• International Journal of Contents
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• v.13 no.1
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• pp.53-61
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• 2017

Recently, the Social Internet of Things (IoT), the follow-up of the IoT, has been studied to expand the existing IoT services, by integrating devices into the social network of people. In the Social IoT environment, humans, devices and digital contents are connected with social relationships, to guarantee the network navigability and establish levels of trustworthiness. However, this environment handles massive data, including social data of humans (e.g., profile, interest and relationship), profiles of IoT devices, and digital contents. Hence, users and service providers in the Social IoT are exposed to arbitrary data when searching for specific information. A study about the recommender system for the Social IoT environment is therefore needed, to provide the required information only. In this paper, we propose the Social correlation group based Recommender System (SRS). The SRS generates a target group, depending on the social correlation of the service requirement. To generate the target group, we have designed an architecture, and proposed a procedure of the SRS based on features of social interest similarity and principles of the Collaborative Filtering and the Content-based Recommender System. With simulation results of the target scenario, we present the possibility of the SRS to be adapted to various Social IoT services.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.4
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• pp.83-91
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• 2010

Generally, the GCM (General Circulation Model) data by IPCC climate change scenarios are used for future weather prediction. IPCC GCM models predict well for the continental scale, but is not good for the regional scale. This paper tried to generate future temperature and precipitation of 8 scattered meteorological stations in South Korea by using the MIROC3.2 hires GCM data and applying LARS-WG downscaling method. The MIROC3.2 A1B scenario data were adopted because it has the similar pattern comparing with the observed data (1977-2006) among the scenarios. The results showed that both the future precipitation and temperature increased. The 2080s annual temperature increased $3.8{\sim}5.0^{\circ}C$. Especially the future temperature increased up to $4.5{\sim}7.8^{\circ}C$ in winter period (December-February). The future annual precipitation of 2020s, 2050s, and 2080s increased 17.5 %, 27.5 %, and 39.0 % respectively. From the trend analysis for the future projected results, the above middle region of South Korea showed a statistical significance for winter precipitation and south region for summer rainfall.

• KCID journal
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• v.21 no.1
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• pp.89-98
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• 2014

This study is to evaluate the future potential impact of climate change on soil erosion loss in a metropolitan area using Revised Universal Soil Loss Equation(RUSLE) with land use information of the Ministry of Environment and rainfall data for present and future years(30-year period). The spatial distribution map of vulnerable areas to soil erosion was prepared to provide the basis information for soil conservation and long-term land use planning. For the future climate change scenario, the MIROC3.2 HiRes A1B($CO_2720ppm$ level 2100) was downscaled for 2040-2069(2040s) and 2070-2099(2080s) using the stochastic weather generator(LARS-WG) with average rainfall data during past 30 years(1980-2010, baseline period). By applying the climate prediction to the RUSLE, the soil erosion loss was evaluated. From the results, the soil erosion loss showed a general tendency to increase with rainfall intensity. The soil loss increased up to 13.7%(55.7 ton/ha/yr) in the 2040s and 29.8%(63.6 ton/ha/yr) in the 2080s based on the baseline data(49.0 ton/ha/yr).

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.697-707
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• 2018

A fast running model comprising the climate change effects is proposed for urban heat environment simulations so as to be used in urban heat island studies and/or the urban planning practices. By combining Hot City Model, a high resolution urban temperature prediction model utilizing the Lagrangian particle tracing technique, and the numerical weather simulation data which are constructed up to year of 2100 under the climate change scenarios, an efficient model is constructed for simulating the future urban heat environments. It is applicable to whole city as well as to a small block area of an urban region, with the computation time being relatively short, requiring the practically manageable amount of the computational resources. The heat environments of the entire metropolitan Seoul area in South Korea are investigated with the aid of the model for the present time and for the future. The results showed that the urban temperature gradually increase up to a significant level in the future. The possible effects of green roofs on the buildings are also studied, and we observe that green roofs don't lower the urban temperature efficiently while making the temperature fields become more homogeneous.

• Journal of Environmental Science International
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• v.25 no.6
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• pp.877-888
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• 2016

To project the effects of climate-induced change on aquatic environments, it is necessary to determine the thermal constraints affecting different fish species and to acquire time series of the current and projected water temperature (WT). Assuming that a nonlinear regression between the WT at individual stations and the ambient air temperature (AT) at nearby weather stations could represent the best relationship of air-water temperature, This study estimates future WT using a general circulation model (GCM). In addition, assuming that the grid-averaged observations of AT correspond to the AT output from GCM simulation, this study constructed a regression curve between the observations of the local WT and the concurrent GCM-simulated surface AT. Because of its low spatial resolution, downscaling is unavoidable. The projected WT under global warming scenario A2 (B2) shows an increase of about $1.6^{\circ}C$ ($0.9^{\circ}C$) for the period 2080-2100. The maximum/minimum WT shows an amount of change similar to that of the mean values. This study will provide guidelines for decision-makers and engineers in climate-induced river environment and ecosystem management.

