• Tuberculosis and Respiratory Diseases
• /
• v.49 no.6
• /
• pp.752-759
• /
• 2000

Background : Many studies have shown that pulmonary function differs widely among race, age and geographical residency. By virtue of the improvement of nutrition and environment, the elderly population in Korea is markedly increasing and so are the ages of patients complaining respiratory symptoms. However, we do not have our own data on the pulmonary functional reserve of elderly persons in Korea. We evaluate the deterioration of pulmonary functional reserve and standardize the predictive values of pulmonary function in the elderly population. Method : Pulmonary function tests were conducted in 100 men and 100 women over the age of 65. We analyzed changes of FVC and $FEV_1$ according to age and height by linear regression. We compared our new multiple linear regression equation with other equations currently used in Korea. Results : In men, the mean age was $71.5{\pm}5.2$(mean${\pm}$SD) years and the mean height was $163.6{\pm}6.2$cm. The mean FVC was $3.42{\pm}0.49{\ell}$ and the mean $FEV_1, $2.72{\pm}v$. In women, the mean age was $72.0{\pm}5.1$ years and the mean height was $149.1{\pm}5.9$cm. The mean FVC was $2.22{\pm}0.42{\ell}$ and the mean $FEV_1$ $1.83{\pm}0.34{\ell}$. Multiple linear regression equation using age and height as an independent factors was as follows : FVC(${\ell}$)=1.857-0.0356$\times$age(year)+0.02517$\times$height(cm) (p<0.01, $R^2$=0.279), $FEV_1(${\ell}$)=1.340-0.02698$\times$age(year)+0.02021$\times$height(cm) (p<0.01, $R^2$=0.255) in men, FVC(${\ell}$) =-0.09765-0.03332$\times$age(year)+0.03164$\times$height(cm) (p<0.01, $R^2$=0.435), $FEV_1(${\ell}$)=-0.l69-0.02469$\times$age(year)+0.02539$\times$height(cm) (p<0.01, $R^2$=0.41) in women. Conclusion : We established prediction regressions for pulmonary functional tests in the elderly Korean population. We also confirmed that currently adopted equations do not exactly anticipate the expected pulmonary functional reserve in the aged person over 65 years old. We suggest that our new equations from this study should be applied to interpret the pulmonary function tests in the elderly population in Korea.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.57-73
• /
• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• 한국사회복지학회:학술대회논문집
• /
• 2003.10a
• /
• pp.185-216
• /
• 2003

This study was aimed at investigating important factors influencing career success among regular workers. The current researcher scrutinized the degree to which variables and factors affect the career success and occupational turnover rates of the research participants. At the same tune, two hypothetical path models established by the researcher were examined using linear multiple regression methods and the LISREL. After examining the differences among the factors of career success, a comparison was made between the disabled worker group and the non-disabled worker group. A questionnaire using the 5-point Likert scale was distributed to a group of 374 workers with disabilities and 463 workers without disabilities. For the data analysis purpose, the structural equation model, factor analysis, correlation analysis, and multiple regression analysis were carried out. The results of this study ran be summarized as follows. First, the results of factor analysis showed important categories of conceptual themes of career success. The initial conceptual factor model did not accord with the empirical one. A three-factorial model revealed categories of personal, family, and organizational factor respectively. The personal factor was composed of the self-esteem and self-efficiency. The family factor was consisted of the multi-roles stress and the number of children. Finally, the organizational factor was composed of the capacity for utilizing resources, networking, and the frequency of mentoring. In addition, the total 10 sub areas of career success were divided by two important aspects; the subjective career success and the objective career success. Second, both research participant groups seemed to be influenced by their occupational types. However, all predictive variables excluding the wage rate and the average length of work years had significant impact on job success for the disabled work group, while all the variables excluding the frequency of advice and length of working years had significant impact on job success for the non-disabled worker group. Third, the turnover rate was significantly influenced by the age and the experience of turnover of the research participants. However, the number of co-workers was the strongest predictive variable for the worker group with disabilities, but the occupation choice variable for the worker group without disabilities. For the disabled worker group, the turnover rate was differently influenced by the type of occupation, the length of working years, while multi-role stress and the average working years at the time of turnover for the worker group without disabilities. Fifth, as a result of verifying the hypothetical path model, it showed that the first model was somewhat proper and could predict the career success on both research participant groups. In the second model, the Chi-square, the degree of freedom (($x^2=64.950$, df=61, P=0.341), and the adjusted Goodness of Fit Index (AGFI) were .964, and the Comparative Fit Index (CFI) were .997, and the Root Mean Squared Residual (RMR) was respectively. .038. The model was best fitted and could predict the career success more highly because the goodness of fit index in the whole models was within the allowed range. In conclusion, the following research implications can be suggested. First, the occupational type of research participants was one of the most important variables to predict the career success for both research participant groups. It means that people with disabilities require human development services including education. They need to improve themselves in this knowledge-based society. Furthermore, for maintaining the career success, people with disabilities should be approached by considering the subjective career success aspects including wages and the promotion opportunities than the objective career success aspects.

