• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 춘계종합학술대회
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• pp.489-492
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• 2005

본 논문은 웨이블릿 변환 특성을 기반으로 조명의 영향을 표현하는 표준 영상 왜곡 모델을 구축하여 조명 및 기타 영상의 왜곡에 강한 얼굴검출 기법을 제시한다. PC카메라 환경에서와 같이 입력 영상의 명암왜곡이 지속적으로 존재하는 응용에서는 히스토그램 평활화, 명세화와 같은 기존의 명암도 보정 방법으로는 효율적인 얼굴탐색이 어렵다. 따라서 입력 영상의 왜곡정보를 분석하고 이 정보가 입력 영상의 보정에 사용될 수 있다면 효율적인 얼굴검출이 수행될 수 있을 것이다. 본 논문에서는 입력 영상의 웨이블릿 변환으로 얻어진 각 고주파 영역의 픽셀을 조사하여 원 영상의 가로, 세로, 대각선 방향의 에지 정보를 분석함으로써 현재 입력된 영상의 명암 상태를 확인하고, 얼굴특징요소 중 눈을 기준으로 검출을 수행하여 아주 어둡거나 밝은 환경에서도 얼굴검출 성능을 높이도록 한다.

• 한국환경과학회지
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• 제24권8호
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.1093-1101
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• 2015

Partial Discharge (PD) is a physical phenomenon, which causes defects and damages to the insulation. This phenomenon is regarded as the most important source of fault and defect in power transformers. Therefore, methods of high speed and precision are considered of special importance for the maintenance of transformers in localization of the origin of partial discharge. In this paper, the transformer winding is first modeled in a transient state by using RLC ladder network and multiconductor transmission line (MTL) models. The parameters of the two models were calculated by Ansoft Maxwell software, and the simulations were performed by Matlab software. Then, the PD pulses were applied to the models with different widths of pulses. With regard to the fact that the signals received after the application of PD had a variable frequency nature over time, and based on the wavelet transform and signal energy, a new method was presented for the localization of PD. Ultimately; the mentioned method was implemented on a 20 kV winding distribution transformer. Then, the performances of the models used in this paper, including RLC and MTL models, were compared in different frequency bands for the correct distinction of partial discharge location.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.902-904
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• 2003

The main objective of this paper is to investigate the potential utility of multisensor remotely sensed data for improved coastal wetland mapping. Five data fusion models, three algebraic models (Multiplicative (MT), Brovey (BT) and Wavelet transform (WT)) and two spectral domain models (Principals component transform (PCT) and Intensity-Hue-Saturation (IHS)) were implemented and tested over the multisensor data. The fused images were then compared based on visual and statistical approaches. The results show that the wavelet transform provides greater flexibility for combining optical data sets and has good potential for preserving the spatial and spectral content of the original images . However, this model yields poor information when combining optical and microwave data. Brovey transform is more reliable for fusing optical and microwave image data and yields improved information about different wetland features of the coastal zone.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.1-10
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• 2020

This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

• 한국음향학회지
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• 제20권8호
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• pp.81-87
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• 2001

The human auditory system is appropriate for the "constant Q"system. The STFT (Short Time Fourier Transform) is not suitable for the auditory perception model since it has constant bandwidth. In this paper, the CWT (continuous wavelet transform) is employed for the auditory filter model. In the CWT, the frequency resolution can be adjusted for auditory sensation models. The proposed CWT is applied to the modeling of the JNVF. In addition, other signal processing methods such as STFT, VER-FFT and VFR-STFT are discussed. Among these methods, the model of JNVF (Just Noticeable Variation in Frequency) by using the CWT fits in with the JNVF of auditory model although it requires quite a long time.

• Smart Structures and Systems
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• 제30권3호
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• pp.263-271
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• 2022

In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice.

• 스마트미디어저널
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• 제12권11호
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• pp.36-47
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• 2023

주요 산업현장에서 설비를 제어하는 산업제어시스템(ICS, Industrial Control System)이 네트워크로 다른 시스템과 연결되는 사례가 증가하고 있다. 또한, 이러한 통합과 함께 한 번의 외부 침입이 전체 시스템 마비로 이루어질 수 있는 지능화된 공격의 발달로, 산업제어시스템에 대한 보안에 대한 위험성과 파급력이 증가하고 있어, 사이버 공격에 대한 보호 및 탐지 방안의 연구가 활발하게 진행되고 있으며, 비지도학습 형태의 딥러닝 모델이 많은 성과를 보여 딥러닝을 기반으로 한 이상(Anomaly) 탐지 기술이 많이 도입되고 있다. 어어, 본 연구에서는 딥러닝 모델에 전처리 방법론을 적용하여 시계열 데이터의 이상 탐지성능을 향상시키는 것에 중점을 두어, 그 결과 웨이블릿 변환(WT, Wavelet Transform) 기반 노이즈 제거 방법론이 딥러닝 기반 이상 탐지의 전처리 방법론으로 효과적임을 알 수 있었으며, 특히 센서에 대한 군집화(Clustering)를 통해 센서의 특성을 반영하여 Dual-Tree Complex 웨이블릿 변환을 차등적으로 적용하였을 때 사이버 공격의 탐지성능을 높이는 것에 가장 효과적임을 확인하였다.

• Structural Monitoring and Maintenance
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• 제9권2호
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• pp.157-177
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• 2022

In this paper, an innovative finite element updating method is presented based on the variation wavelet transform coefficients of Auto/cross-correlations function (WTCF). The Quasi-linear sensitivity of the wavelet coefficients of the WTCF concerning the structural parameters is evaluated based on incomplete measured structural responses. The proposed algorithm is used to estimate the structural parameters of truss and plate models. By the solution of the sensitivity equation through the least-squares method, the finite element model of the structure is updated for estimation of the location and severity of structural damages simultaneously. Several damage scenarios have been considered for the studied structure. The parameter estimation results prove the high accuracy of the method considering measurement and mass modeling errors.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.173-174
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• 2008

Wavelet transform is a new method for power system analysis. On the basis of extensive investigation, optimal mother wavelets for the detection of leakage current are chosen. The recommended mother wavelet is 'Daubechies 4' wavelet. This paper proposes a technique for modeling toe finding point of leakage current in distribution system using wavelet transform and EMTP MODELS.