• Sleep Medicine and Psychophysiology
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• v.21 no.1
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• pp.14-20
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• 2014

Objectives: Several studies suggest that nocturia may be related to obstructive sleep apnea syndrome (OSAS). The mechanism by which OSAS develops nocturia has not been determined. The present study aimed to determine the prevalence of nocturia among adults with OSAS and to identify factors that may be predictive in this regard. Methods: Retrospective review of clinical and polysomnographic data obtained from patients evaluated at the sleep clinics of the St. Paul's Hospital between 2009 and 2012. The urinary symptoms were assessed on the basis of the International Prostate Symptom Score (IPSS). Pathologic nocturia was defined as two or more urination events per night. OSAS was defined as apnea-hypopnea index (AHI) ${\geq}5$. A multivariate analysis using logistic regression was performed to examine the relationship between polysomnographic variables and the presence of pathologic nocturia, while controlling for confounding factor. Results: A total of 161 men >18 years of age (mean age $46.7{\pm}14.1$), who had been referred to a sleep laboratory, were included in the present study. Among these, 27 patients with primary snoring and 134 patients with obstructive sleep apnea were confirmed by polysomnography. Nocturia was found in 53 patients with OSAS (39.6%) and 8 patients with primary snoring (29.6%). The AHI was higher in patients with nocturia than in those without nocturia (p=0.001). OSAS patients with nocturia had higher arousal index (p=0.044), and lower nadir oxyhemoglobin saturation (p=0.001). Multiple regression analysis showed that age (${\beta}$=0.227, p=0.003), and AHI (${\beta}$=0.258, p=0.001) were associated with nocturia, and that the presence of pathologic nocturia was predicted by age (OR 1.04 ; p=0.004) and AHI (OR 1.02 ; p=0.001). Conclusion: Nocturia is common among patients with OSAS. The strongest predictors of nocturia are age and AHI in patients with OSAS.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.21 no.2
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• pp.63-71
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• 2010

Objectives : The aim of this study was to investigate the effect of the clinical and demographic variables such as body weight, dosage, family history of attention-deficit hyperactivity disorder (ADHD), and psychiatric co-morbidity on the side-effects of OROS-Methylphenidate (OROS-MPH), and to evaluate the relationship between drug response and side effect severity. Methods : A total of 144 children (ages 6-18) with diagnosed ADHD were treated with OROS-MPH. Children were examined at baseline and after 1, 3, 6, 9, and 12 weeks of each treatment condition. The stimulant drug side effect rating scale (SERS), pulse rate, systolic blood pressure, diastolic blood pressure, and electrocardiogram (ECG) were evaluated to assess side effect profiles. Changes in these parameters from baseline were examined and analyzed. Results : Anorexia (30.95%) and insomnia (13.10%) were the most commonly reported side effects during this study. Insomnia and loss of appetite score increased at one week follow-up, but was sustained or decreased as treatment progressed. Small but significant increases in pulse rate and diastolic blood pressure were observed during treatment ; however, no clinically meaningful changes in ECG parameters were noted during the study. Low body weight, high dosage of OROS-MPH, and family history of ADHD were associated with cardiovascular side effect. In contrast, there was no significant relationship between OROS-MPH treatment response and the severity of side effect and no difference resulted between the responder and non-responder groups with respect to OROS-MPH dosage in the 12 weeks of follow-up. Conclusion : To the best of our knowledge, this study is the first Korean study to investigate comprehensive side effect profiles and their correlates in OROS-MPH treatment for ADHD children. OROS-MPH was well tolerated with no clinically significant side effects during the treatment period. In conclusion, low body weight, high dosage of OROSMPH, and family history of ADHD could be used as predictive factors in increasing pulse rate and blood pressure.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.154-163
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• 2021

