• Childhood Kidney Diseases
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• v.11 no.1
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• pp.74-82
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• 2007

Purpose : To analyze the clinical characteristics, spontaneous resolution rate and predictive factors of resolution in children with primary vesicoureteral reflux(VUR). Methods : Between October 1991 and July 2003, 149 children diagnosed with primary VUR at Chonbuk National University Hospital were reviewed retrospectively. All of the patients were maintained on low-dose antibiotic prophylaxis and underwent radionuclide cystograms at 1 year intervals over 3 years after the initial diagnosis of VUR by voiding cystourethrogram was made. Results : The median time to resolution of VUR was 24 months and the total 3 year-cumulative resolution rate of VUR was 61.7%. The following variables were associated with resolution of VUR according to univariate analysis-; age<1 year, male gender, mild grade of reflux, unilateral reflux, congenital hydronephrosis as clinical presentation at time of diagnosis of VUR, absence of focal defects in the renal scan at diagnosis, absence of recurrent UTI, renal scars and small kidney during follow-up. After adjustment by Cox regression model, five variables remained as independent predictors of VUR resolution; age<1 yew, relative risk 1.77(P<0.05), VUR grade I+II 2.98(P<0.05), absence of renal scars 2.23(P<0.05), and absence of small kidney 5.20(P<0.01) during follow-up. Conclusion : In this study, spontaneous resolution rate of VUR, even high grade reflux, is high in infants during medical management, and it was related to age, reflux grade at diagnosis, absence of renal scars and small kidney during follow-up. Therefore early surgical intervention should be avoided and reserved for the selected groups.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.3
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• pp.1-12
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• 2016

This study aimed to interpret the fundamental cause of road-kill occurrences and analyzed spatial characteristics of the road-kill locations from Route 44 in Seorak National Park, Korea. Logistic regression analysis was utilized for backward elimination on variables. Seorak National Park Service has constructed GIS-data of 81 road-kill occurrences from 2008 to 2013 and these data were assigned as dependent variables in this study. Considered as independent variables from previous studies and field surveys, vegetation age-class, distance to streams, coverage of fences and retaining walls, and distance to building sites were assigned as road-kill impact factors. The coverage of fences and retaining walls(-1.0135) was shown as the most influential factor whereas vegetation age-class(0.0001) was the least influential among all of the significant factor estimates. Accordingly, the rate of road-kill occurrence can increase as the distance to building sites and stream becomes closer and vegetation age-class becomes higher. The predictive accuracy of road-kill occurrence was shown to be 72.2% as a result of analysis, assuming as partial causes of road-kill occurrences reflecting spatial characteristics. This study can be regarded as beneficial to provide objective basis for spatial decision making including road-kill occurrence mitigation policies and plans in the future.

• Journal of The Korean Ophthalmological Society
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• v.59 no.12
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• pp.1166-1172
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• 2018

Purpose: To investigate the efficacy, and identify predictors of success of selective laser trabeculoplasty (SLT) in open-angle glaucoma (OAG) patients after adjusting for intraocular pressure (IOP) changes in the untreated fellow eye. Methods: This retrospective chart review included 52 eyes of 52 OAG patients who underwent SLT in one eye and were followed-up for at least 1 year after the procedure. The IOP was measured before the treatment, at 1, 2, and 3 months posttreatment, and every 3 months thereafter. To account for the possible influence of IOP fluctuations on laser outcomes, post-laser IOP values of the treated eye of each patient were also analyzed, after adjusting for IOP changes in the untreated fellow eye. Success was defined as an IOP decrease ${\geq}20%$ of the pretreatment IOP. The success rate was determined based on Kaplan-Meier survival analysis and factors predictive of success were analyzed using the Cox proportional hazard model. Results: The mean pretreatment IOP was $23.17{\pm}6.96mmHg$. The mean IOP reduction was $5.59{\pm}4.78mmHg$ (29.7%) and the success rate was 65.4% at 1 year. The adjusted mean IOP reduction was $4.70{\pm}4.67mmHg$ (23.9%) and the adjusted success rate was 53.9%. Pretreatment IOP was associated with SLT success; the higher the pretreatment IOP, the greater the post-laser IOP reduction (p = 0.025). Age and mean deviation index did not show a significant association with SLT success (p = 0.066 and p = 0.464, respectively). Conclusions: SLT is a safe and effective alternative method of IOP reduction in OAG patients. Herein, pretreatment IOP was the only factor significantly associated with SLT success. IOP fluctuations of the untreated eye should be considered for a better understanding of the impact of treatment.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.426-441
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• 2023

This study investigated the predictive accuracy of a model of landslide displacement in Jecheon-si, where a great number of landslides were triggered by heavy rain on both natural (non-clear-cut) and clear-cut slopes during August 2020. This was accomplished by applying three flow direction methods (single flow direction, SFD; multiple flow direction, MFD; infinite flow direction, IFD) and the degree of root cohesion to an infinite slope stability equation. The application assumed that the soil saturation and any changes in root cohesion occurred following the timber harvest (clear-cutting). In the study area, 830 landslide locations were identified via landslide inventory mapping from satellite images and 25 cm resolution aerial photographs. The results of the landslide modeling comparison showed the accuracy of the models that considered changes in the root cohesion following clear-cutting to be improved by 1.3% to 2.6% when compared with those not considered in the area under the receiver operating characteristics (AUROC) analysis. Furthermore, the accuracy of the models that used the MFD algorithm improved by up to 1.3% when compared with the models that used the other algorithms in the AUROC analysis. These results suggest that the discriminatory application of the root cohesion, which considers changes in the vegetation condition, and the selection of the flow direction method may influence the accuracy of landslide predictive modeling. In the future, the results of this study should be verified by examining the root cohesion and its dynamic changes according to the tree species using the field hydrological monitoring technique.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.

