• 비파괴검사학회지
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• 제24권4호
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• pp.341-347
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• 2004

발전설비의 중요한 요소인 배관의 효율적인 비파괴검사를 위해, 배관내에 유도초음파를 comb 트랜스듀서를 이용하여 발생시켰으며, 유도초음파를 비접촉 방식으로 수신하기 위해 An(air-coupled transducer)를 적용하였다. comb 트랜스듀서의 요소간격과 이론적인 분산선도로부터 발생가능 한 유도초음파 모드가 예측되었다. 또한 예측된 모드를 수신하기 위해 각 모드의 이론적인 위상속도를 이용하여 ACT의 수신 각도를 결정하였다. 수신모드의 특성을 규명하기 위해 웨이블릿 변환과 2D-FFT를 이용한 시간-주파수해석을 수행하여 이론적인 분산선도와 비교한 결과, 수신된 보드는 이론적으로 예측된 모드와 일치하는 것으로 나타났다.

• 한국방재학회 논문집
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• 제10권3호
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• pp.119-131
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• 2010

가뭄은 인간이 극복하기 힘든 자연재해로서 가뭄지역의 경제를 어렵게 할 뿐 아니라 생태계까지 파괴하기 때문에 전 세계적으로 가장 두려워하는 관심 재해 중 하나이다. 따라서 본 연구에서는 대표적인 가뭄지수인 팔머가뭄지수와 범지구적 해수면 온도의 상관관계를 분석하였다. 먼저 팔머가뭄지수를 산정하여 과거 가뭄발생연도와 비교분석을 실시하였다. 비교분석을 결과 대부분의 과거 가뭄사상과 지수가 일치하는 것으로 분석되었다. 상관성 분석을 위해 팔머가뭄지수 산정을 위한 지수인 강수자료와 온도자료를 월평균강수량과 월평균온도 자료로 산정하여 군집분석을 실시하였다. 우리나라 기상청관할에 있는 61개 지점을 선정하여 월평균강우량과 월평균온도 자료로 군집분석결과 총 6개의 군집을 형성하는 것으로 분석되었다. 또한, 군집분석결과와 팔머가뭄지수의 주성분 분석을 실시하였다. 주성분 분석을 통해 전체 자료의 분산을 80% 이상 설명할 수 있는 14개의 시계열 자료를 추출하였다. 추출된 팔머가뭄지수의 주요 성분과 범지구적 해수면 온도와의 상관성 분석결과 팔머가뭄지수는 양의 상관관계가 음의 상관관계보다 큰 것으로 분석되었으며, 태평양에서 관측되는 해수면 온도와 통계적으로 유의한 상관관계를 갖는 해수면 온도 구역을 확인할 수 있었다. 이를 통해 해수면 온도를 이용하여 우리나라에 발생할 수 있는 가뭄의 예측 가능성을 제시하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.407-410
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• 2003

We propose a new logo watermark scheme for digital images which embed a watermark by modifying middle-frequency sub-bands of wavelet transform. Independent component analysis (ICA) is introduced to authenticate and copyright protect multimedia products by extracting the watermark. To exploit the Human visual system (HVS) and the robustness, a perceptual model is applied with a stochastic approach based on noise visibility function (NVF) for adaptive watermarking algorithm. Experimental results demonstrated that the watermark is perfectly extracted by ICA technique with excellent invisibility, robust against various image and digital processing operators, and almost all compression algorithms such as Jpeg, jpeg 2000, SPIHT, EZW, and principal components analysis (PCA) based compression.

• 한국공작기계학회논문집
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• 제17권1호
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• 한국염색가공학회지
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• 제21권1호
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• pp.53-58
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• 2009

A real-time inspection system has been developed by combining CCD based image processing algorithm and a standard lighting equipment. The system was tested for defective fabrics showing nozzle contact scratch marks, which were one of the frequently occurring defects. Multi-resolution analysis(MRA) algorithm were used and evaluated according to both their processing time and detection rate. Standard value for defective inspection was the mean of the non-defect image feature. Similarity was decided via comparing standard value with sample image feature value. Totally, we achieved defective inspection accuracy above 95%.

