• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.12-14
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• 2005

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) by means of genetically optimized fuzzy polynomial neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms(GAs). The conventional SOFPNNs hinges on an extended Group Method of Data Handling(GMDH) and exploits a fixed fuzzy inference type in each FPN of the SOFPNN as well as considers a fixed number of input nodes located in each layer. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, a collection of the specific subset of input variables, and the number of membership function) and addresses specific aspects of parametric optimization. Therefore, the proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series).

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• 제어로봇시스템학회논문지
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• 제19권5호
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• pp.429-434
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• 2013

This paper proposes a CPSO (Cooperative Particle Swarm Optimization)-based MPC (Model Predictive Control) scheme to deal with formation control problem of multiple nonholonomic mobile robots. In a distributed MPC framework, each robot needs to optimize control input sequence over a finite prediction horizon considering control inputs of the other robots where their cost functions are coupled by the state variables of the neighboring robots. In order to optimize the control input sequence, a CPSO algorithm is adopted and modified to fit into the formation control problem. Experiments are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the proposed CPSO-based MPC for multi-robot formation.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.771-772
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• 2014

In this paper, the integrated design method of suction muffler in compressor was studied. There are three things to consider when designing this. First, the transmission loss was maximized to consider the noise reduction. Second, dissipation energy of fluid flow was minimized for energy efficiency. Finally, acoustical resonance frequency of suction muffler was controlled because energy efficiency can be increased by supercharging of refrigerant. Therefore, suction muffler was designed to have the specific resonance frequency. The input impedance was used for designing target acoustical resonance frequency. Topology optimization was used for optimization method.

• 한국생산제조학회지
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• 제19권4호
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• pp.434-439
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• 2010

A compliant mechanism is a mechanism that produces its motion by the flexibility of some or all of its members when input force or thermal load is applied. Whereas the topology optimizations based on homogenization and SIMP parameterization have been successfully applied for compliant mechanism design, ESO approach has been hardly considered yet for the optimization of these types of systems. In this paper, traditional ESO method is adopted to achieve the optimum design of a compliant mechanism for thermal load, since AESO method cannot consider the effect of both heat conduction and convection. Sensitivity number, a criterion for element removal in traditional ESO, was newly defined for input thermal loading. The procedure has been tested in numerical applications and compared with the results obtained by other methods to validate these approaches.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제49권4호
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• pp.219-225
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• 2000

This paper presents the shape optimization considering the uncertainty of design variable to find robust optimal solution that has insensitive performance to its change of design variable. Stochastic finite element method (SFEM) is used to treat input data as stochastic variables. It is method that the potential values are series form for the expectation and small variation. Using correlation function of their variables, the statistics of output obtained form the input data distributed. From this, design considering uncertainty of design variables.

• ETRI Journal
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• 제43권1호
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• pp.17-30
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• 2021

At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high-speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C-means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C-means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies-generalized Nash game and cooperative and noncooperative game theory.

• 전기학회논문지
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• 제67권4호
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• pp.569-577
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• 2018

In this paper, with the aid of information which is included within data, preprocessing algorithm-based black plastic classifier is designed. The slope and area of spectrum obtained by using laser induced breakdown spectroscopy(LIBS) are analyzed for each material and its ensuing information is applied as the input data of the proposed classifier. The slope is represented by the rate of change of wavelength and intensity. Also, the area is calculated by the wavelength of the spectrum peak where the material property of chemical elements such as carbon and hydrogen appears. Using informations such as slope and area, input data of the proposed classifier is constructed. In the preprocessing part of the classifier, Principal Component Analysis(PCA) and fuzzy transform are used for dimensional reduction from high dimensional input variables to low dimensional input variables. Characteristic analysis of the materials as well as the processing speed of the classifier is improved. In the condition part, FCM clustering is applied and linear function is used as connection weight in the conclusion part. By means of Particle Swarm Optimization(PSO), parameters such as the number of clusters, fuzzification coefficient and the number of input variables are optimized. To demonstrate the superiority of classification performance, classification rate is compared by using WEKA 3.8 data mining software which contains various classifiers such as Naivebayes, SVM and Multilayer perceptron.

• 한국전자파학회논문지
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• 제30권4호
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• pp.286-289
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• 2019

본 논문에서는 다중 입 출력 무선전력전송 시스템의 최대 전송 효율 구현을 위한 송신기 입력 및 수신기 부하 제어방법을 제안하였다. 최대 전송 효율 구현에 필요한 각 송신기의 입력전압과 수신기의 부하를 송수신기 간의 성능지수를 이용하여 유도하였다. 유도한 최적 입력전압과 최적 부하는 적절한 예시를 통해 Genetic algorithm을 이용해 최적화한 결과나 회로 시뮬레이션 결과와 유사함을 확인하였다. 본 논문에서 제시된 이론적 방법을 적절히 활용한다면, 근거리 다중 입 출력 무선전력전송 시스템의 효과적인 설계가 가능할 것이다.

• Journal of international Conference on Electrical Machines and Systems
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• 제1권4호
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• pp.419-424
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• 2012

This paper presents a tubular reciprocating translational motion permanent magnet synchronous motor for percussive drilling applications for offshore oil & gas industry. The motor model and rock model are built up by doing force analysis of the motor and analyzing the physical procesof impact. The optimization of input voltage waveforms to maximize the rate of penetration is done by simulations. The simulation results show that the motor can be utilized in percussive drilling applications and achieve a very large impact force. Simulation results for optimization also show that second harmonic input voltage produces a higher rate of penetration than the sine wave and fourth harmonic input voltages.