• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.296-296
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• 2023

최근 기후변화로 인해 극한 강우의 발생빈도가 증가하고 있다. 극한 강우의 증가는 수리구조물의 설계홍수량을 초과하는 유입량을 발생시킴으로써 수리구조물의 구조적 안정성을 저해할 수 있다. 농업용 저수지가 기후변화로 인한 이상 강우의 증가에도 불구하고 안정적으로 운영되기 위해서는 적절한 홍수 방재체계의 수립이 필요하다. 저수지의 홍수 방재체계는 구조적 홍수 방재체계와 비구조적 홍수 방재체계로 구분되며, 구조적 홍수 방재체계는 비구조적 홍수 방재체계에 비해 많은 자본이 투입되어야 한다는 특징이 있다. 농업용 저수지의 홍수 방재체계 수립 시 구조적 방법과 비구조적 방법을 종합적으로 고려하여야 하며, 농업용 저수지에 관한 홍수 방재체계 마련 방안이 정립되어야 한다. 본 연구에서는 구조적 방법과 비구조적 방법을 모두 고려한 농업용 저수지의 홍수 방재체계를 마련하고, 이를 적용함으로써 기후변화에 대응하여 농업용 저수지의 홍수조절 능력이 적절히 마련되었는지를 확인하고자 한다. 본 연구에서는 수계, 저수량, 치수 사업 진행 여부 등의 요소를 고려하여 17개의 농업용 저수지를 연구대상지로 선정하였다. 저수지 운영 모의를 위하여 각 연구대상지의 기상자료, 지형자료, 저수지 제원 자료를 수집 및 분석하였다. 저수지 운영방법으로는 저수위가 목표수위 이상일 경우 유입량 전량을 방류하는 Auto-ROM 방식을 채택하였다. 기후변화가 농업용 저수지의 홍수조절능력에 미치는 영향을 파악하기 위해 SSP (Shared Socio-economic Pathways) 기후변화 시나리오를 활용하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.362-362
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• 2023

한정된 수자원의 이용 및 관리로 매년 물 부족과 물 배분 의사결정 문제가 발생하고 있다. 50년간(1965~2014년) 수자원의 총량은 약 1.2배 증가한 반면 인구수 약 1.8배, 생·공·농업용수의 수요는 약 5배가 증가(국회입법조사처, 2018) 했을 뿐 아니라, 기후변화의 영향으로 인한 강수량의 변화와 지역별 편차가 커져 지속가능한 물관리 필요성이 증대되고 있다. 따라서 효율적인 물관리를 위해서는 관리부처가 분절되어 있는 물순환 계통의 데이터를 통합하는 것이 우선시되어야 하고 이를 통해 물순환 모니터링/평가/예측 기술을 개발할 수 있다. 본 연구에서는 생활용수 물순환 계통 통합 DB를 정의 및 구축하였다. 도시의 관점에서 물순환 시스템을 순차적으로 물 유입(수원~취수장)/전달(정수장~급수지역)/유출(하(폐)수처리장~방류구)의 개념으로 설정하고 DB정의서를 마련하였다. 연구대상지는 가뭄이 장기화가 되고 있는 전라남도중 물순환 계통이 비교적 단순한 네트워크로 형성되어 있는 함평군 도시지역으로 선정하였다. 연구 기간은 총 5년(2017년 1월 1일~2021년 12월 31일)이고 일 단위 실계측자료 위주의 원자료를 구축하였다. 이를 이상치 탐지, 제거, 대체의 과정을 거쳐 품질 보정하고 정제된 시계열 자료에 대한 특성 분석을 하였다. 그 결과, 물순환 계통 내 주요 지점 간의 상관관계 및 지연시간을 통한 물흐름의 시계열적 특성을 파악할 수 있었으며 모형의 적합도를 판단하는 데 활용되는 통계량과 유의미하지 않은 잔차의 자기상관성을 볼 때 물 유입-전달-유출의 단기 예측을 위한 ARIMA(Auto-regressive Integrated Moving Average) 모형의 구축도 가능할 것으로 판단되었다. 다만 여름철 발생하는 방류량의 첨두값을 설명하기 위해서는 강우에 의한 불명수 발생으로 증가하는 방류량을 묘사할 수있어야 하므로 향후에는 물순환계통 외 해당 지역의 불명수(강우 효과)도 하수 방류량의 주요 입력 요인으로 추가 검토할 필요가 있다.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.147-154
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• 2009

