• Journal of Digital Convergence
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• v.10 no.1
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• pp.309-315
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• 2012

The dissemination of smartphones is being spread and supplementary services using smartphones are increasing and various as the Mobile network and device are developing rapidly, so smartphones that enables to provide a wide range of services is expected to receive the most attention. It makes users listen to music anytime, anywhere in real-time, use useful applications, and access to Internet to search for information. The service environment is changing on PC into Mobile due to the change of the circumstance mentioned above. these services are done by using just location information rather than other context, and users have to search services and use them. It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also defined the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.

• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.817-827
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• 2007

In the age of ubiquitous environment, huge number of devices and computing services are provided to users. Personalized service, which is modeled according to the character of each and every individual is of particular need. In order to provide various dynamic services according to user's movement, service unit and operating mode should be able to operate automatically with minimum user intervention. In this paper, we discuss the steps of offering approximate service based on user's request in ubiquitous environment. First, we present our simulator designed for modeling the physical resource and computing object in smart space - the infrastructure in ubiquitous. Second, intelligent agents, which we developed based on a FIPA specification compliant multi-agent framework will be discussed. These intelligent agents are developed for achieving the service goal through cooperation between distributed agents. Third, we propose an automated service discovery and composition method in heterogeneous environment using semantic message communication between agents, according to the movement by the user interacting with the service available in the smart space. Fourth, we provide personalized service through agent monitoring anytime, anywhere from user's profile information stored on handhold device. Therefore, our research provides high quality service more than general automated service operation.

• Journal of Korea Water Resources Association
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• v.34 no.2
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• pp.107-118
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• 2001

This study was to develop a tow-dimensional model system for the hydrodynamic analysis and to apply the system on the downstream of Han River. it is performed to design a GIS-based hydrodynamic system for the scientific shallow water profile analysis, and to compare hydrodraulic modeling is the Petrov-Galerkin's finite element method for flow prediction model. This study was to construct a GIS-based river flow system, and it is useful for supporting user's decision making for the on-line status through various analysis. We expect that the results from this study can be used as one of the guidelines for river analysis and management system in order rivers, reservoirs, and estuaries.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.24-30
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• 2008

In this paper we analyze the performance of an adaptive sector cell system, which is adopted to maintain the traffic balance between sectors and to utilize the cell resources effectively, using the data collected from real channel environments. In the data measurements, we transmitted the QPSK modulated signal with carrier frequency of 1.95GHz and received the signals using the 8x4 array antenna equipped on the top of buildings in the urban area. We analyzed the angular distribution and the delay spread of a user signal and analyzed angular distribution of mobile users in a cell using the collected data. Also, we propose the vector channel modeling using the estimated pdf(probability distribution function) of the analyzing results. Through the proposed channel modeling the improvement of the call blocking rate was analyzed when using the adaptive sector cell system, and computer simulations show that the call blocking rate of the adaptive sector cell system was much lower than that of the fixed sector cell system. Additionally, it shows that the call blocking rate increases severely in the fixed sector cell system while the difference of the call blocking rate was smaller in the adaptive sector cell system, as the user density of the spatial distribution increases.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.129-134
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• 2009

Recently, percentage of people who use SMS service is increasing. However, it is difficult to express own complicated emotion with text and emoticon of exited SMS service. This paper focuses on that point and practical uses character animation to express emotion and nuance correctly, funny. Also this paper suggests emotion based gesture animation generation system that use character's facial expression and gesture to delivery emotion excitably and clearly than only speaking. Michel[1] investigated interview movies of a person whose gesturing style they wish to animate and suggested gesture generation graph for stylized gesture animation. In this paper, we make focus to analyze and abstracted emotional gestures of Disney animation characters and did 3D modeling of these emotional gestures expanding Michel[1]'s research. To express emotion of person, suggests a emotion gesture generation graph that reflects emotion flow graph express emotion flow for probability. We investigated user reaction for research the propriety of suggested system and alternation propriety.

