• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.168-171
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• 2005

The present study describes the prediction of the flow induced noise level of a high-speed rotating hexagonal disk and proposes the way how to reduce it. Since a hexagonal disk, which is used in the laser printer and named a polygon mirror, has six sharp comers, there are low and high pressure regions on each of six edges when it rotates. Therefore, the Pressure difference generates three dimension flow field and causes aerodynamic noise. The Ffowcs-Williams and Hawkings(FWH) method is employed for the analysis. We have measured the sound pressure levels and compared them with the computational results. The calculated sound pressure levels agree well with the experimental results. We modified the shape of the edges of a hexagonal disk to reduce the noise level and confirm their effects through numerical computation.

• International Journal of Contents
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• 제14권4호
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.40-49
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• 2018

We propose a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. We propose detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). The feature vector is applied to LDA and using Euclidean distance of intra-class variance and inter class variance in the 2nd dimension, the final analysis and matching is performed. Experimental results show that the proposed method has a wider distribution when the input image is rotated $45^{\circ}$ left / right. We can improve the recognition rate by applying this feature value to a single algorithm and complex algorithm, and it is possible to recognize in real time because it does not require much calculation amount due to dimensional reduction.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 춘계학술발표대회
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• pp.204-207
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• 2013

Music genre classification is an essential component for music information retrieval system. There are two important components to be considered for better genre classification, which are audio feature extraction and classifier. This paper incorporates two different kinds of features for genre classification, timbral texture and rhythmic content features. Timbral texture contains several spectral and Mel-frequency Cepstral Coefficient (MFCC) features. Before choosing a timbral feature we explore which feature contributes less significant role on genre discrimination. This facilitates the reduction of feature dimension. For the timbral features up to the 4-th order central moments and the covariance components of mutual features are considered to improve the overall classification result. For the rhythmic content the features extracted from beat histogram are selected. In the paper Extreme Learning Machine (ELM) with bagging is used as classifier for classifying the genres. Based on the proposed feature sets and classifier, experiment is performed with well-known datasets: GTZAN databases with ten different music genres, respectively. The proposed method acquires the better classification accuracy than the existing approaches.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• Wind and Structures
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• 제35권4호
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• pp.287-296
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• 2022

Analyzing the typhoon wind hazards is crucial to determine the extreme wind load on engineering structures in the typhoon prone region. In essence, the typhoon hazard analysis is a high-dimensional problem with randomness arising from the typhoon genesis, environmental variables and the boundary layer wind field. This study suggests a dimension reduction approach by decoupling the original typhoon hazard analysis into two stages. At the first stage, the randomness of the typhoon genesis and environmental variables are propagated through the typhoon track model and intensity model into the randomness of the key typhoon parameters. At the second stage, the probability distribution information of the key typhoon parameters, combined with the randomness of the boundary layer wind field, could be used to estimate the extreme wind hazard. The Chinese southeast coastline is taken as an example to demonstrate the adequacy and efficiency of the suggested decoupling approach.

• Asian-Australasian Journal of Animal Sciences
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• 제26권7호
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• pp.1030-1037
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• 2013

Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.

• 한국항해학회지
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• 제24권1호
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• pp.85-95
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• 2000

In the country where the population concentrates in the metropolis with the narrow land, development of the ocean space is necessary. Recently, mega-float offshore structure has been studied as one of the effective utilization of the ocean space. And very large floating structures are now being considered for various applications such as floating airports, offshore cities and so on. This very large structure is relatively flexible compared with real floating structures like large ships. when we estimate dynamic responses of these structures in waves, the elastic deformation is important, because vertical dimension is small compared with horizontal. And it is necessary to examine the effect of ocean wave external force received from the natural environment. In this study, the mat-type large floating structure is made to be analytical model. And the analysis of the dynamic response as it receives regular wave is studied. The finite element method is used in the analysis of structural section of this model. And the analysis is carried out using the boundary element method in the fluid division. The validity of analysis method is verified in comparison with the experimental result in the Japan Ministry of Transport Ship Research Institution. In order to know the characteristics of the dynamic response of the large floating structures, effects of wavelength, bending rigidity of the structure, water depth, and wave direction on dynamic response of the floating structure are studied by use of numerical calculation.

• Smart Structures and Systems
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• 제25권3호
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• pp.345-368
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• 2020

The present work proposes a self-controlled multi-stage optimization method for damage identification of structures utilizing standard particle swarm optimization (PSO) algorithm. Damage identification problem is formulated as an inverse optimization problem where damage severity in each element of the structure is considered as optimization variables. An efficient objective function is formed using the first few frequencies and mode shapes of the structure. This objective function is minimized by a self-controlled multi-stage strategy to identify and quantify the damage extent of the structural members. In the first stage, standard PSO is utilized to get an initial solution to the problem. Subsequently, the algorithm identifies the most damage-prone elements of the structure using an adaptable threshold value of damage severity. These identified elements are included in the search space of the standard PSO at the next stage. Thus, the algorithm reduces the dimension of the search space and subsequently increases the accuracy of damage prediction with a considerable reduction in computational cost. The efficiency of the proposed method is investigated and compared with available results through three numerical examples considering both with and without noise. The obtained results demonstrate the accuracy of the present method can accurately estimate the location and severity of multi-damage cases in the structural systems with less computational cost.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.142-149
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• 2004

많은 양의 유즈넷 뉴스 중에서 사용자가 찾고자 하는 정확한 정보를 빠른 시간 안에 검색하고, 원하는 정보만 필터링 하는 것은 중요하다. 그러나 뉴스 문서는 이메일과 달라서 미리 자신에게 맞는 뉴스그룹을 등록해 주어야만 정보를 얻을 수 있다. 하지만, 초보자인 경우는 어떤 뉴스그룹이 자신의 관심사와 관련이 있는지를 판단하기가 용이치 않다. 따라서, 본 연구에서는 다양한 뉴스그룹들 중에서 사용자의 취향과 유사한 뉴스그룹들을 코호넨 신경망을 이용하여 추천해주는 방법을 제공한다. 신경망을 학습시키기 위한 뉴스 문서의 키워드들을 선택하기 위해 예제 문서들로부터 후보 용어들을 추출하고 퍼지 추론을 적용하여 대표 용어들을 선택한다. 하지만 신경망의 학습패턴을 관찰해 보면, 많은 부분이 비어있는 희소성 문제를 발견할 수 있다. 이에 본 연구에서는 통계적인 결정계수를 도입하여 불필요한 차원을 제거한 후 신경망을 학습시키는 새로운 방법을 제안한다. 제안된 방법은 모든 차원을 활용할 때 보다 클러스터내 거리와 클러스터간 거리의 척도를 이용한 클러스터 중첩도 면에서 우수한 분류 성능을 보여줌을 확인하였다.