• Smart Structures and Systems
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• 제20권1호
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• pp.35-42
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• 2017

Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.

• 전기학회논문지
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• 제58권4호
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• pp.840-848
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• 2009

This paper proposes a new multi-start scheme that generates guided random bits in selecting initial search points for global optimization with univariate dynamic encoding algorithm for searches (uDEAS). The proposed method counts the number of 1 in each bit position from all the previously generated initial search matrices and, based on this information, generates 0 in proportion with the probability of selecting 1. This rule is simple and effective for improving diversity of initial search points. The performance improvement of the proposed multi-start is validated through implementation in uDEAS and function optimization experiments.

• 대한수학회논문집
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• 제21권4호
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• pp.779-786
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• 2006

Let $\mathbb{X}_t$ be an m-dimensional linear process defined by $\mathbb{X}_t=\sum{_{j=0}^\infty}\;A_j\;\mathbb{Z}_{t-j}$, t = 1, 2, $\ldots$, where $\mathbb{Z}_t$ is a sequence of m-dimensional random vectors with mean 0 : $m\times1$ and positive definite covariance matrix $\Gamma:m{\times}m$ and $\{A_j\}$ is a sequence of coefficient matrices. In this paper we give sufficient conditions so that $\sum{_{t=1}^{[ns]}\mathbb{X}_t$ (properly normalized) converges weakly to Wiener measure if the corresponding result for $\sum{_{t=1}^{[ns]}\mathbb{Z}_t$ is true.

• Journal of Communications and Networks
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• 제5권2호
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• pp.96-103
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• 2003

Asymptotic theorems are very commonly used in probability. For systems whose performance depends on a set of n random parameters, asymptotic analyses for n${\to}{\infty}$ are often used to simplify calculations and obtain results yielding useful hints at the behavior of the system for finite n. These asymptotic analyses are especially useful whenever the convergence to the asymptotic results is so fast that even for moderate n they yield results close to the true values. This tutorial paper illustrates this principle by applying it to capacity calculations of multiple-antenna systems.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.223-228
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• 2009

We propose an on-line machine learning approach for object recognition, where new images are continuously added and the recognition decision is made without delay. Random forest (RF) classifier has been extensively used as a generative model for classification and regression applications. We extend this technique for the task of building incremental component-based detector. First we employ object descriptor model based on bag of covariance matrices, to represent an object region then run our on-line RF learner to select object descriptors and to learn an object classifier. Experiments of the object recognition are provided to verify the effectiveness of the proposed approach. Results demonstrate that the propose model yields in object recognition performance comparable to the benchmark standard RF, AdaBoost, and SVM classifiers.

• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.523-533
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• 2020

This paper suggests three available methods for finding nonnegative estimates of variance components of the random effects in mixed models. The three proposed methods based on the concepts of projections are called projection method I, II, and III. Each method derives sums of squares uniquely based on its own method of projections. All the sums of squares in quadratic forms are calculated as the squared lengths of projections of an observation vector; therefore, there is discussion on the decomposition of the observation vector into the sum of orthogonal projections for establishing a projection model. The projection model in matrix form is constructed by ascertaining the orthogonal projections defined on vector subspaces. Nonnegative estimates are then obtained by the projection model where all the coefficient matrices of the effects in the model are orthogonal to each other. Each method provides its own system of linear equations in a different way for the estimation of variance components; however, the estimates are given as the same regardless of the methods, whichever is used. Hartley's synthesis is used as a method for finding the coefficients of variance components.

• 호남수학학술지
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• 제27권2호
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• pp.301-315
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• 2005

