• Journal of Life Science
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• v.20 no.3
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• pp.388-395
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• 2010

Cyclooxygenase (COX)-2 is generally known as an inducible enzyme, and it produces arachidonic acid to prostaglandin $E_2$ ($PGE_2$), which has been demonstrated to play a critical role in inflammation. In the present study, we investigated the effects of the extracts of fermented beans including soybean (FS), black agabean (FBA) and yellow agabean (FYA), on the expression of COXs and production of $PGE_2$ in U937 human promonocytic cells. Treatment of phorbol 12-myristate 13-acetate (PMA) significantly induced pro-inflammatory mediators such as COX-2 expression and $PGE_2$ production, whereas the levels of COX-1 remained unchanged. However, pre-treatment with FS, FBA and FYA significantly decreased PMA-induced COX-2 protein as well as mRNA, which is associated with inhibition of $PGE_2$ production. Moreover, FS, FBA and FYA markedly prevented the increase of nuclear translocation of nuclear factor kappa B (NF-${\kappa}B$) p65 by PMA. Our data indicate that the extracts of fermented beans exhibits anti-inflammatory properties by suppressing the transcription of pro-inflammatory cytokine genes through the NF-${\kappa}B$ signaling pathway.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.549-558
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• 2021

In order to keep infrastructures such as tunnels and underground facilities safe, cracks of concrete lining in tunnel should be detected by regular inspections. Since regular inspections are accomplished through manual efforts using maintenance lift vehicles, it brings about traffic jam, exposes works to dangerous circumstances, and deteriorates consistency of crack inspection data. This study aims to provide methodology to automatically extract cracks from tunnel concrete lining images generated by the existing tunnel image acquisition system. Specifically, we train a deep learning based semantic segmentation model with open dataset, and evaluate its performance with the dataset from the existing tunnel image acquisition system. In particular, we compare the model performance in case of using all of a public dataset, subset of the public dataset which are related to tunnel surfaces, and the tunnel-related subset with negative examples. As a result, the model trained using the tunnel-related subset with negative examples reached the best performance. In the future, we expect that this research can be used for planning efficient model training strategy for crack detection.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.69-78
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• 2009

Korea has achieved significant economic growth with building the Gyeongbu Expressway. As the number of new road construction projects has decreased, it becomes more important to maintain optimal status of the current road networks. One of the best ways to accomplish it is weight enforcement as active control measure of traffic load. This study is to develop High-speed Weigh-in-motion System in order to enhance efficiency of weight enforcement, and to analyze patterns of overloaded trucks on highways through the system. Furthermore, it is to review possibilities of developing overweight control system with application of the HS-WIM system. The HS-WIM system developed by this study consists of two sets of an axle load sensor, a loop sensor and a wandering sensor on each lane. A wandering sensor detects whether a travelling vehicle is off the lane or not with the function of checking the location of tire imprint. The sensor of the WIM system has better function of classifying types of vehicles than other existing systems by detecting wheel distance and tire type such as single or dual tire. As a result, its measurement errors regarding 12 types of vehicle classification are very low, which is an advantage of the sensor. The verification tests of the system under all conditions showed that the mean measurement errors of axle weight and gross axle weight were within 15 percent and 7 percent respectively. According to the WIM rate standard of the COST-323, the WIM system of this study is ranked at B(10). It means the system is appropriate for the purpose of design, maintenance and valuation of road infrastructure. The WIM system in testing a 5-axle cargo truck, the most frequently overloaded vehicle among 12 types of vehicles, is ranked at A(5) which means the system is available to control overloaded vehicles. In this case, the measurement errors of axle load and gross axle load were within 8 percent and 5 percent respectively. Weight analysis of all types of vehicles on highways showed that the most frequently overloaded vehicles were type 5, 6, 7 and 12 among 12 vehicle types. As a result, it is necessary to use more effective overweight enforcement system for vehicles which are seriously overloaded due to their lift axles. Traffic volume data depending upon vehicle types is basic information for road design and construction, maintenance, analysis of traffic flow, road policies as well as research.