• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.205-205
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• 2012

국내에서 새로운 댐 저수지 건설을 통한 수자원의 안정적인 확보는 어려운 여건에 있다. 따라서 수자원의 효율적인 확보, 댐 하류하천의 수질 개선, 신규댐 건설 대체 효과를 기대하기 위해 기존 댐 저수지의 연계운영이 중요하게 인식되고 있다. 본 연구에서는 다수의 댐 저수지 수체를 연계하여 모델을 통해 해석하고자 안동-임하호를 연결한 2차원 모델(CE-QUAL-W2)을 구축하고, 2002년과 2006년 수문사상을 재현하였으며, 수리해석을 실시하였다. 안동호의 좌안인 임동면 마리와 임하호의 우안인 망천리를 연결하고, EL. 140 m 위치에 길이 2 km, 직경 5.5 m로 콘크리트 터널을 연결하는 것으로 가정하였다. 관내 바닥 마찰계수와 미소 마찰손실 값은 0.05를 입력하였다. 저수지 실측수위와 모의수위를 시계열로 비교한 결과, 2002년과 2006년 안동호와 임하호에서 여름철 유입량 증가에 따른 수위 상승을 잘 반영하였고, 결정계수값($R^2$)이 모두 0.9953 이상으로 나타나 모델은 두 저수지 물수지 계산에 있어서 높은 신뢰도를 보였다. 2006년을 대상으로 안동호와 임하호의 댐 앞에서 수심별 수온의 실측값과 모의값을 비교한 결과, 안동호는 4월부터 성층이 진행되어 5월에 수온약층이 EL. 130 m에 형성되었다. 7월 홍수가 중층 밀도류를 형성하여 수온 성층구조를 교란하였고, 기존의 수온약층이 EL. 120 m 로 하강하였으며, 표층 EL. 145 m에 새로운 수온약층이 형성되는 2단 성층 구조를 보였다. 여름철 동안 이러한 현상은 지속되었고, 10월부터 대기기온 강하와 함께 수직혼합이 시작되었다. 수온예측 오차는 AME $0.336{\sim}1.806^{\circ}C$, RMSE $0.415{\sim}2.271^{\circ}C$의 범위로 실측값을 잘 반영하는 것으로 나타났다. 임하호도 안동호와 유사한 경향을 보였고, 모델은 두 저수지에서 전 기간에 걸쳐 모두 안정적으로 저수지 수온 성층현상을 모의하였다. 2002년 수문사상에서 안동-임하 연계 운영시 안동호의 평균 수위는 1.38 m 상승하였고, 임하호는 3.75 m 낮아지는 것으로 모의되었다. 수위변동에 따른 유동 유량은 임하호에서 안동호로 3억 6천 4백만 톤, 안동호에서 임하호로 2억 9천 1백만 톤으로 임하호에서 안동호로 유동한 유량이 높게 나타났다. 유역면적에 비해 저수용량이 작은 임하호의 경우 두 저수지간 유량의 이동에 따라서 저수용량의 증가로 인한 홍수 저감 효과가 있을 것으로 판단된다. 반면, 안동-임하 연계 운영시 임하호의 차가운 물이 안동호로 유입되는 경우, 안동호의 수온 성층구조에 영향을 주었다. 안동호의 경우는 단독운영시보다 높은 위치에 수온약층(EL. 140 m)이 형성되었으며, 임하호는 반대로 저수위가 낮아지면서 단독운영시보다 수온약층의 위치가 약간 낮아졌다. 이러한 결과는 두 저수지 연결시 안동호의 탁수와 수질 환경에 변화가 있을 수 있음을 시사한다.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.413-422
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• 2013

In recent, the rainfall is showing different properties in space and time but the ground rain gauge only can observe rainfall at a point. This means the ground rain gauge has the limitations in spatial and temporal resolutions to measure rainfall and so there is a need to utilize radar rainfall which can consider spatial distribution of rainfall This study tried to apply radar rainfall for runoff simulation on an urban drainage system. The study area is Guro-gu, Seoul and we divided study area into subbasins based on rain gauge network of AWS(Automatic Weather station). Then the radar rainfalls were adjusted using rainfall data of rain gauge stations the areal rainfalls were obtained. The runoffs were simulated by using XP-SWMM model in subbasins of an urban drainage system. As the results, the adjusted radar rainfalls were underestimated in the range of 60 to 95% of rain gauge rainfalls and so the simulated runoffs from the adjusted radar and gauge rainfalls also showed the differences. The runoff peak time from radar rainfall was occurred more fast than that from gauge rainfall.

