• 대한전자공학회논문지
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• 제22권3호
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• pp.91-94
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• 1985

A method optimizing selection of a state weighting matrix is presented. The state weight-ing matrix is chosen so that the closed-loop system responses closely match to the ideal model responses. An algorithm is presented which determines a positive semidefinite state weighting matrix in the linear quadratic optimal control design problem and an numerical example is given to show the effect of the present algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• Journal of Communications and Networks
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• 제12권4호
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• pp.289-307
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• 2010

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence-termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carry out model selection and recovery of sparse signals irrespective of the phases of the nonzero entries even if the number of nonzero entries scales almost linearly with the number of rows of the Alltop Gabor frame.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.280-284
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• 2008

In this paper, a best channel matrix selection scheme(BCMS) is proposed to approximate maximum likelihood(ML) detection for a multiple-input multiple-output system. For a one stage BCMS scheme, one of the transmitted symbols is selected to perform ML detection and the other symbols are detected by zero forcing(ZF). To increase the diversity of the symbols that are detected by ZF, multi-stage BCMS detection scheme is used to further improve the system performance. Simulation results show that the performance of the proposed BCMS scheme can approach that of ML detection with a significant reduction in complexity.

• 한국통신학회논문지
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• 제40권7호
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• pp.1257-1265
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• 2015

본 논문은 투영 행렬을 이용한 높은 채널 용량을 획득할 수 있는 사용자 선택 기법을 제안한다. 기존의 Frobenius norm기반의 사용자 선택 기법이 다수의 사용자를 선택할수록 정확도가 감소하는 점을 투영 행렬의 근사화 형태를 이용하여 개선하였다. Flop count를 이용한 제안 기준의 계산복잡도를 분석하였고, 모의 실험을 통하여 안테나 구성에 따른 획득 가능한 채널 용량에 대해서 비교하였다. 모의 실험 결과를 통해 제안한 사용자 선택 기법이 더 높은 채널 용량을 획득하는 것을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 정기총회 및 창립40주년기념 학술대회 학회본부
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• pp.133-136
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• 1987

This paper presents a new algorithm of optimal measurement system by using band matrix characteristic respectively for state estimation. A performance index of measurement system is established to reflect relation among measurement sets, probability of measurement failure and cost of individual meter installation. Selection ranking in the candidates of measurement sets is composed to guarantee the observability for any any single meter outage. Performance index sensitivity is introduced and recursive formula which based on the matrix inversion lemma used for selection. The proposed algorithm is composed of successive addition algorithm, successive elimination algorithm and combinatorial algorithm. The band matrix characteristic could save in memory requirements and calculate the performance index faster than earlier.

• 시스템엔지니어링학술지
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• 제1권2호
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• pp.56-62
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• 2005

In this study, we first define user requirements to fulfill the reconnaissance and the search missions, by analyzing the system characteristics and operation environment. By investigating the design technology level, the development and procurement costs, the strong system design concepts and possible alternatives will be proposed. To analyze the system requirements, the Quality Function Deployment of the systems engineering approach will be implemented. The promising design alternatives that satisfy the user requirements are extracted by constructing the Morphological Matrix, then the best design concept will be obtained using the Pugh Concept Selection Matrix and the TOPSIS(Technique of Order Preference by Similarity to Ideal Solution).

• 신재생에너지
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• 제18권1호
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• pp.17-28
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• 2022

This paper presents an effective Markov transition matrix (EMTM), which will be used to calculate the wind speed at the target site in a wind farm to accurately predict wind energy production. The existing MTS prediction method using a Markov transition matrix (MTM) exhibits a limitation where significant prediction variations are observed owing to random selection errors and its bin width. The proposed method selects the effective states of the MTM and refines its bin width to reduce the error of random selection during a gap filling procedure in MTS. The EMTM reduces the level of variation in the repeated prediction of wind speed by using the coefficient of variations and range of variations. In a case study, MTS exhibited better performance than other MCP models when EMTM was applied to estimate a one-day wind speed, by using mean relative and root mean square errors.

• 방송공학회논문지
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• 제15권5호
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• pp.672-680
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• 2010

본 논문은 H.264 동영상 부호화를 위한 적응적인 양자화 행렬 선택방법을 제안한다. 기존의 H.264 양자화 방법은 각 프레임에 동일한 양자화 행렬을 적용하기 때문에 영상의 지역적 특성을 고려하지 못해 코딩 효율이 저하될 수 있다. 이러한 문제점을 개선하고자 프레임 전체에 동일한 양자화 행렬을 적용하는 대신 매크로블록 단위로 블록이 가지는 방향성을 이용해 적응적으로 양자화 행렬을 적용하는 방법을 제안한다. 먼저, 각 블록의 방향성을 공간적으로 인접 블록의 인트라 예측모드 특성을 이용하여 결정한다. 방향성이 존재하는 블록에 대해서는 제안한 방식의 가중치 양자화 행렬을 적용하고, 방향성이 존재하지 않는 블록에 대해서는 기존의 양자화 행렬을 적용한다. 가중치 양자화 행렬은 인트라 예측모드에 따라 블록의 변환 계수의 통계적인 분포를 기반으로 설계되었기 때문에, 예측모드의 특성에 적합하게 양자화된다. 실험 결과를 통해 제안한 알고리즘이 BD rate 측면에서 기존 방법 대비 약 2% 정도의 부호화 효율이 상승됨을 확인할 수 있다.

• 한국통신학회논문지
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• 제31권10A호
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• pp.943-951
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• 2006

MIMO(Multiple Input Multiple Output) 시스템에서 성능 향상을 위해 안테나의 수를 증가시킬 수 있으나 RF체인의 증가와 하드웨어의 복잡도에 의해 제한될 수 있다. 이때, RF 체인의 수를 고정시키고 그보다 많은 수의 안테나를 채용한 후, 승)수신 양단에서 채널 상태에 따라 동적으로 안테나를 선택함으로써 이와 같은 문제를 완화할 수 있다. 본 논문에서는 전체 $M_R{\times}M_T$채널 이득 행렬에서 가장 큰 채널 이득을 갖는 송신 안테나와 수신안테나를 교차적으로 선택하여 Frobenius norm을 최대화하는 $M_R{\times}M_T$ 채널 행렬을 결정함으로써 송/수신 양단에서 안테나 선택을 동시에 수행하는 송/수신안테나 선택 알고리즘(joint Tx/Rx antenna selection algorithm)을 제안하며, exhaustive search를 통한 최적 방식과 비교할 때 현저하게 계산량을 줄일 수 있어 구현의 복잡도가 매우 낮은 것이 특징이다. $8{\times}8$ 안테나에서 $4{\times}4$ 안테나를 선택하는 경우 성능면에 있어서는 기존의 최적 방식인 exhaustive search 방식에 비해 $0.5{\sim}2dB$가량의 성능 열화가 있으나, 계산량에서는 약 1/10,000 단위로 복잡도를 감소시킬 수 있음을 예시한다.