• The Journal of Engineering Geology
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• v.28 no.4
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• pp.583-592
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• 2018

Recently, concern has arisen regarding the lowering of groundwater levels in the hinterland caused by the development of high-capacity radial collector wells in riverbank filtration areas. In this study, groundwater levels are estimated using Modflow software in relation to the water volume pumped by the radial collector well in Anseongcheon Stream. Using the water volume data, an artificial neural network (ANN) model is developed to determine the amount of water that can be withdrawn while minimizing the reduction of groundwater level. We estimate that increasing the pumping rate of the horizontal well HW-6, which is drilled parallel to the stream direction, is necessary to minimize the reduction of groundwater levels in wells OW-7 and OB-11. We also note that the number of input data and the classification of training and test data affect the results of the ANN model. This type of approach, which supplements ANN modeling with observed data, should contribute to the future groundwater management of hinterland areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.97-102
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• 2019

There are many difficulties to define because there is no systematic classification and analysis method using accurate criteria or numerical values for creative people. In order to solve this problem, this study attempts to analyze how to distinguish creative people and what kind of personality they have when distinguishing creative people. In this study, I first survey the Big 5 personality trait, classify and analyze the data set using the data mining tool WEKA, and then analyze the data set related to the creativity The goal is to analyze the features using various machine learning techniques. I use seven feature selection algorithms, select feature groups classified by feature selection algorithms, apply them to machine learning algorithms to find out the accuracy, and derive the results.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.2
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• pp.85-102
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• 2021

Phytosociological characteristics on Quercus acutissima forests distribution in Daechong-dam basin survey has been carried out using Z.-M. School's methodology and numerical-classification analyses. A total of 43 phytosociological relevés were sampled. Syntaxa were described as Oplismenus undulatifolius-Quercus acutissima community(typicum subcommunity, Phryma leptostachya var. asiatica subcommunity, Ulmus davidiana var. japonica subcommunity), Quercus acutissima community and Quercus variabilis-Quercus acutissima community (typicum subcommunity, Castanea crenata subcommunity). The above three plant communities were classified with species composition reflecting local environmental characteristics of mountain topographies, inclination degrees, and rock exposure rates. Conclusively, those communities were recognized as secondary vegetation affected by high intensity and frequency of human impacts as they inhabited in southward hill lands and low lying grounds in mountains adjacent to human settlements and arable lands. Quercus acutissima community was classified as rural type syntax based on their inlandward distribution and species composition differences from urban forests. Afforest process and natural succession were discussed in relation with habitat environmental elements of Quercus acutissima forest in the survey area.

• Atmosphere
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• v.32 no.4
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• pp.353-365
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• 2022

This paper evaluates precipitation forecast skill of Global/Regional Integrated Model system (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type (C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequencies are 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantly overestimates precipitation occurrence (0.1 mm d^-1) for all types of winter precipitation. Objective measured skill scores of GRIMs are comparably high for L type and O type. Except for precipitation occurrence, the model shows high predictability for L type precipitation with the most unbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuring rare events due to its high dependency on the sample size, as in the case of Critical Success Index as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivity on the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplement the limit of ETS. The evaluation via SEDS shows that the prediction skill score for L type is the highest in the range of 5.0, 10.0 mm d^-1 and the score for O type is the highest in the range of 1.0, 20.0 mm d^-1. C type has the lowest scores in overall range. The difference in precipitation forecast skill by precipitation type can be explained by the spatial distribution and intensity of precipitation in each representative case.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.3
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• pp.74-85
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• 2021

Global Navigation Satellite System (GNSS) receivers are intrinsically vulnerable to radio frequency jamming signals due to the fundamental property of radio navigation systems. A GNSS jamming monitoring system that is capable of jamming detection, classification and localization is essential for infrastructure for autonomous driving systems. For these 3 functionalities, a GNSS jamming monitoring network consisting of a multiple of low-cost GNSS receivers distributed in a certain area is needed, and the precise time synchronizaion between multiple independent GNSS receivers in the network is an essential element. This paper presents a precise time synchronization method based on the direct use of Time Difference of Arrival (TDOA) technique in signal domain. A block interpolation method is additionally incorporated into the method in order to maintain the precision of time synchronization even with the relatively low sampling rate of the received signals for computational efficiency. The feasibility of the proposed approach is verified in the numerical simualtions.

