores critical and success factor of benefits and risks for predicting the acceptance of QR code virtual fashion stores. As an exploratory approach, this study conducted focus group interviews(FGI) with 21 panels who had experience with selected QR virtual stores (e.g., Home plus, 11th Street, Pin & Fit). Content analysis was used to generate 184 excerpts and classify into benefits and risks related to QR code virtual stores. With respect to benefits, content analysis identified four factors: Affective appeal, perceived usefulness, ubiquity, and brand promotion. Also, contents regarding perceived risks were classified into four factors: Complexity, lack of information, lack of technology infrastructure, and perceived cost. A managerial implication was discussed for predicting consumer technology acceptance of a QR code virtual store in the fashion retailing market.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.218-226
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• 1998

In this study a neural network based expert system designed to diagnose operating status of a rotating spindle system supported by ball bearings was introduced. In order to facilitate practical failure situations, five exemplary abnormal status was fabricated. Out of several possible data source locations, seven most effective spots were chosen and proven to be the most successful in predicting single and multiple abnormalities. Increased signal strength was measured around where abnormality was embedded. Signal mea-surement locations producing high prediction rate were also classified. Even though multiple abnormalities were hard to be decoupled into their individual causes, proposed diagnostic system was somewhat effective in predicting such cases under certain combination of sensor locations. Among several abnormal operating conditions, highest prediction rate can be expected when signal is spoiled by the failure or damage in outer race. Proposed diagnostic system was again proven to be the most effective system in analyzing and ranking the importance of data sources.