• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.401-406
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• 2003

This paper deals with stress analysis of single-lap adhesive joints which have uncertain material properties. Basically, material properties have a certain amount of scatter and such uncertainties can affect the performance of joints. In this paper, the convex modeling is introduced to consider such uncertainties in calculating peel and shear stress of adhesive joints and the results are compared with those from the Monte Carlo simulation. Numerical results show that stresses increase when uncertainties considered, which indicates that such uncertainties should not be ignored for estimation of structural safety. Also, the results obtained by the convex modeling and the Monte Carlo simulation show good agreement, which demonstrates the effectiveness of convex modeling.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.7
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• pp.23-31
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• 2012

Resource allocation, such as joint rate control and scheduling, is an important issue in cognitive radio networks. However, it is difficult to jointly consider the rate control and scheduling problem due to the stochastic behavior of channel availability in cognitive radio networks. In this paper, we propose an asymmetric joint rate control and scheduling technique under reliability constraints in cognitive radio networks. The joint rate control and scheduling problem is formulated as a convex optimization problem and substantially decomposed into several sub-problems using a dual decomposition method. An algorithm for secondary users to locally update their rate that maximizes the utility of the overall system is also proposed. The results of simulations revealed that the proposed algorithm converges to a globally optimal solution.

• The Journal of the Acoustical Society of Korea
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• v.29 no.6
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• pp.374-380
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• 2010

It is known that the problem of FIR filtering with noisy input and output data can be solved by a total least squares (TLS) estimation. It is also known that the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose a convex combination algorithm between the ordinary recursive LS based TLS (RTLS) and the ordinary recursive LS (RLS). This combined algorithm is robust to the noise variance ratio and has almost the same complexity as the RTLS. Simulation results show that the proposed algorithm performs near TLS in noise variance ratio ${\gamma}{\approx}1$ and that it outperforms TLS and LS in the rage of 2 < $\gamma$ < 20. Consequently, the practical workability of the TLS method applied to noisy data has been significantly broadened.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.1
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• pp.63-70
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• 1995

퍼지논리제어기는 산업응용에 광범위하게 연구되고 있으며, 계속적으로 사용되고 있다. 그러나 퍼지집합의 조정을 통해 최적규칙을 구축하기 위하여, 시행착오에 의한 매우 능숙한 기술이 요구된다. 이 논문에서는 첫째로, 퍼지논리제어기와 기존의 PID 제어기로 구성된 하이브리드 퍼지제어기를 제안한다. 즉, 시스템의 제어 입력은 퍼지변수로서, 과도상태에서의 FLC출력과 정상상태에서의 PID 출력의 컨벡스(convex) 결합이다. 둘째로, 간략추론법과 개선된 컴플렉스방법을 이용한 강력한 자동동조알고리즘이 퍼지논리제어기의 성능을 자동적으로 개선하기 위하여 사용된다. 이방법은 오차변화율및 제어출력의 제한조건에 의하여, 언어제어규칙, 퍼지계수(scaling factor), PID계수, 하이브리드 퍼지논리제어기의 하중계수의 최적값을 자동적으로 추정한다. 시뮬레이션은 시간지연 플랜트및 하수처리시스템의 활성오니공정과 같은 비선형 플랜트에서 실행되고, 시스템의 성능은 평가지수 ITAE로 평가된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.628-636
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• 2015

In this paper, we propose a non-convex compressed sensing(NCCS)-based channel state information(CSI) feedback scheme for massive multiple-input multiple-output(MIMO) systems. Combining the random vector quantization(RVQ), the proposed scheme permits a transmitter to obtain CSI with acceptable accuracy under substantially reduced feedback load. Furthermore, it recovers CSI from fewer measurements than that of existing convex compressed sensing(CCS)-based schemes even if the measurements are inaccurate and incomplete. Simulation results show that the proposed scheme achieves higher throughput than both existing CCS-based feedback scheme and random vector quantization(RVQ) feedback scheme with the same feedback load.