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particle decomposition</title> <meta name="description" content="Search results for: wavelet particle decomposition"> <meta name="keywords" content="wavelet particle decomposition"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="wavelet particle decomposition"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2426</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: wavelet particle decomposition</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2426</span> Feature Extraction Technique for Prediction the Antigenic Variants of the Influenza Virus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Forghani">Majid Forghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Khachay"> Michael Khachay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antigenic%20variants" title="antigenic variants">antigenic variants</a>, <a href="https://publications.waset.org/abstracts/search?q=neighbor%20effect" title=" neighbor effect"> neighbor effect</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20packet" title=" wavelet packet"> wavelet packet</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20particle%20decomposition" title=" wavelet particle decomposition"> wavelet particle decomposition</a> </p> <a href="https://publications.waset.org/abstracts/96149/feature-extraction-technique-for-prediction-the-antigenic-variants-of-the-influenza-virus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96149.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2425</span> Clutter Suppression Based on Singular Value Decomposition and Fast Wavelet Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ruomeng%20Xiao">Ruomeng Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhulin%20Zong"> Zhulin Zong</a>, <a href="https://publications.waset.org/abstracts/search?q=Longfa%20Yang"> Longfa Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aiming at the problem that the target signal is difficult to detect under the strong ground clutter environment, this paper proposes a clutter suppression algorithm based on the combination of singular value decomposition and the Mallat fast wavelet algorithm. The method first carries out singular value decomposition on the radar echo data matrix, realizes the initial separation of target and clutter through the threshold processing of singular value, and then carries out wavelet decomposition on the echo data to find out the target location, and adopts the discard method to select the appropriate decomposition layer to reconstruct the target signal, which ensures the minimum loss of target information while suppressing the clutter. After the verification of the measured data, the method has a significant effect on the target extraction under low SCR, and the target reconstruction can be realized without the prior position information of the target and the method also has a certain enhancement on the output SCR compared with the traditional single wavelet processing method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clutter%20suppression" title="clutter suppression">clutter suppression</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Mallat%20algorithm" title=" Mallat algorithm"> Mallat algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20SCR" title=" low SCR"> low SCR</a> </p> <a href="https://publications.waset.org/abstracts/181202/clutter-suppression-based-on-singular-value-decomposition-and-fast-wavelet-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181202.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2424</span> Lifting Wavelet Transform and Singular Values Decomposition for Secure Image Watermarking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siraa%20Ben%20Ftima">Siraa Ben Ftima</a>, <a href="https://publications.waset.org/abstracts/search?q=Mourad%20Talbi"> Mourad Talbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tahar%20Ezzedine"> Tahar Ezzedine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lifting%20wavelet%20transform%20%28LWT%29" title="lifting wavelet transform (LWT)">lifting wavelet transform (LWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-space%20vectorial%20decomposition" title=" sub-space vectorial decomposition"> sub-space vectorial decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=secure" title=" secure"> secure</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20watermarking" title=" image watermarking"> image watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=watermark" title=" watermark"> watermark</a> </p> <a href="https://publications.waset.org/abstracts/70998/lifting-wavelet-transform-and-singular-values-decomposition-for-secure-image-watermarking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70998.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">276</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2423</span> Wavelet-Based Classification of Myocardial Ischemia, Arrhythmia, Congestive Heart Failure and Sleep Apnea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Santanu%20Chattopadhyay">Santanu Chattopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Gautam%20Sarkar"> Gautam Sarkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabinda%20Das"> Arabinda Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmia" title="arrhythmia">arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=congestive%20heart%20failure" title=" congestive heart failure"> congestive heart failure</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=myocardial%20ischemia" title=" myocardial ischemia"> myocardial ischemia</a>, <a href="https://publications.waset.org/abstracts/search?q=sleep%20apnea" title=" sleep apnea"> sleep apnea</a> </p> <a href="https://publications.waset.org/abstracts/112333/wavelet-based-classification-of-myocardial-ischemia-arrhythmia-congestive-heart-failure-and-sleep-apnea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112333.