• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5D
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• pp.611-617
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• 2009

With advanced technologies applied to transportation, realtime traffic information has been necessary for not only drivers but also agencies. In normal, traffic conditions have been represented to three levels according to congestion: "free", "slow", and "jammed". Those categories and criteria are set up for traffic management even though traffic information is provided for drivers. This study examines how drivers feel current congestion levels and delves into traffic categories and criteria which they recognize. To collect data for drivers' recognition, a survey of freeway travellers is conducted answering the question about traffic flow speed from video image on a freeway section. In the result of the survey, the surveyee preferred a 4-level traffic condition including "delayed" to 3-level traffic condition. As its criteria, 20 km/h, 50 km/h, and 75 km/h were obtained. These results are expected to contribute to building more appropriate traffic information for drivers and providing an operational guideline for Traffic Monitering Centers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.729-735
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• 2008

For well-constructed concrete, its service life is a long period and it has an enough durability performance. For cracked concrete, however, it is clear that cracks should be a preferential channel for the penetration of aggressive substance such as chloride ions accoding to author's previous researches. Even though crack width can be reduced due to the high reinforcement ratio, the question is to which extend these cracks may jeopardize the durability of cracked concrete. If the size of crack is small, surface treatment system can be considered as one of the best options to extend the service life of concrete structures exposed to marine environment simply in terms of cost effectiveness versus durability performance. Thus, it should be decided to undertake an experimental study to deal with the effect of different types of surface treatment system, which are expected to seal the concrete and the cracks to chloride-induced corrosion in particular. In this study, it is examined the effect of surfaced treated systems such as penetrant, coating, and their combination on chloride penetration through microcracks. Experimental results showed that penetrant can't cure cracks. However, coating and combined treatment can prohibit chloride penetration through cracks upto 0.06 mm, 0.08 mm, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.383-392
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established. Therefore, uncertainty analysis are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an unexpected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Asia Marketing Journal
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• v.8 no.1
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• pp.1-17
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• 2006

A couple of previous studies have warned against the use of homogeneous choice models in assessing the effect of reference price since unaccounted for response heterogeneity may result in spurious reference price effects(Chang, Siddarth and Weinberg 1999; Bell and Lattin 2000). According to Meyer and Kahn(1991), not accounting for consideration set heterogeneity may also bias the effect parameters in the choice model. Therefore, failure to account for these two sources of bias, in fact, have cast doubt on the empirical support for reference price effects in general. In view of aforementioned potential sources of bias, the author investigates the robustness of loss aversion effect in the reference-dependent model after accounting for heterogeneity in response as well as consideration set. The proposed model defines individual household's consideration set based on the posterior distribution of preference obtained from the Empirical Bayes approach. In addition, the same posterior distribution is used to form household-specific reference prices. Response heterogeneity correction is carried out via the Latent Class approach. The proposed model outperforms the Reference-Dependent model that includes the reference price measure most often employed in the previous studies. This implies that as a way of simplifying decision task, consumers restrict their consideration set to a subset of available brands not only in making a brand choice but also in forming reference prices.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.404-414
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• 2023

In this study, digital climate maps with high-resolution (1km, daily) for the period of 1981 to 2020 were produced for the use as reference data within the procedures for statistical downscaling of climate change scenarios. Grid data for the six climate variables including maximum temperature, minimum temperature, precipitation, wind speed, relative humidity, solar radiation was created over Korean Peninsula using statistical downscaling model, so-called IGISRM (Improved GIS-based Regression Model), using global reanalysis data and in-situ observation. The digital climate data reflects topographical effects well in terms of representing general behaviors of observation. In terms of Correlation Coefficient, Slope of scatter plot, and Normalized Root Mean Square Error, temperature-related variables showed satisfactory performance while the other variables showed relatively lower reproducibility performance. These digital climate maps based on observation will be used to downscale future climate change scenario data as well as to get the information of gridded agricultural weather data over the whole Korean Peninsula including North Korea.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.6
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• pp.75-87
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• 2023

This study presents an experimental design for demonstrating the role of nature-based solutions to climate change in the landscape and garden sector. The study suggests spatial strategies for a carbon-neutral society and its role as a cultural industry. This paper describes the use of a low-maintenance garden as part of a strategy for carbon reduction with the goal of protecting the environment and forming a carbon-neutral society. To this end, this study involved the design and construction of a realistic model garden to provide scientific data on the functions, spatial elements, and carbon neutrality of carbon-reducing gardens. The target site is located in the Sejong National Arboretum. The test area in which the carbon-reducing function is measured is located in the centre of the site, and other spaces include dry gardens, community gardens, and flower gardens intended for exhibition and relaxation. The experimental area is divided into several smaller areas within which the carbon-reducing effect is analysed according to the amount of biochar installed, the planting density, and the plant species present. The application of facilities and construction methods to promote carbon reduction were based on the method known as '10 types of carbon gardening for the earth'. In the model garden, we employed rainwater utilization facilities and used low-carbon certified wood and local materials. The carbon reduction effect of each facility and construction method is compared and presented here. The results are expected to serve as an important basis for realizing a carbon-neutral society and can be used as a reference in various fields that require sustainable development, such as the garden industry.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1437-1449
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• 2023

The purpose of this study is to analyze whether pervious and impervious areas in urban areas affect tree growth. In order to determine the differences in the growth of six species of trees planted simultaneously, the effects of pervious and impervious surfaces on tree growth were analyzed using the Normalized Difference Vegetation Index (NDVI) produced using Sentinel-2 and sub-divided land cover map from the Ministry of Environment. For this purpose, the Geospatial eXplainable Artificial Intelligence(GeoXAI) concept was applied. As a result of the analysis, the explanatory power of the model was found to be the best when considering the area of land cover included in the 10m range for Pinus densiflora, the 20 m range for Zelkova Serrata, Metasequoia glyptostroboides, and Ginkgo biloba, the 30 m range for Platanus occidentalis, and the 40 m range for Yoshino cherry trees. In addition, the wider the pervious area, the more active the growth of trees,showing a positive correlation, and the wider the impervious area, such as nearby artificial ground, showed a negative correlation with tree growth. This shows that surrounding pervious and impervious areas affect the growth of trees and that the scope of influence varies depending on the tree species.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.

• Journal of the Economic Geographical Society of Korea
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• v.27 no.1
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• pp.38-52
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• 2024

Digital transformation industry contributes to the improvement of productivity in overall industrial production, the smart green industry for carbon neutrality and sustainable growth is growing as a future industry. The purpose of this paper is to explore the status and role of the industry in the future industry innovation ecosystem through the analysis of the growth drivers and location pattern changes of the smart green industry. The industry is on the rise in both metropolitan and non-metropolitan areas, and the growth of the industry can be seen in non-metropolitan and non-urban areas. In particular, due to the smart green industrial complex pilot project, the creation of Gwangju Jeonnam Innovation City, and the promotion of new and renewable energy policies, the emergence of core aggregation areas (HH type) in the coastal areas of Honam and Chungcheongnam-do, and the formation of isolated centers (HL type) in the Gyeongsang region, new and renewable energy production companies are being accumulated in non-metropolitan areas. Therefore, the smart green industry is expected to promote the formation of various specialized spokes in non-urban areas in the future industrial innovation ecosystem that forms a multipolar hub-spoke network structure, where policy factors are the triggers for growth.