• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.409-420
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• 2012

Since the Industrial Revolution, CO2 levels have been increasing with climate change. In this study, Analyze time-series changes in snow cover quantitatively and predict the vanishing point of snow cover statistically using remote sensing. The study area is Mt. Kilimanjaro, Tanzania. 23 image data of Landsat-5 TM and Landsat-7 ETM+, spanning the 27 years from June 1984 to July 2011, were acquired. For this study, first, atmospheric correction was performed on each image using the COST atmospheric correction model. Second, the snow cover area was extracted using the NDSI (Normalized Difference Snow Index) algorithm. Third, the minimum height of snow cover was determined using SRTM DEM. Finally, the vanishing point of snow cover was predicted using the trend line of a linear function. Analysis was divided using a total of 23 images and 17 images during the dry season. Results show that snow cover area decreased by approximately $6.47km^2$ from $9.01km^2$ to $2.54km^2$, equivalent to a 73% reduction. The minimum height of snow cover increased by approximately 290 m, from 4,603 m to 4,893 m. Using the trend line result shows that the snow cover area decreased by approximately $0.342km^2$ in the dry season and $0.421km^2$ overall each year. In contrast, the annual increase in the minimum height of snow cover was approximately 9.848 m in the dry season and 11.251 m overall. Based on this analysis of vanishing point, there will be no snow cover 2020 at 95% confidence interval. This study can be used to monitor global climate change by providing the change in snow cover area and reference data when studying this area or similar areas in future research.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.169-175
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• 2004

This paper develops a dynamic travel time prediction model for ATIS in a national highway. While there have been many research on travel time prediction, none of them is for national highway in Korea. The study uses AVI data installed on the national highway No.1 with 10km interval for travel time prediction model, and probe vehicle data for evaluating the model. The study area has many access points, so there are many outlying observations in the raw AVI data. Therefore, this study uses the algorithm proposed by the author for removing the outliers, and then Kalman filtering algorithm is applied for the travel time prediction. The prediction model is performed for 5, 10, 15 and 30 minute-aggregating interval and the results are $0.061{\sim}0.066$ for 5, 10 and 15 interval and 0.078 for 30 minute one with a little low performance as MAREs.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.487-492
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• 2013

Underwater acoustic communication has multipath error because of reflection by sea-level and sea-bottom. The multipath of underwater channel causes signal distortion and error floor. In order to improve the performance, it is necessary to employ an iterative coding scheme. Among the iterative coding scheme, turbo codes, LDPC codes and convolutional code based on BCJR algorithm are dominant channel coding schemes in recent. Therefore this paper analyzed the performance of iterative codes based on turbo equalizer with the same coding rate and similar codeword length. The performances of three kinds of iterative codes were evaluated in the environment of underwater acoustic communication channel that are real data collected in Korean east sea. The distance of transmitter and receiver was 5Km and data rate was 1Kbps. As a result, convolutional code based on BCJR algorithm has better performance in underwater channel than turbo codes and LDPC codes.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.4
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• pp.49-54
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• 2018

With increasing satellite-based RS(Remotely Sensed) techniques, RS soil moisture footprints have been providing for various purposes at the spatio-temporal scales in hydrology, agriculture, etc. However, their coarse resolutions still limit the applicability of RS soil moisture to field regions. To overcome these drawbacks, the LDA(Landsat-based Downscaling Algorithm) was developed to downscale RS soil moisture footprints from the coarse- to finer-scales. LDA estimates Landsat-based soil moisture($30m{\times}30m$) values in a spatial domain, and then the weighting values based on the Landsat-based soil moisture estimates were derived at the finer-scale. Then, the coarse-scale RS soil moisture footprints can be downscaled based on the derived weighting values. The LW21(Little Washita) site in Oklahoma(USA) was selected to validate the LDA scheme. In-situ soil moisture data measured at the multiple sampling locations that can reprent the airborne sensing ESTAR(Electronically Scanned Thinned Array Radiometer, $800m{\times}800m$) scale were available at the LW21 site. LDA downscaled the ESTAR soil moisture products, and the downscaled values were validated with the in-situ measurements. The soil moisture values downscaled from ESTAR were identified well with the in-situ measurements, although uncertainties exist. Furthermore, the SMAP(Soil Moisture Active & Passive, $9km{\times}9km$) soil moisture products were downscaled by the LDA. Although the validation works have limitations at the SMAP scale, the downscaled soil moisture values can represent the land surface condition. Thus, the LDA scheme can downscale RS soil moisture products with easy application and be helpful for efficient water management plans in hydrology, agriculture, environment, etc. at field regions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.116-130
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• 2023