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2422</span> Enhancement of Pulsed Eddy Current Response Based on Power Spectral Density after Continuous Wavelet Transform Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Benyahia">A. Benyahia</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Zergoug"> M. Zergoug</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Amir"> M. Amir</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Fodil"> M. Fodil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective of this work is to enhance the Pulsed Eddy Current (PEC) response from the aluminum structure using signal processing. Cracks and metal loss in different structures cause changes in PEC response measurements. In this paper, time-frequency analysis is used to represent PEC response, which generates a large quantity of data and reduce the noise due to measurement. Power Spectral Density (PSD) after Wavelet Decomposition (PSD-WD) is proposed for defect detection. The experimental results demonstrate that the cracks in the surface can be extracted satisfactorily by the proposed methods. The validity of the proposed method is discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DT" title="DT">DT</a>, <a href="https://publications.waset.org/abstracts/search?q=pulsed%20eddy%20current" title=" pulsed eddy current"> pulsed eddy current</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20wavelet%20transform" title=" continuous wavelet transform"> continuous wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Mexican%20hat%20wavelet%20mother" title=" Mexican hat wavelet mother"> Mexican hat wavelet mother</a>, <a href="https://publications.waset.org/abstracts/search?q=defect%20detection" title=" defect detection"> defect detection</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20spectral%20density." title=" power spectral density."> power spectral density.</a> </p> <a href="https://publications.waset.org/abstracts/88425/enhancement-of-pulsed-eddy-current-response-based-on-power-spectral-density-after-continuous-wavelet-transform-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88425.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">237</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2421</span> Multi-Scaled Non-Local Means Filter for Medical Images Denoising: Empirical Mode Decomposition vs. Wavelet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hana%20Rabbouch">Hana Rabbouch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, there has been considerable growth of denoising techniques mainly devoted to medical imaging. This important evolution is not only due to the progress of computing techniques, but also to the emergence of multi-resolution analysis (MRA) on both mathematical and algorithmic bases. In this paper, a comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). The comparison is carried out in a framework of multi-scale denoising, where a Non-Local Means (NLM) filter is performed scale-by-scale to a sample of benchmark medical images. The results prove the effectiveness of the multiscaled denoising, especially when the NLM filtering is coupled with the EMD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20imaging" title="medical imaging">medical imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20local%20means" title=" non local means"> non local means</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscaled%20analysis" title=" multiscaled analysis"> multiscaled analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/115243/multi-scaled-non-local-means-filter-for-medical-images-denoising-empirical-mode-decomposition-vs-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115243.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2420</span> Constructions of Linear and Robust Codes Based on Wavelet Decompositions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alla%20Levina">Alla Levina</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Taranov"> Sergey Taranov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The classical approach to the providing noise immunity and integrity of information that process in computing devices and communication channels is to use linear codes. Linear codes have fast and efficient algorithms of encoding and decoding information, but this codes concentrate their detect and correct abilities in certain error configurations. To protect against any configuration of errors at predetermined probability can robust codes. This is accomplished by the use of perfect nonlinear and almost perfect nonlinear functions to calculate the code redundancy. The paper presents the error-correcting coding scheme using biorthogonal wavelet transform. Wavelet transform applied in various fields of science. Some of the wavelet applications are cleaning of signal from noise, data compression, spectral analysis of the signal components. The article suggests methods for constructing linear codes based on wavelet decomposition. For developed constructions we build generator and check matrix that contain the scaling function coefficients of wavelet. Based on linear wavelet codes we develop robust codes that provide uniform protection against all errors. In article we propose two constructions of robust code. The first class of robust code is based on multiplicative inverse in finite field. In the second robust code construction the redundancy part is a cube of information part. Also, this paper investigates the characteristics of proposed robust and linear codes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robust%20code" title="robust code">robust code</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20code" title=" linear code"> linear code</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20decomposition" title=" wavelet decomposition"> wavelet decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=scaling%20function" title=" scaling function"> scaling function</a>, <a href="https://publications.waset.org/abstracts/search?q=error%20masking%20probability" title=" error masking probability"> error masking probability</a> </p> <a href="https://publications.waset.org/abstracts/16512/constructions-of-linear-and-robust-codes-based-on-wavelet-decompositions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16512.