• 스마트미디어저널
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• 제7권2호
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• pp.23-33
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• 2018

A musical sheet is read by optical music recognition (OMR) systems that automatically recognize and reconstruct the read data to convert them into a machine-readable format such as XML so that the music can be played. This process, however, is very challenging due to the large variety of musical styles, symbol notation, and other distortions. In this paper, we present a model for the recognition of musical symbols through the use of a mobile application, whereby a camera is used to capture the input image; therefore, additional difficulties arise due to variations of the illumination and distortions. For our proposed model, we first generate a line adjacency graph (LAG) to remove the staff lines and to perform primitive detection. After symbol segmentation using the primitive information, we use a covariance-matching method to estimate the similarity between every symbol and pre-defined templates. This method generates the three hypotheses with the highest scores for likelihood measurement. We also add a global consistency (time measurements) to verify the three hypotheses in accordance with the structure of the musical sheets; one of the three hypotheses is chosen through a final decision. The results of the experiment show that our proposed method leads to promising results.

• 전기전자학회논문지
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• 제27권1호
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• pp.38-44
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• 2023

STAP(space-time adaptive processing) 알고리즘의 성능은 CUT(cell under test) 내의 간섭에 대한 공분산 행렬 추정의 정확도가 결정적 역할을 한다. STAP 데이터는 일반적으로 많은 배열 소자 및 사용된 다수의 송신 펄스에 의해 결정되는 2차원 데이터 구조를 가지고 있다. 그러므로 공분산 행렬 추정의 정확도를 높이기 위해서는 매우 많은 트레이닝 데이터가 요구된다. 본 논문에서는 수신된 적은 개수의 데이터를 공간주파수-도플러 평면으로 변환한 후 가상의 트레이닝 데이터를 생성하는 알고리즘을 제시한다. 클러터 점유 위치를 이론적으로 유도하며 이에 근거해서 가상 트레이닝 데이터 생성 절차를 제시하고 STAP 시뮬레이션을 통해서 제시된 알고리즘이 STAP 성능을 개선할 수 있음을 보인다.

• 대한전자공학회논문지SP
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• 제37권6호
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• pp.48-59
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• 2000

안테나 센서 어레이를 이용하여 수신되는 전파의 도래각을 추정하는 방식으로서 MUSIC(multiple signal classification)과 같은 고유분해(eigendecomposition)를 기반으로 한 방식은 백색잡음 환경하에서는 고분해능의 우수한 성능을 보이지만 유색잡음이 존재하는 환경에서는 성능이 크게 저하된다. 본 논문에서는 주기성을 가진 신호에 잡음이 더해진 선호를 웨이브렛 영역으로 변환하여 신호와 잡음을 분리하는 방법을 사용하여 유색잡음이 있는 환경에서 도래각 추정 문제를 접근하였다. 배경잡음만 있는 경우 센서 어레이 출력을 이산 웨이브렛 분해를 하여 얻은 멀티스케일 성분들의 공분산 행렬은 밴드화된 행렬로 근사화 할 수 있는데 비하여 협대역 신호는 멀티스케일 성분간의 상관성은 급속히 감소하는 현상을 보이지 않고 공분산 행렬에서는 신호성분이 전체 행렬에 분포한다. 어레이 출력의 공분산 행렬을 웨이브렛 영역으로 변환하여 유색잡음에 해당하는 특정 밴드를 삭제하고 MUSIC과 같은 기존의 공간 스펙트럼 추정방식을 적용하여 도래각을 추정 한 다음 그 결과로 부터 신호성분을 합성하여 삭제한 밴드를 채우는 과정을 반복하여 정확한 도래각을 얻는 방안을 제안하였다. 제안된 알고리즘의 성능을 여러 가지 형태의 상관함수 특성을 가진 유색잡음 환경에서 모의실험을 통하여 기존 방식과 비교 분석하였다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1993년도 춘계공동학술대회 발표논문 및 초록집; 계명대학교, 대구; 30 Apr.-1 May 1993
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• pp.424-436
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• 1993

