• Title/Summary/Keyword: Wavelet(WT)

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Features Extraction for Classifying Parkinson's Disease Based on Gait Analysis (걸음걸이 분석 기반의 파킨슨병 분류를 위한 특징 추출)

  • Lee, Sang-Hong;Lim, Joon-S.;Shin, Dong-Kun
    • Journal of Internet Computing and Services
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    • v.11 no.6
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    • pp.13-20
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    • 2010
  • This paper presents a measure to classify healthy persons and Parkinson disease patients from the foot pressure of healthy persons and that of Parkinson disease patients using gait analysis based characteristics extraction and Neural Network with Weighted Fuzzy Membership Functions (NEWFM). To extract the inputs to be used in NEWFM, in the first step, the foot pressure data provided by the PhysioBank and changes in foot pressure over time were used to extract four characteristics respectively. In the second step, wavelet coefficients were extracted from the eight characteristics extracted from the previous stage using the wavelet transform (WT). In the final step, 40 inputs were extracted from the extracted wavelet coefficients using statistical methods including the frequency distribution of signals and the amount of variability in the frequency distribution. NEWFM showed high accuracy in the case of the characteristics obtained using differences between the left foot pressure and the right food pressure and in the case of the characteristics obtained using differences in changes in foot pressure over time when healthy persons and Parkinson disease patients were classified by extracting eight characteristics from foot pressure data. Based on these results, the fact that differences between the left and right foot pressures of Parkinson disease patients who show a characteristic of dragging their feet in gaits were relatively smaller than those of healthy persons could be identified through this experiment.

Detecting Ventricular Tachycardia/Fibrillation Using Neural Network with Weighted Fuzzy Membership Functions and Wavelet Transforms (가중 퍼지소속함수 기반 신경망과 웨이블릿 변환을 이용한 심실 빈맥/세동 검출)

  • Shin, Dong-Kun;Zhang, Zhen-Xing;Lee, Sang-Hong;Lim, Joon-S.;Lee, Jung-Hyun
    • The Journal of the Korea Contents Association
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    • v.9 no.7
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    • pp.19-26
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    • 2009
  • This paper presents an approach to classify normal and ventricular tachycardia/fibrillation(VT/VF) from the Creighton University Ventricular Tachyarrhythmia Database(CUDB) using the neural network with weighted fuzzy membership functions(NEWFM) and wavelet transforms. In the first step, wavelet transforms are used to obtain the detail coefficients at levels 3 and 4. In the second step, all of detail coefficients d3 and d4 are classified into four intervals, respectively, and then the standard deviations of the specific intervals are used as eight numbers of input features of NEWFM. NEWFM classifies normal and VT/VF beats using eight numbers of input features, and then the accuracy rate is 90.1%.

A Study on Crane Wire Rope Flaws Signal Processing Using Discrete Wavelet Transform (Wavelet 변환을 이용한 크레인 와이어 로프 결함 신호처리에 관한 연구)

  • Min, Jeong-Tak;Sohn, Dong-Seop;Lee, Jin-Woo;Lee, Kwon-Soon
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2002.11a
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    • pp.155-159
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    • 2002
  • Wire ropes are used in a myriad of various industrial applications such as elevator, mine hoist, construction machinery, lift, and suspension bridge. Especially, wire rope of crane is important component to container transfer. If it happens wire rope failures in operating, it may lead to safety accident, economic power loss by productivity decline, competitive power decline of container terminal and so on. To solve this problem, we developed wire rope fault detecting system as a portable instrument, and this system is consisted of 3 parts that fault detecting part using hall sensor, permanent magnets and analog unit, and digital signal processing part using data acquisition card, monitoring part using wavelet transform, denoising method. In this paper, a wire rope is scanned by this system after makes several broken parts on the surface of wire rope artificially. All detected signal has external noise or disturbance according to circumstances. So, we applied to discrete wavelet transform to extract a signal from noisy data that was used filter. In practical applications of denoising, it is shown that wavelet pursue it with little information loss and smooth signal display. It is verified that the detecting system by denoising has good efficiency for inspecting faults of wire ropes in service. As a result, by developing this system, container terminal could reduce expense because of extension of wire ropes exchange period and could competitive power. Also, this system is possible to apply in several fields like that elevator, lift and so on.

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Pilot-Aided Channel Estimation for OFDM System Using Wavelet Transform and Interpolation (웨이블릿 변환과 보간법을 이용한 OFDM 파일럿 지원 채널 추정기술)

  • Kong Hyung-Yun;Khuong Ho Van;Nam Doo-Hee
    • The KIPS Transactions:PartC
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    • v.12C no.5 s.101
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    • pp.665-672
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    • 2005
  • We present a novel pilot-aided channel estimation method for OFDM (Orthogonal Frequency Division Muitiplexing) system using WT(Wavelet transform) and interpolation. Due to excellent AWGN (Additive White Gaussian Noise) cancellation capability of n, pilot channels are estimated quite exactly and then, Dey are used in 2-degree polynomial interpolating the other remaining data symbol channels. The simulation results for Short WATM (Wireless Asynchronous Transfer Mode) channel show that the degradation in BER (Bit Error Ratio) performance of OFDM system iか this estimator is negligible compared to the case of perfect knowledge of CSI (Channel State Information).

