• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1A
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• pp.10-27
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• 2005

In this paper, we propose a high level design architecture of WCDMA(UMTS) base station modem and synchronization algorithms applied to the proposed architecture. Also analysis of each synchronization algorithm and performance evaluation of fixed point designed modem are shown. Since the target system is base station modem, each synchronization algorithm is designed for its stable operation. To minimize implementation complexity, optimum fixed point design for best operation of synchronization algorithms is performed. We performed symbol level link simulation with fixed point designed modem simulator for data rate of 12.2kbps, 64kbps, 144kbps, and 384kbps. We compared performance results to the minimum requirements specified in 3GPP TS 25.104(Release 5). Extensive computer simulation shows that the proposed modem architecture has stable operation and outperform the minimum requirement by 2 dB. The proposed modem architecture has been applied in the implementation of WCDMA reverse link receiver modem chip successfully.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Steel and Composite Structures
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• v.18 no.5
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• pp.1197-1214
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• 2015

There is a great difference in mechanical behavior between design model one-time loading and step-by-step construction process. This paper presents practical computational methods for simulating the structural behavior of long-span rigid steel structures during construction processes. It introduces the positioning principle of node rectification for installation which is especially suitable for rigid long-span steel structures. Novel improved nonlinear analytical methods, known as element birth and death of node rectification, are introduced based on several calculating methods, as well as a forward iteration of node rectification method. These methods proposed in this paper can solve the problem of element's 'floating' and can be easily incorporated in commercial finite element software. These proposed methods were eventually implemented in the computer simulation and analysis of the main stadium for the Universiade Sports Center during the construction process. The optimum construction scheme of the structure is determined by the improved algorithm and the computational results matched well with the measured values in the project, thus indicating that the novel nonlinear time-varying analysis approach is effective construction simulation of complex rigid long-span steel structures and provides useful reference for future design and construction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.37-46
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• 2014

In this paper, by using Visual C # XNA game development application relates to a technique. This paper proposes the design and an implement effective algorithms Effect Based on XNA Game Development Environment. Existing in the development of the game using Directx C++ game developers, game development, it is necessary to position a lot of the source code. Particularly effective effect was much difficulty in the processing section. However, to eliminate this difficulty was proposed in the paper. The possibility of developing the game in XNA-based group. For the superiority of the proposed paper and the comparative analysis of existing games were designed for the development of the game was. The future study will be the design of various effects and events which give more immersive game implementation.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.653-665
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• 2010

In this study, we introduce a neurofuzzy approach to the design of fuzzy controllers. The development process exploits key technologies of Computational Intelligence (CI), namely, genetic algorithms (GA) and neurofuzzy networks. The crux of the design methodology deals with the selection and determination of optimal values of the scaling factors of fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out. Next, we form a nonlinear mapping for the scaling factors, which are realized by GA-based neurofuzzy networks by using a fuzzy set or fuzzy relation. The proposed approach is applied to control nonlinear systems like the inverted pendulum. Results of comprehensive numerical studies are presented through a detailed comparative analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.216-222
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• 2012

Newly proposed RG-MADS (Randomly Guided Mesh Adaptive Direct Search) has been applied to the optimal design of Interior Permanent Magnet Synchronous Motor (IPMSM) which has the distinctive features of magnetic saturation. RG-MADS, advanced from classical MADS algorithm, has the superiority in computational time and reliable convergence accuracy to the optimal solution, thus it is appropriate to the optimal design of IPMSM coupled with time-consuming Finite Element Analysis (FEA), necessary to the nonlinear magnetic application for better accuracy. Effectiveness of RG-MADS has been verified through the well-known benchmark-functions beforehand. In addition, the proposed RG-MADS has been applied to the optimal design of IPMSM aiming at maximizing the Maximum Torque Per Ampere (MTPA), which is regarded as representative design goal of IPMSM.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.10
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• pp.1335-1342
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• 2000

Three-dimensional flow analysis and numerical optimization methods are presented for the design of an axial-flow fan. Steady, incompressible, three-dimensional Reynolds-averaged Navier-Stokes equations are used as governing equations, and standard k- ${\varepsilon}$ turbulence model is chosen as a turbulence model. Governing equations are discretized using finite volume method. Steepest descent method, conjugate gradient method and BFGS method are compared to determine the searching directions. Golden section method and quadratic fit-sectioning method are tested for one dimensional search. Objective function is defined as a ratio of generation rate of the turbulent kinetic energy to pressure head. Two variables concerning sweep angle distribution are selected as the design variables. Performance of the final fan designed by the optimization was tested experimentally.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.10
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• pp.1621-1626
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• 2001

The structural optimization have been carried out in the continuous design space or in the discrete design space. Methods fur discrete variables such as genetic algorithms , are extremely expensive in computational cost. In this research, an iterative optimization algorithm using orthogonal arrays is developed for design in discrete space. An orthogonal array is selected on a discrete des inn space and levels are selected from candidate values. Matrix experiments with the orthogonal array are conducted. New results of matrix experiments are obtained with penalty functions leer constraints. A new design is determined from analysis of means(ANOM). An orthogonal array is defined around the new values and matrix experiments are conducted. The final optimum design is found from iterative process. The suggested algorithm has been applied to various problems such as truss and frame type structures. The results are compared with those from a genetic algorithm and discussed.