• Journal of Industrial Convergence
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• v.20 no.7
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• pp.1-11
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• 2022

In this paper, numerical values are derived using topic modeling among data-based evaluation methodologies discussed by various research institutes. In addition, we will focus on the ICT field to see if there is a difference in policy perception between the national R&D project and standing committee. First, we create model for classifying ICT documents by learning R&D project data using HAN model. And we perform LDA topic modeling analysis on ICT documents classified by applying the model, compare the distribution with the topics derived from the R&D project data and proceedings of standing committees. Specifically, a total of 26 topics were derived. Also, R&D project data had professionally topics, and the standing committee-discuss relatively social and popular issues. As the difference in perception can be numerically confirmed, it can be used as a basic study on indicators that can be used for future policy or project evaluation.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.210-217
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• 2022

Passive source depth estimation has been studied for decades since the source depth can be used for target classification, target tracking, etc. The purpose of this paper is to solve the problem of ambiguity in the previous paper [S.-il. Cho et al. (in Korean), J. Acoust. Soc. Kr. 38, 120-127 (2019)] that source depth is estimated in two points. The patterns of phase shift of Channel Impulse Response(CIR) reflected in ocean surface and bottom is used for removing ambiguity of the source depth estimation, and after removing ambiguity, source depth is estimated at one point through the intersection of CIR. In order to extract CIR in case of unknown source signal and continuous signal or noise, Ray-based blind deconvolution is used. The proposed algorithm is demonstrated through numerical simulation in ocean waveguide.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.157-166
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• 2020

Automated rebar fabrication, which requires effective information exchange between model designers and fabricators, has brought the integration and interoperability of data from different sources to the notice of both academics and industry practitioners. Industry Foundation Classes (IFC) was one of the most commonly used data formats to represent the semantic information of prefabricated components in buildings, whereas the data format utilized by rebar fabrication machine is BundesVereinigung der Bausoftware (BVBS), which is a numerical data structure exchanging reinforcement information through ASCII encoded files. Seamless transformation between IFC and BVBS empowers the automated rebar fabrication and improve the construction productivity. In order to improve data interoperability between IFC and BVBS, this study presents an IFC extension based on the attributes required by automated rebar fabrication machines with the help of Information Delivery Manual (IDM) and Model View Definition (MVD). IDM is applied to describe and display the information needed for the design, construction and operation of projects, whereas MVD is a subset of IFC schema used to describe the automated rebar fabrication workflow. Firstly, with a rich pool of vocabularies practitioners, OmniClass is used in information exchange between IFC and BVBS, providing a hierarchy classification structure for reinforcing elements. Then, using International Framework for Dictionaries (IFD), the usage of each attribute is defined in a more consistent manner to assist the data mapping process. Besides, in order to address missing information within automated fabrication process, a schematic data mapping diagram has been made to deliver IFC information from BIM models to BVBS format for better data interoperability among different software agents. A case study based on the data mapping will be presented to demonstrate the proposed IFC extension and how it could assist/facilitate the information management.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1279-1288
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• 2023

Recently, domestic leisure boats have been actively researching eco-friendly product development to enter the global market. Since the hulls of existing leisure boats are mainly made of fiber reinforced plastic (FRP) or aluminum, design techniques for securing structural safety by applying related materials have been mainly studied. In this study, an initial structural design safety assessment of a trimaran pontoon leisure boat with a modular hull structure and eco-friendly high-density polyethylene (HDPE) material was conducted, and sensitivity evaluation and optimization analysis for lightweight design were performed. The initial structural design safety assessment was carried out by creating a finite element analysis model and applying the loading conditions specified in the ship classification regulation to check whether the specified allowable stresses are satisfied. For the sensitivity evaluation, the influence of stress and weight of each hull structural member was evaluated using the orthogonal array design of experiments method, and an approximate model based on the response surface method was generated using the results of the design of experiments. The optimization analysis set the thickness of the hull structural members as the design variable and considered the optimal design formulation to minimize the weight while satisfying the allowable stress. The algorithm of the optimization analysis applied the Gradient-population Based Optimizer (GBO) to improve the accuracy of the optimal solution convergence while reducing the numerical cost. Through this study, the optimal design of a newly developed eco-friendly trimaran pontoon leisure boat with a weight reduction of 10% was presented.