• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.157-162
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• 2007

A new level set based topology optimization employing inner-front creation algorithm is presented. In the conventional level set based topology optimization, the optimum topology strongly depends on the initial level set distribution due to the incapability of inner-front creation during optimization process. In the present work, in this regard, an inner-front creation algorithm is proposed. in which the sizes. shapes. positions, and number of new inner-fronts during the optimization process can be globally and consistently identified by considering both the value of a given criterion for inner-front creation and the occupied volume (area) of material domain. To facilitate the inner-front creation process, the inner-front creation map which corresponds to the discrete valued criterion of inner-front creation is applied to the level set function. In order to regularize the design domain during the optimization process, the edge smoothing is carried out by solving the edge smoothing partial differential equation (PDE). Updating the level set function during the optimization process, in the present work, the least-squares finite element method (LSFEM) is employed. As demonstrative examples for the flexibility and usefulness of the proposed method. the level set based topology optimization considering lightweight design of 3D shell structure is carried out.

• Journal of the Korean Home Economics Association
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• v.47 no.10
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• pp.97-108
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• 2009

As one of the conservation strategies, the city of Jeonju established regulations to conserve and revitalize the Hanok village as an attractive tour site. Some of old traditional houses were renovated into traditional inns. A couple of traditional houses were additionally built for lodging houses. The need for urban rehabilitation and adaptive re-use has been growing in Jeonju province. The purpose of this study was to examine how the traditional houses were converted into lodging places balancing the conflict issues such as preserving the unique characteristics of Hanok and updating functional requirement of modern lodging in terms of sustainable reuse and development. For this study, site visits and intensive interview with the owners of the seven traditional lodges were conducted. There was lack of guidelines and strategies renovation or rehabilitation of Hanok as lodging facility for sustainable use and revitalization of city. For the seven traditional Hanok inns, layout of rooms characterized as separate and disconnected, while traditional houses were open, flexible and connected regarding room arrangement. In addition, for sustainable development, the living environment of the community should be secured and align with developing strategies of the area.

• The Journal of Information Technology
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• v.7 no.3
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• pp.11-18
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• 2004

Learning procedure in the neural network is updating of weights between neurons. Unadequate initial learning coefficient causes excessive iterations of learning process or incorrect learning results and degrades learning efficiency. In this paper, adaptive learning algorithm is proposed to increase the efficient in the learning algorithms of Self-Organization Neural Networks. The algorithm updates the weights adaptively when learning procedure runs. To prove the efficiency the algorithm is experimented to classification of strokes which is the reference handwritten character. The result shows improved classification rate about 1.44~3.65% proposed method compare with Kohonan and Mao's algorithms, in this paper.

• Journal of IKEEE
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• v.19 no.2
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• pp.254-259
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• 2015

CABAC is a context-based adaptive binary arithmetic coding method. It increases the encoding efficiency by updating the probability based on the information of the previously coded symbols. Context modeler is a core block of CABAC, which designs a probability model according to the symbol considering statistical correlations. In this paper, an efficient hardware architecture of CABAC context modeler is proposed. The proposed context modeler was designed in Verilog HDL and it was implemented in 0.18 um technology. Its gate count is 29,832 gates including memory. Its operating speed and throughput are 200 MHz and 200 Mbin/s, respectively.

• Advances in Computational Design
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• v.1 no.3
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.248-251
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• 1999

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at i(oration n upon the arrival of new data. The RLS algorithm may be viewed as a special case of the Kalman filter. Indeed this special relationship between the RLS algorithm and the Kalman filter is considered. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. The resulting rate of convergence is therefore typically an order of magnitude faster than the simple LMS algorithm. This improvement in performance, however, Is achieved at the expensive of a large increase in computational complexity.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.353-357
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• 2006

One-sided and two-sided ATF for GNSS receiver are deigned, implemented and evaluated in this paper. The difference f filter characteristics such as the location of zeros and the frequency response is reviewed and examined with experiments. NLMS adaptation algorithm is adopted for updating the weighting coefficients of the 12-tap FIR filter. he performance of ATF is evaluated using real signals consisting of the signals from GPS simulator and the signal generator. The output of ATF is fed into the SDR to evaluate SNR and the position accuracy. The complexity of implementation is also compared and the effects of the time delay and the phase delay are examined. The experimental results show that one-sided and two-sided ATF give similar performance against single tone CWI.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.530-540
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• 2020

In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis of complex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) is proposed. By optimizing the low confidence particles to replace the resampling process, this paper improved the problem of sample impoverishment caused by the sample updating based on risk and cost of CRPF algorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtaining samples. Then, we study the correlation between the current observation value and the prior state. By adjusting the density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noises can be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulation analysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improved obviously under the background of complex noise.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1183-1202
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• 2017

Image restoration has been carried out by texture synthesis mostly for large regions and inpainting algorithms for small cracks in images. In this paper, we propose a new approach that allows for the simultaneous fill-in of different structures and textures by processing in a wavelet domain. A combination of structure inpainting and patch-based texture synthesis is carried out, which is known as patch-based inpainting, for filling and updating the target region. The wavelet transform is used for its very good multiresolution capabilities. The proposed algorithm uses the wavelet domain subbands to resolve the structure and texture components in smooth approximation and high frequency structural details. The subbands are processed separately by the prioritized patch-based inpainting with isophote energy driven texture synthesis at the core. The algorithm automatically estimates the wavelet coefficients of the target regions of various subbands using optimized patches from the surrounding DWT coefficients. The suggested performance improvement drastically improves execution speed over the existing algorithm. The proposed patch optimization strategy improves the quality of the fill. The fill-in is done with higher priority to structures and isophotes arriving at target boundaries. The effectiveness of the algorithm is demonstrated with natural and textured images with varying textural complexions.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.387-391
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• 2008

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.