• Clinical and Experimental Pediatrics
• /
• v.46 no.2
• /
• pp.173-177
• /
• 2003

Purpose : Recently, different results about factors affecting accurate quantitation of 24-hr urinary protein(24UP) amount using spot urine protein/creatinine ratio(PCR) have been reported. The current study was designed to evaluate correlation between 24UP amounts and PCR in children, and the effect of 24UP amounts, age, sex, and glomerular filtration rate(GFR) on this correlation. Methods : Among 94 patients who visited the department of pediatrics in Busan Paik Hospital from March 2002 to August 2002, 68 patients whose urinary creatinine excretion was ${\geq}15mg/kg/day$ were included in this study. All the patients were divided into I, II/A, B group(I : 24UP<500 mg/day, II : $24UP{\geq}500mg/day$, A : <10 years of age, B : ${\geq}10years$ of age). Pearson correlation analysis was performed between 24UP and PCR to evaluate the relationship. We defined fractional difference between 24UP and PCR, and then performed multiple regression analysis with 24UP amount, age, GFR and fractional difference. Results : There was a strong positive linear correlation between 24UP and PCR(R=0.936, P<0.0001) in all patients, and the correlation was also good in each group. Using PCR cutoff values of 0.5, the PCR provided high sensitivity, specificity, positive and negative predictive value in predicting 24UP amount ${\geq}500mg$. The factors affecting accurate quantitation of proteinuria using spot urine PCR was age, not 24UP amount, GFR or sex. Conclusion : Spot urine PCR is a useful test but has limitations in predicting 24UP amount. Therefore, it should be used only as screening method. Age-adjusted PCR cutoff values may be necessary to predict 24UP amount in children with proteinuria.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.11
• /
• pp.4603-4608
• /
• 2015

Background: To determine the potential value of serum tumor markers in predicting pCR (pathological complete response) during neoadjuvant chemotherapy. Materials and Methods: We retrospectively monitored the pro-, mid-, and post-neoadjuvant treatment serum tumor marker concentrations in patients with locally advanced breast cancer (stage II-III) who accepted pre-surgical chemotherapy or chemotherapy in combination with targeted therapy at Fudan University Shanghai Cancer Center between September 2011 and January 2014 and investigated the association of serum tumor marker levels with therapeutic effect. Core needle biopsy samples were assessed using immunohistochemistry (IHC) prior to neoadjuvant treatment to determine hormone receptor, human epidermal growth factor receptor 2(HER2), and proliferation index Ki67 values. In our study, therapeutic response was evaluated by pCR, defined as the disappearance of all invasive cancer cells from excised tissue (including primary lesion and axillary lymph nodes) after completion of chemotherapy. Analysis of variance of repeated measures and receiver operating characteristic (ROC) curves were employed for statistical analysis of the data. Results: A total of 348 patients were recruited in our study after excluding patients with incomplete clinical information. Of these, 106 patients were observed to have acquired pCR status after treatment completion, accounting for approximately 30.5% of study individuals. In addition, 147patients were determined to be Her-2 positive, among whom the pCR rate was 45.6% (69 patients). General linear model analysis (repeated measures analysis of variance) showed that the concentration of cancer antigen (CA) 15-3 increased after neoadjuvant chemotherapy in both pCR and non-pCR groups, and that there were significant differences between the two groups (P=0.008). The areas under the ROC curves (AUCs) of pre-, mid-, and post-treatment CA15-3 concentrations demonstrated low-level predictive value (AUC=0.594, 0.644, 0.621, respectively). No significant differences in carcinoembryonic antigen (CEA) or CA12-5 serum levels were observed between the pCR and non-pCR groups (P=0.196 and 0.693, respectively). No efficient AUC of CEA or CA12-5 concentrations were observed to predict patient response toward neoadjuvant treatment (both less than 0.7), nor were differences between the two groups observed at different time points. We then analyzed the Her-2 positive subset of our cohort. Significant differences in CEA concentrations were identified between the pCR and non-pCR groups (P=0.039), but not in CA15-3 or CA12-5 levels (p=0.092 and 0.89, respectively). None of the ROC curves showed underlying prognostic value, as the AUCs of these three markers were less than 0.7. The ROC-AUCs for the CA12-5 concentrations of inter-and post-neoadjuvant chemotherapy in the estrogen receptor negative HER2 positive subgroup were 0.735 and 0.767, respectively. However, the specificity and sensitivity values were at odds with each other which meant that improving either the sensitivity or specificity would impair the efficiency of the other. Conclusions: Serum tumor markers CA15-3, CA12-5, and CEA might have little clinical significance in predicting neoadjuvant treatment response in locally advanced breast cancer.

