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Wyszukujesz frazę "morphological processing" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Application of morphological analysis for gear fault detection and trending
Autorzy:
Gryllias, K.
Yiakopoulos, C.
Antoniadis, I.
Powiązania:
https://bibliotekanauki.pl/articles/329038.pdf
Data publikacji:
2008
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
koło
uszkodzenie
wykrywanie
analiza morfologiczna
analiza wibracji
defective gears
morphological processing
fault trending
vibration analysis
Opis:
Frequency domain based signal processing methods such as cepstrum analysis, Hilbert Transform based demodulation, cyclostationary analysis, etc have been shown to present a quite effective behaviour in the detection of defects, when applied to the analysis of vibration signals, resulting from gear pairs with one or more defective gears. However, these methods typically require some complex and sophisticated analysis, which renders their application cumbersome for applications requiring unskilled personnel or automated fault detection and trending. Alternatively to these methods, morphological analysis for processing vibration signals has been proposed, addressing the issues of how to quantify the shape and the size of the signals directly in the time domain. Morphological analysis and the resulting morphological index is applied in this paper to a set of twelve successive vibration measurements resulting from a gearbox prior to tooth breakage. As shown, the morphological index is able monitor the evolution of the potential fault, providing a clear warning prior to the breakage of the tooth.
Źródło:
Diagnostyka; 2008, 4(48); 37-42
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses
Autorzy:
Gocławski, J.
Sekulska-Nalewajko, J.
Kuźniak, E.
Powiązania:
https://bibliotekanauki.pl/articles/330961.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
segmentacja obrazu
przestrzeń koloru
przetwarzanie morfologiczne
progowanie obrazu
sztuczna sieć neuronowa
ochrona roślin
image segmentation
colour space
morphological processing
image thresholding
artificial neural network
WTA learning
Widrow-Hoff learning
Cucurbita species
plant stress
ROS detection
Opis:
The increased production of Reactive Oxygen Species (ROS) in plant leaf tissues is a hallmark of a plant's reaction to various environmental stresses. This paper describes an automatic segmentation method for scanned images of cucurbits leaves stained to visualise ROS accumulation sites featured by specific colour hues and intensities. The leaves placed separately in the scanner view field on a colour background are extracted by thresholding in the RGB colour space, then cleaned from petioles to obtain a leaf blade mask. The second stage of the method consists in the classification of within mask pixels in a hue-saturation plane using two classes, determined by leaf regions with and without colour products of the ROS reaction. At this stage a two-layer, hybrid artificial neural network is applied with the first layer as a self-organising Kohonen type network and a linear perceptron output layer (counter propagation network type). The WTA-based, fast competitive learning of the first layer was improved to increase clustering reliability. Widrow-Hoff supervised training used at the output layer utilises manually labelled patterns prepared from training images. The generalisation ability of the network model has been verified by K-fold cross-validation. The method significantly accelerates the measurement of leaf regions containing the ROS reaction colour products and improves measurement accuracy.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 669-684
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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