• 한국실내디자인학회논문집
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• 제38호
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• pp.225-232
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• 2003

The purpose of this study is to select the optimal and minimum transmittance for visual amenity in office buildings. This study progressed as follows. The first, we select 5 films with various transmittance based on the current film makers whose films being used on the windows of office building nowadays and the case study of construction. And then we choose 6 kinds of transmittance as evaluation object including plain glass. The second, we made a mock-up models having different transmittance on the southern windows with model of real scale office building. The third, we select 17 pairs of adjectives for the evaluation of visual comfort on interior or exterior conditions with transmittance factor. The fourth, subjective evaluation experiment was done using selected evaluation adjectives and the result was analyzed. The results are as follows : the minimum transmittance appropriate for the office building is 30%∼40% and the optimal transmittance range is 40%∼60%.

• 한국통신학회논문지
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• 제28권9C호
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• pp.835-842
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• 2003

For an odd prime p and integer n, m and k such that n=(2m＋1)ㆍk, a new family of p-ary sequences of period p$^{n}$ -1 with optimal correlation property is constructed using the p-ary Helleseth-Gong sequences with ideal autocorrelation, where the size of the sequence family is p$^{n}$ . That is, the maximum nontrivial correlation value R$_{max}$ of all pairs of distinct sequences in the family does not exceed p$^{n}$ 2/ ＋1, which means the optimal correlation property in terms of Welch's lower bound. It is also derived that the linear span of the sequences in the family is (m＋2)ㆍn except for the m-sequence in the family.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.566-568
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• 1995

An optimal routing method using hysteresis binary neurons with link failure probability is proposed in this paper. The link failures in computer networks can degrade the performance of the entire networks. We assume the time between successive link failures is exponentially distributed with parameter ${\lambda}$ and the failures are independent. The link failure probability is used for neural networks to find the shortest paths of given source-destination pairs. By using the probability of link failures and hysteresis binary neurons we implement an optimal routing method that can takes routes by coping with link failures.

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

• Journal of Communications and Networks
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• 제18권4호
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• pp.629-638
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• 2016

In this paper, we investigate an energy-efficient resource allocation problem in a two-way relay (TWR) network consisting of multiple user pairs and an amplify-and-forward (AF) relay. As the users and relay have individual energy efficiencies (EE), we formulate a multi-objective optimization problem (MOOP). A single-objective optimization problem (SOOP) of the MOOP is introduced using a weighted-sum method, which achieves a single Pareto optimal point of the MOOP. To derive the algorithm for the SOOP, we propose a more tractable equivalent problem using the Karush-Kuhn-Tucker conditions of the SOOP, which guarantees convergence at the local optimal points. The proposed equivalent problem can be efficiently solved by the proposed iterative algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm in achieving the optimal EE in multi-user AF TWR networks.

• 한국정밀공학회지
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• 제15권9호
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• pp.101-110
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• 1998

In process planning for 3D pocket machining, the critical issues for the optimal process planning are the generation of cutting layers and the tool selection for each cutting layers as well as the other factors such as the determination of machining types, tool path, etc. This paper describes the optimal tool selection on a single cutting layer for 2D pocket machining, the generation of cutting layers for 3D pocket machining, the determination of the thickness of each cutting layers, the determination of the tool combinations for each cutting layers and also the development of an algorithm for determining the machining sequence which reduces the number of tool exchanges, which are based on the backward approach. The branch and bound method is applied to select the optimal tools for each cutting layer, and an algorithmic procedure is developed to determine the machining sequence consisting of the pairs of the cutting layers and cutting tools to be used in the same operation.

• 융합신호처리학회논문지
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• 제11권4호
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• pp.345-353
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• 2010

로봇의 대역적 자기 위치 추정에서의 일반적인 해법은 로봇의 자기 위치에서 복수의 가설을 생성하고, 관측된 랜드마크의 특정을 기초로 각 가설을 평가하여 가장 확실한 자기 위치를 구하는 것이다. 기존의 대표적인 방법인 ML이나 MCL은 랜드마크의 특징과 생성된 가설의 모든 조합을 평가하는 방법으로서 충분한 계산 자원에서는 최적의 방법이라 할 수 있다. 그러나, 일반적으로 계산량은 평가할 조합의 수에 비례하므로 다수의 조합이 존재하는 넓은 환경에서는 이러한 방법은 계산량이 아주 많아진다. 이러한 문제를 해결하기 위하여 본 논문에서는 확실하고 유망한 조합을 우선적으로 선택 평가하는 것으로, 계산시간을 효율적으로 이용하는 새로운 추정방법을 제안한다. 그 기본이 되는 방법으로는 RANSAC 알고리즘과 RANSAC 알고리즘의 효율화 방법인 Preemption scheme을 이용한다. 제안된 방법은 로봇이 관측할 때마다 계산량을 일정치 이하로 억제할 수가 있고, 또한 검증 실험에서 높은 추정 성능을 확인할 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.399-402
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• 2002

In this study, we constructed a four-channel impedance measurement system including a two-channel goniometer to analyze human arm movement. Impedances and joint angles were simultaneously measured for wrist and elbow movements. As the impedance changes resulting from wrist and elbow movements depended heavily on electrode placement, we determined the optimal electrode configurations for those movements by searching for high correlation coefficients, large impedance changes, and minimum interferences in ten subjects (age: 29+6). Our optimal electrode configurations showed very strong relationships between the wrist joint angle and forearm impedance (correlation coefficient = 0.95+0.04), and between the elbow joint angle and upper arm impedance (correlation coefficient = -0.98+0.02). Although the measured impedances changes of the wrist (1.1+1.5 ohm) and elbow (-5.0+2.9 ohm) varied among individuals, the reproducibilities of wrist and elbow impedance changes of five subjects were 5.8+1.8 % and 4.6+1.4 % for the optimal electrode pairs, respectively. We propose that this optimal electrode configuration would be useful for future studies involving the measurement of accurate arm movements by impedance method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.143-146
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• 1996

In this paper, we propose a hopfield model for solving the part-matching which is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and net total path of part-connection. Therefore, this kind of problem is referred to as a combinatorial optimization problem. First of all, we review the theoretical basis for hopfield model to optimization and present two method of part-matching; Traveling Salesman Problem (TSP) and Weighted Matching Problem (WMP). Finally, we show demonstration through computer simulation and analyzes the stability and feasibility of the generated solutions for the proposed connection methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.149.5-149
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...