• Journal of Veterinary Clinics
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• v.33 no.5
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• pp.278-285
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• 2016

Epidemiological studies have shown the association between transportation of live animals and the potential transmission of infectious disease between premises. This finding was also observed in the 2014-2015 foot-and-mouth disease (FMD) outbreak in Korea. Furthermore, slaughterhouses played a key role in the global spread of the FMD virus during the epidemic. In this context, in-depth knowledge of the structure of direct and indirect contact between slaughterhouses is paramount for understanding the dynamics of FMD transmission. But the social network structure of vehicle movements to slaughterhouses in Korea remains unclear. Hence, the aim of this study was to configure a social network topology of vehicle movements between slaughterhouses for a better understanding of how they are potentially connected, and to explore whether FMD outbreaks can be explained by the network properties constructed in the study. We created five monthly directed networks based on the frequency and chronology of on- and off-slaughterhouse vehicle movements. For the monthly network, a node represented a slaughterhouse, and an edge (or link) denoted vehicle movement between two slaughterhouses. Movement data were retrieved from the national Korean Animal Health Integrated System (KAHIS) database, which tracks the routes of individual vehicle movements using a global positioning system (GPS). Electronic registration of livestock movements has been a mandatory requirement since 2013 to ensure traceability of such movements. For each of the five studied networks, the network structures were characterized by small-world properties, with a short mean distance, a high clustering coefficient, and a short diameter. In addition, a strongly connected component was observed in each of the created networks, and this giant component included 94.4% to 100% of all network nodes. The characteristic hub-and-spoke type of structure was not identified. Such a structural vulnerability in the network suggests that once an infectious disease (such as FMD) is introduced in a random slaughterhouse within the cohesive component, it can spread to every other slaughterhouse in the component. From an epidemiological perspective, for disease management, empirically derived small-world networks could inform decision-makers on the higher potential for a large FMD epidemic within the livestock industry, and could provide insights into the rapid-transmission dynamics of the disease across long distances, despite a standstill of animal movements during the epidemic, given a single incursion of infection in any slaughterhouse in the country.

• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.245-257
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• 2016

Although rivers cover only 0.5% of the total land area on the Earth, they are windows that show the integrated effects of watershed biogeochemistry. Studies on the loads and properties of riverine carbon have been conducted because they are directly linked with drinking water quality, and because regional or global net ecosystem production (NEP) can be overestimated, unless riverine carbon loads are subtracted. Globally, ${\sim}0.8-1.5Pg\;yr^{-1}$ and ${\sim}0.62-2.1Pg\;yr^{-1}$ of carbon are transported from terrestrial ecosystems to the ocean via rivers and from inland waters to the atmosphere, respectively. Concentrations, ${\delta}^{13}C$, and fluorescence spectra of riverine carbon have been investigated in South Korea to understand the spatiotemporal changes in the sources. Precipitation as well as land use/land cover can strongly influence the composition of riverine carbon, thus shifting the ratios among DIC, DOC, and POC, which could affect the concentrations, loads, and the degradability of adsorbed organic and inorganic toxic materials. A variety of analyses including $^{14}C$ and high resolution mass spectroscopy need to be employed to precisely define the sources and to quantify the degradability of riverine carbon. Long-term data on concentrations of major ions including alkalinity and daily discharge have been used to show direct evidence of ecosystem changes in the US. The current database managed by the Korean government could be improved further by integrating the data collected by individual researchers, and by adding the major components ions including DIC, DOC, and POC into the database.

