• Journal of Distribution Science
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• v.13 no.11
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• pp.101-109
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• 2015

Purpose - A traditional retail market is a place that offers economic opportunity to employees and employers alike it also is a place where the community can meet. The Korean government has invested three trillion won to improve physical and non-physical aspects in traditional retail markets since 2004. However, little research on this has been conducted. We explore this research gap that could lead to theory extension. We analyze consumption behavior with respect to traditional retail markets through an empirical analysis, thus overcoming limits in previous research. We empirically analyze policy effects of traditional retail market projects supported by the Korean government. Research design, data, and methodology - We propose a traditional retail market improvement plan via the relation between cause and effect resulting from the analysis. More specifically, logit analysis was carried out with 1,754 consumers in 16 cities nationwide. In order to analyze consumer consumption behaviors nationwide, the probability was analyzed using a logit model. This research analyzes the link between support and non-support by the Korean government using binary values. The dependent variable is whether Korean government support is implemented; the binomial logistic regression is used as the statistical estimation technique. The object variables are:1 (support) or 0 (nonsupport), and the prediction value is between 1 and 0. As a result of the factor analysis of questions related to attributes of service quality, four factors were extracted: convenience, product, facilities, and service. Results - The results indicate that convenience, product, and facilities have a significant influence on consumer satisfaction in accordance with the government's traditional retail market support. Additionally, the results reveal that convenience, product, facilities, and service all have a significant influence on consumer satisfaction in a traditional retail market's service quality and consumer satisfaction. Finally, the analysis indicates that the highly satisfied traditional retail market customer has a significant influence on revisit intention. Moreover, the results reveal that the highly satisfied traditional retail market customer has a significant influence on recommendation intention. Conclusions - This research focused on consumers nationwide to measure policy effects of traditional retail markets compared to previous research that focused on one traditional retail market or a specific area. We verified the relationship of service quality and customer satisfaction and consumer behavior based on service quality theory. The results indicate that consumer satisfaction of traditional retail markets supported by service quality factors has a significant impact. In a concrete form, the results indicate that these effects are from facility modernization projects and marketing support projects of the Korean government. The results also imply that these facility and management support effects from the Korean government have been consistent. We realize that the Korean government has to selectively support traditional retail markets in major cities and small and medium-sized cities. To that end, the Korean government needs to select a concentration strategy for the revitalization of traditional retail markets.

• Journal of Satellite, Information and Communications
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• v.2 no.2
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• pp.41-47
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• 2007

The first geostationary satellite made by Korea, COMS, has the three different payload ; Meteorological sensor, Oceanographic sensor and Ka-band communication payload. There are Meteorological & Ocean Data Communication Subsystem(MODCS) and Telemetry, Command and Ranging Subsystem(TC&R) as other RF radiation sources. MODCS transmits and receives Meteo and Ocean measurement data from/to earth using L-band and TC&R using S-band. The Ka-band communication payload will provide high-speed multimedia services and communication services for natural disaster such as prediction, prevention, and recovery services in the government communications network.Ka-band beacon is for the earth antenna pointing and the experiment of rain fading. This paper gives the analysis results about the mutual radiation effect on Ka-band communication payload, Ka-band beacon, MODCS and TC&R. Up/Down link power and coupling factor including the geometrical position and distance of antenna, filter rejection and degradation factor due to the different polarization are considered. The results show MODCS and TC&R are compatible for Ka-band communication payload and Ka-band beacon does not interfere with MODCS and TC&R normal operation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.284-288
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• 2009

With explosively growing PPI databases, the computational approach for a prediction and configuration of PPI network has been a big stream in the bioinformatics area. Recent researches gradually consider physicochemical properties of proteins and support high resolution results with integration of experimental results. With regard to current research trend, it is very close future to complete a PPI network configuration of each organism. However, direct applying the PPI network to real field is complicated problem because PPI network is only a set of co-expressive proteins or gene products, and its network link means simple physical binding rather than in-depth knowledge of biological process. In this paper, we suggest a protein functional flow model which is a directed network based on a protein functions' relation of signaling transduction pathway. The vertex of the suggested model is a molecular function annotated by gene ontology, and the relations among the vertex are considered as edges. Thus, it is easy to trace a specific function's transition, and it can be a constraint to extract a meaningful sub-path from whole PPI network. To evaluate the model, 11 functional flow models of Homo sapiens were built from KEGG, and Cronbach's alpha values were measured (alpha=0.67). Among 1023 functional flows, 765 functional flows showed 0.6 or higher alpha values.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1193-1202
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• 2018

