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SuperGraph: Spatial-Temporal Graph-Based Feature Extraction for Rotating Machinery Diagnosis
IEEE transactions on industrial electronics (1982. Print), 2022Vibration signals always contain noise and irregularities, which makes spectrum analysis difficult to extract high-level features. Recently, graph theory has been applied to spectrum analysis to improve the performance of feature extraction.
Chaoying Yang, Kaibo Zhou, Jie Liu
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Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings
IEEE Transactions on Instrumentation and Measurement, 2021A variety of data-driven methods have been proposed to predict remaining useful life (RUL) of key component for rolling bearings. The accuracy of data-driven RUL prediction model largely depends on the extraction method of performance degradation ...
Huimin Zhao +4 more
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Journal of Applied Science and Technology Trends, 2020 Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results.
R. Zebari +4 more
exaly +2 more sources
IEEE transactions on multimedia, 2021
Recently, convolutional neural networks (CNNs) have brought impressive improvements for object detection. However, detecting targets in infrared images still remains challenging, because the poor texture information, low resolution and high noise levels ...
Ruiheng Zhang +5 more
semanticscholar +1 more source
Recently, convolutional neural networks (CNNs) have brought impressive improvements for object detection. However, detecting targets in infrared images still remains challenging, because the poor texture information, low resolution and high noise levels ...
Ruiheng Zhang +5 more
semanticscholar +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982
A systematic feature extraction procedure is proposed. It is based on successive extractions of features. At each stage a dimensionality reduction is made and a new feature is extracted. A specific example is given using the Gaussian minus-log-likelihood ratio as a basis for the extracted features.
James M. Mantock +2 more
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A systematic feature extraction procedure is proposed. It is based on successive extractions of features. At each stage a dimensionality reduction is made and a new feature is extracted. A specific example is given using the Gaussian minus-log-likelihood ratio as a basis for the extracted features.
James M. Mantock +2 more
openaire +2 more sources
Software feature extraction using infrequent feature extraction
2016 6th International Annual Engineering Seminar (InAES), 2016Evolution and maintenance processes are important but time consuming and expensive. It is very important to make the processes effective and efficient. A software developer can use resource like user opinion data to get information, such as user request, bug report, and user experience.
Daniel Siahaan +1 more
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Feature extraction and feature interaction
Behavioral and Brain Sciences, 1998The idea of the orderly output constraint is compared with recent findings about the representation of vowels in the auditory cortex of an animal model for human speech sound processing (Ohl & Scheich 1997). The comparison allows a critical consideration of the idea of neuronal “feature extractors,” which is of relevance to the noninvariance ...
Frank W. Ohl, Henning Scheich
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Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN
IEEE Transactions on Cybernetics, 2020Multisensor fusion is of great importance in Earth observation related applications. For instance, hyperspectral images (HSIs) provide wealthy spectral information while light detection and ranging (LiDAR) data provide elevation information, and using ...
Mengmeng Zhang +4 more
semanticscholar +1 more source
2016
Most original work on feature extraction has its root in classical 2D image processing (Sec.1) and mainly focuses on edge detection and the localization of interest points and regions. In practice, extracting these features corresponds to segment the image and to analyze its content.
S Biasotti +3 more
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Most original work on feature extraction has its root in classical 2D image processing (Sec.1) and mainly focuses on edge detection and the localization of interest points and regions. In practice, extracting these features corresponds to segment the image and to analyze its content.
S Biasotti +3 more
openaire +2 more sources