• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.317-320
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• 2005

본 논문은 비선형 시스템의 퍼지모델을 설계하기 위해 데이터 입자 기반 퍼지 집합 퍼지 모델의 최적 동정을 제안한다. 퍼지모델은 주로 경험적 방법에 의해 추출되기 때문에 보다 구체적이고 체계적인 방법에 의한 동정 및 최적화 될 필요성이 요구된다. HCM 클러스터링을 통한 데이터 입자는 입력 변수의 개별적인 퍼지 규칙을 형성하고, 퍼지 공간 분할 및 삼각형 멤버쉽 함수의 초기 정점을 정의한다. 또한, 데이터 입자의 중심을 이용하여 후반부의 구조를 결정한다. 초기 퍼지 모델을 동정하기 위해 유전자 알고리즘을 이용하여 입력 변수의 수, 선택될 입력 변수, 멤버쉽 함수의 수, 그리고 후반부 형태를 결정한다. 데이터 입자에 의한 전반부 멤버쉽 파라미터는 유전자 알고리즘을 이용하여 최적으로 동정한다 제안된 모델을 평가하기 위해 수치적인 예를 사용한다.

• Science of Emotion and Sensibility
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• v.7 no.2
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• pp.149-155
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• 2004

This research focus on analyzing the quality of clothing product in view of consumer's sensibility trend with a emphasis on measuring objective quality of pruduct. The fuzzy logic-based multi attribute model has been developed in order to evaluate the quality of clothing product. The overall quality of a clothing products can be divided into two distinct terms, product quality and brand value. Those two values are further analyzed with a relation to human sensibility.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.571-573
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• 2022

최근 금융기관에서는 축적된 금융 빅데이터를 활용하여 차별화된 서비스를 강화하고 있다. 기업고객에 투자하기 위해서는 보다 정밀한 기업분석이 필요하다. 본 연구는 대만기업 6,819개의 95개 재무데이터를 가지고, 비대칭 데이터 문제해결, 데이터 표준화 등 데이터 전처리 작업을 하였다. 해당 데이터는 로지스틱 회기, SVM, K-NN, 나이브 베이즈, 의사결정나무, 랜덤포레스트 등 9가지 분류모델에 5겹 교차검증을 적용하여 학습한 후 모델 성능을 비교하였다. 이 중에서 성능이 가장 우수한 분류모델을 선택하여 예측 결정 이유를 판단하고자 설명 가능한 인공지능(XAI)을 적용하여 예측 결과에 대한 설명을 부여하여 이를 분석하였다. 본 연구를 통해 데이터 전처리에서부터 모델 예측 결과 설명에 이르는 분류예측모델의 전주기를 자동화하는 시스템을 제시하고자 한다.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.429-436
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• 2013

Context aware environment u-health service is to provide health service with recognition of a computer. The computer recognizes that a patient can contact real life in many context. Context aware environment service for recommend have to definition of context data and service recommendations related to factors shall be identified. In this paper, Context aware environment of u-health service will be provide context data related to identifies recommendations factors using multivariate analysis method and recommendations factors creation to decision tree, association rule based decision model. health service recommend for significantly context data can be distinguish through recommendation factors of identify. Also, context data of patient can know preference factors through preference decision model.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.147-153
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• 2017

The Korean government has implemented various policies to introduce the cloud to the public sector. The objectives of the paper are to develop a decision-making model and to propose the roadmap for cloud introduction in the public sector. To achieve these objectives, we analyze the characteristics of public services and types of cloud service. Then we develope a cloud introduction method using fuzzy AHP based convergence decision-making model. As a result of this study, we decided to prioritize the cloud service candidates and proposed a three-step roadmap. The results are expected to contribute to cloud introduction and transition in the public sector and establishment of the cloud policy. In the future, it will be necessary to develop budget plans as well as additional decision-making factors for cloud adoption.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.449-459
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• 2021

