• The Korean Journal of Nuclear Medicine
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• v.34 no.4
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• pp.312-321
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• 2000

Purpose: The aim of this study was to determine whether crossed cerebellar hyperperfusion (CCH) was helpful in discriminating mesial from lateral temporal lobe epilepsy (TLE) and what other factors were related in the development of CCH on ictal brain SPECT. Materials and Methods: We conducted retrospective analysis in 59 patients with TLE (M:41, F:18; $27.4{\pm}7.8$ years old; mesial TLE: 51, lateral TLE: 8), which was confirmed by invasive EEG and surgical outcome (Engel class I, II). All the patients underwent ictal Tc-99m HMPAO brain SPECT and their injection time from ictal EEG onset on video EEG monitoring ranged from 11 sec to 75 sec ($32.6{\pm}19.5sec$) in 39 patients. Multiple factors including age, TLE subtype (mesial TLE or lateral TLE), propagation pattern (hyperperfusion localized to temporal lobes, spread to adjacent lobes or contralateral hemisphere) and injection time were evaluated for their relationship with CCH using multiple logistic regression analysis Results: CCH was observed in 18 among 59 patients. CCH developed in 29% (15/51) of mesial TLE patients and 38% (3/8) of lateral TLE patients. CCH was associated with propagation pattern; no CCH (0/13) in patients with hyperperfusion localized to temporal lobe, 30% (7/23) in patients with propagation to adjacent lobes, 48% (11/23) to contralateral hemisphere. Multiple logistic regression analysis revealed that propagation pattern (p=0.01) and age (p=0.02) were related to the development of CCH. Conclusion: Crossed cerebellar hyperperfusion in ictal brain SPECT did not help differentiate mesial from lateral temporal lobe epilepsy. Crossed cerebellar hyperperfusion was associated with propagation pattern of temporal lobe epilepsy and age.

• Publications of The Korean Astronomical Society
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• v.36 no.3
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• pp.47-63
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• 2021

Variability is one of the major characteristics of Active Galactic Nuclei (AGN), and it is used for understanding the energy generation mechanism in the center of AGN and/or related physical phenomena. It it known that there exists a time lag between AGN light curves simultaneously observed at different wavelengths, which can be used as a tool to estimate the size of the area that produce the radiation. In this paper, We present long term near-infrared variability of optically bright type 1 AGN using the Wide-field Infrared Survey Explorer data. From the Milliquas catalogue v6.4, 73 type 1 QSOs/AGN and 140 quasar candidates are selected that are brighter than 18 mag in optical and located within 5 degree around the ecliptic poles. Light curves in the W1 band (3.4 ㎛) and W2 band (4.6 ㎛) during the period of 2010-2019 were constructed for these objects by extracting multi-epoch photometry data from WISE and NEOWISE all sky survey database. Variability was analyzed based on the excess variance and the probability P_var. Applying both criteria, the numbers of variable objects are 19 (i.e., 26%) for confirmed AGN and 12 (i.e., 9%) for AGN candidates. The characteristic time scale of the variability (τ) and the variability amplitude (σ) were derived by fitting the DRW model to W1 and W2 light curves. No significant correlation is found between the W1/W2 magnitude and the derived variability parameters. Based on the subsample that are identified in the X-ray source catalog, there exists little correlation between the X-ray luminosity and the variability parameters. We also found four AGN with changing W1-W2 color.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.23-33
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• 2016

The current smart lighting is shaped to offer the lighting environment suitable for current context, after identifying user's action and location through a sensor. The sensor-based context awareness technology just considers a single user, and the studies to interpret many users' various context occurrences and conflicts lack. In existing studies, a fuzzy theory and algorithm including ReBa have been used as the methodology to solve context conflict. The fuzzy theory and algorithm including ReBa just avoid an opportunity of context conflict that may occur by providing services by each area, after the spaces where users are located are classified into many areas. Therefore, they actually cannot be regarded as customized service type that can offer personal preference-based context conflict. This paper proposes a priority-based LED lighting control system interpreting multiple context conflicts, which decides services, based on the granted priority according to context type, when service conflict is faced with, due to simultaneous occurrence of various contexts to many users. This study classifies the residential environment into such five areas as living room, 'bed room, study room, kitchen and bath room, and the contexts that may occur within each area are defined as 20 contexts such as exercising, doing makeup, reading, dining and entering, targeting several users. The proposed system defines various contexts of users using an ontology-based model and gives service of user oriented lighting environment through rule based on standard and context reasoning engine. To solve the issue of various context conflicts among users in the same space and at the same time point, the context in which user concentration is required is set in the highest priority. Also, visual comfort is offered as the best alternative priority in the case of the same priority. In this manner, they are utilized as the criteria for service selection upon conflict occurrence.

