• 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.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.113-119
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• 2019

The key features of 5G mobile communications recently commercialized can be represented by High Data Rate, Connection Density and Low Latency, of which the features most distinct from the existing 4G will be low Latency, which will be the foundation for various new service offerings. AR and self-driving technologies are being considered as services that utilize these features, and 5G Network Latency is also being discussed in related standards. However, it is true that the discussion of E2E Latency from a service perspective is much lacking. The final goal to achieve low Latency at 5G is to achieve 1ms of air interface based on RTD, which can be done through Ultra-reliable Low Latency Communications (URLLC) through Rel-16 in early 20 years, and further network parity through Mobile Edge Computing (MEC) is also being studied. In addition to 5G network-related factors, the overall 5G E2E Latency also includes link/equipment Latency on the path between the 5G network and the IDC server for service delivery, and the Processing Latency for service processing within the mobile app and server. Meanwhile, it is also necessary to study detailed service requirements by separating Latency for initial setup of service and Latency for continuous service. In this paper, the following three factors were reviewed for initial setup of service. First, the experiment and analysis presented the impact on Latency on the Latency in the case of 1 Data Lake Setup, 2 CRDX On/Off for efficient power, and finally 3H/O on Latency. Through this, we expect Low Latency to contribute to the service requirements and planning associated with Latency in the initial setup of the required services.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.1-12
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• 2020

Low-Orbit satellite mobile networks can provide services through miniaturized terminals with low transmission power, which can be used as reliable means of communication in the national public disaster network and defense sector. However, the high traffic environment in the emergency preparedness situation increases the new call blocking probability and the handover failure probability of the satellite network, and the increase of the handover failure probability affects the QoS because low orbit satellites move in orbit at a very high speed. Among the channel allocation methods of satellite communication, the FCA shows relatively better performance in a high traffic environment than DCA and is suitable for emergency preparedness situations, but in order to optimize QoS when traffic increases, the new call blocking and the handover failure must be minimized. In this paper, we propose LEO-DBC (LEO satellite dynamic beam width control) technique, which improves QoS by adaptive adjustment of beam width of low-orbit satellites and call time of terminals by improving FCA-QH method. Through the LEO-DBC technique, it is expected that the QoS of the mobile satellite communication network can be optimally maintained in high traffic environments in emergency preparedness situations.