• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.811-824
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• 2023

The purpose of this study was to investigate the communication confidence, self-efficacy, and self confidence in first aid and to identify the predictors of self confidence in first aid. Subjects were 202 caregivers and data were collected by questionnaires from march 1 to 31, 2023. Data were analyzed using t-test, ANOVA, Scheffe's test, Pearson correlation coefficients and Multiple regression analysis using the SPSS 29.0 program. Self-efficacy was a positive correlation with communication confidence (r=.54, p<.001), and self confidence in first aid was a negative correlation with communication confidence(r=-.18, p<.05) and self-efficacy(r=-.31, p<.001). Predictive factors for self confidence in first aid were absence of nurse's aide(β=-.18, p=.009) and self-efficacy(β=-.30, p<.001), and explanatory power was 11.0%(Adj R2=.110, p<.001). Based on the results of this study, to develop and apply an educational program focusing on emergency problems.

• Journal of the Korean Geotechnical Society
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• v.40 no.2
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• pp.65-79
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• 2024

This study employs traditional statistical auto-regressive integrated moving average (ARIMA) and deep learning-based long short-term memory (LSTM) models to predict the deformation of earth retaining walls using inclinometer data from excavation sites. It compares the predictive capabilities of both models. The ARIMA model excels in analyzing linear patterns as time progresses, while the LSTM model is adept at handling complex nonlinear patterns and long-term dependencies in the data. This research includes preprocessing of inclinometer measurement data, performance evaluation across various data lengths and input conditions, and demonstrates that the LSTM model provides statistically significant improvements in prediction accuracy over the ARIMA model. The findings suggest that LSTM models can effectively assess the stability of retaining walls at excavation sites. Additionally, this study is expected to contribute to the development of safety monitoring systems at excavation sites and the advancement of time series prediction models.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1
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• pp.36-42
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• 2006

A Principal component analysis (PCA)-based Mel-Frequency Discrete Wavelet Coefficients (MFDWC) feature Parameters for speaker verification system is Presented in this Paper In this method, we used the 1st-eigenvector obtained from PCA to calculate the energy of each node of level that was approximated by. met-scale. This eigenvector satisfies the constraint of general weighting function that the squared sum of each component of weighting function is unity and is considered to represent speaker's characteristic closely because the 1st-eigenvector of each speaker is fairly different from the others. For verification. we used Universal Background Model (UBM) approach that compares claimed speaker s model with UBM on frame-level. We performed experiments to test the effectiveness of PCA-based parameter and found that our Proposed Parameters could obtain improved average Performance of $0.80\%$compared to MFCC. $5.14\%$ to LPCC and 6.69 to existing MFDWC.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.37-50
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• 2024

This study explored the link between reproductive health knowledge, attitudes, and self-efficacy in young women from Uganda's Luwero district. A survey was conducted on 82 women in the Luwero region from May to July 2016, and the predictive power of knowledge and attitudes toward self-efficacy was evaluated through multiple linear regression analysis. Results showed positive correlations among these factors, with knowledge and attitude predicting self-efficacy. Specifically, understanding healthy puberty habits and valuing women's roles positively influenced self-efficacy for healthy behaviors. These findings emphasize the need to target these aspects in reproductive health education programs, crucial for addressing adolescent pregnancy and related issues in Uganda's rural areas.

• Safety and Health at Work
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• v.15 no.1
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• pp.59-65
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• 2024

