• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.569-582
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• 2020

Recently, Korea has faced a change in the pattern of water use due to urbanization, which has caused difficulties in understanding the rainfall-runoff process and optimizing the allocation of available water resources. In this perspective, spatially downscaled analysis of the water balance is required for the efficient operation of water resources in the National Water Management Plan and the River Basin Water Resource Management Plan. However, the existing water balance analysis does not fully consider water circulation and availability in the basin, thus, the obtained results provide limited information in terms of decision making. This study aims at developing a novel water circulation analysis model that is designed to support a quasi-real-time assessment of water availability along the river. The water circulation model proposed in this study improved the problems that appear in the existing water balance analysis. More importantly, the results showed a significant improvement over the existing model, especially in the low flow simulation. The proposed modeling framework is expected to provide primary information for more realistic hydrological drought monitoring and drought countermeasures by providing streamflow information in quasi-real-time through a more accurate natural flow estimation approach with highly complex network.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.251-264
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• 2008

One of the crucial elements to fully facilitate the various benefits of intelligent transportation systems (ITS) is to obtain more reliable traffic monitoring in real time. To date, point and section-based traffic measurements have been available through existing surveillance technologies, such as loops and automatic vehicle identification (AVI) systems. However, seamless and more reliable traffic data are required for more effective traffic information provision and operations. Technology advancements including vehicle tracking and wireless communication enable the acceleration of the availability of individual vehicle travel information. This study presents a UBIquitous PRObe vehicle Surveillance System (UBIPROSS) using vehicle-to-vehicle (V2V) wireless communications. Seamless vehicle travel information, including origin-destination information, speed, travel times, and other data, can be obtained by the proposed UBIPROSS. A set of parameters associated with functional requirements of the UBIPROSS, which include the market penetration rate (MPR) of equipped vehicles, V2V communication range, and travel time update interval, are investigated by a Monte Carlo simulation- (MCS) based evaluation framework. In addition, this paper describes prototypical implementation. Field test results and identified technical issues are also discussed. It is expected that the proposed system would be an invaluable precursor to develop a next-generation traffic surveillance system.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.905-922
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• 2021

Cluster analysis is one of unsupervised learning techniques used for discovering clusters when there is no prior knowledge of group membership. K-means, one of the commonly used cluster analysis techniques, may fail when the number of variables becomes large. In such high-dimensional cases, it is common to perform tandem analysis, K-means cluster analysis after reducing the number of variables using dimension reduction methods. However, there is no guarantee that the reduced dimension reveals the cluster structure properly. Principal component analysis may mask the structure of clusters, especially when there are large variances for variables that are not related to cluster structure. To overcome this, techniques that perform dimension reduction and cluster analysis simultaneously have been suggested. This study proposes probabilistic reduced K-means, the transition of reduced K-means (De Soete and Caroll, 1994) into a probabilistic framework. Simulation shows that the proposed method performs better than tandem clustering or clustering without any dimension reduction. When the number of the variables is larger than the number of samples in each cluster, probabilistic reduced K-means show better formation of clusters than non-probabilistic reduced K-means. In the application to a real data set, it revealed similar or better cluster structure compared to other methods.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.233-241
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• 2015

In 2014, the United Nations Framework Convention on Climate Change (UNFCCC) agreed to submit the Intended Nationality Determined Contributions (INDCs) at the conference of parties held in Lima, Peru. Then, the South Korean government submitted the INDCs including GHGs reduction target and reduction potential on July, 2015. The goal of this study is to predict GHGs emission and to analyze reduction potential in agricultural sector of Korea. Activity data to estimate GHGs emission was forecast by Korea Agricultural Simulation Model (KASMO) of Korea Rural Economic Institute and estimate methodology was taken by the IPCC and guideline for MRV (Measurement, Reporting and Verification) of national greenhouse gases statistics of Korea. The predicted GHGs emission of agricultural sectors from 2021 to 2030 tended to decrease due to decline in crop production and its gap was less after 2025. Increasing livestock numbers such as sheep, horses, swine, and ducks did not show signigicant impact the total GHGs emission. On a analysis of the reduction potential, GHGs emission was expected to reduce $253Gg\;CO_{2-eq}$. by 2030 with increase of mid-season water drainage area up to 95% of total rice cultivation area. The GHGs reduction potential with intermittent drainage technology applied to 10% of the tatal paddy field area, mid-drainage and no organic matter would be $92Gg\;CO_{2-eq}$. by 2030.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.28-36
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• 2019