Algorithm for Fault Detection and Classification Using Wavelet Singular Value Decomposition for Wide-Area Protection

  • Lee, Jae-Won;Kim, Won-Ki;Oh, Yun-Sik;Seo, Hun-Chul;Jang, Won-Hyeok;Kim, Yoon Sang;Park, Chul-Won;Kim, Chul-Hwan
    • Journal of Electrical Engineering and Technology
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    • v.10 no.3
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    • pp.729-739
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    • 2015
  • An algorithm for fault detection and classification method for wide-area protection in Korean transmission systems is proposed. The modeling of 345-kV and 765-kV Korean power system transmission networks using the Electro Magnetic Transient Program - Restructured Version (EMTP-RV) is presented and the algorithm for fault detection and classification in transmission lines is developed. The proposed algorithm uses the Wavelet Transform (WT) and Singular Value Decomposition (SVD). The Singular value of Approximation coefficient (SA) and part Sum of Detail coefficient (SD) are introduced. The characteristics of the SA and SD at the fault conditions are analyzed and used in the algorithm for fault detection and classification. The validation of the proposed algorithm is verified by various simulation results.

Minimum Fuzzy Membership Function Extraction for Automatic Premature Ventricular Contraction Detection (자동 조기심실수축 탐지를 위한 최소 퍼지소속함수의 추출)

  • Lim, Joon-Shik
    • Journal of Internet Computing and Services
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    • v.8 no.1
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    • pp.125-132
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    • 2007
  • This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM), NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The eight most important coefficients of d3 and d4 are selected by the non-overlap area distribution measurement method. The selected 8 coefficients are used for 3 data sets showing reliable accuracy rates 99,80%, 99,21%, and 98.78%, respectively, which means the selected input features are less dependent to the data sets. The ECG signal segments and fuzzy membership functions of the 8 coefficients enable input features to interpret explicitly.

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High-Velocity Impact Damage Detection of Gr/Ep Composite Laminates Using Piezoelectric Thin Film Sensor Signals (압전필름센서 신호를 이용한 Gr/Ep 복합재 적층판의 고속충격 손상탐지)

  • Kim, Jin-Won;Kim, In-Gul
    • Proceedings of the Korean Society For Composite Materials Conference
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    • 2005.04a
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    • pp.13-16
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    • 2005
  • The mechanical properties of composite materials may degrade severely in the presence of damage. Especially, the high-velocity impact such as bird strike, a hailstorm, and a small piece of tire or stone during high taxing, can cause sever damage to the structures and sub-system in spite of a very small mass. However, it is not easy to detect the damage in composite plates using a single technique or any conventional methods. In this paper, the PYDF(polyvinylidene fluoride) film sensors and strain gages were used for monitoring impact damage initiation and propagation in composite laminates. The WT(wavelet transform) and STFT(short time Fourier transform) are used to decompose the sensor signals. A ultrasonic C-scan and a digital microscope are also used to examine the extent of the damage in each case. This research demonstrate how various sensing techniques, PVDF sensor in particular, can be used to characterize high-velocity impact damage in advanced composites.

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Multisensor Image Fusion for Enhanced Coastal Wetland Mapping

  • Shanmugam, P.;Ahn, Yu-Hwan;Sanjeevi, S.;Yoo, Hong-Ryong
    • Proceedings of the KSRS Conference
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    • 2003.11a
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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.

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Low-velocity Impact Damdage Monitoring for Laminate Composite panels Using PVDF Sensor Signals and Acoustics Emission Signals (압전센서와 음향방출신호를 이용한 적층복합재 판재에 대한 저속 충격손상 모니터링)

  • Kim, Hyoung-Il;Kim, Jin-Won;Kim, In-Gul
    • Proceedings of the Korean Society For Composite Materials Conference
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    • 2005.11a
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    • pp.27-30
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    • 2005
  • This paper studied the PVDF(polyvinylidene fluoride) and Acoustic Emission sensors characteristics of the laminated composite panels under the low velocity impact. The various impact test by changing impact height is performed on the instrumented drop weight impact tester. The STFT(short time Fourier transform) and WT(wavelet transform) are used to decompose the each sensor signals. A ultrasonic C-scan and digital scope are used to define damaged area in each case. The test result indicated that the individual sensor signals involve the damage initiation and development.

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Investigation on PVDE & PZT Sensor Signals for the Low-Velocity Impact Damage of Gr/Ep Composite Laminates (복합적층판의 저속충격손상에 따른 PZT 센서와 PVDF 센서의 신호 분석)

  • 이홍영;김진원;최정민;김인걸
    • Proceedings of the Korean Society For Composite Materials Conference
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    • 2003.04a
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    • pp.125-128
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    • 2003
  • Low-velocity impact damage is a major concern in the design of structures made of composite materials, because impact damage is hidden inside and cannot be detected by visual inspection. The piezoelectric thin film sensor can be used to detect variations in structural and material properties for structural health monitoring. In this paper, the PVDF and PZT sensors were used for monitoring impact damage initiation in Gr/Ep composite panel to illustrate this potential benefit. A series of impact test at various impact energy by changing impact mass and height is performed on the instrumented drop weight impact tester. The wavelet transform(WT) is used to decompose the piezoelectric sensor signals in this study. Test results show that the particular waveform of sensor signals implying the damage initiation and development are detected above the damage initiation impact energy. And it is found that both PZT and PVDF sensors can be used to detect the impact damage.

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