• Economic and Environmental Geology
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• v.38 no.1
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• pp.79-89
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• 2005

Groundwater level changes in coastal aquifers occur due to oceanic tides, where the properties of oceanic tides can be applied to estimate hyadraulic parameters. Hydraulic parameters of coastal aquifers located in eastern part of Jeju island were estimated using the tidal response technique. Groundwater level data from a saltwater intrusion monitoring well system was used which showed tidal effects from 3 to 5 km. The hydraulic gradient was assessed by utilizing the filtering method from 71 consecutive hourly water-level observations. Calculated hydraulic diffusivity ranged from 2.94${\times}10^7m^2d^{-1}$ to 4.36${\times}10^7m^2d^{-1}$ . The hydraulic gradient of the coastal aquifer area was found to be ~$10^{-4}$, whereas the gradient of the area between wells Handong-1 and 2 was found to be ~$10^{-6}$, which is very low comparatively. Analysis of groundwater monitoring data showed that groundwater levels are periodically higher near coastal areas compared to that of inner land areas due to oceanic tide influences. When assessing groundwater flow direction in coastal aquifers it is important to consider tidal fluctuation.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.456-467
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• 2016

Wireless Access in Vehicular Environments (WAVE) communication technology, which provides vehicleto-vehicle and vehicle-to-infrastructure communication and offers safe and convenient service, has been developed for application to an Intelligent Transport System (ITS). This paper provides field test results on a study of the feasibility of WAVE technology application to railway communication systems. A test railway communication system based on WAVE technology has been built along the Daebul line and a newly developed EMU. Field tests have been carried out according to the communication function requirements for LTE - R. The test results show that the railway communication system based on WAVE technology meets the functional requirements: maximum transmission length is 730m, maximum transfer delay is 5.69ms, and maximum interruption time is 1.36s; other tests including throughput test, video data transmission test, VoIP data test, and channel switching test also produced results that meets the functional requirements. These results suggest that WAVE technology can be applied to the railway communication system, enabling Vehicle-to-Wayside communication.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.11
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• pp.263-270
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• 2019

Vehicular cloud computing is a new emerging technology that can provide drivers with cloud services to enable various vehicular applications. A vehicular cloud is defined as a set of vehicles that share their own resources. Vehicles should collaborate with each other to construct vehicular clouds through vehicle-to-vehicle communications. Since collaborating vehicles to construct the vehicular cloud have different speeds, directions and locations respectively, the vehicular cloud is constructed in multi-hop communication range. Due to intermittent wireless connectivity and low density of vehicles with the limited resources, the construction of vehicular cloud with multi-hop communications has become challenging in vehicular environments in terms of the service success ratio, the service delay, and the transmitted packet number. Thus, we propose a multi-hop vehicular cloud construction protocol that increases the service success ratio and decreases the service delay and the transmitted packet number. The proposed protocol uses a connection time-based intermediate vehicle selection scheme to reduce the cloud failure probability of multi-hop vehicular cloud. Simulation results conducted in various environments verify that the proposed protocol achieves better performance than the existing protocol.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.479-487
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• 2021

In this paper, we propose an Sound Event Detection (SED) model using self-training based on a noisy student model. The proposed SED model consists of two stages. In the first stage, a mean-teacher model based on an Residual Convolutional Recurrent Neural Network (RCRNN) is constructed to provide target labels regarding weakly labeled or unlabeled data. In the second stage, a self-training-based noisy student model is constructed by applying different noise types. That is, feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise are used here. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4. The experiments show that the single model and ensemble model of the proposed SED based on the noisy student model improve F1-score by 4.6 % and 3.4 % compared to the top-ranked model in DCASE 2020 challenge Task 4, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.745-760
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• 2021

Prediction of a stock price has been a subject of interest for a long time in financial markets, and thus, many studies have been conducted in various directions. As the efficient market hypothesis introduced in the 1970s acquired supports, it came to be the majority opinion that it was impossible to predict stock prices. However, recent advances in predictive models have led to new attempts to predict the future prices. Here, we summarize past studies on the price prediction by evaluation measures, and predict the direction of stock prices of Samsung Electronics, LG Chem, and NAVER by applying various machine learning models. In addition to widely used technical indicator variables, accounting indicators such as Price Earning Ratio and Price Book-value Ratio and outputs of the hidden Markov Model are used as predictors. From the results of our analysis, we conclude that no models show significantly better accuracy and it is not possible to predict the direction of stock prices with models used. Considering that the models with extra predictors show relatively high test accuracy, we may expect the possibility of a meaningful improvement in prediction accuracy if proper variables that reflect the opinions and sentiments of investors would be utilized.