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">489</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2419</span> Reactive Fabrics for Chemical Warfare Agent Decomposition Using Particle Crystallization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Myungkyu%20Park">Myungkyu Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Minkun%20Kim"> Minkun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunghoon%20Kim"> Sunghoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Samgon%20Ryu"> Samgon Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, research for reactive fabrics which have the characteristics of CWA (Chemical Warfare Agent) decomposition is being performed actively. The performance level of decomposition for CWA decomposition in various environmental condition is one of the critical factors in applicability as protective materials for NBC (Nuclear, Biological, and Chemical) protective clothing. In this study, results of performance test for CWA decomposition by reactive fabric made of electrospinning web and reactive particle are presented. Currently, the MOF (metal organic framework) type of UiO-66-NH₂ is frequently being studied as material for decomposing CWA especially blister agent HD [Bis(2-chloroethyl) sulfide]. When we test decomposition rate with electrospinning web made of PVB (Polyvinyl Butiral) polymer and UiO-66-NH₂ particle, we can get very high protective performance than the case other particles are applied. Furthermore, if the repellant surface fabric is added on reactive material as the component of protective fabric, the performance of layer by layered reactive fabric could be approached to the level of current NBC protective fabric for HD decomposition rate. Reactive fabric we used in this study is manufactured by electrospinning process of polymer which contains the reactive particle of UiO-66-NH₂, and we performed crystalizing process once again on that polymer fiber web in solvent systems as a second step for manufacturing reactive fabric. Three kinds of polymer materials are used in this process, but PVB was most suitable as an electrospinning fiber polymer considering the shape of product. The density of particle on fiber web and HD decomposition rate is enhanced by secondary crystallization compared with the results which are not processed. The amount of HD penetration by 24hr AVLAG (Aerosol Vapor Liquid Assessment Group) swatch test through the reactive fabrics with secondary crystallization and without crystallization is 24 and 146μg/cm² respectively. Even though all of the reactive fiber webs for this test are combined with repellant surface layer at outer side of swatch, the effects of secondary crystallization of particle for the reactive fiber web are remarkable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CWA" title="CWA">CWA</a>, <a href="https://publications.waset.org/abstracts/search?q=Chemical%20Warfare%20Agent" title=" Chemical Warfare Agent"> Chemical Warfare Agent</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20decomposition" title=" gas decomposition"> gas decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20growth" title=" particle growth"> particle growth</a>, <a href="https://publications.waset.org/abstracts/search?q=protective%20clothing" title=" protective clothing"> protective clothing</a>, <a href="https://publications.waset.org/abstracts/search?q=reactive%20fabric" title=" reactive fabric"> reactive fabric</a>, <a href="https://publications.waset.org/abstracts/search?q=swatch%20test" title=" swatch test"> swatch test</a> </p> <a href="https://publications.waset.org/abstracts/90603/reactive-fabrics-for-chemical-warfare-agent-decomposition-using-particle-crystallization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90603.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">295</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2418</span> Review: Wavelet New Tool for Path Loss Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danladi%20Ali">Danladi Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullahi%20Mukaila"> Abdullahi Mukaila</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, GSM signal strength (power) was monitored in an indoor environment. Samples of the GSM signal strength was measured on mobile equipment (ME). One-dimensional multilevel wavelet is used to predict the fading phenomenon of the GSM signal measured and neural network clustering to determine the average power received in the study area. The wavelet prediction revealed that the GSM signal is attenuated due to the fast fading phenomenon which fades about 7 times faster than the radio wavelength while the neural network clustering determined that -75dBm appeared more frequently followed by -85dBm. The work revealed that significant part of the signal measured is dominated by weak signal and the signal followed more of Rayleigh than Gaussian distribution. This confirmed the wavelet prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decomposition" title="decomposition">decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=propagation" title=" propagation"> propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20strength%20and%20spectral%20efficiency" title=" signal strength and spectral efficiency"> signal strength and spectral efficiency</a> </p> <a href="https://publications.waset.org/abstracts/38599/review-wavelet-new-tool-for-path-loss-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38599.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">448</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2417</span> Performance Evaluation and Comparison between the Empirical Mode Decomposition, Wavelet Analysis, and Singular Spectrum Analysis Applied to the Time Series Analysis in Atmospheric Science</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Delage">Olivier Delage</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Bencherif"> Hassan Bencherif</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20Bourdier"> Alain Bourdier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Signal decomposition approaches represent an important step in time series analysis, providing useful knowledge and insight into the data and underlying dynamics characteristics while also facilitating tasks such as noise removal and feature extraction. As most of observational time series are nonlinear and nonstationary, resulting of several physical processes interaction at different time scales, experimental time series have fluctuations at all time scales and requires the development of specific signal decomposition techniques. Most commonly used techniques are data driven, enabling to obtain well-behaved signal components without making any prior-assumptions on input data. Among the most popular time series decomposition techniques, most cited in the literature, are the empirical mode decomposition and its variants, the empirical wavelet transform and singular spectrum analysis. With increasing popularity and utility of these methods in wide ranging applications, it is imperative to gain a good understanding and insight into the operation of these algorithms. In this work, we describe all of the techniques mentioned above as well as their ability to denoise signals, to capture trends, to identify components corresponding to the physical processes involved in the evolution of the observed system and deduce the dimensionality of the underlying dynamics. Results obtained with all of these methods on experimental total ozone columns and rainfall time series will be discussed and compared <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denoising" title="denoising">denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20spectrum%20analysis" title=" singular spectrum analysis"> singular spectrum analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=underlying%20dynamics" title=" underlying dynamics"> underlying dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20analysis" title=" wavelet analysis"> wavelet analysis</a> </p> <a href="https://publications.waset.org/abstracts/165886/performance-evaluation-and-comparison-between-the-empirical-mode-decomposition-wavelet-analysis-and-singular-spectrum-analysis-applied-to-the-time-series-analysis-in-atmospheric-science" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165886.