• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Bulletin of the Society of Naval Architects of Korea
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• v.25 no.4
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• pp.7-12
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• 1988

The Resistance Committee of Korea Towing Tank Conference extended the Cooperative Experimental Study Program(1985)[1] to perform the geosim tests by exchanging the five different scaled Series 60, $C_B=0.60$ models between the participating organizations and 13 sets of resistance data have been obtained. The test results are compared among the participating towing tanks and also with the results given in the report of the Resistance and Flow Com-identify the mittee of 18th ITTC. The form factor of each model is derived by Prohaska's method to investigate its dependency on $R_n$. On the other hand, at each $F_n$, form factors are also derived by Telfer's method to relation between $F_n$ and form factor. For this hull form, form factors show relatively weak dependency on $R_n$ and strong dependency on $F_n$. And it is also found that dependencies on both have a cross relation. It seems that further study should be continued to understand more clearly the physical phenomena involved in this problem.

• Food Engineering Progress
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• v.14 no.1
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• pp.49-53
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• 2010

Surface plasmon resonance (SPR) which is utilized in thin film refractometry-based sensors has been concerned on measurement of physical, chemical and biological quantities because of its high sensitivity and label-free feature. In this paper, an application of SPR to detection of alcohol content in wine and liquor was investigated. The result showed that SPR sensor had high potential to evaluate alcohol content. Nevertheless, food industry may need SPR sensor with higher sensitivity. Herein, we introduced a nano-technique into fabrication of SPR chip to enhance SPR sensitivity. Using Langmuir-Blodgett (LB) method, gold film with nano-structured surface was devised. In order to make a new SPR chip, firstly, a single layer of nano-scaled silica particles adhered to plain surface of gold film. Thereafter, gold was deposited on the template by an e-beam evaporator. Finally, the nano-structured surface with basin-like shape was obtained after removing the silica particles by sonication. In this study, two types of silica particles, or 130 nm and 300 nm, were used as template beads and sensitivity of the new SPR chip was tested with ethanol solution, respectively. Applying the new developed SPR sensor to a model food of alcoholic beverage, the sensitivity showed improvement of 95% over the conventional one.