• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.574-578
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• 2006

Tidal amplification by construction of the sea-dike and sea-walls had been detected not only near Mokpo Harbor but also at Chungkye Bay which is connected with Mokpo Harbor by a narrow channel. This brings about increase of tidal flat area and in particular increase of surge-wave combined runup during storms. The purpose of this study is to examine an efficient operational model that can be used by civil defense agencies for real-time prediction and fast warnings on wind waves and storm surges. Instead of using commercialized wave models such as WAM, SWAN, the wind waves are simulated by using a new concept of wavelength modulation to enhance broader application of the hyperbolic wave model of the mild-slope equation type. Furthermore, The predicting system is composed of easy and economical tools for inputting depth data of complex bathymetry and enormous tidal flats such as Mokpo coastal zone. The method is applied to Chungkye Bay, and possible inundation features at Mokpo Harbor are analyzed.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.3
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• pp.95-107
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• 2020

The failures of the agricultural reservoirs that most have more than 50 years, have increased due to the abnormal weather and localized heavy rains. There are many studies on the prediction of damage from reservoir collapse, however, these referenced studies focused on evaluating reservoir collapse as single unit and applyed to one and two dimensional hydrodynamic model to identify the fluid flow. This study is to estimate failure probability of spillway, sliding, bearing capacity and overflowing targeting small and medium scale agricultural reservoirs. In addition, we calculate failure probability by complex mode. Moreover, we predict downstream flood damage by reservoir failure applying three dimensional hydrodynamic model. When the reservoir destroyed, the results are as follows; (1) the flow of fluid proceeds to same stream direction and to a lower slope by potential and kinetic energy; (2) The predicted damage in downstream is evaluated that damage due to building destruction is the highest.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.11 no.4
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• pp.81-89
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• 1991

An interesting property of meandering river patterns is that they slowly deform, as bank erosion on one side of a channel and deposition on the other side result in the location of the channel. In this study we used a sine-generated meander pattern proposed by Langbein and Leopold(1966) to develop a solution of a linear, second-order differential equation of transverse bed slope(bed topography) proposed by Odgaard(1986). A new model for transverse bed slope(bed topography), that accounts for the phase lag and the influence of the width to depth aspect ratio, was developed in this study and compared with results of field measurements.

• The Journal of Engineering Geology
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• v.18 no.3
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• pp.303-309
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• 2008

A probability of slope hazards was predicted at a natural terrain around the stone relics of Jinjeon-saji area, which is located in Yangyang, Kangwon Province. As the analyzing results of field investigation, laboratory test and geology and geomorphology data, the effect factors of landslides occurrence were evaluated. Also, the landslides prediction map was made up using the prediction model by the effect factors. The landslide susceptibility of stone relics was investigated as the grading classification of occurrence probability. In the landslides prediction map, the high probability area was $3,489m^2$ and it was 10.1% of total prediction area. The high probability area has over 70% of occurrence probability. If landslides are occurred at the predicted area, the three stories stone pagoda of Jinjeon-saji(National treasure No. 122) and the stone lantern of Jinjeon-saji(Treasure No.439) will be collapsed by debris flow.

• Journal of the Korean Geotechnical Society
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• v.20 no.8
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• pp.67-75
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• 2004

The landslide is one of the most significant natural disasters, which cause a lot of loss of human lives and properties. The landslides in natural slopes generally occur by complicated problems such as soil properties, topography, and geology. Artificial Neural Network (ANN) model is efficient computing technique that is widely used to solve complicated problems in many research fields. In this paper, the ANN model with application of error back propagation method was proposed for estimation of landslide hazard in natural slope. This model can evaluate the possibility of landslide hazard with two different approaches: one considering only soil properties; the other considering soil properties, topography, and geology. In order to evaluate reasonably the landslide hazard, the SlideEval (Ver, 1.0) program was developed using the ANN model. The evaluation of slope stability using the ANN model shows a high accuracy. Especially, the prediction of landslides using the ANN model gives more stable and accurate results in the case of considering such factors as soil, topographic and geological properties together. As a result of comparison with the statistical analysis(Korea Institute of Geosciences and Mineral Resources, 2003), the analysis using the ANN model is approximately equal to the statistical analysis. Therefore, the SlideEval (Ver. 1.0) program using ANN model can predict landslides hazard and estimate the slope stability.