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2416</span> Analysis of EEG Signals Using Wavelet Entropy and Approximate Entropy: A Case Study on Depression Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subha%20D.%20Puthankattil">Subha D. Puthankattil</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20K.%20Joseph"> Paul K. Joseph</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analyzing brain signals of the patients suffering from the state of depression may lead to interesting observations in the signal parameters that is quite different from a normal control. The present study adopts two different methods: Time frequency domain and nonlinear method for the analysis of EEG signals acquired from depression patients and age and sex matched normal controls. The time frequency domain analysis is realized using wavelet entropy and approximate entropy is employed for the nonlinear method of analysis. The ability of the signal processing technique and the nonlinear method in differentiating the physiological aspects of the brain state are revealed using Wavelet entropy and Approximate entropy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=depression" title=" depression"> depression</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20entropy" title=" wavelet entropy"> wavelet entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=approximate%20entropy" title=" approximate entropy"> approximate entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=relative%20wavelet%20energy" title=" relative wavelet energy"> relative wavelet energy</a>, <a href="https://publications.waset.org/abstracts/search?q=multiresolution%20decomposition" title=" multiresolution decomposition"> multiresolution decomposition</a> </p> <a href="https://publications.waset.org/abstracts/11836/analysis-of-eeg-signals-using-wavelet-entropy-and-approximate-entropy-a-case-study-on-depression-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11836.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2415</span> Analysis of Dynamics Underlying the Observation Time Series by Using a Singular Spectrum Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Delage">O. Delage</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Bencherif"> H. Bencherif</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Portafaix"> T. Portafaix</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Bourdier"> A. Bourdier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of time series analysis is to learn about the dynamics behind some time ordered measurement data. Two approaches are used in the literature to get a better knowledge of the dynamics contained in observation data sequences. The first of these approaches concerns time series decomposition, which is an important analysis step allowing patterns and behaviors to be extracted as components providing insight into the mechanisms producing the time series. As in many cases, time series are short, noisy, and non-stationary. To provide components which are physically meaningful, methods such as Empirical Mode Decomposition (EMD), Empirical Wavelet Transform (EWT) or, more recently, Empirical Adaptive Wavelet Decomposition (EAWD) have been proposed. The second approach is to reconstruct the dynamics underlying the time series as a trajectory in state space by mapping a time series into a set of Rᵐ lag vectors by using the method of delays (MOD). Takens has proved that the trajectory obtained with the MOD technic is equivalent to the trajectory representing the dynamics behind the original time series. This work introduces the singular spectrum decomposition (SSD), which is a new adaptive method for decomposing non-linear and non-stationary time series in narrow-banded components. This method takes its origin from singular spectrum analysis (SSA), a nonparametric spectral estimation method used for the analysis and prediction of time series. As the first step of SSD is to constitute a trajectory matrix by embedding a one-dimensional time series into a set of lagged vectors, SSD can also be seen as a reconstruction method like MOD. We will first give a brief overview of the existing decomposition methods (EMD-EWT-EAWD). The SSD method will then be described in detail and applied to experimental time series of observations resulting from total columns of ozone measurements. The results obtained will be compared with those provided by the previously mentioned decomposition methods. We will also compare the reconstruction qualities of the observed dynamics obtained from the SSD and MOD methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series%20analysis" title="time series analysis">time series analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20time%20series%20decomposition" title=" adaptive time series decomposition"> adaptive time series decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space%20reconstruction" title=" phase space reconstruction"> phase space reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20spectrum%20analysis" title=" singular spectrum analysis"> singular spectrum analysis</a> </p> <a href="https://publications.waset.org/abstracts/147886/analysis-of-dynamics-underlying-the-observation-time-series-by-using-a-singular-spectrum-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147886.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2414</span> Bayesian Inference for High Dimensional Dynamic Spatio-Temporal Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20M.