• Environmental Analysis Health and Toxicology
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• 제23권4호
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• pp.231-245
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• 2008

Various methods for detecting changes in response behaviors of indicator specimens are presented for monitoring effects of toxic treatments. The movement patterns of individuals are quantitatively characterized by statistical (i.e., ANOVA, multivariate analysis) and computational (i.e., fractal dimension, Fourier transform) methods. Extraction of information in complex behavioral data is further illustrated by techniques in ecological informatics. Multi-Layer Perceptron and Self-Organizing Map are applied for detection and patterning of response behaviors of indicator specimens. The recent techniques of Wavelet analysis and line detection by Recurrent Self-Organizing Map are additionally discussed as an efficient tool for checking time-series movement data. Behavioral monitoring could be established as new methodology in integrative ecological assessment, tilling the gap between large-scale (e.g., community structure) and small-scale (e.g., molecular response) measurements.

• Smart Structures and Systems
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• 제19권2호
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• pp.195-202
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• 2017

The aim of this paper is to develop a novel method to determine the severity of a damage in a thin plate. This paper presents a novel fault detection and diagnosis approach employing a new electromagnetic acoustic transducer, called EMAT, together with a complex signal processing method. The method consists in the recognition of a fault that exists within the structure, the fault location, i.e. the identification of the geometric position of damage, and the determining the significance of the damage, which indicates the importance or severity of the defect. The main scientific novelties presented in this paper is: to develop of a new type of electromagnetic acoustic transducer; to incorporate wavelet transforms for signal representation enhancements; to investigate multi-parametric analysis for noise identification and defect classification; to study attenuation curves properties for defect localization improvement; flaw sizing and location algorithm development.

• 한국산업정보학회논문지
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• 제19권1호
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• pp.43-52
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• 2014

P-wave characteristic in the human electrocardiogram (ECG) is important in the diagnosis of atrial conduction pathology. In this paper, we measured an ECG signal from patient with cardiovascular disease using one lead ECG electrode system which is based on the wireless cardiac monitoring system. And we detected a P-wave in ECG signal using the complex-valued continuous wavelet transforms (CWT) according to two kinds of patch type electrodes such as an existing narrow patch type electrode and the improved wide patch type electrode presented in this paper. Also, we compared the characteristics in detecting the P-wave in terms of the magnitude and the width of P-waves. From the results of comparison we found that the width and the magnitude of P-wave detected using the wide patch type electrode is improved to be interpreted easier compared to those using the narrow patch type electrode. Furthermore, we have also proven that the complex-valued CWT can be used as a robust detector for P-wave in ECG signal analysis.

• Smart Structures and Systems
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• 제22권2호
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• 한국정보통신학회논문지
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• 제16권7호
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• pp.1437-1446
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• 2012

본 논문에서는 스테레오 카메라 환경에서 고속으로 깊이 정보를 얻기 위한 응용분야에 적합한 스테레오 정합 기법을 제안하고자 한다. 이러한 조건을 만족하기 위해서 DWT 영역에서의 주파수 정보와 가변 정합창을 이용하는 적응적인 스테레오 정합 기법을 제안한다. 공간 영역에서 영상의 국부적인 특성을 분석하여 정합창의 크기를 결정하고, 주파수 영역에서 영상의 주파수 특성을 분석하여 정합창의 형태 및 스케일링 요소를 결정한다. 주파수 영역에 대한 정보를 이용하기 위해서 로컬 DWT와 전역 DWT를 활용하는 기법을 모두 적용하였다. 본 논문은 스테레오 정합을 위한 제안한 기법은 유사한 수준의 복잡도에서 고정 정합창 기반의 기법과 비교할 때 PSNR이 향상되는 것을 확인하였다.