• Proceedings of the KOR-KST Conference
• /
• 1999.10a
• /
• pp.489-494
• /
• 1999

• International Journal of Oral Biology
• /
• v.46 no.1
• /
• pp.45-50
• /
• 2021

This study evaluated the antimicrobial activity of Endoseal TCS, an mineral trioxide aggregate-based root canal sealer, mixed with water-soluble mangostin derivatives (WsMD) of Garcinia mangostana L. (mangosteen) ethanol extract against Enterococcus faecalis and Staphylococcus aureus. The antibacterial activity of Endoseal TCS mixed with WsMD against three strains of E. faecalis and three strains of S. aureus was performed using agar diffusion test. The data showed that Endoseal TCS mixed with 0.115% WsMD had a zone of inhibition of 0.7 ± 0.2-2.4 ± 0.1 mm. The results suggest that Endoseal TCS mixed with WsMD of Garcinia mangostana L. ethanol extract is useful as a root canal sealer with antibacterial activity against E. faecalis and S. aureus.

• Journal of Korean Society of Transportation
• /
• v.33 no.1
• /
• pp.14-28
• /
• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.6
• /
• pp.1873-1879
• /
• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Journal of Korean Society of Transportation
• /
• v.27 no.5
• /
• pp.209-221
• /
• 2009

Travel time estimation under given traffic conditions is important for providing drivers with travel time prediction information. But the present expressway travel time estimation process cannot calculate a reliable travel time. The objective of this study is to estimate the path travel time spent in a through lane between origin tollgates and destination tollgates on an expressway as a prerequisite result to offer reliable prediction information. Useful and abundant toll collection system (TCS) data were used. When estimating the path travel time, the path travel time is estimated combining the link travel time obtained through a preprocessing process. In the case of a lack of TCS data, the TCS travel time for previous intervals is referenced using the linear interpolation method after analyzing the increase pattern for the travel time. When the TCS data are absent over a long-term period, the dynamic travel time using the VDS time space diagram is estimated. The travel time estimated by the model proposed can be validated statistically when compared to the travel time obtained from vehicles traveling the path directly. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variaty of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.1021-1024
• /
• 2009

현재 사용되고 있는 통행시간 분류방법은 하나의 통행시간을 대푯값으로 가지고 있다. 이에 문제점은 고속도로 특성으로 규정 속도 이상의 속도로 주행하는 차량, 규정 속도 및 휴게소 이용차량, 운전자의 운전 습성, 통행 목적, 피로의 정도, 운전자 성향과 도로상황에 따라 통행시간이 다르게 나타나는 점이다. TCS(Toll Collection System) 자료는 고속도로의 다양한 특성이 포함되어 있으며, 대상 구간의 거리가 멀수록 목적지에 도달하는 통행시간의 분산이 커지는 특성 또한 보인다. 따라서 이를 처리하기 위한 효율적인 통행시간 분류, 구간대표통행시간 추출 알고리즘이 필요하다. 기존의 방법은 전체 통행차량의 통행시간을 감안한 방법으로 통행시간 예측시 정확성이 저하된다. 본 연구에서는 TCS 자료를 Fuzzy c-means 알고리즘을 이용하여 일일 고속도로 통행시간의 시간별 주행특성을 고려한 대푯 값을 추출하는 알고리즘을 제안하였다. 실제 서울-청주 구간을 운행한 TCS 자료를 가지고 실시한 실험으로, 주행특성 및 도로상황을 고려한 Fuzzy c-means를 이용한 통행시간 분류방법과 기존의 통행시간 분류 방법을 통한 통행시간을 PIFAB를 사용 TCS 자료의 실제 통행시간과 경로통행시간을 비교 평가하였다. 평가한 결과 본 연구에서 제안하는 Fuzzy c-means기법은 기존 방법인 MAD기법보다 75%, 신뢰구간(95%) 추출법 대비 81%의 정확성을 제고하였다.

• Wind and Structures
• /
• v.38 no.4
• /
• pp.309-323
• /
• 2024

The gust factor and turbulence intensity are two crucial parameters that characterize the properties of turbulence. In tropical cyclones (TCs), these parameters exhibit significant variability, yet there is a lack of established formulas to account for their probabilistic characteristics with consideration of their inherent connection. On this condition, a probabilistic analysis of gust factors and turbulence intensities of TCs is conducted based on fourteen sets of wind data collected at the Sutong Cable-stayed Bridge site. Initially, the turbulence intensities and gust factors of recorded data are computed, followed by an analysis of their probability densities across different ranges categorized by mean wind speed. The Gaussian, lognormal, and generalized extreme value (GEV) distributions are employed to fit the measured probability densities, with subsequent evaluation of their effectiveness. The Gumbel distribution, which is a specific instance of the GEV distribution, has been identified as an optimal choice for probabilistic characterizations of turbulence intensity and gust factor in TCs. The corresponding empirical models are then established through curve fitting. By utilizing the Gumbel distribution as a template, the nexus between the probability density functions of turbulence intensity and gust factor is built, leading to the development of a generalized probabilistic model that statistically describe turbulence intensity and gust factor in TCs. Finally, these empirical models are validated using measured data and compared with suggestions recommended by specifications.