패널내 추계적 성분들의 공분산 관계(variance-covariance structure)를 이용한 ML 추정법을 항상소득가설(PIH)의 검증에 적용하였다. Hall & Mishkin의 모형을 기초로 분기별 이분산성(heteroscedasticity)을 고려한 모형의 추정결과 전체 소비변동 중 약 11%가 과도민감성에 의한 것으로 나타났다.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.575-587
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• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제49권4호
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• pp.249-257
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• 2000

In this paper, we propose the use of stochastic finite element method, that is popularly employed in mechanical structure analysis, for more practical designing purpose of RF device. The proposed method is formulated based on the vector finite element method cooperated by pertubation analysis. The method utilizes sensitivity analysis algorithm with covariance matrix of the random variables that represent for uncertain physical quantities such as length or various electrical constants to compute the probabilities of the measure of performance of the structure. For this computation one need to know the variance and covariance of the random variables that might be determined by practical experiences. The presenting algorithm has been verified by analyzing several device with different be determined by practical experiences. The presenting algorithm has been verified by analysis several device with different measure of performanes. For the convenience of formulation, two dimensional analysis has been performed to apply it into waveguide with dielectric slab. In the problem the dielectric constant of the dielectric slab is considered as random variable. Another example is matched waveguide and cavity problem. In the problem, the dimension of them are assumed to be as random variables and the expectations and variances of quality factor have been computed.

• 디지털콘텐츠학회 논문지
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• 제19권3호
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• pp.489-496
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• 2018

본 연구는 공분산구조분석을 이용하여 소방공무원의 직무스트레스, 사회심리적 요인(A형 행동유형, 자기존중감, 통제신념) 및 수면의 질이 피로수준에 미치는 영향을 규명하고자 하였다. 공분산 구조분석 결과, 피로수준에 가장 큰 영향을 미치는 요소는 직무스트레스 요인에 이어, 사회심리적 요인, 수면의 질 순서로 나타났다. 이와 같은 결과는 소방공무원의 피로수준이 직무스트레스, 사회심리적 요인, 수면의 질과의 인과관계가 성립됨을 시사한다. 따라서 소방공무원의 피로수준 감소를 위한 근무환경의 개선뿐만 아니라 제도적인 뒷받침이 필요하리라 생각된다.

• Industrial Engineering and Management Systems
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• 제15권3호
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• pp.251-258
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• 2016

Companies have developed a keen interest in bolstering their CSR activities. Especially in Japan, CSR is strongly associated with social contribution and $m{\acute{e}}c{\acute{e}}nat$ activities. On the other hand, the primary purpose of companies is to provide their products and services to consumers and thereby return value to their shareholders through sales and profits. It is, however, difficult to estimate directly the relationship between CSR and sales/profits. This study focuses particularly on CSR activities related to environmental and consumer issues and community involvement and development. It is also analyzed the relationship between the degree of CSR empathy and product purchase motivation. For accomplishment the purpose, a questionnaire is designed that elicits the respondent's degree of purchase intention and empathy toward CSR activities supported through sales. The object industry for questionnaires is TV maker. For results of the questionnaire, a covariance structure analysis is conducted to understand potential relationships among CSR activities, sales, and disposable budget. Through this study, it is able to clear the relationship between CSR activities and sales/profits. It is one of the advice to future prospect, strategy and CSR concept for the companies.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1326-1328
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• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.701-718
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• 2006

In cancer microarray experiments, the experimenter or patient which is nested in each experimenter often shows quite heterogeneous error variability, which should be estimated for identifying a source of variation. Our study describes a Bayesian method which utilizes clinical information for identifying a set of DE genes for the class of subtypes as well as assesses and examines the experimenter effect and patient effect which is nested in each experimenter as a source of variation. We propose a Bayesian multilevel mixed effect model based on analysis of covariance (ANACOVA). The Bayesian multilevel mixed effect model is a combination of the multilevel mixed effect model and the Bayesian hierarchical model, which provides a flexible way of defining a suitable correlation structure among genes.