Let $\{A_u,\;u=0,\;1,\;2,\;{\cdots}\}$ be a sequence of coefficient matrices such that ${\sum}_{u=0}^{\infty}{\parallel}A_u{\parallel}<{\infty}$ and ${\sum}_{u=0}^{\infty}\;A_u{\neq}O_{m{\times}m}$, where for any $m{\times}m(m{\geq}1)$, matrix $A=(a_{ij})$, ${\parallel}A{\parallel}={\sum}_{i=1}^m{\sum}_{j=1}^m{\mid}a_{ij}{\mid}$ and $O_{m{\times}m}$ denotes the $m{\times}m$ zero matrix. In this paper, a functional central limit theorem is derived for a stationary m-dimensional linear process ${\mathbb{X}}_t$ of the form ${\mathbb{X}_t}={\sum}_{u=0}^{\infty}A_u{\mathbb{Z}_{t-u}}$, where $\{\mathbb{Z}_t,\;t=0,\;{\pm}1,\;{\pm}2,\;{\cdots}\}$ is a stationary sequence of linearly positive quadrant dependent m-dimensional random vectors with $E({\mathbb{Z}_t})={{\mathbb{O}}$ and $E{\parallel}{\mathbb{Z}_t}{\parallel}^2<{\infty}$.

• 한국정보통신학회논문지
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• 제23권12호
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• pp.1638-1643
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• 2019

본 논문에서는 MIMO (Multiple-Input Multiple Output, 다중입출력) 시스템을 위한 적응형 섭동을 이용한 기회적 프리코딩(Adaptive Perturbation-aided Opportunistic Precoding) 방식을 제안한다. 제안 프리코딩 방식에서는 MIMO 시스템을 위한 프리코딩 행렬을 생성할 때 랜덤한 섭동 뿐 아니라 사용자로부터 받은 전송률 정보에 의해 결정되는 적응적 변화값을 함께 이용한다. 이전 시간의 랜덤 섭동이 전송속도를 상승시켰을 경우 적응형 섭동을 이전 랜덤 섭동과 동일하게 하고, 그렇지 않을 경우 이전 랜덤한 섭동 값의 음의 값에 해당하는 값을 적용시킨다. 또한 전송 속도 최적화를 위해 스케줄링에서 현재 생성된 프리코딩 행렬 뿐 아니라 메모리에 저장된 최근 프리코딩 행렬 정보도 함께 이용한다. 모의실험 결과에서 기존 프리코딩 방식에 비해 제안한 섭동 기반 기회적 프리코딩 방식이 높은 전송속도를 얻으며, 특히 사용자의 수가 적은 환경에서 전송 속도 이득이 큰 것을 확인할 수 있다.

• Journal of Microbiology and Biotechnology
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• 제8권5호
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• pp.490-496
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• 1998

Random amplified polymorphic DNA (RAPD) markers were used to survey genetic variability among 34 Botrytis cinerea isolates from nine different host plants in Korea. For RAPD analysis, 115 arbitrary decamer primers were initially screened for polymorphic major DNA bands with 11 representative B. cinerea isolates. Eleven primers that initially detected polymorphisms were tested a second time with additional 23 isolates of B. cinerea as well as one isolate of Botrytis squamosa as an outgroup. The RAPD analyses revealed that all isolates except one showed different molecular phenotypes. Dendrograms obtained from dissimilarity matrices using the unweighted paired group method of arithmetic means (UPGMA) showed the 36.4% to 90.0% similarity among all B. cinerea isolates. The B. squamosa isolate showed the least similarity to all B. cinerea isolates. The cluster analyses indicated no correlation among all the characteristics examined including molecular phenotypes, host and geographic origins, year of isolation, or pathogenicity. The RAPD data suggest that a high level of genetic variation exists among Korean populations of B. cinerea and it seems to be caused by heterokaryosis among preexisting molecular phenotypes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2634-2648
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• 2020

In this paper, an adaptive sparse singular value decomposition (ASSVD) algorithm is proposed to estimate the signal matrix when only one data matrix is observed and there is high dimensional white noise, in which we assume that the signal matrix is low-rank and has sparse singular vectors, i.e. it is a simultaneously low-rank and sparse matrix. It is a structured matrix since the non-zero entries are confined on some small blocks. The proposed algorithm estimates the singular values and vectors separable by exploring the structure of singular vectors, in which the recent developments in Random Matrix Theory known as anisotropic Marchenko-Pastur law are used. And then we prove that when the signal is strong in the sense that the signal to noise ratio is above some threshold, our estimator is consistent and outperforms over many state-of-the-art algorithms. Moreover, our estimator is adaptive to the data set and does not require the variance of the noise to be known or estimated. Numerical simulations indicate that ASSVD still works well when the signal matrix is not very sparse.