• IE interfaces
• /
• v.6 no.2
• /
• pp.151-169
• /
• 1993

In this research, we proposed "coefficient of inequality" as a measure of distributional inequality for an alternative, which is defined as the area between the diagonal line from 0 to 1 and the Lorenz curve of the given alternative. Next, we showed theoretical relationship between stochastic dominance and the coefficient of inequality as a means to determine the preferred alternative when decision is made with incomplete information about decision maker's utility function. Then, two experiments were performed to test subject‘s attitude toward risk. The results of the experiments support the idea that when a decision maker is risk averse or risk prone, he/she can use the coefficient of inequality as a decision rule to choose the preferred alternative instead of using stochastic dominance. Thus, according to decision maker’s attitude toward risk, the decision rule proposed here can be used as a valuable aid in decision making under uncertainty with incomplete information.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2007.11a
• /
• pp.357-361
• /
• 2007

This paper presents a multi-robot localization based on Bayesian Multidimensional Scaling (BMDS). We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr${\ddot{o}}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2014.01a
• /
• pp.125-126
• /
• 2014

센서 스트림 데이터는 센서 네트워크를 통해 수집되는 데이터로 실시간 처리를 요구하며, 연속적으로 끊임없이 발생하는 스트림 데이터이다. 이러한 스트림 데이터는 양이 방대하여 이를 저장하기가 매우 어려우며, 동시에 데이터를 검색하는 데에는 많은 시간이 소요된다. 본 논문에서는 센서 네트워크에서의 효율적인 스트림 데이터 처리 시스템을 제안한다. 이 시스템은 캐시테이블을 사용함으로써 데이터베이스에 최소화된 접근으로 데이터 스트림 관리 시스템의 성능을 개선하였다. 그리고 센서 네트워크에서 읽어 들여온 불완전 데이터를 효율적으로 정제하고 상위 단계로 전송한다.

• Proceedings of the Korea Society of Costume Conference
• /
• 2005.10a
• /
• pp.190-191
• /
• 2005

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.2
• /
• pp.386-393
• /
• 2004

In order to maintain load balancing in distributed systems in a decentralized manner, every node should obtain workload information from all the nodes on the network. It requires $Ο({v^2})$ traffic overheads, where v is the number of nodes. This paper presents a new synchronous dynamic distributed load balancing algorithm for a ( v,k+1,1)-configured network topology, which is a kind of 2k regular graph, based on symmetric balanced incomplete block design, where v equals ${k^2}+k+1$. Our algorithm needs only Ο(v√v) message overheads and each node receives workload information from all the nodes without redundancy. And load balancing in this algorithm is maintained so that every link has same amount of traffic by √v for transferring workload information.

• Journal of the Korea Convergence Society
• /
• v.10 no.11
• /
• pp.149-159
• /
• 2019

Six sigma is an innovative management movement which provides improved business process by adapting the paradigm and the trend of market and customers. Suitable selection of six sigma project could highly reduce the costs, improve the quality, and enhance the customer satisfaction. There are existing studies on the selection of Six Sigma projects, but few studies have been conducted to select the correct project under an incomplete information environment. The purpose of this study is to propose the application of integrated MCDM techniques for correct project selection under incomplete information. The project selection process of six sigma involves four steps as follows: 1) determination of project selection criteria 2) calculation of relative importance of team member's competencies 3) assessment with project preference scale 4) finalization of ranking the projects. This study proposes the combination methods by applying group fuzzy Analytical Hierarchy Process (AHP), an easy defuzzified number of Trapezoidal Fuzzy Number (TrFN) and Grey Relational Analysis (GRA). Both of the weight of project selection criteria and the relative importance of team member's competencies can be evaluated by group fuzzy AHP. Project preferences are assessed by easy defuzzified scale of TrFN in case of incomplete information.)

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.151-158
• /
• 2015

This paper discusses how to use projections for the analysis of data from balanced incomplete block designs. A model is suggested as a matrix form for the interblock analysis. A second set of treatment effects can be found by projections from the suggested interblock model. The variance and covariance matrix of two estimated vectors of treatment effects is derived. The uncorrelation of two estimated vectors can be verified from their covaraince structure. The fitting constants method is employed for the calculation of block sum of squares adjusted for treatment effects.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.355-361
• /
• 1999

An application of case-based reasoning is proposed to build an influence diagram for identifying successful new ventures. The decision to invest in new ventures in characterized by incomplete information and uncertainty, where some measures of firm performance are quantitative, while some others are substituted by qualitative indicators. Influence diagrams are used as a model for representing investment decision problems based on incomplete and uncertain information from a variety of sources. The building of influence diagrams needs much time and efforts and the resulting model such as a decision model is applicable to only one specific problem. However, some prior knowledge from the experience to build decision model can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem solving experience to solve a new decision. In this paper, we suggest a case-based reasoning approach to build an influence diagram for the class of investment decision problems. This is composed of a retrieval procedure and an adaptation procedure. The retrieval procedure use two suggested measures, the fitting ratio and the garbage ratio. An adaptation procedure is based on a decision-analytic knowledge and decision participants knowledge. Each step of procedure is explained step by step, and it is applied to the investment decision problem in new ventures.

• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.31-34
• /
• 2000

This paper proposes a reconstruction scheme for overlapping characters in Thai printed document. Overlapping characters are characters that overlap with surrounding characters. The problem of overlapping characters is still an unsolved problem In commercially available software of Thai character recognition systems. The algorithm of reconstruction scheme is based on structural analysis of overlapping Thai printed characters. It consists of 2 steps: overlapping point determination and reconstruction of segmented characters. The overlapping point is defined as the intersection point between characters and can be determined by using templates. Then, an overlapping character is separated into segments at the intersection point. The structure of each segment may be an incomplete character and is not identical to the original one. Therefore, the reconstruction process is employed to add the incomplete part of these segments. The proposed scheme has been implemented and tested with 70 patterns of conventionally found in overlapping printed Thai characters with different typefaces and type sizes. The experimental results show that the proposed scheme can segment and reconstruct overlapping characters correctly. The proposed scheme can improve the recognition rate of commercially available software, ThaiOCR1.5 and ArnThai1.0, more than 60 percents

• Journal of KIISE:Computer Systems and Theory
• /
• v.33 no.12
• /
• pp.885-893
• /
• 2006

The Bayesian network(BN) has emerged in recent years as a powerful technique for handling uncertainty iii complex domains. Parameter learning of BN to find the most proper network from given data set has been investigated to decrease the time and effort for designing BN. Off-line learning needs much time and effort to gather the enough data and since there are uncertainties in real world, it is hard to get the complete data. In this paper, we propose an online learning method of Bayesian network parameters from incomplete data. It provides higher flexibility through learning from incomplete data and higher adaptability on environments through online learning. The results of comparison with Voting EM algorithm proposed by Cohen at el. confirm that the proposed method has the same performance in complete data set and higher performance in incomplete data set, comparing with Voting EM algorithm.