• 한국수자원학회논문집
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• 제40권9호
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• pp.687-696
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• 2007

본 연구는 다목적 유전자알고리즘을 이용하여 Tank 모형의 매개변수를 추정하는데 있어서 선호적순서화(preference ordering)를 적용한 연구로써, 목적함수의 개수가 여러 개인 경우에 발생할 수 있는 파레토최적화의 단점을 해결하기 위한 것이다. 최적화를 위한 목적함수는 모두 4가지를 사용하였으며, 선호적순서화를 통해서 구한 2차 효율성(2nd order efficiency)을 가지면서 정도(degree)가 3인 4개의 해 중에서 1개의 해만을 최우선해로 선정하였다. NSGA-II로 도출된 최우선해의 적합성을 살펴보기 위해서, 자동보정방법인 Powell 방법과 SGA(simple genetic algorithm)를 매개변수 자동보정 방법으로 이용하고 하나의 단일목적함수로 사용해서 최적화한 결과와 비교해보았으며, 비교결과 다목적 유전자 알고리즘을 4개의 목적함수에 모두 적용해서 한번에 도출된 매개변수를 이용한 결과가 보정기간뿐만 아니라 검정기간에 대해서도 비교적 양호한 결과를 나타내는 것으로 나타났다.

• Management Science and Financial Engineering
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• 제10권1호
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• pp.25-41
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• 2004

There have been many studies to build a model that can help investors construct optimal portfolio. Most of the previous models, however, are based upon the path-breaking Markowitz model (1959) which is a quantitative model. One of the most important problems with that kind of quantitative model is that, in reality, most of the investors use not only quantitative, but also qualitative information when they select their optimal portfolio. Since collecting both types of information from the markets are time consuming and expensive, making a set of target assets smaller, without suffering heavy loss in the rate of return, would attract investors. To extract only desired assets among all available assets, we need knowledge that identifies investors' preference for the risk of the assets. This study suggests two-layer decision-making rules capable of identifying an investor's risk preference and an architecture applying them to a quantitative portfolio model based on risk and expected return. Our knowledge-based portfolio system is to build an investor's preference-oriented portfolio. The empirical tests using the data from Korean capital markets show the results that our model contributes significantly to the construction of a better portfolio in the perspective of an investor's benefit/cost ratio than that produced by the existing portfolio models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• 응용통계연구
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• 제14권2호
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• pp.305-320
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• 2001

다차원선호분석(mutidimensional preference analysis)은 여러 상품들에 대한 개인(또는 그룹)의 선호도를 알아보기 위한 분석방법으로 결과는 보통 2차원 그림으로 제공된다. 본 연구에서는 의미있는 두 가지 최적척도 기준을 제안하고 이와 연관된 행 및 열표시자를 유도하고 있으며, 아울러 사전지식을 반영하기 위해 부분수량화를 다차원선호분석에 도입하는 방법을 제시한다. 또한 본 연구에서 제시한 다차원분석기법들을 실제 인터넷 검색엔진에 대한 선호도 자료에 적용한다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.229-236
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• 2004

This article presents a multiple comparison ranking procedure for several products of the Poisson rates. A preference probability matrix that warrants the optimal comparison ranking is introduced. Using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. Necessary theory and two illustrative examples are provided.

• 한국조리학회지
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• 제17권3호
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• pp.221-235
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• 2011

본 연구는 허브 추출물로 마리네이드 한 고등어의 품질 특성에 대하여 알아보고자 하였다. 마늘, 생강, 바질의 첨가량을 달리하여 최적의 첨가량을 알아보았다. 실험 결과, 마늘 3%, 생강 3%, 바질 2%의 기호도가 가장 높아 최적의 첨가량으로 도출하였다. 최적의 첨가량인 마늘 3%, 생강 3%, 바질 2%를 첨가하여 마리네이드 한 고등어의 pH 변화는 적색육 어류의 초기 부패점인 pH 6.2-6.4의 범위 안에 들어가 제품 품질 특성에 적합하였으며 특성차이검사에서 견고성은 GA3이 가장 낮아 부드러웠고, 탄력성도 GA3이 가장 좋았으며 촉촉함 또한 GA3 이 촉촉하여 생강, 바질 보다는 마늘이 더 좋은 것으로 나타났다. 기호도 검사에서 외관, 향, 절감, 맛 모두 GA3를 가장 선호하였다. 이상의 실험 결과를 볼 때 GA3(마늘 3%)이 최적의 첨가량으로 보아진다.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.135-141
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• 2019

Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user-item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user-item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user-item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user-item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.237-245
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• 2003

MDS(multi-dimensional scaling) for preference data is a graphical tool which usually figures out how consumers recognize, evaluate certain products. This article is mainly concerned with an optimal scaling for MDS when target configuration is available. Rotation of axis and SUR(seemingly unrelated regression) methods are employed to get a new configuration which is obtained as close to the target as we can. Methodologies developed here are also illustrated via a real data set.

• 정보기술과데이타베이스저널
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• 제7권1호
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• pp.42-53
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• 2000

The major goal of this research is to develop an optimal route search algorithm for an intelligent route guidance system, one sub-area of ITS. ITS stands for intelligent Transportation System. ITS offers a fundamental solution to various issues concerning transportation and it will eventually help comfortable and swift moves of drivers by receiving and transmitting information on humans, roads and automobiles. Genetic algorithm, and fuzzy logic are utilized in order to implement the proposed algorithm. Using genetic algorithm, the proposed algorithm searches shortest routes in terms of travel time in consideration of stochastic traffic volume, diverse turn constraints, etc. Then using fuzzy logic, it selects driver-preference optimal route among the candidate routes searched by GA, taking into account various driver's preferences such as difficulty degree of driving and surrounding scenery of road, etc. In order to evaluate this algorithm, a virtual road-traffic network DB with various road attributes is simulated, where the suggested algorithm promptly produces the best route for a driver with reference to his or her preferences.

• 한국경영과학회지
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• 제20권2호
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• pp.61-76
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• 1995

The "optimal" solution for a decision making problem should be the one that best reflects the decision-maker's preference. For MADM (Multi-Attribute Decision-Making) problems, however, finding an optimal solution is difficult, especially when the number of alternatives, or that of attributes is relatively large. Most of the existing mathematical approaches arrive at a final solution on the basis of many unrealistic assumptions, without reflecting the decision-maker's preference structure exactly. To remedy this, some interactive methods have been proposed, but most of them require a large amount of information growing exponentially as the number of alternatives, or that of attributes increases. Therefore it is difficult for the decision-maker to maintain consistency throughout the decision making process. In this paper, an interactive method which finds optimal solutions for deterministic MADM problems with many attributes and alternatives is proposed. Instead of considering all the attributes simultaneously, this method partitions all the attributes into several mutually independent subgroups and considers one of them at each of preordered steps, where the alternatives are eliminated until the optimal one is obtained. The efficiency of the method lies in the fact that the amount of neccessary information is reduced significantly, and even further if a suboptimal solution is acceptable to the decision-maker.ion-maker.