• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.

• Journal of Korea Water Resources Association
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• v.30 no.6
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• pp.641-648
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• 1997

Mutual information is useful for analyzing nonlinear dependence in time series in much the same way as correlation is used to characterize linear dependence. We use multivariate kernel density estimators for the estimation of mutual information at different time lags for single and multiple time series. This approach is tested on a variety of hydrologic data sets, and suggested an appropriate delay time $ au$ at which the mutual information is almost zerothen multi-dimensional phase portraits could be constructed from measurements of a single scalar time series.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1289-1312
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• 2015

This paper studies the comovement of industry defaults among listed companies. Rank correlation coefficients of Spearman's ${\rho}$ and Kendall's ${\tau}$ measure the concordance of default. These non-parametric coefficients do not require distributional assumptions and are easily used even with less data and extreme values. This study predicts a future financial crisis by looking at the comovement of industry defaults. We expect our analyses will aid market participants (including company executives) in making investment or risk management decisions.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.117-126
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• 2020

This paper is aimed to explore the co-movement capital market in Southeast Asia and analysis the correlation of conventional and Islamic Index in the regional and global equity. This research become necessary to represent the risk on the capital market and measure market performance, as investor considers the volatility before investing. The time series daily data use from April 2012 to April 2020 both conventional and Islamic stock index in Malaysia and Indonesia. This paper examines the dynamics of conditional volatilities and correlations between those markets by using Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH). Our result shows that conventional or composite index in Malaysia less volatile than Islamic, but on the other hand, both drive correlation movement. The other output captures that Islamic Index in Indonesian capital market more gradual volatilities than the Composite Index that tends to be low in risk so that investors intend to keep the shares. Generally, the result shows a correlation in each country for conventional and the Islamic index. However, Internationally Indonesia and Malaysia composite and Islamic is low correlated. Regionally Indonesia's indices movement looks to be more correlated and it's similar to Malaysian Capital Market counterparts. In the global market distress condition, the diversification portfolio between Indonesia and Malaysia does not give many benefits.

• Journal of Chest Surgery
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• v.27 no.10
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• pp.833-837
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• 1994

Lung contusion due to blunt chest trauma is the most common lung injury and correlated with the clinical course and prognosis. Its diagnosis by CT[Computerized Tomogram] gives a more clear and understandable three dimensional view, by which we are able to measure the volume of the contused and entire lung. Other variables are arterial blood gas, number of rib fracture, presence of hemopneumothorax, sternal fracture and clavicle fracture, number of associated non-thoracic injuries, ventilator time and presence of pulmonary complication. Percentage[%] of lung contusion are expressed as mean $\pm$ standard deviation and data analysis was performed by means of multivariate repeated measures analysis of variance to detect significant differences in variables between positive thoracic injury group and negative group. The paired t-test was used. Differences of percentage of lung contusion between groups were assessed by one-way analysis of variance. Simple linear regression was used to perform correlation analysis in the number of rib fracture and ventilator time. A p value less than 0.05 was considered statistically significant. Pneumothorax and the number of associated other injuries affect the amount of lung contusion and pulmonary complication group has more contused lung volume. Arterial blood gas study shows no correlation with the amount of lung contusion statistically. The number of rib fracture correlated with the amount of lung contusion, which also correlated with ventilator time[r=0.56, p<0.05]. In conclusion, quantitative anlysis of lung contusion by CT predicts the clinical course and treatment such as ventilator care.

• Journal of Biosystems Engineering
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• v.26 no.5
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• pp.475-480
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• 2001

Sensing soil organic matter is crucial for precision farming and environment friendly agriculture. Near infra-red(NIR) was utilized to measure the soil organic matter. Multivariate calibration methods, including stepwise multiple linear regression(MLR), principal components recession(PCR) and partial least squares regression(PLS), were applied to soil spectral reflectance data to predict the organic matter content. The effect of soil particle size and water content was studied. The range of soil organic matter contents was from 0.5 to 11%. Near infrared (NIR) region from 700 to 2,500nm was applied. For uniform soil particle size, result had good correlation (R$\^$2/ = 0.984, standard error of prediction= 0.596). The effect of soil particle size could be eliminated with 1st order derivative of the NIR signal. However. moist soil had a little lower correlation. R$\^$2/ was 0.95 and standard error of prediction was 0.94% using the PLS method. The results showed the possibility of soil organic matter measurement using NIR reflectance on the field.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.335-351
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• 2021