• Tuberculosis and Respiratory Diseases
• /
• v.46 no.6
• /
• pp.786-794
• /
• 1999

Back ground : Arm span measurements provide a practical substitute for standing height to predict normal spirometric values in subjects unable to stand or those with a skeletal deformity such as kyphoscoliosis. The relationship between arm span and height has previously been reported as either a fixed ratio unaffected by age or as a regression equation in which the ratio varies as a function of age. The fixed ratio or regression equation is known to be specific for sex and race. Methods : We studied the relationship between standing height, arm span, and age in 381 Korean adult female subjects (ages 20 to 69 yrs) sampled in a general population. Results : The mean ratio for arm span to height is 1.004. Multiple linear analysis found arm span and age to be predictive of standing height (p=0.0001, $r^2$=0.76). We performed the analysis of the difference between the predicted height using either fixed ratio or regression equation and actual height. At the extremes of arm span and age, the ratio method either underestimated(at smaller arm span or younger age) or overestimated(at larger arm span or older age) as compared with actual height (p=0.0001). Conclusion : This results indicate that the estimated height using the fixed ratio method provides a less acceptable method of estimating height for the prediction of lung volumes in the Korean adult women when compared with the regression equations, especially at the extremes of stature or age.

• Korean Journal of Medicinal Crop Science
• /
• v.2 no.1
• /
• pp.7-13
• /
• 1994

This study was conducted to investigate the relationships between yearly variations of climatic factors and yearly variations of productivity in Alisma plantago L. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were collected from the Statistical Year Book of Seungju province, Reserach Report of Seungju Extension Station of Rural Development Administration, and farmers for 10 years from 1983 to 1992. The meteorological data gathered at the Seungju Weather Station for the same period were used to find out the relationships between climatic factors and productivity. Yearly variation of the amount of precipitation in October and the minimum temperature in November were large with coefficients of variation(C.V.) of 106.44, 144.08%, respectively, but the variation of the average temperature, maximum temperature, minimum temperature from July to September were relatively small. Fresh weight and dry weight of roots vary greatly with C. V. of 30.62, 31.85%, respectivly. Plant height and stem length show more or less small C. V. of 5.51, 6. 26%, respectively and leaf width, leaf length, number of stems and root diameter show still less variation. Correlation coefficients between maximum temperature in November and plant height, stem diamter, number of stems, root diamter and dry weight of roots are positively significant at the 5% level. There are high signficant positive correlations observed, between yield and yield components. The maximum temperature would be used as a predictive variable for the estimation of dry weight of roots and number of stems. Simple linear regression equations by the least square method are estimated for number of stems $(Y_1)$ and the maximum temperature in November(X) as $Y_1=4.7114+0.5333\;X\;(R^2=0.4410)$, and for dry weight of roots$(Y_2)$ and the maximum temperature in November(X) as $Y_2=55.0405+14.3233\;X\;(R^2=0.4511)$

• The Korean Journal of Applied Statistics
• /
• v.36 no.1
• /
• pp.49-62
• /
• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.