Expressways play pivotal roles in cargo transportation because of their superior accessibility and mobility compared to rail and air. On the other hand, there is a limit to the accurate calculation of cargo transportation performance using existing highways owing to the mixture of vehicle types and difficulty in identifying cargo loads of individual cargo vehicles. This paper presents an algorithm for calculating more reliable cargo transportation performance using big data. The traffic performance (veh·km/day) was derived using the data collected from Toll Collecting System. The average tolerance weight for each vehicle type and the cargo load unit (ton/unit) considering it was calculated using vehicle specification information data and high-speed and low-speed axis data. This study calculated the cargo transportation performance by section and type using various online integrated highway data and presented a method for calculating the transportation performance by linking open business offices and private highways.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.233-242
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• 2010

SEBAL (Surface Energy Balance Algorithm for Land) developed by Bastiaanssen (1995) is an image-processing model comprisedof twenty-five sub models that calculates spatial evapotranspiration (ET) and other energy exchanges at the surface. SEBAL uses image data from Landsat or other satellites measuring thermal infrared radiation, visible and near infrared. In this study, the model was applied to Gyeongancheon watershed, the main tributary of Han river Basin. ET was computed on apixel-by-pixel basis from an energy balance using 4 years (2001-2004) Landsat and MODIS images. The scale effect between Landsat (30 m) and MODIS (1 km) was evaluated. The results both from Landsat and MODIS were compared with FAO Penman-Monteith ET. The absolute errors between satellite ETs and Penman-Monteith ET were within 12%. The spatial and temporal characteristics of ET distribution within the watershed were also analyzed.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.561-573
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• 2023

Korea Atomic Energy Research Institute (KAERI) constructed the KURT (KAERI Underground Research Tunnel) to analyze the hydrogeological/geochemical characteristics of deep rock mass. Numerous boreholes have been drilled to conduct various field tests. The selection of suitable investigation intervals within a borehole is of great importance. When objectives are centered around hydraulic flow and groundwater sampling, intervals with sufficient groundwater flow are the most suitable. This study defines such points as hydraulic outliers and aimed to detect them using borehole geophysical logging data (temperature and EC) from a 1 km depth borehole. For systematic and efficient outlier detection, machine learning algorithms, such as DBSCAN, OCSVM, kNN, and isolation forest, were applied and their applicability was assessed. Following data preprocessing and algorithm optimization, the four algorithms detected 55, 12, 52, and 68 outliers, respectively. Though this study confirms applicability of the machine learning algorithms, it is suggested that further verification and supplements are desirable since the input data were relatively limited.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.371-377
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• 2007

Ionospheric time delay is one of the main error source for single-frequency DGPS applications, including time transfer and Wide Area Differential GPS (WADGPS). Grid-based algorithm was already developed for WADGPS but that algorithm is not applicable to geomagnetic storm condition in accuracy and management. In geomagnetic storm condition, the spatial distribution of vertical ionospheric delay is noisy and therefore the accuracy of modeling become low in grid-based algorithm. For better accuracy, function based algorithm can be used but the continuity of correction message is not guranteed. In this paper, we propose the ionospheric model using wavelet based algorithm. This algorithm shows better accuracy with the same number of correction message than the existing spherical harmonics algorithm and guarantees the continuity of correction messages when the number of message is expanded for geomagnetic storm condition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2360-2366
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• 2011

The Unmanned Ground Vehicle is comprised of four systems of obstacle detection: The navigation system, vehicle controlling system, obstacle detecting and an integration system that use the various sensors. The research introduced utilizes 6 lasers to recognize obstacles. The system operates an avoidance system within the unmanned ground vehicle, using six lasers. The Unmanned Ground Vehicle's parallel parking and right angle parking is in development using algorithms. This algorithms' certification is intended to be installed in the encoder, in the GPS. By using the Laser Scannerfor the position's calculation, errors are both reduced and minimized, so the tire's slip minimized to the point where the vehicle had a limit of about 5Km/h.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.76-84
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• 2003

Now days, It is based on GIS and GPS, it can search for the shortest path and estimation of arrival time by using the internet and cell phone to driver. But, even though good car navigation system does not create which is the shortest path when there average vehicle speed is 10 -20 Km. Therefore In order to reduce vehicle waiting time and average vehicle speed, we suggest optimal green time algorithm using fuzzy adaptive control, where there are different traffic intersection length and lane. In this paper, it will be able to forecast the optimal traffic information, estimation of destination arrival time, under construction road, and dangerous road using internet.