Purpose: An underlying factor for the failure of several clinical trials of anti-epidermal growth factor receptor (EGFR) therapies is the lack of an effective method to identify patients who overexpress EGFR protein. The quantitative dot blot method (QDB) was used to measure EGFR protein levels objectively, absolutely, and quantitatively. Its feasibility was evaluated for the prognosis of overall survival (OS) of patients with gastric cancer. Materials and Methods: Slices of 2×5 ㎛ from formalin-fixed paraffin-embedded gastric cancer specimens were used to extract total tissue lysates for QDB measurement. Absolutely quantitated EGFR protein levels were used for the Kaplan-Meier OS analysis. Results: EGFR protein levels ranged from 0 to 772.6 pmol/g (n=246) for all gastric cancer patients. A poor correlation was observed between quantitated EGFR levels and immunohistochemistry scores with ρ=0.024 and P=0.717 in Spearman's correlation analysis. EGFR was identified as an independent negative prognostic biomarker for gastric cancer patients only through absolute quantitation, with a hazard ratio of 1.92 (95% confidence interval, 1.05-3.53; P=0.034) in multivariate Cox regression OS analysis. A cutoff of 208 pmol/g was proposed to stratify patients with a 3-year survival probability of 44% for patients with EGFR levels above the cutoff versus 68% for those below the cutoff based on Kaplan-Meier OS analysis (log rank test, P=0.002). Conclusions: A QDB-based assay was developed for gastric cancer specimens to measure EGFR protein levels absolutely, quantitatively, and objectively. This assay should facilitate clinical trials aimed at evaluation of anti-EGFR therapies retrospectively and prospectively for gastric cancer.

• Journal of Trauma and Injury
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• v.35 no.1
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• pp.25-33
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• 2022

Purpose: The scoring system for traumatic liver injury (SSTLI) was developed in 2015 to predict mortality in patients with polytraumatic liver injury. This study aimed to validate the SSTLI as a prognostic factor in patients with polytrauma and liver injury through a generalized estimating equation analysis. Methods: The medical records of 521 patients with traumatic liver injury from January 2015 to December 2019 were reviewed. The primary outcome variable was in-hospital mortality. All the risk factors were analyzed using multivariate logistic regression analysis. The SSTLI has five clinical measures (age, Injury Severity Score, serum total bilirubin level, prothrombin time, and creatinine level) chosen based on their predictive power. Each measure is scored as 0-1 (age and Injury Severity Score) or 0-3 (serum total bilirubin level, prothrombin time, and creatinine level). The SSTLI score corresponds to the total points for each item (0-11 points). Results: The areas under the curve of the SSTLI to predict mortality on post-traumatic days 0, 1, 3, and 5 were 0.736, 0.783, 0.830, and 0.824, respectively. A very good to excellent positive correlation was observed between the probability of mortality and the SSTLI score (γ=0.997, P<0.001). A value of 5 points was used as the threshold to distinguish low-risk (<5) from high-risk (≥5) patients. Multivariate analysis using the generalized estimating equation in the logistic regression model indicated that the SSTLI score was an independent predictor of mortality (odds ratio, 1.027; 95% confidence interval, 1.018-1.036; P<0.001). Conclusions: The SSTLI was verified to predict mortality in patients with polytrauma and liver injury. A score of ≥5 on the SSTLI indicated a high-risk of post-traumatic mortality.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.3
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• pp.435-442
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• 2014

Purpose: The purpose of this study was to investigate influencing factors in self-efficacy among college students. Methods: The sample consisted of 553 college students in C & K cities. Self-report questionnaires were used to measure the variables. In data analysis, SPSS WIN 20.0 program was utilized for descriptive statistics, Pearson's correlation coefficients, and regression analysis. Results: The mean score for Self-Efficacy was 66.62. Self-Efficacy was significantly different according to gender(t=2.076, p=.038), grade(F=11.077, p<.001), religion(F=4.666, p=.003), major(F=19.598, p<.001), economic status(F=6.570, p<.001), perceived health status(F=6.168, p<.001), and self-rated knowledge about health(F=12.936, p<.001). Multivariate analysis showed that major, self-rated knowledge about health, economic status, and perceived health status were significant predictors of self-efficacy and could explain 15.9% of total variability in this model. Conclusion: This study suggests that perceived health status and self-rated knowledge about health are significant factors in self-efficacy among college students. Therefore, the strategies of nursing intervention which improve these variables must be developed for college students.

• Korean Journal of Adult Nursing
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• v.23 no.4
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• pp.309-320
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• 2011

Purpose: The purpose of the study was to examine the related factors of quality of life (QOL) among patients with Chronic Obstructive Pulmonary Disease (COPD). Methods: Patients diagnosed with COPD (N=230) were recruited from four hospitals in Kyeong-Nam province, from March 2 to November 30, 2010. The data collection instruments were the Short Form 36, perceived dyspnea measure by Modified Medical Research Council, COPD and Asthma Sleep Impact Scale, COPD Self-efficacy Scale, and Center for Epidemiologic Studies Depression Scale were used. Following the completion of the data collection instruments Pulmonary function was tested. Data were analyzed with descriptive statistics, Pearson correlation and simultaneous multiple regression using SPSS/WIN. Results: The mean QOL of this study was 68.24. Using a multivariate approach, the significant correlates of QOL were depression (${\beta}$=-.37), dyspnea (${\beta}$=-.28), self-efficacy (${\beta}$=.20), and a sufficient degree of household income (${\beta}$=.16). These variables explained 49% of variance in QOL. Conclusion: The study suggests that psychological aspects are an important factor in explaining QOL of the patients. Screening and minimizing depression could be effective strategies in enhancing QOL of patients with COPD and further investigation to reduce depression could warrant the improvement of QOL in patients with COPD.