The APR1400 (Advanced Power Reactor 1400MW) nuclear power plant is a large-scale national infrastructure facility with a total project cost of 8.6 trillion won and a project period of 10 years or more. The total project area is about 2.17 million square meters and consists of more than 20 buildings and structures. And the total number of drawings required for construction is about 65,000. In order to design such a large facility, it is important to establish a design standard that reflects the design intent and can increase conformity between documents (drawings). To this end, a design bases document (DBD) reflecting the design bases that extracted in regulatory requirements (e.g. 10CFR50, Korean Law, etc.) is created. However, although the design bases are important concepts that are a big framework for the whole design of the nuclear power plant, they are managed in 2-dimensional by the experts in each field fragmentarily. Therefore, in order to improve the usability of building information, we developed BIM(Building Information Model) based 3-dimensional design bases management system. For this purpose, the concept of design bases information layer (DBIL) was introduced. Through the simulation of developed system, design bases attribute and element data extraction for each DBIL was confirmed, and walls, floors, doors, and penetrations with DBIL were successfully extracted.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.287-292
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• 2021

Load and resistance factor design is an efficient design approach that provides a system of consistent design solutions. This study aims to determine the load and resistance factors needed for the design of breakwater foundations within a probabilistic framework. In the study, four typical types of Korean breakwaters, namely, rubble mound breakwaters, vertical composite caisson breakwaters, perforated caisson breakwaters, and horizontal composite breakwaters, are investigated. The bearing capacity of breakwater foundations under wave loading conditions is thoroughly examined. Two levels of the target reliability index (RI) of 2.5 and 3.0 are selected to implement the load and resistance factors calibration using Monte Carlo simulations with 100,000 cycles. The normalized resistance factors are found to be lower for the higher target RI as expected. Their ranges are from 0.668 to 0.687 for the target RI of 2.5 and from 0.576 to 0.634 for the target RI of 3.0.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.7
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• pp.445-454
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• 2022

Ice accretion on the aircraft components, such as wings, fuselage, and empennage, can occur when the aircraft encounters a cloud zone with high humidity and low temperature. The prevention of ice accretion is important because it causes a decrease in the aerodynamic performance and flight stability, thus leading to fatal safety problems. In this study, a shape design optimization of a multi-element airfoil is performed to minimize the amount of ice accretion on the high-lift device including leading-edge slat, main element, and trailing-edge flap. The design optimization framework proposed in this paper consists of four major parts: air flow, droplet impingement and ice accretion simulations and gradient-free optimization algorithm. Reynolds-averaged Navier-Stokes (RANS) simulation is used to predict the aerodynamic performance and flow field around the multi-element airfoil at the angle of attack 8°. Droplet impingement and ice accretion simulations are conducted using the multi-physics computational analysis tool. The objective function is to minimize the total mass of ice accretion and the design variables are the deflection angle, gap, and overhang of the flap and slat. Kriging surrogate model is used to construct the response surface, providing rapid approximations of time-consuming function evaluation, and genetic algorithm is employed to find the optimal solution. As a result of optimization, the total mass of ice accretion on the optimized multielement airfoil is reduced by about 8% compared to the baseline configuration.

• Journal of Popular Narrative
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• v.26 no.4
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• pp.215-237
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• 2020

This paper is written to introduce and review Shinji Matsunaga's The Aesthetics of Video Games which published in Japan in 2018. Shinji Matsunaga has studied video games from a philosophical and aesthetic perspective. In The Aesthetics of Video Games, he took video games as a hybrid form of traditional games. Shinji Matsunaga particularly notes that video games can design human behaviors and experiences. From this point of view, he tries to construct a theoretical framework that will be able to describe the ways of signification in games and fiction respectively. In previous studies, video games have been mainly discussed in the context of cultural studies and entertainment culture in Japan. The Aesthetics of Video Games is distinguished from the previous studies in the following points. First, The Aesthetics of Video Games pioneered the method of studying video games in art theory. Second, it established various types of relationships with video games and traditional aesthetic concepts. Third, this book connects new concepts that emerged in the age of artificial intelligence to video games as an aesthetic action. Through this work, not only video games were discussed academically, but also the fields of aesthetics and art were expanded. The Aesthetics of Video Game is like a collection of philosophical concepts for video games. Through this book, it can be said that the path for artificial intelligence to approach human secrets is closer than before.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.397-408
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• 2009

In this study, a stochastic precipitation generation framework for simultaneous simulation of daily precipitation at multiple sites is presented. The precipitation occurrence at individual sites is generated using hybrid-order Markov chain model which allows higher-order dependence for dry sequences. The precipitation amounts are reproduced using Anscombe residuals and gamma distributions. Multisite spatial correlations in the precipitation occurrence and amount series are represented with spatially correlated random numbers. The proposed model is applied for a network of 17 locations in the middle of Korean peninsular. Evaluation statistics are reported by generating 50 realizations of the precipitation of length equal to the observed record. The analysis of results show that the model reproduces wet day number, wet and dry day spell, and mean and standard deviation of wet day amount fairly well. However, mean values of 50 realizations of generated precipitation series yield around 23% Root Mean Square Errors (RMSE) of the average value of observed maximum numbers of consecutive wet and dry days and 17% RMSE of the average value of observed annual maximum precipitations for return periods of 100 and 200 years. The provided model also reproduces spatial correlations in observed precipitation occurrence and amount series accurately.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.