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  • In Jour Smart Sensing And Intelligent Systems

 

Article

EMPIRICAL MODE DECOMPOSITION AND ROUGH SET ATTRIBUTE REDUCTION FOR ULTRASONIC FLAW SIGNAL CLASSIFICATION

Feature extraction and selection are the most important techniques for ultrasonic flaw signal classification. In this study, empirical mode decomposition (EMD) is first used to obtain the intrinsic mode functions (IMFs) of original ultrasonic signals. Such IMFs and traditional time as well as frequency domain based statistical parameters are extracted as the initial features of flaw signal. After that, spectral clustering method is used for feature value discretization so that rough set

Yu Wang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1401–1420

Research Article

FEATURES OF SLEEP APNEA RECOGNITION AND ANALYSIS

Sleep apnea is a growing sleep disorder issue and estimate to affect 7% of the adult population in Malaysia. In this study, the electrical activity of the brain is studied using Electroencephalogram (EEG). The data obtained was then decomposed using three methods; Empirical Mode Decomposition (EMD), Bivariate EMD and finally Ensemble EMD. The Index of Orthogonatility (IO) was obtained which shows EMD performed the most poorly, EEMD the best and Bivariate in between. The performance of EMD

LEONG WAI YIE, JOEL THAN CHIA MING

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 481–497

Research Article

Analyzing and Processing EEG-based Multichannel Signals Acquired during Sleeping

Processing of signals acquired from sensor systems needs accurate algorithms to extract information of interest concerning the problem under study. In this work Empirical Mode Decomposition method is used on EEG signals obtained during polysomnography examination, when electromyographic (EMG) signals are acquired too. EMD method decomposes a signal into components named Intrinsic Mode Functions (IMF) which can exhibit important time-frequency information related to signals under observation

P. Vergallo, A. Lay-Ekuakille, N.I. Giannocaro, A. Trabacca, R. Della Porta, M. De Rinaldis

International Journal on Smart Sensing and Intelligent Systems , ISSUE 5, 1–4

Article

FILM THICKNESS MEASUREMENT OF MECHANICAL SEAL BASED ON CASCADED ARTIFICIAL NEURAL NETWORK RECOGNITION MODEL

stationary ring of mechanical seals are used to directly measure the thickness of the liquid-lubricated film. The Eddy current signal is decomposed by empirical mode decomposition into a series of intrinsic mode function. The information reflecting the film thickness is obtained by eliminating the false intrinsic mode function components. Acoustic emission sensor placed on the lateral of stationary ring is used to detect the friction of end faces. In order to decrease the acoustic emission signal’s noise

Erqing Zhang, Pan Fu, Kesi LI, Xiaohui Li, Zhongrong Zhou

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1870–1889

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