• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2015.11a
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• pp.251-251
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• 2015

최근 질병 조기진단에 대한 사회적 요구가 높아짐에 따라 이에 대한 기술에 관심이 집중되고 있다. 그 중 표면증강라만산란(surface enhanced Raman scattering(SERS))을 이용하여 인체 내 소량의 바이오마커를 검출하는 연구가 활발히 진행중이다. 본 연구에서는 바이오마커의 검지감도를 최대치로 증가시키기 위해 SERS 기판의 나노구조를 최적화 하였다. SERS 기판 표면의 나노구조, 크기, 형상, 밀도 등에 따라 검지감도가 변화되기 때문에 이를 제어하기 위해 증착공정 변수에 변화를 주어 표면의 나노구조를 형성하였다. 이를 분석하기 위해 SEM, XRD를 사용하였으며 최적화된 SERS 기판을 활용하여 Rhodamine 6G의 신호가 $1{\times}10^5$ 이상의 enhancing factor를 확인하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.80-92
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• 2023

As cities are becoming densely populated, previously unexpected events such as crimes, accidents, and infectious diseases are bound to affect user demands. With a time-series prediction of demand using information with uncertainty, it is impossible to derive reliable results. In particular, the COVID-19 outbreak in early 2020 caused changes in abnormal travel patterns and made it difficult to predict demand for time series. A methodology that accurately predicts demand by detecting and reflecting these changes is, therefore, required. The current study suggests a time series modeling pipeline that automatically detects and predicts abnormal events caused by COVID-19. We expect its wide application in various situations where there is a change in demand due to irregular and abnormal events.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.21-30
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• 2008

The travel speed collected by the prove-car equipped with the GPS has the problems, which are the data's stability and finding out the representative travel speed, by the influence of the traffic signal and etc. at the interrupted traffic. This study was conducted to develop the method of filtering the outlier data from the data collected by the prove-car. The method to remove the outlier data from the serial data which were collected by the prove-car was adapted to each of the quartile deviation statistics model and the management graphic statistics model. The rate of removing the outlier data by the quartile deviation method was $0{\sim}3.7%$ while the rate by the management graphic statistic methods was $0.3{\sim}7.2%$. Both methods show the low removal rate at the dawn time when the traffic is inactivity, on the other hand the remove rate is high during the daytime. However, both methods have the problem such that the threshold level for removing the outlier data was established at the low bound in the case as good as the statistics model. Therefore, it is required for the experience calibration.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.53-63
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• 2008

In this paper, we propose the regional travel time measurement algorithm using the sequence pattern matching to the type of vehicles between the origin of the region and the end of the region, that could be able to overcome the limit of conventional method such as Probe Car Method or AVI Method by License Plate Recognition. This algorithm recognizes the vehicles as a sequence group with a definite length, and measures the regional travel time by searching the sequence of the origin which is the most highly similar to the sequence of the end. According to the assumption of similarity cost function, there are proposed three types of algorithm, and it will be able to estimate the average travel time that is the most adequate to the information providing period by eliminating the abnormal value caused by inflow and outflow of vehicles. In the result of computer simulation by the length of region, the number of passing cars, the length of sequence, and the average maximum error rate are measured within 3.46%, which means that this algorithm is verified for its superior performance.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.11
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• pp.1529-1533
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• 2008

The detection characteristics of gamma-irradiated ($0{\sim}10.0\;kGy$) medicinal herbs (Platycodon grandiflorum, Acanthopanax chiisanensis) were investigated by photostimulated luminescence (PSL), thermoluminescence (TL), and electron spin resonance (ESR). The results of the PSL, a first screening method in comparison with the TL, showed photon counts greater than 5,000 counts/60 s (positive) in the irradiated samples, while the non-irradiated samples yielded photon counts less than 700 counts/60 s (negative). The TL was also applied for the detection method of irradiated medicinal herbs and showed that the non-irradiated sample revealed a glow curve with a low intensity, while the irradiated samples showed a higher intensity. These results were normalized by re-irradiating the mineral grains with a irradiation dose of 1.0 kGy, and a second glow curve was recorded. The ratio of the intensity of the first glow curve ($TL_1$) to that after the normalization dose ($TL_2$) was determined and compared with the recommended threshold values. TL ratio ($TL_1/TL_2$) was below 0.007 for the non-irradiated sample and higher than 0.1 for all irradiated samples (above 1.0 kGy). ESR spectroscopy revealed specific signals (6.065 mT) derived from free radicals in cellulose containing irradiated medicinal herbs. The P. grandiflorum showed clearer signals than A. chiisanensis. From the results of our studies, the PSL, TL, and ESR determinations were found to be suitable for the detection of irradiated medicinal herbs such as P. grandiflorum and A. chiisanensis.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.51-61
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• 2002

In this dissertation, We demonstrated the Travel Time forecasting model in the freeway of multi-section with regard of drives' attitude. Recently, the forecasted travel time that is furnished based on expected travel time data and advanced experiment isn't being able to reflect the time-lag phenomenon specially in case of long distance trip, so drivers don't believe any more forecasted travel time. And that's why the effects of ATIS(Advanced Traveler Information System) are reduced. Therefore, in this dissertation to forecast the travel time of the freeway of multi-section reflecting the time-lag phenomenon & the delay of tollgate, we used traffic volume data & TCS data that are collected by Korea Highway Cooperation. Also keep the data of mixed unusual to applicate real system. The applied model for forecasting is consisted of feed-forward structure which has three input units & two output units and the back-propagation is utilized as studying method. Furthermore, the optimal alternative was chosen through the twelve alternative ideas which is composed of the unit number of hidden-layer & repeating number which affect studying speed & forecasting capability. In order to compare the forecasting capability of developed ANN model. the algorithm which are currently used as an information source for freeway travel time. During the comparison with reference model, MSE, MARE, MAE & T-test were executed, as the result, the model which utilized the artificial neural network performed more superior forecasting capability among the comparison index. Moreover, the calculated through the particularity of data structure which was used in this experiment.

• Journal of Navigation and Port Research
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• v.38 no.1
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• pp.65-71
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• 2014

Gyeongbu and Namhae expressways in the country, are the major arterial highways which are connected with the Busan port in the north-south and east-west directions, respectively, and required to study the traffic characteristics about the hourly volume factors(K-factor) by concentrated midium-size and large-size cargo trucks of 20% or higher in expressways. We therefore attempted to predict the K-factor in expressways through the correlation analysis between K-factor and K-factor estimates on the basis of the short-term VDS data collected at the basic segments of the above major expressways. As a result, power model appeared to be appropriate in predicting K-factor by the K-factor estimate based on VDS data for 7 days with a high explanatory power and validity.