• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.45-48
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• 2000

In this paper algorithms of frequency hopping sequences generation such as Lempel-Greenberger, optimum Lempel-Greenberger and Kumar sequences for spread spectrum communications are described. Using the scalogram based on wavelet transform, time-frequency characteristics of frequency hopped signals corresponding to the considered hopping sequences are analyzed.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.2
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• pp.125-133
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• 2022

This paper proposes a fault detection method for a Unmanned Surface Vehicle (USV) with overactuated system. Current status information for fault detection is expressed as a scalogram image. The scalogram image is obtained by wavelet-transforming the USV's control input and sensor information. The fault detection scheme is based on Convolutional Neural Network (CNN) algorithm. The previously generated scalogram data was transferred learning to GoogLeNet algorithm. The data are generated as scalogram images in real time, and fault is detected through a learning model. The result of fault detection is very robust and highly accurate.

• Smart Structures and Systems
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• v.14 no.3
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• pp.377-395
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• 2014

Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.

• Journal of IKEEE
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• v.26 no.2
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• pp.319-328
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• 2022

Bearing plays an important role in the operation of most machinery, Therefore, when a defect occurs in the bearing, a fatal defect throughout the machine is generated. In this reason, bearing defects should be detected early. In this paper, we describe a method using Convolutional Neural Networks (SN-CNNs) based on continuous wavelet transformations and Switchable normalization for bearing defect detection models. The accuracy of the model was measured using the Case Western Reserve University (CWRU) bearing dataset. In addition, batch normalization methods and spectrogram images are used to compare model performance. The proposed model achieved over 99% testing accuracy in CWRU dataset.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.252-257
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• 2006

기본적으로 모든 소비자들은 조금씩 다르며, 제품은 그 차이를 극대화 시킴으로써 다양한 소비를 촉진하게 된다. 이와 같은 시장 세분화와 포지셔닝 전략은 디자인 경영에 있어 매우 중요한 전략적 단계라 할 수 있으며, 기업의 소비자 분석의 목적이기도 하다. 다차원 척도법은 군집 분석에서와 마찬가지로 자료에 내재된 구조를 찾아내어 자료를 함축적으로 표현하고자 하는 자료축약형 다변량 분석 기법이다. 패턴 분류의 수량화를 이용하는 POSA(Partial Order Scalogram Analysis)는 MSA(Multidimensional Scalogram Analysis)의 구조화된 방법으로 디자인 전략을 수립하는 단계에서 소비자의 성향을 보다 세분화할 수 있다. 본 논문에서는 디자인 리서치 단계에 POSA 방법론을 적용하였을 때 소비자 유형 분류가 가능하다고 보고, 창의적 디자인 컨셉의 도출에 어느 정도 역할을 하는지 알아보고자 함을 목적으로 하였다. 본 연구에서는 부분적 계층 분석법인 POSA 분석방법을 통하여 사용자의 계층을 세분화하는 방법을 고안하고, 이를 분석함으로써 소비자의 유형을 분류하여 디자인 포지셔닝과 방향을 제시하는 방법론을 제안하고자 하였다. 이를 위하여 설문조사를 통하여 POSA 기법을 이용한 소비자 유형 분류 방법이 고안되었고, 이를 기반으로 모바일 기기를 위한 프로젝트에 실제 디자인 사례로 적용되었으며, 이러한 소비자 유형 분석을 통하여 타겟 유저의 시나리오 작성 단계에서 창의적 발상을 지원한다는 점을 발견할 수 있었다.

• Journal of Advanced Research in Ocean Engineering
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• v.2 no.4
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• pp.202-208
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• 2016

In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.260-265
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• 2001

본 연구에서는 연속 웨이블렛 변환을 이용하여 Ringing 현상을 연구하였다. 사용되어진 웨이블렛은 Morlet 웨이블렛이었고, 실험은 파수조에서 수행되었다. 또한 Ringing 현상을 다루고자 쇄파를 발생시켰다. 실험에 쓰인 모델은 수면을 통과하여 수직으로 고정된 원주 실린더였고, 이 실린더에 작용된 힘과 파고가 측정되어졌다. 이들은 연속 웨이블렛 변환으로 분석되어졌고, 이러한 분석으로 얻어진 scalogram 들은 고주파 성분이 쇄파 충격시 만들어진다는 사실을 시간영역상에서 보여주었다. 이는 기존의 스펙트럼 분석에서는 찾기 힘든 것이다. Coherence 분석도 위의 결론을 뒷받침해 주었다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.3
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• pp.291-297
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• 2012

Conventional Fourier analysis can give only limited information about the dynamic characteristics of nonstationary signals. Instead, time-frequency analysis is widely used to investigate the nonstationary signal in detail. Several time-frequency analysis methods are compared for a typical acoustic emission burst generated during the impact between a ferrite ceramic and aluminum plate. This AE burst is inherently nonstationary and random containing many frequency contents, which leads to severe interference between cross terms in bilinear convolution type distributions. The smoothing and reassignment processes can improve the readability and resolution of the results. Spectrogram and scalogram of the AE burst are obtained and compared to get the characteristics information. Renyi entropies are computed for various bilinear time-frequency transforms to evaluate the randomness. These bilinear transforms are reassigned by using the improved algorithm in discrete computation.

• Korean Journal of Child Studies
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• v.5
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• pp.53-64
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• 1984

The purpose of this research was to supplement the preceding study and to examine the feasibility of standardizing the Bayley Scales of Infant Development (BSID) for Korean infants. The subjects of this research were 93 healthy infants at 1-12 months of age living in Seoul. Seventy were the same infants as the subjects of the preceding research. Statistical analysis of the results was done by Item analysis and scalogram analysis using Green's method. The scalability of the BSID for Korean infants was .78 on the mental scale and .8l on the motor scale. Item order by counting positive responses was slightly different from the original BSID item order. On the feasibility of standardization of the BSID in Korea, the results showed that: 1) The BSID is scalable for Korean infant development, and 2) The BSID needs correction in item rank order.