• The KIPS Transactions:PartC
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• v.8C no.1
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• pp.97-104
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• 2001

In this paper, we present a new hierarchical model for performance analysis of channel allocation and packet service protocol in wireless n network. The proposed hierarchical model consists of two parts : upper and lower layer models. The upper layer model is the structure state model representing the state of the channel allocation and call service. The lower layer model, which captures the performance of the system within a given structure state, is the wireless packet retransmission protocol model. These models are developed using SRN which is an modeling tool. SRN, an extension of stochastic Petri net, provides compact modeling facilities for system analysis. To get the performance index, appropriate reward rates are assigned to its SRN. Fixed point iteration is used to determine the model parameters that are not available directly as input. That is, the call service time of the upper model can be obtained by packet delay in the lower model, and the packet generation rates of the lower model come from call generation rates of the upper model.

• Bulletin of the Korean Mathematical Society
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• v.48 no.5
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• pp.991-1002
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• 2011

An iterative algorithm is provided to find a common element of the set of fixed points of a nonexpansive mapping and the set of solutions of some variational inequality in a Hilbert space. Using this result, we consider a strong convergence result for finding a common fixed point of a nonexpansive mapping and a strictly pseudocontractive mapping. Our results include the previous results as special cases and can be viewed as an improvement and refinement of the previously known results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.3-16
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• 2010

Key derivation functions are used within many cryptographic systems in order to generate various keys from a fixed short key string. In this paper we survey a state-of-the-art in the key derivation functions and wish to examine the soundness of the functions on the view point of provable security. Especially we focus on the key derivation functions using pseudorandom functions which are recommended by NISI recently, and show that the variant of Double-Pipeline Iteration mode using pseudorandom permutations is a pseudorandom function. Block ciphers can be regarded as practical primitives of pseudorandom permutations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.11-13
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• 2011

양방향 필터 (Bilateral filter)는 에지 보전 평활화 필터로써 디노이징, 반사 제거, 스테레오 매칭 등 다양한 분야에서 사용되고 있다. 이는 기존의 가우시안 필터에 사용되는 공간 도메인 커널 (spatial kernel)이외에 강도 도메인 커널 (range kernel)을 추가로 사용하여 비슷한 강도의 픽셀에 높은 가중치를 부여함으로써 에지를 보전하면서 평활화를 한다. 또한 양방향 필터는 비등방성 확산 필터 (Anisotropic diffusion filter)와 달리 항상 수렴을 보장한다. 따라서 본 논문에서는 고정점 반복 이론을 적용하여 양방향 필터의 수렴을 수학적으로 증명한다.

• Journal of the Korean Mathematical Society
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• v.45 no.1
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• pp.29-40
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• 2008

In this paper, we introduce a modified three-step iteration scheme with errors for three classes of uniformly equi-continuous and asymptotically quasi-nonexpansive mappings in the framework of uniformly convex Banach spaces. We then use this scheme to approximate a common fixed point of these mappings. The results obtained in this paper extend and improve the recent ones announced by Khan, Fukhar-ud-di, Zhou, Cho, Noor and some others.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1815-1818
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• 2002

In this paper, a predistorter is presented for the orthogonal frequency division multiplxing (OFDM) systems with a transmit raised cosine (RC) pulse shaping filter and a high power amplifier (HPA). By exploiting zero-intersymbol interference (ISI) nature of the RC filter, the proposed predistorter placed before the filter only utilizes memoryless nonlinearity of the HPA, not the overall nonlinearity with memory induced by a combination of the filter and HPA. The predistirtion is realized upon fixed point iteration on OFDM symbols. Simulation results show that the proposed predisorter can be effectively employed to achieve significant performance improvement.

• Nonlinear Functional Analysis and Applications
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• v.26 no.2
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• pp.347-371
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• 2021

A plethora of applications from mathematical programmings, such as minimax, mathematical programming, penalization and fixed point problems can be framed as variational inequality problems. Most of the methods that used to solve such problems involve iterative methods, that is why, in this paper, we introduce a new extragradient-like method to solve pseudomonotone variational inequalities in a real Hilbert space. The proposed method has the advantage of a variable step size rule that is updated for each iteration based on previous iterations. The main advantage of this method is that it operates without the previous knowledge of the Lipschitz constants of an operator. A strong convergence theorem for the proposed method is proved by letting the mild conditions on an operator 𝒢. Numerical experiments have been studied in order to validate the numerical performance of the proposed method and to compare it with existing methods.

• East Asian mathematical journal
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• v.25 no.4
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• pp.415-423
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• 2009

In this paper, we consider the hybrid monotone projection algorithm for asymptotically quasi-pseudocontractive mappings. A strong convergence theorem is established in the framework of Hilbert spaces. Our results mainly improve the corresponding results announced by [H. Zhou, Demiclosedness principle with applications for asymptotically pseudo-contractions in Hilbert spaces, Nonlinear Anal. 70 (2009) 3140-3145] and also include Kim and Xu [T.H. Kim, H.K. Xu, Strong convergence of modified Mann iterations for asymptotically nonexpansive mappings and semigroups, Nonlinear Anal. 64 (2006) 1140-1152; Convergence of the modified Mann's iteration method for asymptotically strict pseudo-contractions, Nonlinear Anal. 68 (2008) 2828-2836] as special cases.

• ETRI Journal
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• v.45 no.1
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• pp.119-130
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• 2023

We propose a quantized gradient search algorithm that can achieve global optimization by monotonically reducing the quantization step with respect to time when quantization is composed of integer or fixed-point fractional values applied to an optimization algorithm. According to the white noise hypothesis states, a quantization step is sufficiently small and the quantization is well defined, the round-off error caused by quantization can be regarded as a random variable with identically independent distribution. Thus, we rewrite the searching equation based on a gradient descent as a stochastic differential equation and obtain the monotonically decreasing rate of the quantization step, enabling the global optimization by stochastic analysis for deriving an objective function. Consequently, when the search equation is quantized by a monotonically decreasing quantization step, which suitably reduces the round-off error, we can derive the searching algorithm evolving from an optimization algorithm. Numerical simulations indicate that due to the property of quantization-based global optimization, the proposed algorithm shows better optimization performance on a search space to each iteration than the conventional algorithm with a higher success rate and fewer iterations.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.210-218
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• 2003

MDO (multidisciplinary design optimization) technology has been proposed and applied to solve large and complex optimization problems where multiple disciplinaries are involved. In this research. an MDO problem is defined for automobile design which has crashworthiness analyses. Crash model which are consisted of airbag, belt integrated seat (BIS), energy absorbing steering system .and safety belt is selected as a practical example for MDO application to vehicle system. Through disciplinary analysis, vehicle system is decomposed into structure subspace and occupant subspace, and coupling variables are identified. Before subspace optimization, values of coupling variables at given design point must be determined with system analysis. The system analysis in MDO is very important in that the coupling between disciplines can be temporary disconnected through the system analysis. As a result of system analysis, subspace optimizations are independently conducted. However, in vehicle crash, system analysis methods such as Newton method and fixed-point iteration can not be applied to one. Therefore, new system analysis algorithm is developed to apply to crashworthiness. It is conducted for system analysis to determine values of coupling variables. MDO algorithm which is applied to vehicle crash is MDOIS (Multidisciplinary Design Optimization Based on Independent Subspaces). Then, structure and occupant subspaces are independently optimized by using MDOIS.