• Journal of agricultural medicine and community health
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• v.23 no.1
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• pp.51-64
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• 1998

This study was carried out during 3 years from March 1992 to March 1995, 140 healthy people and 140 C.V.A patients were investigated, at an oriental clinic in Taejon. The data collected was analysed using a SAS. The results are as follows. 1. The Relationships between patients who have undergone one or more abortion procedures and C.V.A occurrence. A statistical significant difference was recorded between the control group and the outpatients group of P<0.05 2. Number of years after initial C.V.A diagnosis before C.V.A symptom onset. within 1 years 52% (2-3 years, 3-4 years, 4-5 years, 5 years and over) 3. Additional diseases occurring after initial stroke 1 disease 50.7% (2, 3, 4, 5 or more) 4. Alcohol Consumption "A statistical significant difference was recorded between the control group and the outpatients group of" P<0.01 5. Previous Treatment Experience for stoke outpatients Oriental Hospital, clinic 64.3% (General Hospital, clinic, a public health center drug store, home remedies, etc) 6. Exercise Frequency Outpatients Regular 28.6%, Irregular 71.4% Control sample Regular 27%, Irregular 72.9% 7. Degree of Appetite "A statistical significant difference was recorded between the control group and the outpatients group of" P<0.01 8. People who include fish in their diet "A statistical difference was recorded between the control group and the outpatients group of" P<0.01.

• Environmental Mutagens and Carcinogens
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• v.24 no.4
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• pp.180-188
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• 2004

CYP1A1 is known to be inducible by xenobiotic compouds such as polyciclic aromatic hydrocarbons(PAHs) and 2,3,7,8-tetrachloro-dibenzo-p-dioxin(TCDD). These chemicals have been identified worldwide and can have a significant impact on the human health and well being of human and wildlife. Given these issues, the detection and quantification of these chemicals in biological, environmental and food samples is important. We investigated the effect of dietaty flavonoid, such as CYP1A1 promoter activity, 7-ethoxyresorufin-O-deethylase(EROD) activity and CYP1A1 mRNA expression induced by benzo(k)fluoranthene(B(k)F) in MCF-7 cells. Based on the three criteria of frequency of occurrence in the environment, toxicity and potential exposure to humans, B(k)F is one of the top-listed PAHs. We found that B(k)F significantly up-regulates the level of CYP1A1 promoter activity, EROD and CYP1A1 mRNA. when cells were treated with daidzein inhibited the B(k)-induced CYP1A1 prompter activity and mRNA level at high concentration. But daidzein exhibited stimulatory effects B(k)F-induced CYP1A1 promoter activity and mRNA level at low concentration. Overall, results from these studies demonstrate flavonoids might interfere the action of B(k) with AhR system to stimulate CYP1A1 gene expression.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.535-537
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• 2013

Recently, the quality of the incident of school violence is getting worse and the frequency of its occurrence is gradually increasing. One of the biggest reasons that violence in schools does not reduce is because the attitude that they mean to hide damage from the violence, thus the improvement of such conditions enables school violence to decrease in the short-term effect. This study designed an intelligent reporting platform to prevent additional violence and victim's suicide, by related agencies and parents reporting the circumstance of school violence real-time, as both victims and attackers register real-time troubles anonymously, at any time, anywhere, then are provided 1:1 counseling and customized services, and all contents of the counseling are accumulated in the intelligent reporting platform.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.51-58
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• 2018

As the amount of users and data of NS explosively increased, research based on SNS Big data became active. In social mining, Latent Dirichlet Allocation(LDA), which is a typical topic model technique, is used to identify the similarity of each text from non-classified large-volume SNS text big data and to extract trends therefrom. However, LDA has the limitation that it is difficult to deduce a high-level topic due to the semantic sparsity of non-frequent word occurrence in the short sentence data. The BTM study improved the limitations of this LDA through a combination of two words. However, BTM also has a limitation that it is impossible to calculate the weight considering the relation with each subject because it is influenced more by the high frequency word among the combined words. In this paper, we propose a technique to improve the accuracy of existing BTM by reflecting semantic relation between words.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.197-206
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• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of Astronomy and Space Sciences
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• v.36 no.2
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• pp.61-68
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• 2019

