• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1217-1228
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• 2005

Recently, since DNA computing has been widely studied for various applications, DNA sequence design which is the most basic and important step for DNA computing has been highlighted. In previous works, DNA sequence design has been formulated as a multi-objective optimization task, and solved by elitist non-dominated sorting genetic algorithm (NSGA-II). However, NSGA-II needed lots of computational time. Therefore, we use an $\varepsilon$- multiobjective evolutionarv algorithm ($\varepsilon$-MOEA) to overcome the drawbacks of NSGA-II in this paper. To compare the performance of two algorithms in detail, we apply both algorithms to the DTLZ2 benchmark function. $\varepsilon$-MOEA outperformed NSGA-II in both convergence and diversity, $70\%$ and $73\%$ respectively. Especially, $\varepsilon$-MOEA finds optimal solutions using small computational time. Based on these results, we redesign the DNA sequences generated by the previous DNA sequence design tools and the DNA sequences for the 7-travelling salesman problem (TSP). The experimental results show that $\varepsilon$-MOEA outperforms the most cases. Especially, for 7-TSP, $\varepsilon$-MOEA achieves the comparative results two tines faster while finding $22\%$ improved diversity and $92\%$ improved convergence in final solutions using the same time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1765-1772
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• 2015

In the underwater acoustic communication channels, multipath reflection become the cause of obstacle. Generally, equalizer has been applied to overcome these problems. In this paper, the method was proposed to optimize tap-length of decision feedback equalizer using genetic algorithm. After inputting feed-forward filter length and feed-back filter length as genetic information of the genetic algorithm, it optimize tap-length using BER(bit error rate) calculation in accordance with object function. The object function consist of decision feedback equalizer and BER calculation. For the purpose of BER calculation in the object function, the method was proposed to optimize the tap-length of decision feedback equalizer with genetic algorithm using preamble signals. As a result of experiments, the optimized BER is 0.0355 for signals which were received through a 25m receiver and which were applied to calculate BER merely using preamble signals in object function. When all data were used to calculate BER in object function, the optimized BER is 0.0215.

• Journal of the Korean Magnetics Society
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• v.7 no.2
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• pp.103-108
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• 1997

In this paper, the application of fuzzy decision to optimization of induction motor design is proposed. This method can reflect the designer's experience, view, and judgment, but also can be applied to multi-objective optimization design easily. The electromagnetic performance of the induction motor are calculated by means of the equivalent magnetic circuit method. The design method is The $D^2L$ method which is combined with fuzzy decision and optimization algorithm. As the optimization algorithm, the evolution strategy(ES) is applied. The proposed algorithm is applied to a multiobjective optimization of an induction motor design where the motor should have less weight and, at the same time, have higher efficiency and power factor at rated operating points.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.10a
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• pp.153-157
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• 1997

The investigation deals with the process design in cold rolling mill of electric plant. In this study, multiple objective optimization is conducted by a genetic algorithm, where the fitness values are evaluated on the basis of one - dimensional model of flat rolling. The approach is applied to the determination of the process conditions which are optimal with regard to minimization of roll power and maximization of productivity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1022-1029
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• 2020

We investigate the joint selection problem of the transmit power level and the best access point for multi-access points. We further reduce the transmit power by jointly optimizing the transmit power and the access point selection. Our aim is to minimize the total transmit power, while each terminal maintains minimum signal to interference ratio requirement. We observe that the optimum solution can be achieved through proposed iterative algorithm for both TA link and AT link. Simulation results show that proposed algorithm (joint optimization of transmit power level and access point) outperforms the algorithm which optimizes the transmit power only. We also observe that the duality between the TA link and AT link does not hold in multi-access points environment. Accordingly, the resulting power vectors and the access point vectors for TA link and AT link are different in general.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.69-78
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• 2015