• Clinical and Experimental Pediatrics
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• v.51 no.10
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• pp.1065-1070
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• 2008

Purpose : This study aims to determine whether early closure (within 7 d) of significant patent ductus arteriosus (PDA) with indomethacin or ligation reduces neonatal morbidity when compared with delayed closure (after 7 d). Methods : Fifty-eight extremely-low-birth-weight infants admitted to the NICU of Seoul National University Hospital from April 2005 to May 2007 with PDA were studied retrospectively. Results : The mean gestational age (GA) was $26{\pm}2weeks$ (range, 23-32 wk), and the birth weight was $782{\pm}146g$ (range, 430-990 g). The delayed closure group was associated with early GA ($25.7{\pm}1.7wk$ vs $27.1{\pm}2.0wk$, P=0.013), in vitro fertilization (IVF) (55% vs 24%, P=0.017), and the absence of preeclampsia (5% vs. 34%, P=0.013). There was no difference in ductal size between the early closure and delayed closure groups. The incidence of bronchopulmonary dysplasia (95% vs 65%, P=0.012) and intraventricular hemorrhage (70% vs. 39%, P=0.027) increased in the delayed closure group. Using regression analysis adjusted for gestational age, delayed closure correlated positively with the duration of ventilator support (P=0.008), hospitalization (P=0.020), time to full enteral feeding (P<0.001), and total parenteral nutrition (P=0.010). Conclusion : Delayed closure of the hemodynamically significant patent ductus arteriosus in extremely-low-birth-weight infants is significantly related to the development of various morbidities. Thus, early closure of PDA is needed within the first week of life.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.4
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• pp.129-144
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• 2013

The consumption of coffee has been drastically increased last two decades. Now almost all the Korean adult people enjoy the coffee and diverse cultures related coffee have been spread widely in Korea. Therefore new marketing strategies are necessary to satisfy consumers according to ages, attitudes, and other characters. It has been continuously discussed whether the coffee gives negative impacts to health. Regardless of the discussions of the effects to health, now coffee became a part of modern daily lives. In this study the motivations of coffee consumption were classified to five; wellbeing motivation, refreshment motivation, social motivation, habitual motivation, and emotional motivation. Future intention of coffee consumption were also classified to five factors: sound mental intention, addictive intention, side-effect recovery intention, economic intention, and psychological intention. The survey was conducted in Seoul City and Kyeongki Province from January 3 to February 2, 2013. Total 500 questionaries were distributed and 450 were collected and 428 samples were used for the analysis of this study. The data were analyzed by SPSS Win 18 Version. The methods used in this study were factors analysis test, reliability test, validity test, t-testy, One-Way ANOVA, and regression analysis. The hypnosis in this study were as follows. First, The motivations of coffee consumption would influence to the intention of coffee consumption. Second, there would be statistical differences to the intention of coffee consumption according to the demographic characteristics. According to the result of the study, the motivation of coffee partially affected to the intention of coffee consumption. And there were statistical differences according to age, occupations, educational levels, and monthly incomes. The implications of this study were the factors related health and emotional feeling were considered more important than tastes and characters of coffee-shop that people thought more important before.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.498-508
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• 2017