We have investigated the variations of sporadic E (Es) layer using the measurements of digisondes at Icheon ($37.14^{\circ}N$, $127.54^{\circ}E$, IC) and Jeju ($33.4^{\circ}N$, $126.30^{\circ}E$, JJ) in 2011-2018. The Es occurrence rate and its critical frequency (foEs) have peak values in summer at both IC and JJ in consistent with their known seasonal variations at mid-latitudes. The virtual height of the Es layer (h'Es) during equinox months is greater than that in other months. It may be related to the similar variation of meteor peak heights. The h'Es shows the semidiurnal variations with two peaks at early in the morning and late in the afternoon during equinoxes and summer. However, the semi-diurnal variation is not obvious in winter. The semi-diurnal variation is generally thought to be caused by the semi-diurnal tidal variation in the neutral wind shear, whose measurements, however, are rare and not available in the region of interest. To investigate the formation mechanism of Es, we have derived the vertical ion drift velocity using the Horizontal Wind Model (HWM) 14, International Geomagnetic Reference Field, and Naval Research Laboratory Mass Spectrometer and Incoherent Scatter Radar-00 models. Our results show that h'Es preferentially occur at the altitudes where the direction of the vertical ion velocity changes. This result indicates the significant role of ion convergence in the creation of Es.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.593-599
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• 2023

There is risk of derailment due to various factors such as vehicle-track load imbalance in curved parts, so urban railways install and operate derailing prevent guard rails. The angle-type derailing prevent guard rail is composed of various parts including the guard angle. Even if derailment does not occur, various damages occur in the components, so continuous maintenance is required. Through the damage status analysis, the components of the angle-type derailing prevent guard rail with high damage frequency were classified, and conditions for the occurrence of various damages were investigated. In addition, a numerical analysis using a precise 3D numerical model was performed to analyze the cause of the damage analytically. In order to analytically simulate the derailment situation, the static ultimate load condition was applied, and the actual drawing of the angle-type derailing prevent guard rails, rails, and wheels was used for modeling. By analyzing the results of the damage status investigation and finite element analysis, we tried to investigate the damage of the components.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1A
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• pp.135-146
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• 2008

In this paper, a hybrid damage monitoring scheme for prestressed concrete (PSC) girder bridges by using sequential acceleration and impedance signatures is newly proposed. Damage types of interest include prestress-loss in tendon and flexural stiffness-loss in a concrete girder. The hybrid scheme mainly consists of three sequential phases: damage alarming, damage classification, and damage estimation. In the first phase, the global occurrence of damage is alarmed by monitoring changes in acceleration features. In the second phase, the type of damage is classified into either prestress-loss or flexural stiffness-loss by recognizing patterns of impedance features. In the third phase, the location and the extent of damage are estimated by using two different ways: a mode shape-based damage detection to detect flexural stiffness-loss and a natural frequency-based prestress prediction to identify prestress-loss. The feasibility of the proposed scheme is evaluated on a laboratory-scaled PSC girder model for which hybrid vibration-impedance signatures were measured for several damage scenarios of prestress-loss and flexural stiffness-loss.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.43-51
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• 2011

Since a variety of factors are associated with crash occurrence, the analysis of causes of crash is a hard task for traffic researchers and engineers. Among contributing factors leading to crash, the characteristics of driver is of keen interest. This study attempted to identify factors affecting the severity of pedestrian in the collision between pedestrian and vehicle. In particular, our analyses were focused on the novice driver. A binary logistic regression technique was adopted for the analyses. The results showed that driver's age, crash location, and the frequency of violations were dominant factors for the severity. Findings are expected to be useful information for deffective policy- and education-based countermeasures.