• Journal of Korean Society of Transportation
• /
• v.30 no.3
• /
• pp.31-41
• /
• 2012

Due to growing interests in the distribution of traffic volume through information dissemination such as VMS and traffic broadcasting system, the research on the driver's reaction and effect of the traffic report has continued. In this study, we propose a methodology, which estimates the traffic volume of diversion and the consequential diversion rate using FTMS data and TCS data, and the estimation is based on the analysis of the national highway and IC, in which real-time FTMS and TCS data are established. We also calculate the diversion rate of actual targeted sections and analyze the changes in time and spatial diversion rate. In this study, we define a deviation (considering a deviation due to dynamic properties of traffic conditions) found when the outflow traffic volume is temporarily higher than the average outflow traffic volume on a relevant time slot after providing traffic information. The diverting volume is considered to be caused by the traffic information, and the study determines the ratio of traffic volume on highways to that of route diversion as the diversion rate. The analysis on changes in the diversion rate in accordance with the time flow, the initial change in the diversion rate on upstream IC that first acquires the report on the traffic congestion is significant. After that, the change in the diversion rate on upstream IC affects the route diversion on downstream IC with spatial and time flow, and this again leads the change in upstream IC. Thereby, we confirmed that there is a feedback-control circulation system in the route diversion.

• Toxicological Research
• /
• v.35 no.2
• /
• pp.137-154
• /
• 2019

Triclosan (TCS) is an antimicrobial compound used in consumer products. The purpose of current study was to examine toxicology and risk assessment of TCS based on available data. Acute toxicities of oral, transdermal and inhalation routes were low, and phototoxicity and neurotoxicity were not observed. Topical treatment of TCS to animal caused mild irritation. TCS did not induce reproductive and developmental toxicity in rodents. In addition, genotoxicity was not considered based on in vitro and in vivo tests of TCS. It is not classified as a carcinogen in international authorities such as International Agency for Research on Cancer (IARC). No-observed-adverse-effect level (NOAEL) was determined 12 mg/kg bw/day for TCS, based on haematoxicity and reduction of absolute and relative spleen weights in a 104-week oral toxicity study in rats. Percutaneous absorption rate was set as 14%, which was human skin absorption study reported by National Industrial Chemicals Notification and Assessment Scheme (NICNAS) (2009). The systemic exposure dosage (SED) of TCS has been derived by two scenarios depending on the cosmetics usage of Koreans. The first scenario is the combined use of representative cosmetics and oral care products. The second scenario is the combined use of rinse-off products of cleansing, deodorants, coloring products, and oral care products. SEDs have been calculated as 0.14337 mg/kg bw/day for the first scenario and 0.04733 mg/kg bw/day for the second scenario. As a result, margin of safety (MOS) for the first and second scenarios was estimated to 84 and 253.5, respectively. Based on these results, exposure of TCS contained in rinse-off products, deodorants, and coloring products would not pose a significant health risk when it is used up to 0.3%.

• Atmosphere
• /
• v.30 no.3
• /
• pp.209-220
• /
• 2020

Seasonal predictability and variability of tropical storms (TCs) simulated in the Global Seasonal Forecast System version 5 (GloSea5) of the Korea Meteorological Administration (KMA) is assessed in Northern Hemisphere in 1996~2009. In the KMA, the GloSea5-Global Atmosphere version 3.0 (GloSea5-GA3) that was previously operated was switched to the GloSea5-Global Coupled version 2.0 (GloSea5-GC2) with data assimilation system since May 2016. In this study, frequency, track, duration, and strength of the TCs in the North Indian Ocean, Western Pacific, Eastern Pacific, and North Atlantic regions derived from the GloSea5-GC2 and GloSea5-GA3 are examined against the best track data during the research period. In general, the GloSea5 shows a good skill for the prediction of seasonally averaged number of the TCs in the Eastern and Western Pacific regions, but underestimation of those in the North Atlantic region. Both the GloSea5-GA3 and GC2 are not able to predict the recurvature of the TCs in the North Western Pacific Ocean (NWPO), which implies that there is no skill for the prediction of landfalls in the Korean peninsula. The GloSea5-GC2 has higher skills for predictability and variability of the TCs than the GloSea5-GA3, although continuous improvements in the operational system for seasonal forecast are still necessary to simulate TCs more realistically in the future.