It is important to quantitatively analyze the energy budget for understanding of long-term climate change in Arctic. High-quality and long-term radiative parameters are needed to understand the energy budget. Since most of radiative flux components based on satellite are provide for a short period, several data must be used together. It is important to acquaint differences between data to link for conjunction with several data. In this study, we investigated the comparative analysis of Arctic radiative flux product such as CERES and GEWEX to provide basic information for data linkage and analysis of changes in Arctic climate. As a result, GEWEX was underestimated the radiative variables, and it difference between the two data was about $3{\sim}25W/m^2$. In addition, the difference in high-latitude and sea ice regions have increased. In case of comparing with monthly means, the other variables except for longwave downward flux represent high difference of $9.26{\sim}26.71W/m^2$ in spring-summer season. The results of this study can be used standard data for blending and selecting GEWEX and CERES radiative flux data due to recognition of characteristics according to ice-ocean area, season, and regions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.387-392
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• 2022

To participate in global carbon neutrality, the Korean government is also planning to carry out zero-energy building certification for all buildings by 2030 through the enforcement decree of the 'Green Building Support Act'. Accordingly, the government is providing various projects related to solar power generation, which are relatively close to life. In particular, roof-mounted photovoltaic power generation systems are attracting attention in terms of using unused space to produce energy without destroying the environment, but low power generation efficiency compared to other photovoltaic power generation facilities is pointed out as a disadvantage. Therefore, in this paper, to solve this problem, we propose an efficient solar panel angle variable system through research on the solar panel structure for single-axial solar tracking, and also consider the application environment of the roof-mounted solar power generation system. Suggests measures to prevent damage and secondary damage. In addition, it is judged that it is possible to control the solar panel based on ICT convergence and configure the accident prediction safety system to link the project-based education program.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.2
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• pp.109-117
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• 2013

The Global Framework on Climate Services (GFCS) will guide the development of climate services that link science-based climate information and predictions with climate-risk management and adaptation to climate change. GFCS structure is made up of 5 pillars; Observations/Monitoring (OBS), Research/ Modeling/ Prediction (RES), Climate Services Information System (CSIS) and User Interface Platform (UIP) which are all supplemented with Capacity Development (CD). Corresponding to each GFCS pillar, the Commission for Agricultural Meteorology (CAgM) has been proposing "Global Initiatives in AgroMeteorology" (GIAM) in order to facilitate GFCS implementation scheme from the perspective of AgroMeteorology - Global AgroMeteorological Outlook System (GAMOS) for OBS, Global AgroMeteorological Pilot Projects (GAMPP) for RES, Global Federation of AgroMeteorological Society (GFAMS) for UIP/RES, WAMIS next phase for CSIS/UIP, and Global Centers of Research and Excellence in AgroMeteorology (GCREAM) for CD, through which next generation experts will be brought up as virtuous cycle for human resource procurements. The World AgroMeteorological Information Service (WAMIS) is a dedicated web server in which agrometeorological bulletins and advisories from members are placed. CAgM is about to extend its service into a Grid portal to share computer resources, information and human resources with user communities as a part of GFCS. To facilitate ICT resources sharing, a specialized or dedicated Data Center or Production Center (DCPC) of WMO Information System for WAMIS is under implementation by Korea Meteorological Administration. CAgM will provide land surface information to support LDAS (Land Data Assimilation System) of next generation Earth System as an information provider. The International Society for Agricultural Meteorology (INSAM) is an Internet market place for agrometeorologists. In an effort to strengthen INSAM as UIP for research community in AgroMeteorology, it was proposed by CAgM to establish Global Federation of AgroMeteorological Society (GFAMS). CAgM will try to encourage the next generation agrometeorological experts through Global Center of Excellence in Research and Education in AgroMeteorology (GCREAM) including graduate programmes under the framework of GENRI as a governing hub of Global Initiatives in AgroMeteorology (GIAM of CAgM). It would be coordinated under the framework of GENRI as a governing hub for all global initiatives such as GFAMS, GAMPP, GAPON including WAMIS II, primarily targeting on GFCS implementations.