• Korean Journal of Environmental Agriculture
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• v.27 no.4
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• pp.314-320
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• 2008

During the summer season, more than half of the annual precipitation in Korea occurs during the summer season due to the geographical location in the Asian monsoon belt. So, this causes severe soil erosion from croplands, which is directly linked to the deterioration of crop/land productivity and surface water quality. Therefore, much attention has been given to develop accurate estimation tools of soil erosion. The aim of this study is to assess the performance of using the empirical Universal Soil Loss Equation (USLE) and the physical-based model of the Water Erosion Prediction Project (WEPP) to quantify eroded amount of soil from agricultural fields. Input data files, including climate, soil, slope, and cropping management, were modified to fit into Korean conditions. Chuncheon (forest) and Jeonju (level-plain) were selected as two Korean cities with different topographic characteristics for model analysis. The results of this current study indicated that better soil erosion prediction can be achieved using the WEPP model since it has better power to illustrate a higher degree of spatial variability than USLE in topography, precipitation, soils, and crop management practices. These present findings are expected to contribute to the development of the environmental assessment program as well as the conservation of the agricultural environment in Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.165-175
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• 2005

Geographic Information System(GIS) is useful to preserve cultural heritage and land use management using both spatial information management technique and spatial analysis function in cultural heritage management. The purpose of this study is to build a database of pottery and kiln sites in South Korea, to analyze site locations and finally to make prediction model. The locations of 1,200 sites are put into GIS database. Such factor elevation, slope angle, aspect, horizontal/vertical distance from the nearest water are analyzed. Each factor was statistically analyzed on GIS and represented to rank 1-5. Pottery/kiln can be predicted by the spatial analysis function in overlay methods. As a result of this study, preliminary application of prediction model shows that the high potential area is between the slope and alluvial plain. Field survey in the Sungbuk-dong in Daejeon city supports the preliminary result. More data can make improve efficient prediction model in unknown areas.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.728-735
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• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.145-151
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• 2011

In view of the hypothesis that the effects of Parkinson's disease on voice production can be detected before pharmacological intervention, the prosodic features of patients with idiopathic Parkinson's disease (IPD) and a healthy aging group were diagnostically analyzed with the long term object of establishing, for clinical purposes, early disease-progression biomarkers. Twenty patients (male 8; female 12) with IPD (prior to pharmacological intervention) and a healthy control group of 22 (male 10; female 12) were selected. Ten sentences were recorded with a head-worn microphone. One sentence was chosen for the analysis of this paper. Relevant parameters, i.e. 3-dimensional model (F0, intensity, duration) and pitch and intensity related slopes (maxEnergy, maxF0, meanAbS, semiT, meanEnergy, meanF0), were analyzed by two-group discriminant analysis. The stepwise estimation method of discriminant analysis was performed by gender. The discriminant functions predicted 83.9% of the male test data correctly while the prediction rate was 93.1% for the female group. The results showed that meanF0_slope and semiT_slope were more important parameters than the others for the male group. For the female group, the meanEnergy_slope and maxEnergy_slope were the important ones. These findings indicate that significant parameters are different for the male and female group. Gender lifestyle may be responsible for this difference. Dysprosodic features of IPD show not simultaneously but progressively in terms of F0, intensity and duration.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.21-30
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• 2022

Shear strength is the most important indicator in the evaluation of rock slope stability. It is generally estimated by comparing the results of existing literature data, back analysis, experiments and etc. There are additional variables related to the state of discontinuity to consider in the shear strength of the rock slope. It is difficult to determine whether these variables exist through drilling, and it is also difficult to find an exact relationship with shear strength. In this study, the data calculated through back analysis were used. The relationship between previously considered variables was applied to deep learning and the possibility for estimating shear strength of rock slope was explored. For comparison, an existing simple linear regression model and a deep learning algorithm, a deep neural network(DNN) model, were used. Although each analysis model derived similar prediction results, the explanatory power of DNN was improved with a small differences.