Background: Different job strain formulations based on the Job Demand-Control model have been developed. This study evaluated longitudinal associations between job strain and psychological distress and whether associations were influenced by six formulations of job strain, including quadrant (original and simplified), subtraction, quotient, logarithm quotient, and quartile based on quotient, in randomly selected Japanese workers. Methods: Data were from waves I and II of the Survey of Midlife in Japan (MIDJA), with a 4-year followup period. The study sample consisted of 412 participants working at baseline and had complete data on variables of interest. Associations between job strain at baseline and psychological distress at follow-up were assessed via multivariable linear regression, and results were expressed as β coefficients and 95% confidence intervals including R² and Akaike information criterion (AIC) evaluation. Results: Crude models revealed that job strain formulations explained 6.93-10.30% of variance. The AIC ranged from 1475.87 to 1489.12. After accounting for sociodemographic and behavioral factors and psychological distress at baseline, fully-adjusted models indicated significant associations between all job strain formulations at baseline and psychological distress at follow-up: original quadrant (β: 1.16, 95% CI: 0.12, 2.21), simplified quadrant (β: 1.01, 95% CI: 0.18, 1.85), subtraction (β: 0.39, 95% CI: 0.09, 0.70), quotient (β: 0.37, 95% CI: 0.08, 0.67), logarithm quotient (β: 0.42, 95% CI: 0.12, 0.72), and quartile based on quotient (β: 1.22, 95% CI: 0.36, 2.08). Conclusion: Six job strain formulations showed robust predictive power regarding psychological distress over 4 years among Japanese workers.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Journal of Chest Surgery
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• v.35 no.1
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• pp.27-35
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• 2002

Background: A perioperative myocardial infarction(PMI) is one of the major complications after CABG. Among diagnostic methods of PMI, CK-MB activity assays have been increasingly replaced by CK-MB mass assays, which have more sensitive, simple measurement. Also, new cardiac-specific and -sensitive marker, cardiac troponin I(cTnl), has been shown to be a marker of myocardial infarction. We report our evaluation of clinical significance of CK-MB mass and cTnl as a marker of PMI after CABG. Material and Method: We studied 32 patients who underwent CABG at Kangdong Sacred Hospital between April 2000 and April 2001. Postoperative serum CK-MB activity level, serum CK-MB mass, cTnl, electrocardiogram, echocardiogram, and clinical data were recorded prospectively The diagnosis of PMI was defined as positive 2 among 3 or all of the following , by a new Q wave on the electrocardiogram, by serum CK-MB activity higher than 200 lU/L within 72 hours after operation, and by new regional wall motion abnormality on the echocardiogram. Result: After CABG, 3 patients had sustained a PMI according to current diagnostic criteria. As serum CK-MB activity time course, a level of CK-MB activity 12 hours after CABG had very linear correlated significance with serum CK-MB mass 24hours(R=0.946) and cTnl 48 hours(R=0.933) after CABG(p=0.000). As we used a receiver operating characteristics curve(ROC curve) for a diagnostic cutoff value in patients with PMI, serum CK-MB mass levels higher than 30.05 ug/L 24 hours after CABG detected the presence of PMI with an area under the ROC curve of 1.0, a sensitivity of 100%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 100%. Also serum cTnl levels higher than 17.15 ug/L 48 hours after CABG detected the presence of PMI with an area under the ROC curve of 0.98, a sensitivity of 100%, a specificity of 96.6%, a positive preclictive value of 75%, and a negative predictive value of 100% Conclusion: We concluded that both the measurement of CK-MB mass and cTnl are the easier, accurate methods as a diagnostic marker of PMT after CABG, also as a proposal of diagnostic cutoff value enables to an early detection of PMI. However, a 1arger number of patient will be needed because of statistic limitation that a small number of participating patients, a small number of PMI.

• Journal of Chest Surgery
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• v.40 no.8
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• pp.552-557
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• 2007

Background: B-type natriuretic peptide (BNP) is a cardiac hormone that is primarily synthesized by the ventricular cardiac myocytes. Increased plasma BNP levels have been observed in patients suffering with congestive heart failure, ventricular hypertrophy and myocaridits and also during heart transplantation rejection. We investigated the serum BNP level as a predictive marker for rejection after heart transplantation. Material and Method: To test the usefulness of measuring the BNP level in cardiac transplant patients, consecutive blood samplings for BNP, right ventricular endomyocardial biopsies, hemodynamic measurements and transthoracic echocardiogram were all done in 10 such patients between January 2004 and August 2005 at the Department of Thoracic and Cardiovascular Surgery in Asan Medical Center. Two groups were identified with using the median value: the low BNP group (n=28, BNP: ${\le}290$ pg/mL) and the high BNP group (n=29, BNP: >290 pg/mL). We retrospectively analyzed rejection, the ejection fraction, tricuspid regurgitation, left ventricular hypertrophy, the pulmonary capillary wedge pressure and the right atrial pressure between the 2 groups. Result: There were no differences in age, gender, rejection, the ejection fraction, tricuspid regurgitation, left ventricular hypertrophy and the right atrial pressure between the 2 groups (p>0.05). However, a higher pulmonary capillary wedge pressure and a higher mean pulmonary atrial pressure were observed in the high BNP group (p<0.05). Further, BNP has linear correlation with the pulmonary capillary wedge pressure (r=0.590, p<0.001). Using the cut-off value of 620 pg/mL, the BNP predicted a high PCWP (>12 mmHg) with a sensitivity of 83.3% and a specificity of 91.1% (AUC: $0.900{\pm}0.045$, p<0.001). Conclusion: The BNP level after heart transplantation does not show any significant correlation with rejection, yet it might be a predictive marker of ventricular diastolic dysfunction.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.14 no.2
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• pp.110-116
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• 2003