In this study we investigate the problem of reducing color change cost at painting operations in an automobile assembly plant. Changing control logic at conveyor junction points prior to the top coat line has been proposed and analyzed using the discrete event simulation model we developed using AutoMod. We also discussed the project which initiated this research as well as the details of painting operations. Simulation analysis showed that the grouping ratio increases from 1.8 to 2.5 if the proposed control logic change is applied to the plant. Contrary to other approaches such as using dedicated equipment for resequencing, our approach has the merit of less investment cost, no need for additional space consumption. We finally note that the grouping ratio can be further increased if our algorithms is implemented as well as CRS (Color Rescheduling Storage) is installed.

• Design Convergence Study
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• v.17 no.4
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• pp.17-29
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• 2018

In the semi-autonomous vehicle, before reaching a fully autonomous driving stage, it is imperative for the system to issue a take-over request(TOR) that asks a driver to operate manually in a specific situation. The purpose of this study is to compare whether head-up display(HUD) is a better human-vehicle interaction than head-down display(HUD) in the event of TOR. Upon recognition of TOR in the experiment with a driving simulator, participants were prompted to switch over to manual driving after performing a secondart task, that is, playing a game, while in auto-driving mode. The results show that HUD is superior to HDD in 'ease of use' and 'satisfaction' although there is no significant difference in reaction time and subjective workload. Therefore, designing secondary tasks through HUD during autonomous driving situation improves the user experience of the TOR function. The implication of this study lies in the establishing an empirical case for setting up UX design guidelines for autonomous driving context.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.115-127
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• 2023

Video stabilization is one of the camera technologies that the importance is gradually increasing as the personal media market has recently become huge. For deep learning-based video stabilization, existing methods collect pairs of video datas before and after stabilization, but it takes a lot of time and effort to create synchronized datas. Recently, to solve this problem, unsupervised learning method using only unstable video data has been proposed. In this paper, we propose a network structure that learns the stabilized trajectory only with the unstable video image without the pair of unstable and stable video pair using the Convolutional Auto Encoder structure, one of the unsupervised learning methods. Optical flow data is used as network input and output, and optical flow data was mapped into grid units to simplify the network and minimize noise. In addition, to generate a stabilized trajectory with an unsupervised learning method, we define the loss function that smoothing the input optical flow data. And through comparison of the results, we confirmed that the network is learned as intended by the loss function.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.127-139
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• 2023

Numerous genuine issues, for example, financial exchange expectation, climate determining and so forth has inalienable arbitrariness related with them. Receiving a probabilistic system for forecast can oblige this dubious connection among past and future. Commonly the interest is in the contingent likelihood thickness of the arbitrary variable included. One methodology for expectation is with time arrangement and auto relapse models. In this work, liner expectation technique and approach for computation of forecast coefficient are given and likelihood of blunder for various assessors is determined. The current methods all need in some regard assessing a boundary of some accepted arrangement. In this way, an elective methodology is proposed. The elective methodology is to gauge the restrictive thickness of the irregular variable included. The methodology proposed in this theory includes assessing the (discretized) restrictive thickness utilizing a Markovian definition when two arbitrary factors are genuinely needy, knowing the estimation of one of them allows us to improve gauge of the estimation of the other one. The restrictive thickness is assessed as the proportion of the two dimensional joint thickness to the one-dimensional thickness of irregular variable at whatever point the later is positive. Markov models are utilized in the issues of settling on an arrangement of choices and issue that have an innate transience that comprises of an interaction that unfurls on schedule on schedule. In the nonstop time Markov chain models the time stretches between two successive changes may likewise be a ceaseless irregular variable. The Markovian methodology is especially basic and quick for practically all classes of classes of issues requiring the assessment of contingent densities.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.258-258
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• 2020

