• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.7
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• pp.56-64
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• 2012

A smart grid is the next generation power grid which combines the existing power grid with information technology, so an energy efficient power grid can be provided. In this paper, in order to build an efficient smart grid an AMI system, which gears with the existing home network and provides an user friendly management function, is proposed. The proposed AMI system, which is based on an extended home network, consists of various functional units; smart meters, communication modules, home gateway, security modules, meter data management modules (MDMM), electric power application modules and so on. The proposed home network system, which can reduce electric power consumption and transmit data more effectively, is designed by using IEEE 802.15.4. The extended home gateway can exchange energy consumption information with the outside management system via web services. The proposed AMI system is designed to enable two-way communication between the home gateway and MDMM via the Internet. The AES(Advanced Encryption Standard) algorithm, which is a symmetric block cipher algorithm, is used to ensure secure information exchange. Even though the results in this study could be limited to our experimental environment, the result of the simulation test shows that the proposed system reduces electric power consumption by 4~42% on average compared to the case of using no control.

• Archives of design research
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• v.18 no.1 s.59
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• pp.17-26
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• 2005

Nowadays, display-contents are developing to an informative environment. that is under the logic of the media operating system. To perceive the media-environments and produce the cultural contents, the cultural designers seek to understand a skin structure from making up for shape. To appreciate operating system in data and database is not only systematization of form and contents of visual contents but also variety contents into multiple-platform and integrative environments. These days, the spectacle exhibition try to express for their surface design between algorithm of data and database. the information is expressing aesthetic which means presents the integrated contents through the play instinct environment to end-user. That was given web or game to participation is developing with the cellular device and ubiquitous computing system. in the linear perspective, the end-user should be immerse more and more hyper-simulation system because of the operating algorithm of database. To do this, human have need to get the information-ability from multi-platform society. In the virtual environment, database offer the experience of an unheard-of event to end-user that prepare the participants the circumstances priority of signifiers. To do that already based on a fixed sensibility endow with narrative of the freshness- experience.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.315-315
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• 2017

In Japan, more than 80 % of soybean growing area is converted fields and excess water is one of the major problems in soybean production. For example, recent study (Yoshifuji et al., 2016) suggested that in the fields of shallow groundwater level (GWL) (< 1m depth), rising GWL even in a short period (e.g. 1 day) causes inhibition of soybean growth. Thus it becomes more and more important to predict GWL and soil moisture in detail. In addition to conventional surface drainage and underdrain, FOEAS (Farm Oriented Enhancing Aquatic System), which is expected to control GWL in fields adequately, has been developed recently. In this study we attempted to predict GWL and soil moisture condition at the converted field with FOEAS in Biwa lake reclamation area, Shiga prefecture, near the center of the main island of Japan. Two dimensional HYDRUS model (Simuinek et al., 1999) based on common Richards' equation, was used for the calculation of soil water movement. The calculation domain was considered to be 10 and 5 meter in horizontal and vertical direction, respectively, with two layers, i.e. 20cm-thick of plowed layer and underlying subsoil layer. The center of main underdrain (10 cm in diameter) was assumed to be 5 meter from the both ends of the domain and 10-60cm depth from the surface in accordance with the field experiment. The hydraulic parameters of the soil was estimated with the digital soil map in "Soil information web viewer" and Agricultural soil-profile physical properties database, Japan (SolphyJ) (Kato and Nishimura, 2016). Hourly rainfall depth and daily potential evapo-transpiration rate data were given as the upper boundary condition (B.C.). For the bottom B.C., constant upward flux, which meant the inflow flux to the field from outside, was given. Seepage face condition was employed for the surrounding of the underdrain. Initial condition was employed as GWL=60cm. Then we compared the simulated and observed results of volumetric water content at depth of 15cm and GWL. While the model described the variation of GWL well, it tended to overestimate the soil moisture through the growing period. Judging from the field condition, and observed data of soil moisture and GWL, consideration of soil structure (e.g. cracks and clods) in determination of soil hydraulic parameters at the plowed layer may improve the simulation results of soil moisture.

• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.27-38
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• 2011

This study was aimed to investigate the effects of restaurant in-store color and music congruency on consumer's emotional responses and behavioral intentions. The web survey was conducted among 400 customers(aged from 20~39 years old) who lived in Seoul and Kyunggi, Incheon Province. To find ensemble effect of color and music, 3D studio MAX were used to make high-stimulus(exciting) and low-stimulus(calm) and 3D virtual reality restaurant simulation stimulus were applied. The statistical data analyses were performed using SPSS/WIN 18.0 and reliability analysis, factor analysis, regression analysis were used. Based on the result of the conducting factor analysis, emotional responses were classified into 2 factors: positive emotion and negative emotion. Satisfaction was classified into 1 factor: satisfaction. Loyalty was classified into 1 factor: loyalty. Cronbach's alpha was calculated for the reliability of the survey instrument. Consequently, restaurant in-store color and music congruency were shown to affect positive emotion and negative emotion. Positive emotion and negative emotion were shown to affect satisfaction. Satisfaction were shown to affect loyalty. Music congruency had a higher effect on positive emotion than color congruency. Color congruency had a higher effect on negative emotion than music congruency. The results of this study will serve as a basis of color and music congruency with restaurant atmospherics.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.474-481
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• 2007

