• 한국인터넷방송통신학회논문지
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• 제18권2호
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• pp.81-88
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• 2018

부사용자가 주사용자의 주파수 사용 상태를 판별하기 위해 인지 무선 시스템의 핵심 기술인 스펙트럼 센싱을 사용한다. 스펙트럼 센싱 기법 중 에너지 검출법은 할당 된 채널 신호의 강도에 따라서 주사용자의 주파수 사용 유무를 판별한다. 이 기법은 단순히 신호의 크기를 이용해 스펙트럼 센싱하기 때문에 SNR 대역이 낮아질수록 주사용자의 신호를 검출하기 어렵다는 단점이 있다. 본 논문은 낮은 SNR 대역에서의 성능 열화를 극복하기 위해 웨이블릿 패킷 분해를 사용한 서포트 벡터 머신을 스펙트럼 센싱과 융합하는 방식을 제안하였다. 이 방식은 센싱 신호를 웨이블릿 패킷 분해를 기반으로 특징 추출하여 Support Vector Machine의 훈련과 실험용 데이터로 사용한다. 제안한 방식의 실험 결과를 SNR대역에 대해 정확도와 ROC 커브 그래프의 AUC를 이용하여 에너지 검출법과 비교하였다. 실험 결과, 제안한 시스템은 낮은 SNR대역에서 에너지 검출법 보다 더 향상된 판별 성능을 보였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.476-478
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• 2005

This paper describes a method for the detection of shorted-turn in the cylindrical synchronous generator rotor windings based on the discrete wavelet transform. Multi-resolution analysis(MRA) based on discrete wavelet transform provides a set of decomposed signals in independent frequency bands. In the proposed method, shorted-turn detection in rotor windings is based on the decomposition of the rotor currents, where wavelet coefficients of these signals have been extracted. Comparing these extracted coefficients is used for diagnosing the healthy machine from faulty machine. Experimental results show the effectiveness of the proposed method for shorted-turn detection in the cylindrical synchronous generator rotor windings.

• Journal of information and communication convergence engineering
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• 제4권4호
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• pp.174-179
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• 2006

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.190-192
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• 2004

In this paper, we propose a low bit-rate embedded video compression scheme with 3-D block partition coding in the wavelet domain. The proposed video compression scheme includes multi-level 3-dimensional dyadic wavelet decomposition, raster scanning within each subband, formation of block, 3-D partitioning of block, and adaptive arithmetic entropy coding. Although the proposed video compression scheme is quit simple, it produces bit-stream with good features, including SNR scalability from the embedded nature. Experimental results demonstrate that the proposed video compression scheme is quit competitive to other good wavelet-based video coders in the literature.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1832-1833
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• 2006

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of Autonomous Underwater Vehicle(AUV) steering systems. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome nonlinearities and uncertainty. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and original signal of AUV model that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by gradient-descent method. Through computer simulations, we demonstrate the effectiveness of the proposed control method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2327-2330
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• 2003

This paper deals with face detection and recognition using ellipsodal information and wavelet packet analysis. We proposed two methods. First, Face detection method uses general ellipsodal information of human face contour and we find eye position on wavelet transformed face images A novel method for recognition of views of human faces under roughly constant illumination is presented. Second, The proposed Face recognition scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtered images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, the Euclidian distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the MIT FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method. The proposed system achieved an rate of 97％(MIT data), 95.8％(FERET databace)

• 전기전자학회논문지
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• 제4권2호
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• pp.233-240
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• 2000

This paper presents fuzzy clustering and wavelet transform analysis based technique for the industrial hourly load forecasting fur the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using fuzzy clustering and then wavelet transform is adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of fuzzy clustering and wavelet transform approach can be used as an attractive and effective means for the industrial hourly peak load forecasting.

• Smart Structures and Systems
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• 제2권2호
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• pp.171-188
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• 2006

This paper deals with damage detection using active Lamb waves. The wavelet transform and empirical mode decomposition methods are discussed for measuring the Lamb wave's arrival time of the group velocity. An experimental system to diagnose the damage in the composite plate is developed. A method to optimize this system is also given for practical applications of active Lamb waves, which involve optimal arrangement of the piezoelectric elements to produce single mode Lamb waves. In the paper, the single mode Lamb wave means that there exists no overlapping among different Lamb wave modes and the original Lamb wave signal with the boundary reflection signals. Based on this optimized PZT arrangement method, five damage localizations on different plates are completed and the results using wavelet transform and empirical mode decomposition methods are compared.

• 인터넷정보학회논문지
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• 제11권2호
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• pp.99-107
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• 2010

본 연구에서는 디지털 영상의 블라인드 워터마킹 알고리즘을 제안하였다. 제안한 알고리즘은 웨이블릿 평면에서 처리되는 변환 평면 워터마킹 알고리즘으로서 2차원 EMD을 이용하여 분해된 워터마크의 EMF 성분들이 웨이블릿 대역에 삽입되어, 각 웨이블릿 대역은 워터마크의 일부 정보만을 포함하고 있는 것을 특징으로 하고 있다. 워터마크의 추출은 각 웨이블릿 대역에서 추출한 워터마크의 일부 정보들을 2차원 EMD의 선형적인 특징에 의해 산술적 또는 논리적 연산을 통하여 회복할 수 있도록 하였다. 개발한 워터마킹 알고리즘은 비가시성, 강인성 등 워터마킹 알고리즘에서 요구되는 조건들에 대한 실험을 수행하여 성능을 비교 분석하였다.

• Journal of Advanced Marine Engineering and Technology
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• 제36권5호
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• pp.662-673
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• 2012

This study introduces a condition diagnosis technique for a turbine governor system. The governor system is an important control system to handle turbine speed in a nuclear power plant. The turbine governor system includes turbine valves and stop valves which have their own functions in the system. Because a turbine governor system is operated by high oil pressure, it is very difficult to maintain under stable operating conditions. Turbine valves supply oil pressure to the governor system for proper operation. Using the pressure variation of turbine and governor valves, operating conditions of the turbine governor control system are detected and identified. To achieve automatic detection of valve status, time-based and frequency-based analysis is employed. In this study, a new approach, wavelet decomposition, was used to extract specific features from the pressure signals of the governor and stop valves. The extracted features, which represent the operating conditions of the turbine governor system, include important information to control and diagnose the valves. After extracting the specific features, decision rules were used to classify the valve conditions. The rules were generated by a decision tree algorithm (a typical simple method for data-based rule generation). The results given by the wavelet-based analysis were compared to detection results using time- and frequency-based approaches. Compared with the several related studies, the wavelet transform-based analysis, the proposed in this study has the advantage of easier application without auxiliary features.