홍수기시 저수지 방류량으로 인한 피해를 저감하기 위한 방법으로는 구조적인 방법과 비구조적인 방법이 있으며, 구조적인 방법으로는 저수지 제체를 증고하는 등의 방법이 있고, 비구조적인 방법으로는 홍수 관리 수리시설물의 홍수기시 효율적인 운영 등이 있다. 그동안 둑높이기 저수지를 평가하는 다양한 연구에서는 제체의 증고를 통한 홍수조절능력 평가가 주를 이뤄왔으나 홍수기시 수공구조물의 운영방법별 홍수조절효과를 다각적으로 비교 연구한 연구는 부족한 실정이다. 따라서 본 연구에서는 황룡강 유역의 장성저수지를 대상으로 빈도별 홍수량을 산정하고, 둑높이기 전후의 수공구조물 운영 방법별 홍수조절능력을 평가하였으며, 효율적인 홍수조절방법을 검토하였다. 본 연구에서는 대상지구인 장성저수지의 지배관측소인 광주관측소의 과거 기상자료를 수집하였으며 HEC-HMS (Hydrologic Modeling System)을 이용하여 50년, 100년, 200년 빈도 및 PMF 유입시 설계홍수량을 산정하였다. 각 설계홍수량의 유입 시 저수지 운영 모의에는 HEC-5 (Hydrologic Engineering Center-5) 모형을 이용하였으며, 저수지 운영기법으로 Auto ROM 과 Rigid ROM 방식을 적용하였다. 또한 둑 높이기 전후 홍수조절능력 평가는 지체시간, 시간별 홍수위, 첨두홍수량, 홍수조절률 등 다양한 평가 지표를 고려하여 수행하였다. 본 연구에서 모의한 저수지 운영기법별 둑높이기 전 후의 홍수조절효과 평가를 통해 저수지의 최적 운영방안을 도출하고 둑높이기 저수지의 하류하천에 대한 치수계획의 수립 및 보완에 활용될 수 있을 것으로 기대된다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

• Journal of Korean Society of Rural Planning
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• v.30 no.3
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• pp.75-84
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• 2024

This study developed the procedure and method for the accuracy assessment of unmanned boat survey data, based on the reservoir water depth data of Misan Reservoir, measured by the manned and unmanned boats in 2009 by Korea Rural Community Corporation. In the first step, this study devised the method to extract the contour map of NGIS data in AutoCAD to generate easily the reservoir boundary map used to set the survey range of reservoir water depth and to test the survey accuracy. The surveyed data coordinate systems of the manned and the unmanned boat were also unified by using ArcGIS for the standards of accuracy assessment. In the accuracy assessment, the spatial correlation coefficient of the grid maps of the two measurement results was 0.95, showing high pattern similarity, although the average error was high at 78cm. To analyze in more detail assessment, this study generated randomly the 3,250m transverse profile route (PR), and then extracted grid values of water depth on the PR. In the results of analysis to the extracted depth data on PR, the error average difference of the unmanned boat measurements was 73.18cm and the standard deviation of the error was 55cm compared to the manned boat. This study set these values as the standard for the correction value by average shift and noise removal of the unmanned boat measurement data. By correcting the unmanned boat measurements with these values, this study has high accuracy results, the reservoir water depth and surface area curve with R² = 0.97 and the water depth and storage volume curve with R² = 0.999.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1283-1283
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• 2024

Recently, construction sites have faced significant challenges due to arbitrary changes and poor communication between general contractors and subcontractors. This study proposes a technological solution by integrating Unmanned Ground Vehicles (UGVs) into the existing workflow of apartment construction. By analyzing current processes, we identified a scenario where UGVs, equipped with LiDAR (Light Detection and Ranging) systems, can generate and provide real-time 3D models of construction sites. These models can be linked with extended reality (XR) technology or office PCs for intuitive comparisons between digital and actual site conditions as a digital twin of the construction site. The study suggests an improved construction process that enhances contractors' understanding and on-site efficiency and enables managers to monitor progress effectively. To address challenging terrain on construction sites, a caterpillar driven UGV was developed, equipped with stereo cameras, a LiDAR sensor for scanning and gathering environmental data, and an embedded PC for data processing. Utilizing SLAM (Simultaneous Localization and Mapping) technology, the UGV autonomously navigates and scans the site at night, minimizing disruptions. Additionally, an embedded system analyzes images from stereo cameras to assess the quality of construction, mapping the findings onto 3D models. This innovation allows site managers to efficiently verify construction quality and identify issues without manual inspections, significantly improving site management efficiency.