With population growth, industrialization, and urbanization within the watershed, the hydrologic response changed dramatically, resulting in increases in peak flow with lesser time to peak and total runoff with shortened time of concentration. Infiltration is directly affected by initial soil moisture condition, which is a key element to determine runoff. Influence of the initial soil moisture condition on hydrograph analysis should be evaluated to assess land use change impacts on runoff and non-point source pollution characteristics. The Long-Term Hydrologic Impact Assessment (L-THIA) model has been widely used for the estimation of the direct runoff worldwide. The L-THIA model was applied to the Little Eagle Creek (LEC) watershed and Its estimated direct runoff values were compared with the BFLOW filtered direct runoff values by other researchers. The $R^2$ value Was 0.68 and the Nash-Sutcliffe coefficient value was 0.64. Also, the L-THIA estimates were compared with those separated using optimized $BFI_{max}$ value for the Eckhardt filter. The $R^2$ value and the Nash-Sutcliffe coefficient value were 0.66 and 0.63, respectively. Although these higher statistics could indicate that the L-THIA model is good in estimating the direct runoff reasonably well, the Antecedent Moisture Condition (AMC) was not adjusted in that study, which might be responsible for mismatches in peak flow between the L-THIA estimated and the measured peak values. In this study, the L-THIA model was run with AMC adjustment for direct runoff estimation. The $R^2$ value was 0.80 and the Nash-Sutcliffe coefficient value was 0.78 for the comparison of L-THIA simulated direct runoff with the filtered direct runoff. However there was 42.44% differences in the L-THIA estimated direct runoff and filtered direct runoff. This can be explained in that about 80% of the simulation period is classified as 'AMC I' condition, which caused lower CN values and lower direct runoff estimation. Thus, the coefficients of the equation to adjust CN II to CN I and CN III depending on AMC condition were modified to minimize adjustments impacts on runoff estimation. The $R^2$ and the Nash-Sutcliffe coefficient values increase, 0.80 and 0.80 respectively. The difference in the estimated and filtered direct runoff decreased from 42.44% to 7.99%. The results obtained in this study indicate the AMC needs to be considered for accurate direct runoff estimation using the L-THIA model. Also, more researches are needed for realistic adjustment of the AMC in the L-THIA model.

• Cartoon and Animation Studies
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• s.29
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• pp.129-149
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• 2012

The purpose of this study was to investigate the clustering and Big Five Personality domains in discriminating groups by level of school-related adjustment, as experienced by Social Sims game users. Social Games are based on web that has simple rules to play in fictional time and space background. This paper is to analyze the relationships between social networks and user behaviors through the social games . In general, characteristics of social games are simple, fun and easy to play, popular to the public, and based on personal connections in reality. These features of social games make themselves different from video games with one player or MMORPG with many unspecific players. Especially Social Game show a noticeable characteristic related to social learning. The object of this research is to provide a possibility that game that its social perspective can be strengthened in social game environment and analyze whether it actually influences on problem solving of real life problems, therefore suggesting its direction of alternative play means and positive simulation game. Data was collected by administering 4 questionnaires (the short version of BFI, Satisfaction with life, Career Decision-.Making Self-.Efficacy, Depression) to the participants who were 20 people in Seoul and Daejeon. For the purposes of the data analysis, both Stepwise Discriminant analysis and Cluster analysis was employed. Neuroticism, Openness, Conscientiousness within the Big Five Personality domains were seen to be significant variables when it came to discriminating the groups. These findings indicated that the short version of the BFI may be useful in understanding for game user behaviors When it comes to cultural research, digital game takes up a significant role. We can see that from the fact that game, which has only been considered as a leisure activity or commercial means, is being actively research for its methodological, social role and function. Among digital game's several meanings, one of the most noticeable ones is the research on its critical, social participating function. According to Jame Paul gee, the most important merit of game is 'projected identity'. This means that experiences from various perspectives is possible.[1] In his recent autobiography , he described gamer as an active problem solver. In addition, Gonzalo Francesca also suggested an alternative game developing method through 'game that conveys critical messages by strengthening critical reasons'. [2] They all provided evidences showing game can be a strong academic tool. Not only does a genre called social game exist in the field of media and Social Network Game, but there are also some efforts to positively evaluate its value Through these kinds of researches, we can study how game can give positive influence along with the change in its general perception, which would eventually lead to spreading healthy game culture and enabling fresh life experience. This would better bring out the educative side of the game and become a social communicative tool. The object of this game is to provide a possibility that the social aspect can be strengthened within the game environment and analyze whether it actually influences the problem solving of real life problems. Therefore suggesting it's direction of alternative play means positive game simulation.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.