• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.363-371
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• 1992

This study was aimed at developing a computer model to determine rational road networks in mountainous forests. The computer model is composed of two major subroutines for digital terrain analyses and route selection. The digital terrain model(DTM) provides various information on topographic and vegetative characteristics of forest stands. The DTM also evaluates the effectiveness of road construction based on slope gradients. Using the results of digital terrain analyses, the route selection subroutine, heuristically, determines the optimal road layout satisfying the predefined road densities. The route selection subroutine uses the area-partitioning method in order to fully of roads. This method leads to unbiased road layouts in forest areas. The size of the unit partitiones area can be calculated as a function of the predefined road density. In addition, the user-defined road density of the area-partitioning method provides flexibility in applying the model to real situations. The rational road network can be easily achived for varying road densities, which would be an essential element for network design of forest roads. The optimality conditions are evaluated in conjuction with longitudinal gradients, investment efficiency earthwork quantity or the mixed criteria of these three. The performance of the model was measured and, then, compared with those of conventional ones in terns of average skidding distance, accessibility of stands, development index and circulated road network index. The results of the performance analysis indicate that selection of roading routes for network design using the digital terrain analysis and the area-partitioning method improves performance of the network design medel.

• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.420-430
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• 2001

The object of this study is to develop a computer program for the arrangement of the forest-road network maximizing the investment effect in forest-road construction with factors such as terrains, forest physiognomy, management plan, logging system, cost of forest-road construction, capacity of inputted labour, capacity of timber production and so on. The operating system developed by this study is Korean Windows 95/98 and Microsoft Visual Basic ver. 5.0. User interface was designed as systematic structure, it is presented as a kind of GUI(graphic user interface). The developed program has result of the most suitable forest-road arrangement, has suitable forest-road density calculated with cost of logging, cost of forest-road construction, diversion ratio of forest-road, cost of walking in forest. And the most suitable forest-road arrangement was designed for forest-road arrangement network which maximized investment effect through minimizing the sum of cost of logging and cost of forest-road construction. Input data were divided into map data and control data. Digital terrain model, division of forest-road layout plan, division of forest function and the existing road network are obtained from map data. on the other hand, cost of logging related terrain division, diversion ratio of forest-road and working road, cost of forest-road construction, cost of walking, cost of labor, walking speed, capacity of inputted labor, capacity of timber production and total distance of forest-road are inputted from control data. And map data was designed to be inputted by mesh method for common matrix. This program can be used to construct a new forest-road or vice forest-road which compensate already existing forest-road for the functional forestry.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.108-120
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• 2008

This paper discusses the prediction of deforestation areas using probability models from forest census database, Geographic information system (GIS) database and the land cover database. The land cover data was analyzed using remotely-sensed (RS) data of the Landsat TM data from 1989 to 2001. Over the analysis period of 12 years, the deforestation area was about 40ha. Most of the deforestation areas were attributable to road construction and residential development activities. About 80% of the deforestation areas for residential development were found within 100m of the road network. More than 20% of the deforestation areas for forest road construction were within 100m of the road network. Geographic factors and vegetation change detection (VCD) factors were used in probability models to construct deforestation occurrence map. We examined the size effect of area partition as training area and validation area for the probability models. The Bayes model provided a better deforestation prediction rate than that of the regression model.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of Forest and Environmental Science
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• v.27 no.3
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• pp.195-203
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• 2011

Forest in the our country is in the age that needs positive operation in order to foster economical forest. Multiple operations for making valuable forest should be conducted steadily and timely from afforestation to harvesting. In order to execute these kinds of forest operations, the construction of skid trail network that can be effectively used as a pathway for forestry machine and working space is necessary. To investigate facility effect of skid trail network, we executed the location of skid trail network through centroid method by GIS for 50ha of harvesting workplace in mechanized model forest located in Hongcheon, Gangwon Province. As a result of this research, skid trail density in this area changed from 79m/ha with current method to 42m/ha with improved method. It appeared that skid trail density with improved method is nearly half of current method even though the cutting area is the same as the current cutting area. Also, skidding distance changed from 117m with current method to 57m with improved method. It appears that skidding distance with improved method is nearly half of current method even though cutting area was enlarged in adjacent tending cutting area.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.86-87
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• 2021

As non-road mobile pollutants such as construction equipment are emerging as the main cause of air pollutants emission, construction equipment regulations are gradually strengthening. Research was conducted by correcting the emission coefficient to calculate and predict air pollutant emissions of construction equipment, but it did not reflect site variables such as field and equipment conditions that affect actual emissions. This study derived an Artificial Neural Network emission prediction model based on the actual emission data of excavators and trucks measured at the site and proposed a platform to predict the emission of air pollutants at the site according to the working size and conditions. Through this, it is possible to establish an eco-friendly process plan using a model from the construction plan.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.3
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• pp.237-248
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• 1998

Landcover has been largely influenced by human activities, especially in recent days. The analysis of the change of land use by urbanized development is useful for determining development plan hereafter. This study aimed to the quantitative analysis about the urban sprawl within 12 years from 1985 to 1996, at Chonan, and for extracting the characteristics of change. For this purpose, this study performed land cover classifications using Landsat TM data . A hybrid classification method was used to classify satellite images into seven types of land cover. Road network digitied from 1:25,000 topographic map was rasterized and overlaid on the landcover map. A result of this study showed that area of forest and paddy decreased due to urban sprawl. Especially from 1993 to 1996, the change of land use progressed rapidly because of merging a city and a country in Chonan. The size of patch in forest had been smaller and irregular form. It is a general progress that size of patch in forest had been smaller and irregular form. It is a general progress that the forest have changed the paddy and bare land paddy and bare land have changed low-density urban or high-density urban. This explained how urbanized Chonan was and applied the suggeston of plan in landuse with the result of this study.

• Journal of Korean Society of Rural Planning
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• v.21 no.2
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• pp.11-21
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• 2015

This study carried out evaluation of vulnerability in accessability and functionality using road network that was extracted from Intelligent Transportation System(ITS) and digital map. It was built in order to figure out accessability that locational data which include community center, public facilities, medical facilities and highway IC. The method for grasping functionality are Digital Elevation Model(DEM) and land slide hazard map provided by Korea Forest Service. The evaluation criteria for figure out accessability was set to related comparison of average time in urban area. Functionality value was calculated by the possibility of backing the vehicle possibility of snowfall and landslides. At last, this research computed weighting value through Analytic Hierarchy Process (AHP), calculated a vulnerable score. As the result, the accessability of rural village came out that would spend more time by 1.4 to 3.2 times in comparison with urban area. Even though, vulnerability of the road by a snowfall was estimated that more than 50% satisfies the first class, however, it show up that the road were still vulnerable due snowing because over the 14% of the road being evaluated the fifth class. The functionality has been satisfied most of the road, however, It was vulnerable around Lake Daechung and Piban-ryung, Yumti-jae, Suriti-jae where on the way Boeun. Also, the fifth class road are about 35 km away from the city hall on distance, take an hour to an hour and a half. The fourth class road are about 25 km away from the city hall on distance, take 25 min to an hour. The other class of the road take in 30 min from the city hall or aren't affected of weather and have been analyzed that a density of road is high. In A result that compare between distribution and a housing density came out different the southern and the eastern area, so this result could be suggested quantitative data for possibility of development.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.282-294
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• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.