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Tool Condition Monitoring Using Spectral Subtraction Algorithm and Artificial Intelligence Methods in Milling Process
2017
International Journal of Mechanical Engineering and Robotics Research
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Wavelet time-frequency transform is used as a superior tool to simultaneously investigate time-varying characteristics of the signal and its frequency components. ...
Finally, five advanced machine-learning algorithms are implemented for modeling the system. ...
In this study, a TCM system is developed based on current signal of spindle motor as the fault indicator signal. Wavelet time-frequency analysis method is employed for signal processing step. ...
doi:10.18178/ijmerr.7.1.30-34
fatcat:rbdalpvf6ba7lo5rwhg7vtxs2y
ULF wave activity during the 2003 Halloween superstorm: multipoint observations from CHAMP, Cluster and Geotail missions
2012
Annales Geophysicae
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We use a suite of wavelet-based algorithms, which are a subset of a tool that is being developed for the analysis of multi-instrument multi-satellite and ground-based observations to identify ULF waves ...
We then expand these three time intervals for purposes of comparison between CHAMP, Cluster and Geotail Pc3 observations but also to be able to search for Pc4–5 wave signatures (frequency 1–10 mHz) into ...
We gratefully acknowledge ESA's Cluster Active Archive, DARTS at the Institute of Space and Astronautical Science, JAXA in Japan, and Helmholtz Centre Potsdam GFZ, German Research Centre for Geosciences ...
doi:10.5194/angeo-30-1751-2012
fatcat:aaozjbxgnrdyvmm3gtjo5qa76y
Improved Method of OCT Image Segmentation for the Detection and Classification of Retinopathy Diseases
2019
International journal of recent technology and engineering
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Discrete wavelet transform (DWT) is a multiresolution approach and is widely used for OCT image segmentation. ...
This work compares the segmentation process based on DWT with KMC and presents a better segmentation method comprising of K-Means Cluster with Genetic Algorithm Optimization (KMC-GAO) that identifies cluster ...
the Nethralayam , Senior Consultant of the Rajan Eye Care Hospital, Honorary Doctor of the Lions Club International, Founder of the Uyiralayam and Advisor of the Self Help Women"s Association, Chennai for ...
doi:10.35940/ijrte.d9146.118419
fatcat:kawgiuh4jfc3hhy6acmk2zsvau
Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis
2013
International Journal of Image Graphics and Signal Processing
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In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. ...
Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. ...
Ltd, Kochi for supporting us with required medical guidance in this work. ...
doi:10.5815/ijigsp.2013.08.04
fatcat:c4f3rrlczjbhxmkt4xtps2xuj4
An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques
2022
Journal of Spectral Imaging
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As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification. ...
Moreover, these images have a large spectral dimensionality, which adds computational complexity and affects classification precision. ...
Therefore, their outcomes are not always good enough. 4) Graph-based approaches, like spectral clustering, perform well in HSI cluster analysis, but the affinity matrix and eigenvalue decomposition take ...
doi:10.1255/jsi.2022.a1
fatcat:rue5klkmlfcrzftepc6lzfcbfe
Disentangling Multispectral Functional Connectivity With Wavelets
2018
Frontiers in Neuroscience
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Information theoretic criteria measure relatedness between spectrally-delimited FC graphs. ...
The wavelet domain presents a transform space well suited to the examination of multiscale systems as the wavelet basis set is constructed from a self-similar rescaling of a time and frequency delimited ...
Since their development, wavelets have become an important tool in fMRI analysis (Bullmore et al., 2004) . ...
doi:10.3389/fnins.2018.00812
pmid:30459548
pmcid:PMC6232345
fatcat:c64azer2s5dgnakpq36rlg57ae
Graph Signal Processing – Part II: Processing and Analyzing Signals on Graphs
[article]
2019
arXiv
pre-print
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A link between the LGFT with spectral varying window and the spectral graph wavelet transform (SGWT) is also established. ...
At the core of the spectral domain representation of graph signals and systems is the Graph Discrete Fourier Transform (GDFT). ...
While traditional approaches for graph analysis, clustering and segmentation consider only graph topology and spectral properties of graphs, when dealing with signals on graphs, localized analyzes should ...
arXiv:1909.10325v1
fatcat:dbqmdg3sobaaxaldsit5dp6h2a
Magnetospheric ULF wave studies in the frame of Swarm mission: a time-frequency analysis tool for automated detection of pulsations in magnetic and electric field observations
2013
Earth, Planets and Space
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In line with this aim, we also develop and deliver relevant analysis tools based on wavelet transforms and tailored to the Swarm mission. ...
analysis tools and highlight the options opened to treat various categories of multipoint multi-instrument measurements (both spaceborne and ground-based) for signatures of ULF wave signals as well as ...
