• Journal of the Korean Geotechnical Society
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• v.26 no.9
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• pp.15-24
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• 2010

Because the thickness and depth of consolidation layer vary at every location, the consolidation settlement and time have to be evaluated spatially. Also, for a rational evaluation of the uncertainty of the spatial distribution of consolidation settlement and time, it is necessary to adopt a probabilistic method. In this study, mean and standard deviation of consolidation settlement and time of whole analysis region are evaluated by using the spatial distribution of consolidation layer which is estimated from ordinary kriging and statistics of soil properties. Using these results and probabilistic method, the area that needs adopting the prefabricated vertical drain as well as raising the ground level for balancing the final design ground level is determined. It is observed that such areas are influenced by the variability of soil properties. The design procedure and method presented in this paper can be used in the decision making process for a geotechnical engineering design.

• Atmosphere
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• v.32 no.1
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• pp.1-15
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• 2022

Recently, Japan's Meteorological Research Institute presented the d4PDF database (Database for Policy Decision-Making for Future Climate Change, d4PDF) through large-scale climate ensemble simulations to overcome uncertainty arising from variability when the general circulation model represents extreme-scale precipitation. In this study, the change of precipitation characteristics between the historical and future climate conditions in the Yongdam-dam basin was analyzed using the d4PDF data. The result shows that annual mean precipitation and seasonal mean precipitation increased by more than 10% in future climate conditions. This study also performed an analysis on the change of the return period rainfall. The annual maximum daily rainfall was extracted for each climatic condition, and the rainfall with each return period was estimated. In this process, we represent the extreme-scale rainfall corresponding to a very long return period without any statistical model and method as the d4PDF provides rainfall data during 3,000 years for historical climate conditions and during 5,400 years for future climate conditions. The rainfall with a 50-year return period under future climate conditions exceeded the rainfall with a 100-year return period under historical climate conditions. Consequently, in future climate conditions, the magnitude of rainfall increased at the same return period and, the return period decreased at the same magnitude of rainfall. In this study, by using the d4PDF data, it was possible to analyze the change in extreme magnitude of rainfall.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.11-16
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• 2023

The technology of 3D reconstruction, primarily relying on point cloud data, is essential for digitizing objects or spaces. This paper aims to utilize reinforcement learning to achieve the acquisition of point clouds in a given environment. To accomplish this, a simulation environment is constructed using Unity, and reinforcement learning is implemented using the Unity package known as ML-Agents. The process of point cloud acquisition involves initially setting a goal and calculating a traversable path around the goal. The traversal path is segmented at regular intervals, with rewards assigned at each step. To prevent the agent from deviating from the path, rewards are increased. Additionally, rewards are granted each time the agent fixates on the goal during traversal, facilitating the learning of optimal points for point cloud acquisition at each traversal step. Experimental results demonstrate that despite the variability in traversal paths, the approach enables the acquisition of relatively accurate point clouds.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.855-866
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• 2023

With the rapid advancement and sophistication of defense weapon systems, the government, military, and the defense industry have conducted various innovative attempts to improve the efficiency of post-logistics support(PLS). The Ministry of Defense has mandated RAM-C(Reliability, Availability, and Maintainability-Cost) analysis as a requirement according to revised Total Life Cycle System Management Code of Practice in May 2022. Especially, for the project budget forecast of new PBL(Performance Based Logistics) business contacts, RAM-C is recognized as an obligatory factor. However, relevant entities have not officially provided guidelines or manuals for RAM-C analysis, and each defense contractor conducts RAM-C analysis with different standards and methods to win PBL-related business contract. Hence, this study aims to contribute to the generalization of the analysis procedure by presenting a cost analysis case based on RAM-C for the supply of military depot maintenance PBL project. This study presents formulas and procedures to determine requirements of military depot maintenance PBL project for repair parts supply. Moreover, a sensitivity analysis was conducted to find the optimal cost/utilization ratio. During the process, a correlation was found between supply delay and total cost of ownership as well as between cost variability and utilization rate. The analysis results are expected to provide an important basis for the conceptualization of the cost analysis for the supply of military depot maintenance PBL project and are capable of proposing the optimal utilization rate in relation to cost.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.895-903
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• 2023