• Asian Journal of Atmospheric Environment
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• v.5 no.2
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• pp.86-96
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• 2011

The positive matrix factorization (PMF2) and multilinear engine (ME2) models have been shown to be powerful environmental analysis techniques and have been successfully applied to the assessment of ambient particulate matter (PM) source contributions. Because these models are difficult to apply practically, the US EPA developed a more user-friendly version of the PMF. The initial version of the EPA PMF model does not provide any rotational capabilities; for this reason, the model was upgraded to include rotational functions in the EPA PMF ver. 2.0. In this study, PMF and EPA PMF modeling identified ten particulate matter sources including secondary sulfate I, vehicle gasoline, secondary sulfate II, secondary nitrate, secondary sulfate III, incinerators, aged sea salt, airborne soil particles, oil combustion, and diesel emissions. All of the source profiles determined by the two models showed excellent agreement. The calculated average concentrations of $PM_{2.5}$ were consistent between the PMF2 and EPA PMF ($17.94{\pm}0.30{\mu}g/m^3$ and $17.94{\pm}0.30\;{\mu}g/m^3$, respectively). Also, each set of estimated source contributions of the PMF2 and EPA PMF showed good agreement. The results from the new EPA PMF version applying rotational functions were consistent with those of PMF2. Therefore, the updated version of EPA PMF with rotational capabilities will provide more reasonable solutions compared with those of PMF2 and can be more widely applied to air quality management.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1566-1574
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• 2008

Researches of current information searching focus on providing personalized results as well as matching needed queries in an enormous amount of information. This paper aims at discovering hidden knowledge to provide personalized and inferred search results based on the ontology with categorized concepts and relations among data. The current searching occasionally presents too much redundant information or offers no matching results from large volumes of data. To lessen this disadvantages in the information searching, we propose an inference algorithm that supports associated and inferred searching through the Jess engine based on the OWL ontology constraints and knowledge expressed by SWRL with association rules. After constructing the personalized preference ontology for domains such as restaurants, gas stations, bakeries, and so on, it shows that new knowledge information generated from the ontology and the rules is provided with an example of the domain of gas stations.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Wind and Structures
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• v.30 no.4
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• pp.433-450
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• 2020

The strong turbulence characteristic of typhoon not only will significantly change flow field characteristics surrounding the large-scale wind turbine and aerodynamic force distribution on surface, but also may cause morphological evolution of coast dune and thereby form sand storms. A 5MW horizontal-axis wind turbine in a wind power plant of southeastern coastal areas in China was chosen to investigate the distribution law of additional loads caused by wind-sand coupling movement of coast dune at landing of strong typhoons. Firstly, a mesoscale Weather Research and Forecasting (WRF) mode was introduced in for high spatial resolution simulation of typhoon "Megi". Wind speed profile on the boundary layer of typhoon was gained through fitting based on nonlinear least squares and then it was integrated into the user-defined function (UDF) as an entry condition of small-scaled CFD numerical simulation. On this basis, a synchronous iterative modeling of wind field and sand particle combination was carried out by using a continuous phase and discrete phase. Influencing laws of typhoon and normal wind on moving characteristics of sand particles, equivalent pressure distribution mode of structural surface and characteristics of lift resistance coefficient were compared. Results demonstrated that: Compared with normal wind, mesoscale typhoon intensifies the 3D aerodynamic distribution mode on structural surface of wind turbine significantly. Different from wind loads, sand loads mainly impact on 30° ranges at two sides of the lower windward region on the tower. The ratio between sand loads and wind load reaches 3.937% and the maximum sand pressure coefficient is 0.09. The coupling impact effect of strong typhoon and large sand particles is more significant, in which the resistance coefficient of tower is increased by 9.80% to the maximum extent. The maximum resistance coefficient in typhoon field is 13.79% higher than that in the normal wind field.