Background and Objectives : Laryngeal cancer discrimination using voice signals is a non-invasive method that can carry out the examination rapidly and simply without giving discomfort to the patients. n appropriate analysis parameters and classifiers are developed, this method can be used effectively in various applications including telemedicine. This study examines voice analysis parameters used for laryngeal disease discrimination to help discriminate laryngeal diseases by voice signal analysis. The study also estimates the laryngeal cancer discrimination activity of the Gaussian mixture model (GMM) classifier based on the statistical modelling of voice analysis parameters. Materials and Methods : The Multi-dimensional voice program (MDVP) parameters, which have been widely used for the analysis of laryngeal cancer voice, sometimes fail to analyze the voice of a laryngeal cancer patient whose cycle is seriously damaged. Accordingly, it is necessary to develop a new method that enables an analysis of high reliability for the voice signals that cannot be analyzed by the MDVP. To conduct the experiments of laryngeal cancer discrimination, the authors used three types of voices collected at the Department of Otorhinorlaryngology, Pusan National University Hospital. 50 normal males voice data, 50 voices of males with benign laryngeal diseases and 105 voices of males laryngeal cancer. In addition, the experiment also included 11 voices data of males with laryngeal cancer that cannot be analyzed by the MDVP, Only monosyllabic vowel /a/ was used as voice data. Since there were only 11 voices of laryngeal cancer patients that cannot be analyzed by the MDVP, those voices were used only for discrimination. This study examined the linear predictive cepstral coefficients (LPCC) and the met-frequency cepstral coefficients (MFCC) that are the two major cepstrum analysis methods in the area of acoustic recognition. Results : The results showed that this met frequency scaling process was effective in acoustic recognition but not useful for laryngeal cancer discrimination. Accordingly, the linear frequency cepstral coefficients (LFCC) that excluded the met frequency scaling from the MFCC was introduced. The LFCC showed more excellent discrimination activity rather than the MFCC in predictability of laryngeal cancer. Conclusion : In conclusion, the parameters applied in this study could discriminate accurately even the terminal laryngeal cancer whose periodicity is disturbed. Also it is thought that future studies on various classification algorithms and parameters representing pathophysiology of vocal cords will make it possible to discriminate benign laryngeal diseases as well, in addition to laryngeal cancer.

• Korean Journal of Biological Psychiatry
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• v.20 no.2
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• pp.54-60
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• 2013

Objectives The purpose of this study is to find the influential clinical and physical characteristics which affect apnea-hypopnea index (AHI) in suspected obstructive sleep apnea (OSA) patients. Methods We evaluated the comprehensive factors including sleep related symptoms, clinical scales, medical history, substance use, and anthropometric data of the 119 participants who complained of the symptoms of OSA. All the participants underwent attended-full night laboratory polysomnography. The correlation and multiple regression analysis were conducted to find the influential and predictive factors of AHI. Results A multiple linear regression model 1 showed that higher AHI was associated with higher body mass index (BMI)(p < 0.001) and higher frequency of observed apnea (p = 0.002). In multiple linear regression model 2, AHI was associated with higher BMI (p < 0.001) and loudness of snoring (p = 0.018). Conclusions The present preliminary results suggest that BMI and observed apnea are most influential factors that affect AHI in suspected OSA patients. In the future study we will design the prediction formula for the OSA and AHI, which is useful in the clinical medical field.