%20Karadimitriou">Sofia M. Karadimitriou</a>, <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Triantafyllopoulos"> Kostas Triantafyllopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Timothy%20Heaton"> Timothy Heaton</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20Laplace%20prior" title="multidimensional Laplace prior">multidimensional Laplace prior</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filtering" title=" particle filtering"> particle filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal%20modelling" title=" spatio-temporal modelling"> spatio-temporal modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/43799/bayesian-inference-for-high-dimensional-dynamic-spatio-temporal-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43799.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2413</span> Wavelet Based Signal Processing for Fault Location in Airplane Cable </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Rezaeipour%20Honarmandzad">Reza Rezaeipour Honarmandzad </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet analysis is an exciting method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, signal processing, image processing, pattern recognition, etc. Wavelets allow complex information such as signals, images and patterns to be decomposed into elementary forms at different positions and scales and subsequently reconstructed with high precision. In this paper a wavelet-based signal processing algorithm for airplane cable fault location is proposed. An orthogonal discrete wavelet decomposition and reconstruction algorithm is used to eliminate the noise in the aircraft cable fault signal. The experiment result has shown that the character of emission pulse and reflect pulse used to test the aircraft cable fault point are reserved and the high-frequency noise are eliminated by means of the proposed algorithm in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wavelet%20analysis" title="wavelet analysis">wavelet analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20discrete%20wavelet" title=" orthogonal discrete wavelet"> orthogonal discrete wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=aircraft%20cable%20fault%20signal" title=" aircraft cable fault signal"> aircraft cable fault signal</a> </p> <a href="https://publications.waset.org/abstracts/29799/wavelet-based-signal-processing-for-fault-location-in-airplane-cable" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29799.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">524</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2412</span> Speech Intelligibility Improvement Using Variable Level Decomposition DWT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samba%20Raju">Samba Raju</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiluveru"> Chiluveru</a>, <a href="https://publications.waset.org/abstracts/search?q=Manoj%20Tripathy"> Manoj Tripathy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intelligibility is an essential characteristic of a speech signal, which is used to help in the understanding of information in speech signal. Background noise in the environment can deteriorate the intelligibility of a recorded speech. In this paper, we presented a simple variance subtracted - variable level discrete wavelet transform, which improve the intelligibility of speech. The proposed algorithm does not require an explicit estimation of noise, i.e., prior knowledge of the noise; hence, it is easy to implement, and it reduces the computational burden. The proposed algorithm decides a separate decomposition level for each frame based on signal dominant and dominant noise criteria. The performance of the proposed algorithm is evaluated with speech intelligibility measure (STOI), and results obtained are compared with Universal Discrete Wavelet Transform (DWT) thresholding and Minimum Mean Square Error (MMSE) methods. The experimental results revealed that the proposed scheme outperformed competing methods <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title="discrete wavelet transform">discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20intelligibility" title=" speech intelligibility"> speech intelligibility</a>, <a href="https://publications.waset.org/abstracts/search?q=STOI" title=" STOI"> STOI</a>, <a href="https://publications.waset.org/abstracts/search?q=standard%20deviation" title=" standard deviation"> standard deviation</a> </p> <a href="https://publications.waset.org/abstracts/115620/speech-intelligibility-improvement-using-variable-level-decomposition-dwt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115620.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2411</span> Fuzzy Wavelet Model to Forecast the Exchange Rate of IDR/USD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tri%20Wijayanti%20Septiarini">Tri Wijayanti Septiarini</a>, <a href="https://publications.waset.org/abstracts/search?q=Agus%20Maman%20Abadi"> Agus Maman Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Rifki%20Taufik"> Muhammad Rifki Taufik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The exchange rate of IDR/USD can be the indicator to analysis Indonesian economy. The exchange rate as a important factor because it has big effect in Indonesian economy overall. So, it needs the analysis data of exchange rate. There is decomposition data of exchange rate of IDR/USD to be frequency and time. It can help the government to monitor the Indonesian economy. This method is very effective to identify the case, have high accurate result and have simple structure. In this paper, data of exchange rate that used is weekly data from December 17, 2010 until November 11, 2014. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=the%20exchange%20rate" title="the exchange rate">the exchange rate</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20mamdani" title=" fuzzy mamdani"> fuzzy mamdani</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transforms" title=" discrete wavelet transforms"> discrete wavelet transforms</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20wavelet" title=" fuzzy wavelet "> fuzzy wavelet </a> </p> <a href="https://publications.waset.org/abstracts/21207/fuzzy-wavelet-model-to-forecast-the-exchange-rate-of-idrusd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21207.