More than 80 % of agricultural reservoirs are old facilities over 50 years old, and safety and function declines occur. As a result, safety accidents such as the collapse of the reservoir have occurred. Precise safety diagnosis is conducted in advance to prevent accidents such as reservoir collapse, and Water resources facility improvement project are implemented based on priority. However, the priority of the business is selected based on the subjective judgment of the facility manager. In this study, we set 80 hypotheses based on the results of precision safety diagnosis and decision-making examples of existing Water resources facility improvement project and selected 45 variables using correlation analysis and significance test. Using logistic regression analysis, the final 21 variables were selected and a decision support model was presented, and the classification accuracy of the model was 86.8 %. In this research, the part that presented the quantitative index for decision support when selecting the Water resources facility improvement project has important significance.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.369-377
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• 2010

In vocabulary recognition systems based on HMM(Hidden Markov Model)s, training process unseen model bring on show a low recognition rate. If recognition vocabulary modify and make an addition then recreated modeling of executed database collected and training sequence on account of bring on additional expenses and take more time. This study suggest efficient context dependent process modeling method using decision tree-based state tying. On study suggest method is reduce recreated of model and it's offered that robustness and accuracy of context dependent acoustic modeling. Also reduce amount of model and offered training process unseen model as concerns context dependent a likely phoneme model has been used unseen model solve the matter. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.38%.

• Korean Journal of Construction Engineering and Management
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• v.10 no.5
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• pp.135-147
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• 2009

The world has witnessed the dramatic expansion of international construction markets during the last decades, particularly around the developing economies and energy resource-rich countries. However, despite the booming markets, the risks of emerging regions have also increased under the rapidly changing environments confronting the global contractors. Most of all, success in overseas business mainly depends on selecting the right market to enter. Accordingly, the right market selection requires global firms to carefully carry out the scientific market entry decision by evaluating country risks, market prospects, firm's capability, level of competition, and among others. This study aims at developing a market entry model by the use of real option analysis (ROA) and scenario planning, which addresses the corporate strategic flexibility against the uncertainties encompassing the overseas construction markets. Based on the suggested approach, global contractors are expected to make a better decision rather than a typically static approach in pursuing, postponing, or abandoning a prospective market to their capacity with a concurrent consideration of uncertainties as well as its option value.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3C
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• pp.115-125
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• 2009

The preconsolidation pressure has been commonly determined by oedometer test. However, it can also be determined by insitu test, such as piezocone test with theoretical and(or) empirical correlations. Recently, Neural Network (NN) theory was applied and some models were proposed to estimate the preconsolidation pressure or OCR. It was already found that NN model can come over the site dependency and prediction accuracy is greatly improved when compared with present theoretical and empirical models. However, since the optimization process of synaptic weights of NN model is dependent on the initial synaptic weights, NN models which are trained with different initial weights can't avoid the variability on prediction result for new database even though they have same structure and use same transfer function. In this study, Committee Neural Network (CNN) model is proposed to improve the initial weight dependency of multi-layered neural network model on the prediction of preconsolidation pressure of soft clay from piezocone test result. Prediction results of CNN model are compared with those of conventional empirical and theoretical models and multi-layered neural network model, which has the optimized structure. It was found that even though the NN model has the optimized structure for given training data set, it still has the initial weight dependency, while the proposed CNN model can improve the initial weight dependency of the NN model and provide a consistent and precise inference result than existing NN models.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.73-80
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• 2013

In this paper, an experimental circuit model for an accurate high-frequency characterization of transmission line is proposed. Inherent resonance effects during measurements make it difficult to determine characteristic impedance and propagation constant at the resonance frequencies corresponding to the line length. Thus, resonance-effect-free transmission line parameter determination technique based on the physical insight and theory is proposed. Then, by using the parameters high-frequency circuit model is proposed for high-speed signal propagation characterization. The proposed frequency-variant transmission line model is verified with measurement and it can be usefully exploited in high-speed signal propagation characterization.