• Korean Journal of Culture and Social Issue
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• v.28 no.2
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• pp.187-217
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• 2022

The aim of this study is to examine the psychometric properties of the Korean version of the Post-traumatic Stress Disorder Checklist for DSM-5(PCL-5). For this purpose, online surveys were conducted for two times with a one year interval using the data from 1,077 Korean adults at time 1, and 563 Korean adults at time 2. First, from the result of the confirmatory factor analysis, comparing the model fit of the 1, 4, 6, and 7-factor model, the 4, 6, and 7-factor model showed a acceptable fit, and the best fit was seen in the order of the 7, 6, 4-factor model. Second, the internal consistency, omega coefficient, construct validity, average variance extracted, and test-retest reliability results were all satisfactory.. Third, a correlation analysis with the K-PC-PTSD-5 and the sub-factors of BSI-18 was conducted to check the validity of the Korean Version of PCL-5. As a result, a positive correlation was seen with both K-PC-PTSD-5 and BSI-18. Fourth, a hierarchical multiple regression was performed to examine whether the Korean Version of PCL-5 predicts future PTSD, depression, anxiety, and somatization. As a result, the Korean Version of PCL-5 measured at time 1 significantly predicted PTSD, depression, anxiety, and somatization symptoms at time 2. Fifth, by analyzing the ROC curve, the discriminant power of PCL-5 for screening PTSD symptom groups was confirmed, and the best cut-off score was suggested. As a result of the longitudinal validation of Korean version of PCL-5, it was found that this scale is a reliable and valid measure for Korean adults. By looking into the predictive validity of the scale, it was found that the Korean version of PCL-5 can predict not only PTSD symptoms but also PTSD-related symptoms such as depression, anxiety, and somatization. Also, this study differs from previous validation studies measuring PTSD symptoms in that it suggested a cut-off score to help differentiate PTSD symptom groups.

• 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.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.4
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• pp.281-291
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• 2012

In this study, WET (whole effluent toxicity) test with Skeletonema costatum, Tigriopus japonicus and Paralichthys olivaceus and ERA (environmental risk assessment) were conducted to assess the unacceptable effect on marine ecosystem by emitting the treated discharge water from 'ARA Plasma BWTS' BWMS (ballast water management system) using filtration, Plasma and MPUV module. 34 psu treated discharge water from ARA Plasma BWTS shown slight chronic toxicity effect on the P. olivaceus ($7d-LC_{50}{\Rightarrow}100.00%$ treated discharge water, $7d-LC_{25}{\Rightarrow}85.15%$ treated discharge water). Bromobenzene, chlorobenzene and 4-chlorotoluene in 34 psu treated discharge water from ARA Plasma BWTS were higher than in the background original content of seawater. The PECs (predictive environmental concentrations) of bromobenzene, chlorobenzene and 4-chlorotoluene calculated by MAMPEC (marine antifoulant model to predict environmental concentrations) program (ver. 3.0) were 3.34E-03, 2.10E-03 and 1.73E-03 ${\mu}g\;L^{-1}$, respectively and PNECs (predicted no effect concentrations) of them were 1.6, 0.5 and 1.9 ${\mu}g\;L^{-1}$. The PEC/PNEC ratio of bromobenzene, chlorobenzene and 4-chlorotoluene did not exceed one and 3 substances did not consider as persistence, bioaccumulative and toxic. Therefore, it was suggested that treated discharge water from ARA Plasma BWTS did not pose unacceptable effect on marine ecosystem.

• Korean Journal of Environment and Ecology
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• v.26 no.5
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• pp.812-826
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• 2012

The selection rate of tree burials (TB) is still low in spite of increasing concerns about TB and government's efforts to increase TB participation. It is necessary to understand the factors affecting TB selection. This study investigated the relationship between major variables (attitude: ATT; subjective norm: SN; perceived behavioral control: PBC) of Ajzen's theory of planned behavior (TPB), additional variable (custom: CUST), and intention to select TB by using structural equation modelling (SEM). Samples were selected from Gwang-ju citizens using proportionate stratified sampling (PST) by region during September of 2011. Four hundred and twelve responses were used for data analysis. The model showed fair goodness of fit. All four variables (ATT, SN, PBC, CUST) influenced intention to select TB. The four variables explained 53.0% of intention to select TB. SN(${\beta}$=0.459) was the most predictive variable on the intention, followed by ATT(${\beta}$=0.247), PBC(${\beta}$=0.152), and CUST(${\beta}$=0.102) in decreasing order. The results were discussed and some suggestions to increase the intention of tree burial selection were made.