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">571</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2410</span> A New Approach of Preprocessing with SVM Optimization Based on PSO for Bearing Fault Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tawfik%20Thelaidjia">Tawfik Thelaidjia</a>, <a href="https://publications.waset.org/abstracts/search?q=Salah%20Chenikher"> Salah Chenikher </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title="condition monitoring">condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=kurtosis" title=" kurtosis"> kurtosis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=roller%20bearing" title=" roller bearing"> roller bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=rotating%20machines" title=" rotating machines"> rotating machines</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20measurement" title=" vibration measurement "> vibration measurement </a> </p> <a href="https://publications.waset.org/abstracts/2554/a-new-approach-of-preprocessing-with-svm-optimization-based-on-pso-for-bearing-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2554.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2409</span> Fault Detection and Diagnosis of Broken Bar Problem in Induction Motors Base Wavelet Analysis and EMD Method: Case Study of Mobarakeh Steel Company in Iran </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ahmadi">M. Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Kafil"> M. Kafil</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Ebrahimi"> H. Ebrahimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=broken%20bar" title="broken bar">broken bar</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title=" condition monitoring"> condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostics" title=" diagnostics"> diagnostics</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=fourier%20transform" title=" fourier transform"> fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/98730/fault-detection-and-diagnosis-of-broken-bar-problem-in-induction-motors-base-wavelet-analysis-and-emd-method-case-study-of-mobarakeh-steel-company-in-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98730.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2408</span> Wavelet Based Advanced Encryption Standard Algorithm for Image Encryption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ajish%20Sreedharan">Ajish Sreedharan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. As encryption process is applied to the whole image in AES ,it is difficult to improve the efficiency. In this paper, wavelet decomposition is used to concentrate the main information of image to the low frequency part. Then, AES encryption is applied to the low frequency part. The high frequency parts are XORed with the encrypted low frequency part and a wavelet reconstruction is applied. Theoretical analysis and experimental results show that the proposed algorithm has high efficiency, and satisfied security suits for image data transmission. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transforms" title="discrete wavelet transforms">discrete wavelet transforms</a>, <a href="https://publications.waset.org/abstracts/search?q=AES" title=" AES"> AES</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20SBox" title=" dynamic SBox"> dynamic SBox</a> </p> <a href="https://publications.waset.org/abstracts/16582/wavelet-based-advanced-encryption-standard-algorithm-for-image-encryption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16582.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2407</span> Labview-Based System for Fiber Links Events Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Liu">Bo Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Qingshan%20Kong"> Qingshan Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Weiqing%20Huang"> Weiqing Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid development of modern communication, diagnosing the fiber-optic quality and faults in real-time is widely focused. In this paper, a Labview-based system is proposed for fiber-optic faults detection. The wavelet threshold denoising method combined with Empirical Mode Decomposition (EMD) is applied to denoise the optical time domain reflectometer (OTDR) signal. Then the method based on Gabor representation is used to detect events. Experimental measurements show that signal to noise ratio (SNR) of the OTDR signal is improved by 1.34dB on average, compared with using the wavelet threshold denosing method. The proposed system has a high score in event detection capability and accuracy. The maximum detectable fiber length of the proposed Labview-based system can be 65km. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title="empirical mode decomposition">empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=events%20detection" title=" events detection"> events detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20transform" title=" Gabor transform"> Gabor transform</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20time%20domain%20reflectometer" title=" optical time domain reflectometer"> optical time domain reflectometer</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20threshold%20denoising" title=" wavelet threshold denoising"> wavelet threshold denoising</a> </p> <a href="https://publications.waset.org/abstracts/105512/labview-based-system-for-fiber-links-events-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105512.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2406</span> Fault Diagnosis in Induction Motors Using the Discrete Wavelet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Yahia">Khaled Yahia </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park’s vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motors%20%28IMs%29" title="induction motors (IMs)">induction motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform%20%28DWT%29" title=" discrete wavelet transform (DWT)"> discrete wavelet transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20park%E2%80%99s%20vector%20modulus%20%28CPVM%29" title=" current park’s vector modulus (CPVM) "> current park’s vector modulus (CPVM) </a> </p> <a href="https://publications.waset.org/abstracts/31450/fault-diagnosis-in-induction-motors-using-the-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31450.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">569</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2405</span> A Study on Kinetic of Nitrous Oxide Catalytic Decomposition over CuO/HZSM-5</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20J.%20Song">Y. J. Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Q.%20S.%20Xu"> Q. S. Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=X.%20C.%20Wang"> X. C. Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Wang"> H. Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Q.%20Li"> C. Q. Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The catalyst of copper oxide loaded on HZSM-5 was developed for nitrous oxide (N₂O) direct decomposition. The kinetic of nitrous oxide decomposition was studied for CuO/HZSM-5 catalyst prepared by incipient wetness impregnation method. The external and internal diffusion of catalytic reaction were considered in the investigation. Experiment results indicated that the external diffusion was basically eliminated when the reaction gas mixture gas hourly space velocity (GHSV) was higher than 9000h⁻¹ and the influence of the internal diffusion was negligible when the particle size of the catalyst CuO/HZSM-5 was small than 40-60 mesh. The experiment results showed that the kinetic of catalytic decomposition of N₂O was a first-order reaction and the activation energy and the pre-factor of the kinetic equation were 115.15kJ/mol and of 1.6×109, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=catalytic%20decomposition" title="catalytic decomposition">catalytic decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=CuO%2FHZSM-5" title=" CuO/HZSM-5"> CuO/HZSM-5</a>, <a href="https://publications.waset.org/abstracts/search?q=kinetic" title=" kinetic"> kinetic</a>, <a href="https://publications.waset.org/abstracts/search?q=nitrous%20oxide" title=" nitrous oxide"> nitrous oxide</a> </p> <a href="https://publications.waset.org/abstracts/130896/a-study-on-kinetic-of-nitrous-oxide-catalytic-decomposition-over-cuohzsm-5" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130896.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">185</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2404</span> Fault Diagnosis in Induction Motors Using Discrete Wavelet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Yahia">K. Yahia</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Titaouine"> A. Titaouine</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ghoggal"> A. Ghoggal</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20E.%20Zouzou"> S. E. Zouzou</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Benchabane"> F. Benchabane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park’s vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Induction%20Motors%20%28IMs%29" title="Induction Motors (IMs)">Induction Motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Current%20Park%E2%80%99s%20Vector%20Modulus%20%28CPVM%29" title=" Current Park’s Vector Modulus (CPVM)"> Current Park’s Vector Modulus (CPVM)</a> </p> <a href="https://publications.waset.org/abstracts/22046/fault-diagnosis-in-induction-motors-using-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22046.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">553</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2403</span> 3D Object Model Reconstruction Based on Polywogs Wavelet Network Parametrization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Othmani">Mohamed Othmani</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Khlifi"> Yassine Khlifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3d%20object" title="3d object">3d object</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=parametrization" title=" parametrization"> parametrization</a>, <a href="https://publications.waset.org/abstracts/search?q=polywog%20wavelets" title=" polywog wavelets"> polywog wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=reconstruction" title=" reconstruction"> reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20networks" title=" wavelet networks"> wavelet networks</a> </p> <a href="https://publications.waset.org/abstracts/49814/3d-object-model-reconstruction-based-on-polywogs-wavelet-network-parametrization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49814.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2402</span> Chaotic Sequence Noise Reduction and Chaotic Recognition Rate Improvement Based on Improved Local Geometric Projection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rubin%20Dan">Rubin Dan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingcai%20Wang"> Xingcai Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziyang%20Chen"> Ziyang Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A chaotic time series noise reduction method based on the fusion of the local projection method, wavelet transform, and particle swarm algorithm (referred to as the LW-PSO method) is proposed to address the problem of false recognition due to noise in the recognition process of chaotic time series containing noise. The method first uses phase space reconstruction to recover the original dynamical system characteristics and removes the noise subspace by selecting the neighborhood radius; then it uses wavelet transform to remove D1-D3 high-frequency components to maximize the retention of signal information while least-squares optimization is performed by the particle swarm algorithm. The Lorenz system containing 30% Gaussian white noise is simulated and verified, and the phase space, SNR value, RMSE value, and K value of the 0-1 test method before and after noise reduction of the Schreiber method, local projection method, wavelet transform method, and LW-PSO method are compared and analyzed, which proves that the LW-PSO method has a better noise reduction effect compared with the other three common methods. The method is also applied to the classical system to evaluate the noise reduction effect of the four methods and the original system identification effect, which further verifies the superiority of the LW-PSO method. Finally, it is applied to the Chengdu rainfall chaotic sequence for research, and the results prove that the LW-PSO method can effectively reduce the noise and improve the chaos recognition rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Schreiber%20noise%20reduction" title="Schreiber noise reduction">Schreiber noise reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=0-1%20test%20method" title=" 0-1 test method"> 0-1 test method</a>, <a href="https://publications.waset.org/abstracts/search?q=chaotic%20sequence%20denoising" title=" chaotic sequence denoising"> chaotic sequence denoising</a> </p> <a href="https://publications.waset.org/abstracts/150600/chaotic-sequence-noise-reduction-and-chaotic-recognition-rate-improvement-based-on-improved-local-geometric-projection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150600.