This study investigates the impact of riparian vegetation in the floodplain on channel stability, changes in bend curvature, and meandering channel migration. In channels with riparian vegetation, over time, meander width remains relatively constant, but selective bank erosion leads to meander development and downstream movement. During this process, bank erosion and changes in the riverbed are not significant, and the channel maintains relatively constant conditions with reduced sediment discharge and minimal variability. As the density of vegetation increases, bank erosion rates decrease. The erosion rates along the riverbanks increase with the density of vegetation on the floodplain, thus affecting the development of meanders. This factor notably contributes to enhancing riverbank stability and influencing channel changes through floodplain vegetation. Bank erosion rates and dimensionless bend curvature are greatest when there is no riparian vegetation but decrease in conditions with vegetation. Furthermore, the relationship between lateral migration rate and dimensionless bend curvature is similar to that of bank erosion rates. Therefore, riparian vegetation enhances channel stability, influencing bank erosion, meander curvature, and meander migration.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.177-189
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• 2024

The run length is defined as the number of samples or subgroups taken before the control chart statistic exceeds the control limits. Because the distribution of run length is typically asymmetric and has a large variability, it may not be appropriate to use ARL (average run length) alone to design control charts and evaluate performance. In this paper, we introduce the concept of percentile (PL)-based design of control charts, and propose the procedure for PL-based design of EWMA (exponentially weighted moving average) charts. For the PL-based design of EWMA, we present a fitted function for the control chart coefficient, given specific percentile parameters. Additionally, we perform simulations to compare the proposed design with the ARL-based design. The simulation results show that the proposed design yields improvements in monitoring in-control processes while maintaining the ability to detect out-of-control performance.

• The Mathematical Education
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• v.63 no.2
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• pp.209-231
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• 2024

The education sector is undergoing significant changes due to the Fourth Industrial Revolution and the advancement of artificial intelligence. Particularly, the importance of education based on artificial intelligence is being emphasized. Accordingly, the purpose of this study is to develop a statistics education program using deep learning prediction in high school mathematics and to examine the impact of such statistically problem-solvingcentered statistics education programs on high school students' statistical literacy and computational thinking. To achieve this goal, a statistics education program using deep learning prediction applicable to high school mathematics was developed. The analysis revealed that students' understanding of context improved through experiencing how data was generated and collected. Additionally, they enhanced their comprehension of data variability while exploring and analyzing various datasets. Moreover, they demonstrated the ability to critically analyze data during the process of validating its reliability. In order to analyze the impact of the statistics education program on high school students' computational thinking, a paired sample t-test was conducted, confirming a statistically significant difference in computational thinking between before and after classes (t=-11.657, p<0.001).

• Atmosphere
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• v.34 no.2
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• pp.187-202
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• 2024

We analyzed the observed characteristics of monoterpene and developed an empirical formula for monoterpene concentration in the pine-dominated mixed forest of the National Center for Forest Therapy. Monoterpene was measured at 0800, 1200 and 1700 LST once a month using sorbent tube sampling coupled with thermal desorption gas chromatography and mass spectrometry. Monoterpene concentration is low in winter and shows a maximum in June and July. The major components of monoterpene are alpha-pinene, camphene and beta-pinene. During the warm period from May to November, monoterpene concentration is higher at 0800 and 1700 LST than at 1200 LST. The empirical formula takes into account the vegetation variables, temperature-controlled emission, oxidation processes and dilution by wind. The vegetation variable accounts for the difference in observed monoterpene concentration between two sites. The observed monoterpene concentration normalized by the vegetation variable increases exponentially with air temperature. The oxidation process explains the lower monoterpene concentration at 1200 LST than at 0800 and 1700 LST during the warm period. The monoterpene estimates using the empirical formula shows a correlation of 0.52 with the observation for the development period (2018~2020), while it shows a correlation of 0.72 for the validation year (2021). Such higher correlation for the validation year than for the development period is due to the fact that variability of monoterpene concentration is better explained by air temperature in 2021 than in the development period. However, the developed formula underestimates the monoterpene concentration in May and June, showing the limitation in accurately capturing the monthly variation of monoterpene.