• Journal of Power Electronics
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• v.18 no.3
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• pp.879-891
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• 2018

A bidirectional dc-dc converter is used in battery energy storage systems owing to the growing requirements of a charging and discharging mode of battery. The magnetic coupling of output or input inductors in parallel-connected multi modules of a bidirectional dc-dc converter is often utilized to reduce the peak-to-peak ripple size of the inductor current. This study proposes a novel design guideline to achieve minimal ripple size of the inductor current under bidirectional power flow. The newly proposed design guideline of optimized coupling factor is applicable to the buck and boost operation modes of a bidirectional dc-dc converter. Therefore, the coupling factor value of the coupled inductor does not have to be optimized separately for buck and boost operation modes. This new observation is explained using the theoretical model of coupled inductor and confirmed through simulation and experimental test.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.517-524
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• 2004

For real world applications of reinforcement learning techniques, function approximation or generalization will be required to avoid curse of dimensionality. For this, an improved function approximation-based reinforcement teaming method is proposed to speed up convergence by using CA-CMAC(Credit-Assigned Cerebellar Model Articulation Controller). To show that our proposed CACRL(CA-CMAC-based Reinforcement Learning) performs better than the CRL(CMAC- based Reinforcement Learning), computer simulation and experiment results are illustrated, where a swing-up control Problem of an acrobot is considered.

• 한국기계연구소 소보
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• s.19
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• pp.93-115
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• 1989

Cerebellar Model Arithmetic Controller(CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d. o. f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process; A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.114-117
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• 2000

In this paper, Fuzzy Self-organizing Neural Networks(FSONN) based on Fuzzy Neural Networks(FNN) is proposed to overcome some problems, such as the conflict between ovefitting and good generation, and low reliability. The proposed FSONN consists of FNN and SONN. Here, FNN is used as the premise part of FSONN and SONN is the consequnt part of FSONN. The FUN plays the preceding role of FSONN. For the fuzzy reasoning and learning method in FNN, Simplified fuzzy reasoning and backpropagation learning rule are utilized. The number of layers and the number of nodes in each layers of SONN that is based on the GMDH method are not predetermined, unlike in the case of the popular multi layer perceptron structure and can be generated. Also the partial descriptions of nodes can use various forms such as linear, modified quadratic, cubic, high-order polynomial and so on. In this paper, the optimal design procedure of the proposed FSONN is shown in each step and performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

• The Journal of Information Technology
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• v.5 no.2
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• pp.47-54
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• 2002

OFDM ambient noise and entrain error that this research proposes in IEEE 802.11g, use 256-State 2/3 binary scale Convoulutional 8-PSK Modulations, FEC coding, PBCC and did speed of 12 Mbpses that belong on category of 5 GHzs Band FHSS way so that can be applied in 20 Mbpses DSSS'S generalization that is establishing Pyojunan in current IEEE embodying transmission device model who operate with the equal speed from 2.4 GHzs ISM Band important duty to DSSS way.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.285-295
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• 1999

Developing control functions that operate and cooperate each equipment in order to achieve a goal is one of the most important problems in the installation of automated manufacturing systems. This paper discusses the development of a control system for heterarchical architecture and a simulator to verify operations of the control system. The object-oriented paradigm that has excellent reusability, portability, and extensibility is currently being used in many application fields as a software development methodology. Especially, UML(Unified Modeling Language), the third generation object-oriented modeling methodology, has advantages such as model generalization, clearness, and so on. In this research, software objects to accomodate the real time environments of automated manufacturing systems are modeled with the diagrams of UML. Based on these models, control software is developed as a format of pseudo-codes. A simulator is implemented to validate the developed control system.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1235-1240
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• 2006

Previous isotropy analysis of a caster wheeled omnidirectional mobile robot(COMR) has been made under the assumption that the steering link offset is equal to the caster wheel radius. Nevertheless, many practical COMR's in use take advantage of the steering link offset different from the wheel radius, mainly because of improved stability. This paper presents the isotropy analysis of a fully actuated COMR with variable steering link offset, which can be considered as the generalization of the previous analysis. First, the kinematic model of a COMR under full actuation is obtained based on the orthogonal decomposition of the wheel velocities. Second, the necessary and sufficient conditions for the isotropy of a COMR are derived and examined to categorize three different groups, each of which can be dealt with in a similar way. Third, for each group, the isotropy conditions are further explored so as to identify all possible isotropic configurations completely.

• Structural Engineering and Mechanics
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• v.29 no.1
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• pp.77-89
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• 2008

Presence of high degree of orthotropy enhances shear lag phenomenon in laminated composite box-beams and it persists till failure. In this paper three key parameters governing shear lag behavior of laminated composite box beams are identified and defined by simple expressions. Uniqueness of the identified key parameters is proved with the help of finite element method (FEM) based studies. In addition to this, for the sake of generalization of prediction of shear lag effect in symmetrical laminated composite box beams a feed forward back propagation neural network (BPNN) model is developed. The network is trained and tested using the data base generated by extensive FEM studies carried out for various b/D, b/tF, tF/tW and laminate configurations. An optimum network architecture has been established which can effectively learn the pattern. Computational efficiency of the developed ANN makes it suitable for use in optimum design of laminated composite box-beams.