This study was carried out to present the habitat distribution status and the habitat distribution prediction of Sophora koreensis, which is the Korean Endemic Plant included in the EN (Endangered) class of the IUCN Red List. The habit distribution survey of Sophora koreensis confirmed 19 habitats in Gangwon Province, including 13 habitats in Yanggu-gun, 3 habitats in Inje-gun, 2 habitats in Chuncheon-si, and 1 habitat in Hongcheon-gun. The northernmost habitat of Sophora koreensis in Korea was in Imdang-ri, Yanggu-gun; the easternmost habitat in Hangye-ri, Inje-gun; the westernmost habitat in Jinae-ri, Chuncheon-si; and the southernmost habitat in Sungdong-ri, Hongcheon-gun. The altitude of the Sophora koreensis habitats ranged from 169 to 711 m, with an average altitude of 375m. The area of the habitats was 8,000-734,000 m², with an average area of 202,789 m². Most habitats were the managed forests, such as thinning and pruning forests. The MaxEnt program analysis for the potential habitat of Sophora koreensis showed the AUC value of 0.9762. The predictive habitat distribution was Yanggu-gun, Inje-gun, Hwacheon-gun, and Chuncheon-si in Gangwon Province. The variables that influence the prediction of the habitat distribution were the annual precipitation, soil carbon content, and maximum monthly temperature. This study confirmed that habitats of Sophora koreensis were mostly found in the ridge area with rich light intensity. They can be used as basic data for the designation of protected areas of Sophora koreensis habitat.

• Sleep Medicine and Psychophysiology
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• v.26 no.1
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• pp.33-43
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• 2019

Objectives: Bipolar disorder is characterized by repetitive relapses that result in psychosocial dysfunctions. The functioning of bipolar disorder patients is related to the severity of symptoms, quality of sleep, drug compliance, and social support. The purpose of this study was to investigate the association between sociodemographic and clinical factors and functional status in bipolar disorder patients. Methods: A total of 52 bipolar disorder patients participated in the study. The following scales were utilized: Korean version of personal and social performance scale (K-PSP), Korean version of Hamilton rating scale for depression (K-HDRS), Korean version of young mania rating scale (K-YMRS), Korean version of pittsburgh sleep quality index (PSQI-K), Korean version of drug attitude inventory (K-DAI), mood disorders insight scale (MDIS), and multidimensional scale of perceived social support (MSPSS). Results: The K-PSP score showed a negative relationship with K-HDRS score (r = -0.387, p = 0.005), but not with K-YMRS score (r = -0.205, p = 0.145). The K-PSP score showed a negative relationship with global PSQI-K score (r = -0.378, p = 0.005) and overall sleep quality (r = -0.353, p = 0.010). The K-PSP scores were positively associated with the KDAI score (r = 0.409, p = 0.003) and MSPSS score (r = 0.334, p = 0.015). The predictive factors for K-PSP were overall sleep quality and social support from family. Conclusion: Our study showed that depressive symptoms were related to overall function in bipolar disorder. Also, our study suggested that improving sleep quality is important in maintaining functional status. Appropriate social support and positive perception toward the drug may lead to the higher level of functioning. This study is meaningful in that the functional status of bipolar disorder patients is analyzed in a multivariate manner in relation to various variables in psychosocial aspects.

• Korean Journal of Psychosomatic Medicine
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• v.26 no.2
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• pp.77-85
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• 2018

Objectives : This study aimed to investigate the level of distress using the distress thermometer (DT) and the factors associated with distress in postoperative breast cancer (BC) patients. Methods : DT and WHOQOL-BREF (World Health Organization Quality of Life Scale Abbreviated Version) along with sociodemographic variables were assessed in patients undergoing surgery for their first treatment of BC within one week postoperatively. The distress group consisted of participants with a DT score ${\geq}4$. The prevalence and associative factors of distress were examined by descriptive, univariable, and logistic regression analysis. Results : Three hundred seven women were recruited, and 264 subjects were finally analyzed. A total of 173 (65.5%) were classified into the distress group. The distress group showed significantly younger age (p=0.045), living without a spouse (p=0.032), and worse quality of life (QOL) as measured by overall QOL (p=0.009), general health (p=0.005), physical health domain (p<0.000), and psychological health domain (p=0.002). The logistic regression analysis showed that patients aged 40-49 years were more likely to experience distress than those aged ${\geq}60years$ (Odds ratios [OR]=2.992, 95% confidence interval [CI] 1.241-7.215). Moreover, the WHOQOL-BREF physical health domain was a predictive factor of distress (OR=0.777, 95% CI 0.692-0.873). Conclusions : A substantial proportion of patients are experiencing significant distress after BC surgery. It would be expected that distress management, especially in the middle-aged patients and in the domain of physical QOL (e.g., pain, insomnia, fatigue), from the early BC treatment stage might reduce chronic distress.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

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