• Journal of the Korean GEO-environmental Society
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• v.22 no.6
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• pp.27-32
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• 2021

Due to heavy rainfall and typhoons caused by climate change, it has become common to witness heavy rain that exceeds the design frequency of agricultural reservoirs. This has brought greater attention to the safety of irrigation facilities including agricultural reservoirs. Out of approximately 17,740 reservoirs available in Korea, 83.87% were built before 1970. To ensure the safety of these old reservoirs, their embankments are being repaired and reinforced using various techniques. Among these techniques, using the cement-bentonite cutoff wall makes it possible to construct diaphragm walls with slurry composed of cement and bentonite, while excavation. The advantages of this technique include that it is simple and fast, and ensures the uniformity of cutoff walls by enabling the immediate application of the replacement method to excavation areas; thus excellent performance is guaranteed. However, despite these advantages, the technique is not commonly used in Korea. Thus, this study investigated the changes in strength and permeability by varying the mix ratio of cement and bentonite. As a major experimental results, when the cement of 200 kg/m³ and the bentonite of 60 to 80 kg/m³ is most suitable for the repair and reinforcement of the reservoir embankments.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.244-255
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• 2022

This study estimated and evaluated the high resolution (1km) gridded mountain meteorology data of daily mean, maximum and minimum temperature based on ASOS (Automated Surface Observing System), AWS (Automatic Weather Stations) and AMOS (Automatic Mountain Meteorology Observation System) in South Korea. The ASOS, AWS, and AMOS meteorology data which were located above 200m was classified as mountainous area. And the ASOS, AWS, and AMOS meteorology data which were located under 200m was classified as non-mountainous area. The bias-correction method was used for correct air temperature over complex mountainous area and the performance of enhanced daily coefficients based on the AMOS and mountainous area observing meteorology data was evaluated using the observed daily mean, maximum and minimum temperature. As a result, the evaluation results show that RMSE (Root Mean Square Error) of air temperature using the enhanced coefficients based on the mountainous area observed meteorology data is smaller as 30% (mean), 50% (minimum), and 37% (maximum) than that of using non-mountainous area observed meteorology data. It indicates that the enhanced weather coefficients based on the AMOS and mountain ASOS can estimate mean, maximum, and minimum temperature data reasonably and the temperature results can provide useful input data on several climatological and forest disaster prediction studies.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.74-99
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• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.887-901
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• 2022

Estimation of the parameters for individual rainfall-rainfall events can lead to a different set of parameters for each event which result in lack of parameter identifiability. Moreover, it becomes even more ambiguous to determine a representative set of parameters for the watershed due to enhanced variability exceeding the range of model parameters. To reduce the variability of estimated parameters, this study proposed a parameter optimization framework with the simultaneous use of multiple rainfall-runoff events based on NSE as an objective function. It was found that the proposed optimization framework could effectively estimate the representative set of parameters pertained to their physical range over the entire watershed. It is found that the difference in NSE value of optimization when it performed individual and multiple rainfall events, is 0.08. Furthermore, In terms of estimating the observed floods, the representative parameters showed a more improved (or similar) performance compared to the results obtained from the single-event optimization process on an NSE basis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.363-373
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• 2023

Forest biomass surveys are regularly conducted to assess and manage forests as carbon sinks. LiDAR (Light Detection and Ranging), a remote sensing technology, has attracted considerable attention, as it allows for objective acquisition of forest structure information with minimal labor. In this study, we propose a method for estimating overstory and understory biomass in forest stands using backpack laser scanning (BPLS) and unmanned aerial vehicle laser scanning (UAV-LS), and assessed its accuracy. For overstory biomass, we analyzed the accuracy of BPLS and UAV-LS in estimating diameter at breast height (DBH) and tree height. For understory biomass, we developed a multiple regression model for estimating understory biomass using the best combination of vertical structure metrics extracted from the BPLS data. The results indicated that BPLS provided accurate estimations of DBH (R² =0.92), but underestimated tree height (R² =0.63, bias=-5.56 m), whereas UAV-LS showed strong performance in estimating tree height (R² =0.91). For understory biomass, metrics representing the mean height of the points and the point density of the fourth layer were selected to develop the model. The cross-validation result of the understory biomass estimation model showed a coefficient of determination of 0.68. The study findings suggest that the proposed overstory and understory biomass survey methods using BPLS and UAV-LS can effectively replace traditional biomass survey methods.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.1-7
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• 2023