We gratefully acknowledge ESA's Cluster Active Archive, NASA's Virtual Magnetospheric Observatory, Helmholtz Centre Potsdam GFZ, German Research Centre for Geosciences, and the Canadian Space Agency, for ...
doi:10.5047/eps.2013.10.003
fatcat:yup2ucryjfhenovqrpklw72ofe
Data Driven Modeling for System-Level Condition Monitoring on Wind Power Plants
2015
IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes
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This paper presents an innovative approach to condition monitoring of wind power plants, that provides a system-level anomaly detection for preventive maintenance. ...
Additionally, this automatically learned model is used as a basis for the second algorithm presented in this work, which detects anomalous system behavior and can alarm its operator. ...
Acknowledgments Funded by the German Federal Ministry for Economic Affairs and Energy, KF2074717KM3 & KF2074719KM3 ...
dblp:conf/safeprocess/EickmeyerLGPN15
fatcat:hphot7vc5vfwvled72qqntdire
WAVELET BASED FINGERPRINT AUTHENTICATION SYSTEM: A REVIEW
2016
Electrical and Electronics Engineering An International Journal
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Wavelet based algorithm do not require any preprocessing and post processing steps and hence is the key for low cost fingerprint identification system. ...
In comparison to older fingerprint recognition system that is based on minutiae and FFT, the wavelet based system has high recognition rates. ...
Vaishali Pawar and Mukesh Zaveri [23] implemented a graph clustering and matching algorithms based fingerprint recognition system. ...
doi:10.14810/elelij.2016.5105
fatcat:ngmj2l3ovnfb3i7v4dn24ajgye
Clustering Nonstationary Circadian Rhythms using Locally Stationary Wavelet Representations
2018
Multiscale Modeling & simulation
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A functional principal components 58 analysis on the spectral data treated as an 'image' (as suggested in a Fourier context 59 by Holan et al. (2010)) is then used to reduce the data dimensionality and ...
Section 3 develops our proposed 66 novel locally stationary wavelet-based clustering method. The findings of an extensive 67 simulation study are presented in Section 4. ...
analysis for the wavelet 324 spectral content. ...
doi:10.1137/16m1108078
fatcat:45mxdmk4vrh6fpg5gt656fio74
An Approach of Neural Network For Electrocardiogram Classification
2016
APTIKOM Journal on Computer Science and Information Technologies
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The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier ...
for identifying the abnormalities of heart disease. ...
A rule-based roughset decision system is developed from time-domain features to make an inference engine for arrhythmia detection. ...
doi:10.11591/aptikom.j.csit.120
fatcat:bv4urzi65fgwtpbvxzhn57oylu
Multi-resolution Fuzzy Clustering Approach for Image-Based Particle Characterization
2010
IFAC Proceedings Volumes
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This paper presents a novel technique based on combing wavelet transform and Fuzzy C-means Clustering (FCM) for particle image analysis. ...
Through performing wavelet transform on images, the noise and high frequency components of images can be eliminated and the textures and features can be obtained. ...
Architecture of the image-based monitoring framework For reliable performance, a monitoring system must include components of acquisition, storage and processing of the data as well as display and storage ...
doi:10.3182/20100705-3-be-2011.00025
fatcat:osgjtmaepzfzte4exqilvdj5ou
An Approach of Neural Network For Electrocardiogram Classification
2020
APTIKOM Journal on Computer Science and Information Technologies
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The ECG signal feature extraction parameters suchas spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includesartificial neural network as a classifier ...
for identifying the abnormalities of heart disease. ...
A rule-based roughset decision system is developed from time-domain features to make an inference engine for arrhythmia detection. ...
doi:10.34306/csit.v1i3.57
fatcat:kogtng5zjnfshmctsjpc7mpvny
Hand Motion Recognition from Single Channel Surface EMG Using Wavelet & Artificial Neural Network
2015
Procedia Computer Science
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Single channel sEMG analysis is preferred over multi-channel due to its simplicity, computational cost and efficiency. ...
Wavelet transformation and artificial neural network (ANN) classifier are utilized to classify and analyze the sEMG signal in a better way. c 2014 The Authors. Published by Elsevier B.V. ...
The wavelet transform, a multi-resolution time-frequency analysis, is preferred for EMG analysis 11, 12 . ...
doi:10.1016/j.procs.2015.04.227
fatcat:sg4i6oo2frbzvfehsqtvonawaa
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