• Title/Summary/Keyword: 힐버트 변환

Search Result 72, Processing Time 0.045 seconds

Applications of the improved Hilbert-Huang transform method to the detection of thermo-acoustic instabilities (열음향학적 불안정성 검출에 대한 개선된 힐버트-후앙 변환의 적용)

  • Cha, Ji-Hyeong;Kim, Young-Seok;Ko, Sang-Ho
    • Proceedings of the Korean Society of Propulsion Engineers Conference
    • /
    • 2012.05a
    • /
    • pp.555-561
    • /
    • 2012
  • The Hilbert Huang Transform (HHT) technigue with Empirical Mode Decomposition (EMD) is one of the time-frequency domain analysis methods and it has several advantages such that analyzing non-stationary and nonlinear signal is possible. However, there are shortcomings in detecting near-range of frequencies and added noise signals. In this paper, to analyze characteristics of each method, HHT and Short-Time Fourier Transform (STFT) effective in dealing with stationary signals are compared. And with thermoacoustic instabilities signals from a Rijke tube test, HHT and the improved HHT with Ensemble Empirical Mode Decomposition (EEMD) are compared. The results show that the improved HHT is more appropriate than the original HHT due to the relative insensitivity to noise. Therefore it will result in more accurate analysis.

  • PDF

Transient Characteristics Analysis of Structural Systems Undergoing Impact Employing Hilbert-Huang Transformation (힐버트 황 변환을 이용한 충격을 받는 시스템의 과도특성 분석)

  • Lee, Seung-Kyu;Yoo, Hong-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.33 no.12
    • /
    • pp.1442-1448
    • /
    • 2009
  • Transient characteristics of a signal can be effectively exhibited in time-frequency domain. Hilbert-Huang Transform (HHT) is one of the time-frequency domain analysis methods. HHT is known for its several advantages over other signal analysis methods. The capability of analyzing non-stationary or nonlinear characteristics of a signal is the primary advantage of HHT. Moreover, it is known that HHT can provide fine resolution in high frequency region and handle large size data efficiently. In this study, the effectiveness of Hilbert-Huang transform is illustrated by employing structural systems undergoing impact. A simple discrete system and an axially oscillating cantilever beam undertaking periodic impulsive force are chosen to show the effectiveness of HHT.

Transient Response of 1 DOF Complex Stiffness System via Hilbert-transform (힐버트 변환을 이용한 복소강성을 지니는 1자유도 시스템의 과도응답)

  • Bae, Seung-Hoon;Jeong, Weui Bong;Cho, Jin Rae
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
    • /
    • 2014.10a
    • /
    • pp.298-299
    • /
    • 2014
  • The solution of transient response of complex stiffness system was obtained using a green function of this system. To derive the green function, governing equation of this systems was expressed in Steady Space and solved by the diagonalization. The solution of this system are written as a convolution integral form. The result that are calculated by the numerical integration process for transient responses was showed properly.

  • PDF

Identification of Underwater Ambient Noise Sources Using Hilbert-Huang Transfer (힐버트-후앙 변환을 이용한 수중소음원의 식별)

  • Hwang, Do-Jin;Kim, Jea-Soo
    • Journal of Ocean Engineering and Technology
    • /
    • v.22 no.1
    • /
    • pp.30-36
    • /
    • 2008
  • Underwater ambient noise originating from geophysical, biological, and man-made acoustic sources contains information on the source and the ocean environment. Such noise affectsthe performance of sonar equipment. In this paper, three steps are used to identify the ambient noise source, detection, feature extraction, and similarity measurement. First, we use the zero-crossing rate to detect the ambient noisesource from background noise. Then, a set of feature vectors is proposed forthe ambient noise source using the Hilbert-Huang transform and the Karhunen-Loeve transform. Finally, the Euclidean distance is used to measure the similarity between the standard feature vector and the feature vector of the unknown ambient noise source. The developed algorithm is applied to the observed ocean data, and the results are presented and discussed.

A K-Nearest Neighbour Search Algorithm based on Hilbert Curve for Outsourced Spatial Database (아웃소싱된 공간 데이터베이스를 위한 힐버트 커브 기반 k-최근접점 질의처리 알고리즘)

  • Yoo, Hye-Kyeom;Chang, Jae-Woo
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • 2011.11a
    • /
    • pp.1199-1202
    • /
    • 2011
  • 최근 클라우드 컴퓨팅에 대한 관심이 고조됨에 따라, 이를 활용한 데이터베이스 아웃소싱에 대한 연구가 활발히 진행되고 있다. 한편, 데이터 소유자가 자신이 가지고 있는 공간 데이터베이스를 그대로 아웃소싱 할 경우, 서비스 제공자는 이를 불법으로 취득하여 악용할 수 있고, 질의 요청자들의 통계 정보를 통해 개인정보를 획득할 수 있다. 따라서 아웃소싱 환경에서 개인정보 보호 및 공간 데이터베이스를 보호하기 위한 데이터 변환기법 및 변환된 데이터베이스 상에서 질의를 처리하는 연구가 필요하다. 따라서, 본 논문에서는 아웃소싱 환경에서 공간 네트워크를 고려한 가공 데이터 생성 기법 및 암호화 기법을 설계한다. 아울러, 인증된 사용자가 질의 요청 시, 서비스 제공자가 저장한 가공 데이터를 이용하여 효율적으로 k-최근접점 질의를 수행하기 위한 힐버트 커브 기반 k-최근접점 질의처리 알고리즘을 제안한다.

A New Controller of Single Phase Active Power Filter Using Rotating Synchronous Frame d-q Transformation (회전하는 동기 좌표계 d-q 변환을 이용한 단상 능동 전력 필터의 새로운 제어기)

  • Kang, Min Gu
    • Journal of the Institute of Electronics and Information Engineers
    • /
    • v.51 no.6
    • /
    • pp.271-275
    • /
    • 2014
  • A New Single Phase Active Power Filter Controller is proposed using Rotating Synchronous Frame d-q transformation. Instantaneous Active Power is calculated using d-q transformation. Average Value of Instantaneous Active Power is obtained using Low Pass Filter. Because power factor is corrected, source current is in phase with source voltage. Amplitude of source current is calculated using single phase power formula. Reference signal of compensated current of Active power filter is obtained from source current reference signal minus load current. Simulation is performed using hysteresis current controller in proposed new controller. Simulation result shows that because active power filter compensates load current, source current is in phase with source voltage and source current is sinusoidal. And Hilbert transformer is builded using all pass filter.