• 한국정보처리학회논문지
• /
• 제6권9호
• /
• pp.2332-2342
• /
• 1999

멀티캐스트는 중요한 시스템 레벨의 그룹 프로세스들을 수반하는 통신 서비스의 한 클래스이다. 소프트웨어 멀티캐스트 알고리즘을 설계하는데 있어서의 주된 문제는 성능과 이식성 사이의 교환조건(trade-off)을 고려하는 것이다. 본 논문에서 제안하는 변수화 된 통신 모델은 LogP 모델의 확장으로 병렬 플랫폼의 통신 네트워크를 더 정확하게 특성화 할 수 있다. 이 변수화 된 모델에서, 컴퓨터 구조에 의존적이지 않고 이식성 있는 OPT-tree라는 최적의 멀티캐스트를 형성하는 알고리즘을 제안한다. 실제 여러 네트워크에 구현했을 때 진정한 최적의 수행을 달성하기 위해서 OPT-tree로 생성된 트리에서의 네트워크 위상에 따른 튜닝(tuning)에 대해 연구한다. 특히 웜홀 스위치를 사용하는 메쉬(mesh) 네트워크에서 변수화 된 멀티캐스트 알고리즘의 최적화 한 버전인 OPT-mesh 알고리즘을 개발하여 다른 알고리즘들과 비교하여 그 우수성을 검증한다.

• 대한인간공학회지
• /
• 제15권2호
• /
• pp.157-163
• /
• 1996

A single line display menu (SDM) is widely used for the user interface of many electronic consumer products, and the designers need useful guidelines applicable to the SDM. In many studies on menus, major focus has been placed on the optimal menu structure, but only a few standard menu structures, such as $64^{1},8^{2},4^{3}$,and $2^{6}$ are usually tested for optimality. In many cases, however, ill defined or asymmetric structures are suggested as design alternatives. To determine the optimal menu structure, user performance should be obtained in terms of quantitative measures. Hence, a model is needed to provide a predicted value of user performance for a given menu structure. Altough several models have been proposed for ordinary menus, none is available for the SDM yet. To solve this problem a performance model was developed in this study using the neural network approach. This model is capable of providing quantitative measures of human performance for any menu structures without conducting additional experiments, which will save much time and reduce the design cost.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2000년도 추계학술대회 논문집
• /
• pp.299-302
• /
• 2000

Various types of measuring devices are used for reverse engineering and inspection in different fields of industry such as automotive, aerospace, computer graphics, and home appliance. In order to measure a part easily and efficiently, it is important to select appropriate measuring device considering the characteristics of each measuring machine and part information. In this research, an optimal measuring device selection system using neural networks is proposed. There are two major steps: Firstly, the measuring information such as curvature, normal, type of surface, edge, and facet approximation is extracted from the CAD model. Second, the best suitable measuring device is proposed using the neural network system based on the knowledge of the measuring parameters and the measuring resources. An example of machine selection is implemented to evaluate the performance of the system.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
• /
• pp.972-974
• /
• 1993

This paper presents a new method to automatically design fuzzy logic controller(FLC). The main problems of designing FLC are how to optimally and automatically select the control rules and the parameters of membership function (MF). Cell state space algorithms (CSS), differential competitive learning (DCL) and multialyer neural network are combined in this paper to solve the problems. When the dynamical model of a control process is known. CSS can be used to generate a group of optimal input output pairs(X, Y) used by a controller. The(X, Y) then can be used to determine the FLC rules by DCL and to determine the optimal parameters of MF by DCL and to determine the optimal parameters of MF by multilayer neural network trained by BP algorithm.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2023년도 춘계학술대회
• /
• pp.256-257
• /
• 2023

This paper aims to propose optimal method to assess and cumulate the daily profit for liner shipping to support the shipping lines in making optimal decision with the highest average daily profit. This paper not only explains the actual calculated results align with decision-makers' behavior from concepts indicated in cumulative prospect theory but also contributes to an easy-to-apply method for liner shipping network predictability in and provides optimal decision-making is helpful for shipping managers for the best effective selection of the most appropriate alternative under uncertainties.

• 한국정보처리학회논문지
• /
• 제4권9호
• /
• pp.2309-2314
• /
• 1997

본 연구는 보험 회사의 파산 예측을 위하여 신경회로망이 사용되는데 이를 최적화하기 위하여 유전자 알고리즘이 사용된다. 유전자 알고리즘은 최적의 네트워크 구조와 매개변수들을 제시해 준다. 유전자 알고리즘에 의해 설계된 신경회로망은 파산 예측을 함에 있어 discriminant analysis, logistic regression, ID3, CART 등과 비교되는데 가장 좋은 성능을 보여준다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2003년도 통신소사이어티 추계학술대회논문집
• /
• pp.140-144
• /
• 2003

A sensor network consists of many low-cost, low-power, and multi-functional sensor nodes. One of most important issues in of sensor networks is to increase network lifetime, and there have been researches on the problem. In this paper, we propose a routing mechanism to prolong network lifetime, in which each node adjusts its transmission power to send data to its neighbors. We model the energy efficient routing with power control and present an algorithm to obtain the optimal flow solution for maximum network lifetime. Then, we derive an upper bound on the network lifetime for specific network topologies.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
• /
• pp.2876-2878
• /
• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimal Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. The study is illustrated with the ACI Distance Relay Data for application to power systems.

• 한국정보통신학회논문지
• /
• 제11권2호
• /
• pp.430-435
• /
• 2007

본 논문에서는 센서 네트워크의 원활한 전송을 위해 SOFM을 이용한 센서 네트워크의 지능적인 배치를 제안한다. 제안한 방법은 무선 채널 분석을 통해서 센서 노드 사이의 통신이 가능한 거리를 구하고, 신경회로망의 SOFM(Self-Organizing Feature Map)방식을 이용하여 지능적으로 최적의 센서 노드의 개수와 센서 노드가 배치할 최적 위치를 결정한다. Log-normal path loss 모델을 이용하여 거리에 따른 PRR(Packet Reception Rate)을 구하고, 이것으로부터 센서 노드의 통신 범위를 결정한다. 제안한 방식의 유용성을 확인하기 위하여 센서 노드의 지능적인 위치 탐색과 센서 네트워크의 연결 상태에 대한 시뮬레이션을 수행하였다.

• 한국IT서비스학회지
• /
• 제13권3호
• /
• pp.201-219
• /
• 2014

This paper considers service deployment and priority optimization for multiple service-oriented applications sharing reusable services, which are deployed as multiple instances in the cloud. In order to handle variations in the workloads of the multiple applications, service instances of the individual reusable services are dynamically provisioned in the cloud. Also service priorities for each application in a particular reusable service are dynamically adjusted. In this paper, we propose an analytic performance model, based on a queueing network model, to predict the expected sojourn times of multiple service-oriented applications, given the number of service instances and priority disciplines in individual reusable services. We also propose a simple heuristic algorithm to search an optimal number of service instances in the cloud and service priority disciplines for each application in individual reusable services. A numerical example is also presented to demonstrate the applicability of the proposed performance model and algorithm to the proposed optimal decision problem.