• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.149-150
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• 2019

기존의 Flow 규칙 구분을 위해 연구되었던 기법들은 적응적 또는 사전 처리의 접근법이 제안되었으나 각각의 장단점을 기반으로 효율적인 접근법이 연구되어야한다. 본 연구에서는 Flow 규칙을 삽입하기 전에, 스위치의 계산 작업을 완화하기 위하여 전이 학습 기법인 TrAdaBoost를 이용함으로써 Flow 규칙들을 구분하는 접근법을 제안한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.8
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• pp.1-9
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• 2002

Axisymmetric separated flows in a converging-diverging conical nozzle are investigated through numerical simulations for various pressure ratios. We employ AUSM scheme for spatial derivatives and Pulliam's 2nd order subiteration time stepping scheme for implicit time integration. Numerical results indicate that the separated flow structures are very complex when compared to the simple quasi-one dimensional flow. Depending on the pressure ratio, the flow within the nozzle is either separated or non-separated. Various separated flow patterns with distinctive features are illustrated and discussed in detail.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.401-406
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• 2019

Recently, as multi-platform game environments become common, many games of convergence genre have been produced, and the boundaries of genre division by existing platforms have become blurred. The game genre is convergence content consisting of user-centered 'narrative and play'. In this paper, we propose a game genre classification according to the user 's behavior type based on the essential recognition that the subject of the game is the user. The user's actions are done in different genres and goals and rules, and the interaction is an important act for immersion. Therefore, the user's behavioral classification and perception by the game genre are important and expected to help redefine the game structure.

• Journal of KIISE:Software and Applications
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• v.28 no.12
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• pp.885-895
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• 2001

Existing system analysis models suggest various ideas to present entity relations and event flows for consistency between analysis and design paradigms. However, they are preferred to derive and arrange related entities on system flow than to identify entities. To identify entities systematically is a basic and important work of software development, and identified entities can be major assets of business system. In case of business systems the business rules or the computed or derived information like attendance lists of lecture system can be the most important system assets. The management information or meta data are also. In this paper, we suggest a business system analysis models to derive and present entities. System is identified entities, interfaces and event or behaviors through this model then entities are extended to independent entities, dependent entities, which are dependent to independent entities, constraint shows the physical and administrative notices. Various entity identification can reduce the incompleteness of entity analysis.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.4
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• pp.206-216
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• 2017

The temporal distribution of the turbulence kinetic energy (TKE) and the vertical component of Reynolds stresses ($-{\bar{u^{\prime}w^{\prime}}}$) was measured during one wave period under high wave energy conditions. The wave data were obtained at Hujeong Beach in the east coast of Korea at January 14~18 of 2017 when an extratropical cyclone was developed in the East Sea. Among the whole thousands of waves measured during the period, hundreds of regular waves that had with similar pattern were selected for the analysis in order to give three representing mean wave patterns using the ensemble average technique. The turbulence properties were then estimated based on the selected wave data. It is interesting to find out that $-{\bar{u^{\prime}w^{\prime}}}$ has one clear peak near the time of flow reversal while TKE has two peaks at the corresponding times of maximum cross-shore velocity magnitudes. The distinguished pattern of Reynolds stress indicates that vertical fluxes of such properties as suspended sediments may be enhanced at the time when the horizontal flow direction is reversed to disturb the flows, supporting the turbulence convection process proposed by Nielsen (1992). The characteristic patterns of turbulence properties are examined using the CADMAS-SURF Reynolds-Averaged Navier-Stokes (RANS) model. Although the model can reasonably simulate the distribution of TKE pattern, it fails to produce the $-{\bar{u^{\prime}w^{\prime}}}$ peak at the time of flow reversal, which indicates that the application of RANS model is limited in the prediction of some turbulence properties such as Reynolds stresses.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.149-158
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• 2002

For the purpose of preciously describing real time traffic pattern in urban road network, dynamic network loading(DNL) models able to simulate traffic behavior are required. A number of different methods are available, including macroscopic, microscopic dynamic network models, as well as analytical model. Equivalency minimization problem and Variation inequality problem are the analytical models, which include explicit mathematical travel cost function for describing traffic behaviors on the network. While microscopic simulation models move vehicles according to behavioral car-following and cell-transmission. However, DNL models embedding such travel time function have some limitations ; analytical model has lacking of describing traffic characteristics such as relations between flow and speed, between speed and density Microscopic simulation models are the most detailed and realistic, but they are difficult to calibrate and may not be the most practical tools for large-scale networks. To cope with such problems, this paper develops a new DNL model appropriate for dynamic traffic assignment(DTA), The model is combined with vertical queue model representing vehicles as vertical queues at the end of links. In order to compare and to assess the model, we use a contrived example network. From the numerical results, we found that the DNL model presented in the paper were able to describe traffic characteristics with reasonable amount of computing time. The model also showed good relationship between travel time and traffic flow and expressed the feature of backward turn at near capacity.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.419-431
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• 2018

In this paper, we propose a novel method for monitoring mood trend of Twitter users by analyzing their daily tweets for a long period. Then, to more accurately understand their tweets, we analyze all types of content in tweets, i.e., texts and emoticons, and images, thus develop a multimodal sentiment analysis method. In the proposed method, two single-modal analyses first are performed to extract the users' moods hidden in texts and images: a lexicon-based and learning-based text classifier and a learning-based image classifier. Thereafter, the extracted moods from the respective analyses are combined into a tweet mood and aggregated a daily mood. As a result, the proposed method generates a user daily mood flow graph, which allows us for monitoring the mood trend of users more intuitively. For evaluation, we perform two sets of experiment. First, we collect the data sets of 40,447 data. We evaluate our method via comparing the state-of-the-art techniques. In our experiments, we demonstrate that the proposed multimodal analysis method outperforms other baselines and our own methods using text-based tweets or images only. Furthermore, to evaluate the potential of the proposed method in monitoring users' mood trend, we tested the proposed method with 40 depressive users and 40 normal users. It proves that the proposed method can be effectively used in finding depressed users.