• ETRI Journal
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• v.34 no.5
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• pp.666-673
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• 2012

Non-real-time delivery of stereoscopic video has been considered as a service scenario for 3DTV to overcome the limited bandwidth in the terrestrial digital television system. A hybrid codec combining MPEG-2 and H.264/AVC has been suggested for the compression of stereoscopic video for 3DTV. In this paper, we propose a stereoscopic video coding scheme using adaptive pre-/post-filters (APPF) to improve the quality of 3D video while retaining compatibility with legacy video coding standards. The APPF are applied adaptively to blocks of various sizes determined by the macroblock coding mode and reference frame index. Experiment results show that the proposed method achieves up to 24.86% bit rate savings relative to a hybrid codec of MPEG-2 and H.264/AVC including the inter-view prediction.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2292-2296
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• 2010

2020 Korea GHG reduction goal is decreasing 4% compared with that of 2005. Effective counterplan of GHG redection goal needs to set for inductrial allocation and various reduction GHG technologies and policies for transportation have been developed. Modal shifr is one of these main policy and it focused on shifting as much freight as economically meaningful under current market conditions. It improves energy efficiency, consequently reduces GHG effect. This study is proposed as a preliminary studay of analyzing Modal shift effect. modal shift of car travelers to train is concerned in Seoul-Busan section, This study is based on a scenario which can maximize passenger occupancy rate to get the GHG reduction effect and the effect of modal shift of car to train is identified. According to this result, we can get GHG reduction effect through dealing with maximizing passenger occupancy rate on train. Therefore, in order to enhance this modal shift effect, train using rate need to increased and also improvement of policies and cost system are need to be considered to promote increasing use of train.

• Journal of KIISE
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• v.44 no.7
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• pp.726-732
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• 2017

In practical distributed sensing and prediction applications over wireless sensor networks (WSN), environmental sensing activities are highly dynamic because of noisy sensory information from moving source signals. The recent distributed online convex optimization frameworks have been developed as promising approaches for solving approximately stochastic learning problems over network of sensors in a distributed manner. Negligence of mobility consequence in the original distributed saddle point algorithm (DSPA) could strongly affect the convergence rate and stability of learning results. In this paper, we propose an integrated sliding windows mechanism in order to stabilize predictions and achieve better convergence rates in cooperative detection of a moving source signal scenario.

• Asian Journal of Business Environment
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• v.7 no.4
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• pp.35-41
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• 2017

Purpose - This paper aims to examine several time series models to predict sales of department stores and discount store markets in South Korea, while other previous trial has performed sales of convenience stores and supermarkets. In addition, optimal predicted values on the underlying model can be got and be applied to distribution industry. Research design, data, and methodology - Two retailing types, under investigation, are homogeneous and comparable in size based on 86 realizations sampled from January 2010 to February in 2017. To accomplish the purpose of this research, both ARIMA model and exponential smoothing methods are, simultaneously, utilized. Furthermore, model-fit measures may be exploited as important tools of the optimal model-building. Results - By applying Holt-Winters' additive seasonality method to sales of two large-scale retailing types, persisting increasing trend and fluctuation around the constant level with seasonal pattern, respectively, will be predicted from May in 2017 to February in 2018. Conclusions - Considering 2017-2018 forecasts for sales of two large-scale retailing types, it is important to predict future sales magnitude and to produce the useful information for reforming financial conditions and related policies, so that the impacts of any marketing or management scheme can be compared against the do-nothing scenario.

• Environmental Engineering Research
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• v.25 no.4
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• pp.462-469
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• 2020

This study is proposing a System Dynamics Model for estimating Greenhouse Gas (GHG) emission from treating Municipal Solid Waste (MSW) in South Korea for years 2000 to 2030. The government of country decided to decrease the total GHG emission from waste sector in 2030 as per Business-as-usual level. In context, four scenarios are generated to predict GHG emission from treating the MSW with three processes i.e., landfill, incineration and recycling. For prior step, MSW generation rate is projected for present and future case using population and waste generation per capita data. It is found that population and total MSW are directly correlated. The total population will increase to 56.27 million and total MSW will be 21.59 million tons in 2030. The methods for estimating GHG emission from landfill, incineration and recycling are adopted from IPCC, 2006 guidelines. The study indicates that Scenario 2 is best to adopt for decreasing the total GHG emission in future where recycling waste is increased to 75% and landfill waste is decreased to 7.6%. Lastly, it is concluded that choosing proper method for treating the MSW in country can result into savings of GHG emission.