• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.196-211
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• 2007

In the Product Line Engineering (PLE), current studies about an analysis of the feature have uncertain and ad-hoc criteria of analysis based on developer’s intuition or domain expert’s heuristic approach and difficulty to extract explicit features from a product in a product line because the stakeholders lack comprehensive understanding of the features in feature modeling. Therefore, this paper proposes a model of the analyzing commonality and variability of the feature based on the Ontology. The proposed model in this paper suggests two approaches in order to solve the problems mentioned above: First, the model explicitly expresses the feature by making an individual feature attribute list based on the meta feature modeling to understand common feature. Second, the model projects an analysis model of commonality and variability using the semantic similarity between features based on the Ontology to the stakeholders. The main contribution of this paper is to improve the reusability of distinguished features on developing products of same line henceforth.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.269-277
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• 2012

The future battlefield environment has changed platform centric warfare into a network centric warfare. The NCO is a operational concept to improve combat power through information sharing, shared situational awareness, decision making and synchronized action based on powerful network grid. In addition, these operational environment is composed of physical, information, cognitive and social domains. The platform environment system is associated with the OSI 7 layer. However, OSI 7 layer is limited to express NCW environment including cognitive and social domains. Therefore, we requires a new model for expressing cognitive and social domains. After we developed a new model, this model applied to the NCW architecture taxonomy.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Journal of the Korean Society of Radiology
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• v.12 no.4
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• pp.505-510
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• 2018

In this study, we allocate bits by quantizing these fractal coefficients through a quantizer which can extract the probability distribution. In the coding process of IFS, a variable size block method is used to shorten the coding time and improve the compression ratio. In the future, it will be necessary to further improve the coding time and the compression rate while maintaining the best image quality in the fractal coding process.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.165-187
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• 2004

Constraint Programming in AS and Optimization in OR started and have grown in different backgrounds to solve common decision-making problems in real world. This paper tries to integrate results from those different fields by suggesting a hybrid solver as an integration framework. Starting with an integrating modeling language, a way to implement a hybrid solver will be discussed using Warren's abstract machine and an finite domain constraint programming solver structures. This paper will also propose some issues rising when implementing the hybrid solver and provide their solutions.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2018.06a
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• pp.127-127
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• 2018

열전변환기술은 폐열을 전기로 변환하는 제벡효과를 이용한 기술이다. 열전변환효율은 재료의 성능에 따라 결정되며 성능지수 $ZT=S^2{\sigma}T/k$로 표현할 수 있다. 여기서 S는 제벡계수, ${\sigma}$는 전기전도도, k는 열전도도, T는 절대온도이다. GeTe계 열전재료는 $200{\sim}500^{\circ}C$에서 쓰이는 중온용 열전재료이다. 높은 성능지수를 가지기 위해서는 파워펙터($S2{\sigma}$)의 향상과 열전도도의 감소가 필요하다. GeTe계 화합물은 Ge의 공공 때문에 높은 캐리어 농도를 가지게 되고, 이로 인해 낮은 제벡계수 값과 높은 열전도도를 가지게 된다. 따라서 GeTe계 화합물의 성능 향상을 위해서는 캐리어농도 제어가 필수적이다. TEM을 통하여 GeTe를 관찰하면 밝고 어두운 콘트라스트들이 형성되어 있는 헤링본구조를 확인 할 수 있다. 콘트라스트를 보여주는 작은 평행사변형 하나는 헤링본구조의 가장 작은 단위인 도메인이며 이 도메인들이 특정한 방향으로 배열되어 콜로니를 형성하고 콜로니들이 특정한 방향으로 배열되어 헤링본구조를 이룬다. 헤링본의 폭과 길이를 제어 할 수 있다면 GeTe계 화합물의 열전특성 향상에 영향을 미칠 수 있을 것으로 예상된다. 따라서 본 연구에서는 GeTe계 화합물내에 도핑원소 첨가를 통한 캐리어 농도제어와 도핑원소 첨가에 따른 헤링본구조의 변화에 관하여 연구하였다.

• Journal of Broadcast Engineering
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• v.4 no.2
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• pp.127-133
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• 1999

The conventional fractal image compression based on discrete wavelet transform uses the fixed block size in fractal coding and reduces PSNR at low bit rate. This paper proposes a fractal image coding based on discrete wavelet transform which improves PSNR by using variable block size in fractal coding. In the proposed method. the absolute values of discrete wavelet transform coefficients are computed. and the discrete wavelet transform coefficients of different highpass subbands corresponding to the same spatial block are assembled. and the fractal code for the range block of each range block level is assigned. and then a decision tree C. the set of choices among fractal coding. "0" encoding. and scalar quantization is generated and a set of scalar quantizers q is chosen. And then the wavelet coefficients. fractal codes. and the choice items in the decision tree are entropy coded by using an adaptive arithmetic coder. This proposed method improved PSNR at low bit rate and could achieve a blockless reconstructed image. As the results of experiment. the proposed method obtained better PSNR and higher compression ratio than the conventional fractal coding method and wavelet transform coding.rm coding.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.512-517
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• 2008

It is show that increasing of aged and handicapped people requires development of Ubiquitous computing technique to offer the specialized service for handicapped-people. For this, we need a development of Context Awareness and Service Reasoning Technique that the technique is supplied interaction between user and U-environment instead of the old unilateral relation. The old research of context awareness needed probabilistic presentation model like a Bayesian Network based on expert Systems for recognize given circumstance by a domain of uncertain real world. In this article, we define a domain of disorder activity assistant service application and context model based on ontology in diversified environment and minimized intervention of user and developer. By use this context model, we apply the structure learning of Bayesian Network and decide the service and activity to development of application service for handicapped people. Finally, we define the proper Conditional Probability Table of the structured Bayesian Network and if random situation is given to user, then present state variable of Activity and Service by given Causal relation of Bayesian Network based on Conditional Probability Table and it can be result of context awareness.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.47-66
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• 2008

This study is aimed at proposing a new type of ubiquitous decision support system (u-DSS) portal which is embedded with two important mechanisms like an intelligent ontology management module (i-OMM) and multi-agent coordination mechanism (MACM). The proposed portal provides timely decision support to the involved decision entities (represented as agents) by taking advantage of the two mechanisms embedded on the portal. The most important virtue of the proposed portal is that it can resolve two problems such as semantic discordance and data confliction which are occurring very often in an ubiquitous computing environment. Frequent requests of revising the already established decision information due to the rapid changes in decision entities' requirements require the extremely flexible and intelligent u-DSS vehicle like theproposed mechanism. In this sense, the i-OMM is designed to provide support to solving the semantic discordance in the way that the i-OMM virtually integrates ontology view (IOV) to integrate heterogeneous ontology among the agents engaged inubiquitous commerce situations. Then the i-OMM sends the IOV to the MACM to resolve the conflicts among the involved agents. The proposed u-DSS portal was applied to the u-fulfillment problem in which all the involved decisionagents need their own requirements to be satisfied seamlessly and timely. The experimental results show that the proposed u-DSS portal is very robust and promising in the field of u-DSS and context modeling.