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2401</span> Optimal Mother Wavelet Function for Shoulder Muscles of Upper Limb Amputees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amanpreet%20Kaur">Amanpreet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet transform (WT) is a powerful statistical tool used in applied mathematics for signal and image processing. The different mother, wavelet basis function, has been compared to select the optimal wavelet function that represents the electromyogram signal characteristics of upper limb amputees. Four different EMG electrode has placed on different location of shoulder muscles. Twenty one wavelet functions from different wavelet families were investigated. These functions included Daubechies (db1-db10), Symlets (sym1-sym5), Coiflets (coif1-coif5) and Discrete Meyer. Using mean square error value, the significance of the mother wavelet functions has been determined for teres, pectorals, and infraspinatus around shoulder muscles. The results show that the best mother wavelet is the db3 from the Daubechies family for efficient classification of the signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daubechies" title="Daubechies">Daubechies</a>, <a href="https://publications.waset.org/abstracts/search?q=upper%20limb%20amputation" title=" upper limb amputation"> upper limb amputation</a>, <a href="https://publications.waset.org/abstracts/search?q=shoulder%20muscles" title=" shoulder muscles"> shoulder muscles</a>, <a href="https://publications.waset.org/abstracts/search?q=Symlets" title=" Symlets"> Symlets</a>, <a href="https://publications.waset.org/abstracts/search?q=Coiflets" title=" Coiflets"> Coiflets</a> </p> <a href="https://publications.waset.org/abstracts/103654/optimal-mother-wavelet-function-for-shoulder-muscles-of-upper-limb-amputees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103654.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2400</span> Stator Short-Circuits Fault Diagnosis in Induction Motors Using Extended Park’s Vector Approach through the Discrete Wavelet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Yahia">K. Yahia</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ghoggal"> A. Ghoggal</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Titaouine"> A. Titaouine</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20E.%20Zouzou"> S. E. Zouzou</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Benchabane"> F. Benchabane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park’s vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Induction%20Motors%20%28IMs%29" title="Induction Motors (IMs)">Induction Motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=Inter-turn%20Short-Circuits%20Diagnosis" title=" Inter-turn Short-Circuits Diagnosis"> Inter-turn Short-Circuits Diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Current%20Park%E2%80%99s%20Vector%20Modulus%20%28CPVM%29" title=" Current Park’s Vector Modulus (CPVM)"> Current Park’s Vector Modulus (CPVM)</a> </p> <a href="https://publications.waset.org/abstracts/22006/stator-short-circuits-fault-diagnosis-in-induction-motors-using-extended-parks-vector-approach-through-the-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22006.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">564</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2399</span> A Spatial Information Network Traffic Prediction Method Based on Hybrid Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jingling%20Li">Jingling Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi%20Zhang"> Yi Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Liang"> Wei Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Cui"> Tao Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Li"> Jun Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20information%20network" title="spatial information network">spatial information network</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20prediction" title=" traffic prediction"> traffic prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20decomposition" title=" wavelet decomposition"> wavelet decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20model" title=" time series model"> time series model</a> </p> <a href="https://publications.waset.org/abstracts/106062/a-spatial-information-network-traffic-prediction-method-based-on-hybrid-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106062.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2398</span> Short Term Distribution Load Forecasting Using Wavelet Transform and Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Neelima">S. Neelima</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Subramanyam"> P. S. Subramanyam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20distribution%20systems" title="electrical distribution systems">electrical distribution systems</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform%20%28WT%29" title=" wavelet transform (WT)"> wavelet transform (WT)</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20term%20load%20forecasting%20%28STLF%29" title=" short term load forecasting (STLF)"> short term load forecasting (STLF)</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20%28ANN%29" title=" artificial neural network (ANN) "> artificial neural network (ANN) </a> </p> <a href="https://publications.waset.org/abstracts/34572/short-term-distribution-load-forecasting-using-wavelet-transform-and-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34572.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2397</span> Stator Short-Circuits Fault Diagnosis in Induction Motors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Yahia">K. Yahia</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sahraoui"> M. Sahraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Guettaf"> A. Guettaf </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park’s vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental results, show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motors%20%28IMs%29" title="induction motors (IMs)">induction motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform%20%28DWT%29" title=" discrete wavelet transform (DWT)"> discrete wavelet transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Current%20Park%E2%80%99s%20Vector%20Modulus%20%28CPVM%29" title=" Current Park’s Vector Modulus (CPVM)"> Current Park’s Vector Modulus (CPVM)</a> </p> <a href="https://publications.waset.org/abstracts/82115/stator-short-circuits-fault-diagnosis-in-induction-motors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=wavelet%20particle%20decomposition&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=wavelet%20particle%20decomposition&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=wavelet%20particle%20decomposition&page=4">4</a></li> <li class="page-item"><a class="page-link" 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