An artificial intelligence (AI) method based on image deep learning is proposed to predict the entire displacement shape of a structure using the feature of partial displacements. The performance of the method was investigated through a structural test of a steel frame. An image-to-image regression (I2IR) training method was developed based on the U-Net layer for image recognition. In the I2IR method, the U-Net is modified to generate images of entire displacement shapes when images of partial displacement shapes of structures are input to the AI network. Furthermore, the training of displacements combined with the location feature was developed so that nodal displacement values with corresponding nodal coordinates could be used in AI training. The proposed training methods can consider correlations between nodal displacements in 3D space, and the accuracy of displacement predictions is improved compared with artificial neural network training methods. Displacements of the steel frame were predicted during the structural tests using the proposed methods and compared with 3D scanning data of displacement shapes. The results show that the proposed AI prediction properly follows the measured displacements using 3D scanning.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.9
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• pp.265-273
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• 2018

In this paper, we propose an acoustic event detection method in tunnels using non-negative tensor factorization (NTF) and hidden Markov model (HMM) applied to multi-channel audio signals. Incidents in tunnel are inherent to the system and occur unavoidably with known probability. Incidents can easily happen minor accidents and extend right through to major disaster. Most incident detection systems deploy visual incident detection (VID) systems that often cause false alarms due to various constraints such as night obstacles and a limit of viewing angle. To this end, the proposed method first tries to separate and detect every acoustic event, which is assumed to be an in-tunnel incident, from noisy acoustic signals by using an NTF technique. Then, maximum likelihood estimation using Gaussian mixture model (GMM)-HMMs is carried out to verify whether or not each detected event is an actual incident. Performance evaluation shows that the proposed method operates in real time and achieves high detection accuracy under simulated tunnel conditions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.135-143
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• 2009

Integrating the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor technologies using the precise GPS Carrier phase measurements is a methodology that has been widely applied in those application fields requiring accurate and reliable positioning and attitude determination; ranging from 'kinematic geodesy', to mobile mapping and imaging, to precise navigation. However, such integrated system may not fulfil the demanding performance requirements when the baseline length between reference and mobil user GPS receiver is grater than a few tens of kilometers. This is because their positioning/attitude determination is still very dependent on the errors of the GPS observations, so-called "baseline dependent errors". This limitation can be remedied by the integration of GPS and INS sensors, using multiple reference stations. Hence, in order to derive the GPS distance dependent errors, this research proposes measurement processing algorithms for multiple reference stations, such as a reference station ambiguity resolution procedure using linear combination techniques, a error estimation based on Kalman filter and a error interpolation. In addition, all the algorithms are evaluated by processing real observations and results are summarized in this paper.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.363-374
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• 2008

Intensity-Duration-Frequency (IDF) curve that is essential to calculate rainfall quantiles for designing hydraulic structures in Korea is generally formulated by regression analysis. In this study, IDF curve derived by the cumulative distribution function ("IDF by CDF") of the proper probability distribution function (PDF) of each site is suggested, and the corresponding parameters of IDF curve are computed using genetic algorithm (GA). For this purpose, IDF by CDF and the conventional IDF derived by regression analysis ("IDF by REG") were computed for 22 Korea Meteorological Administration (KMA) rainfall recording sites. Comparisons of RMSE (root mean squared error) and RRMSE (Relative RMSE) of rainfall intensities computed from IDF by CDF and IDF by REG show that IDF by CDF is more accurate than IDF by REG. In order to accommodate the effect of the recent intensive rainfall of Korea, the rainfall intensities computed by the two IDF curves are compared with that by at-site frequency analysis using the rainfall data recorded by 2006, and the result from IDF by CDF show the better performance than that from IDF by REG. As a result, it can be said that the suggested IDF by CDF curve would be the more efficient IDF curve than that computed by regression analysis and could be applied for Korean rainfall data.