Recently, research on the multi-agent system is developed actively in the various fields, especially on the control of complex system and optimization. In this study, we develop a multi-agent system for NPC simulation in game environment. The purpose of the development is to support quick and precise decision by inferencing the situation of the dynamic discrete domain, and to support an optimization process of the agent system. Our approach employed Petri-net as a basic agent model to simplify structure of the system, and used fuzzy inference engine to support decision making in various situation. Our experimentation describes situation of the virtual battlefield between the NPCs, which are divided two groups, such as fuzzy rule based agent and automata based agent. We calculate the percentage of winning and survival rate from the several simulations, and the result describes that the fuzzy rule based agent showed better performance than the automata based agent.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.100-107
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• 1999

무인 운반차량 시스템 (AGV System) 의 이용도가 날로 증가함에 따라 시스템의 최적화 및 운영 방법에 관한 많은 연구가 진행되고 있다. 이에 본 연구에서는 AGV System에서 사용하는 Routing 및 Scheduling 정책들을 연구하고 이를 개선할 수 있는 새로운 방안을 모색한 후, 컴퓨터 모델링 기법을 이용한 보다 객관적인 시뮬레이션을 수행하여 최적의 AGV System과 그에 적합한 운영 정책을 제시하는데 그 목적이 있다. 따라서 본 논문은 크게 AGV Routing 과 Scheduling에 관한 연구로 나누어진다. AGV Routing은 AGV의 이동경로를 설정하는 것으로서 충돌 방지 (Collision Avoidance)와 최단경로 탐색 (Minimal Cost Path Find) 이라는 두 개의 주요 알고리즘으로 이루어진다. AGV Scheduling 은 장비의 공정시간과 AGV의 Loading/Unloading, Charging 시간으로 인해 불규칙한 Event 가 일어났을 경우 AGV 각각의 Jop을 알맞게 선정해주는 정책을 말하며 일반적으로 AGV Selection Rule, Charging Rule이 여기에 속한다. 본 연구에서는 이러한 알고리즘들이 반영된 AGV System을 컴퓨터 모델로 구축하여, 기존 시스템에서 사용되고 있는 여러 운영 정책들의 문제점들을 분석하였으며, Multiple AGV System을 최적화 시키는 운영 정책이 보다 객관적으로 제시되었다.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.826-833
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• 2010

Electric-thermal-structural actuated compliant mechanisms are mechanisms onto which electric voltage drop is applied as input instead of force. This mechanism is based on thermal expansion of material while being heated. Compliant mechanisms are designed subjected to electric charge input using BESO(bi-directional evolutionary structural optimization) method. Reliability-based topology optimization (RBTO) is applied to the topology design of actuators. performance measure approach (PMA), which has probabilistic constraints that are formulated in terms of the reliability index, is adopted to evaluate the probabilistic constraints. In this study, BESO method is used to obtain optimal topology of compliant mechanisms from initial design domain. PMA approach is used to evaluate reliability index. The procedure has been tested in numerical applications and compared with the results obtained by other methods to validate these approaches.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.4
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• pp.7-15
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• 2010

This study proposes a rad o resource management scheme to maximize the performance of the LTE(Long Term Evolution) uplink, using SC-FDMA(Single Carrier-Frequency Division Multiple Access). Rather than the single-cell SC-FDMA system the existing studies are mainly concerning, this study focuses on multi-cell system which needs considering the interaction among cells. Radio resource management is divided into two phases, planning and operation phases. The former is for the master eNB(e-NodeB) to allocate RBs(radio bearer) to eNB, the latter for eNB to assign RBs to the mobiles in the cell. For each phase, an optimization model and greedy algorithm are proposed. Optimization models aim to maximize the system performance while satisfying the constraints for both QoS and RB continuity. The greedy algorithms, like generic ones, move from a solution to a neighboring one having the best objective value among neighboring ones. From the numerous numerical experiments, the performance and characteristics of the algorithms are analyzed. This study is expected to play a volunteering role in radio resource management for the multi-cell SC-FDMA system.