Purpose: The present study investigated nutrient supplement intake to examine the relationship between the health behaviors of nutrient supplement users and nonusers and nutrient supplement users and other drug users. The results provide baseline data to understand whether nutrient supplements actually perform as expected in view of the fact that healthy people that take nutritional supplements may become healthier, but may also develop nutritional supplement abuse problems. Among 7,006 household heads of 24,614 household members from the Korea Health Panel data in 2008, a total of 6,009 household heads were the respondents of the Korea Health Panel Survey (appendix) in 2009. Method: The subjects of the present study were targeted household heads. The respondents who reported that they had taken (planned to take) life/health promotion-related drugs (01. vitamins/nutritional supplements) for more than three months that were purchased at pharmacies during the past one year at the time of the survey were defined as nutritional supplement users. Those who took other drugs (05. hair-loss treatments, 06. obesity treatments, 10. others) were regarded as other drug users. A chi-squared test was performed to analyze the sociodemographic characteristics of the subjects and differences between groups. Multiple regression analyses were conducted to analyze health behaviors according to nutrient supplement intake. Result: Comparison of (A) nutritional supplement users and nonusers revealed that those who were women, 50 years or older, and spent more than average living expenses were more likely to take nutritional supplements, which was not significant in health behavior variables. Analysis of nutritional supplement users and other drug users (B) revealed that those who were high school graduates or above, had a spouse, were non-smokers, took drugs, ate regular meals, and were not stressed by economic or family conflicts were more likely to take nutritional supplements. Conclusion: The results of the present study indicated that people take nutritional supplements because of their psychological desire to be healthy, not because they are not healthy, have problems, or believe supplements will make them healthier.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.144-159
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• 2014

Techniques for making a single panoramic image using multiple pictures are widely studied in many areas such as computer vision, computer graphics, etc. The panorama image can be applied to various fields like virtual reality, robot vision areas which require wide-angled shots as an useful way to overcome the limitations such as picture-angle, resolutions, and internal informations of an image taken from a single camera. It is so much meaningful in a point that a panoramic image usually provides better immersion feeling than a plain image. Although there are many ways to build a panoramic image, most of them are using the way of extracting feature points and matching points of each images for making a single panoramic image. In addition, those methods use the RANSAC(RANdom SAmple Consensus) algorithm with matching points and the Homography matrix to transform the image. The SURF(Speeded Up Robust Features) algorithm which is used in this paper to extract featuring points uses an image's black and white informations and local spatial informations. The SURF is widely being used since it is very much robust at detecting image's size, view-point changes, and additionally, faster than the SIFT(Scale Invariant Features Transform) algorithm. The SURF has a shortcoming of making an error which results in decreasing the RANSAC algorithm's performance speed when extracting image's feature points. As a result, this may increase the CPU usage occupation rate. The error of detecting matching points may role as a critical reason for disqualifying panoramic image's accuracy and lucidity. In this paper, in order to minimize errors of extracting matching points, we used $3{\times}3$ region's RGB pixel values around the matching points' coordinates to perform intermediate filtering process for removing wrong matching points. We have also presented analysis and evaluation results relating to enhanced working speed for producing a panorama image, CPU usage rate, extracted matching points' decreasing rate and accuracy.

• Journal of agricultural medicine and community health
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• v.28 no.2
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• pp.87-105
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• 2003

Objectives: The purpose of this study was to examine the health care utilization pattern and its related factors of low-income population with abnormal results through health examination. Methods: Analysed data were collected through a questionnaire survey, which was given to 263 persons who 30 years or over with abnormal results through health examination at Health Center. This survey was conducted in March, 2003. This study employed Andersen's prediction model as most well known medical demand mode and data were analysed through 2-test, and multiple logistic regression analysis. Results: The proportion of medical utilization for thorough examination or treatment among study subjects was 51.0%. In multiple logistic regression analysis as dependent variable with medical utilization, the variables affecting the medical utilization were 'feeling about abnormal result(anxiety versus no anxiety: odds ratio 2.25, 95% confidence intervals 1.07-4.75)', 'type of health security(medicaid type I versus health insurance: odds ratio 2.82, 95% confidence intervals 1.04-7.66; medicaid type II versus health insurance: odds ratio 3.22, 95% confidence intervals 1.37-7.53)', 'experience of health examination during past 2 years(odds ratio 2.39, 95% confidence intervals 1.09-5.21)' and 'family member's response for abnormal result(recommendation for medical utilization versus no response: odds ratio 4.90, 95% confidence intervals 1.75-13.75; family member recommended to utilize medical facilities with him/her versus no response: odds ratio 19.47, 95% confidence intervals 5.01-75.73)'. The time of medical utilization was 8-15 days after they received the result(29.9%), 16-30 days after they receive the result(27.6%), 2-7 days after they received the result(20.9%) in order. The most important reason why they didn't take a medical utilization was that it seemed insignificant to them(32.4%). Conclusions: In order to promote medical utilization of low-income population, health education for abnormal result and its management would be necessary to family member as well as person with abnormal result. And follow-up management program for person with abnormal result through health examination such as home-visit health care would be necessary.