The main purpose of thLs paper is to detect and follow the pipeline in sonar image. This work is performed in two steps. The first is to split an transformed line image of pipeline signal into regions of uuiform texture usiug the Gray Level Cooccurrence Matrix Method (GLCM) which ts widely used in texture segmentation application. The second one addresses the unsupervised learning method based on the Arti5cial Neural Networks (SeU-Organizing Map or SOM) used for determining the comparative model of pipeline from the image. To increase the performance of SOM, we propose a penalty function based on data histogram visualization for detecting the position of pipeline. After a brief review of both techniques (GLCM and SOM), we will present our method and some results from several experiments on the real world data set
Moisture content is one of the factors measured to evaluate the quality of Camellia oleifera seeds. High quality C. oleifera seeds used for trading must have a low moisture content, specifically not more than 15% on a dry basis (db). Moisture content analysis requires a prolonged laboratory investigation so that the development of fast and effective determination methods is helpful. The objective of this paper was to develop a low-cost portable NIR reflectance spectrometer collaborating with an android application for the rapid prediction of the moisture content in C. oleifera seeds. To calibrate the prediction model, an effective chemometric algorithm, based on partial least squares regression was established, and models based on wavelength selection algorithms such as backward interval partial least squares (biPLS) and partial least squares coupled with variable importance projection (VIP-PLS) were implemented as an improved version of PLS. Both algorithms (biPLS and VIP-PLS) improved the predictive performance and accuracy of the model. The experimental results showed that the biPLS model with the 1 st derivative transformation provided the best prediction for measuring the moisture content of C. oleifera seeds with a coefficient of determination (R 2 ) value of 0.927, standard error of prediction (SEP) of 0.848%db, bias of -0.067%db, function slope of 1.005, and ratio of performance deviation (RPD) of 3.696. Finally, the device was tested according to the ISO 12099:2017(E) standard and confirmed the reliability of the device for infield use.INDEX TERMS C. oleifera seed, moisture content, portable spectrometer, backward interval partial least squares, variable importance projection.
The aim of this paper is to detect and follow the pipeline in sonar imagery. This work is performed in two steps. The first is to split an image (first experiment) or an transformed line image of pipeline image (second experiment) into regions of uniform texture using the Gray Level Co-occurrence Matrix Method (GLCM). The second addresses the unsupervised learning method based on the Artificial Neural Networks (Self-Organizing Map or SOM) used for determining the comparative model of pipeline from the image. To increase the performance of SOM, we propose a penalty function based on data histogram visualization for detecting the position of pipeline. After a brief review of both techniques (GLCM and SOM), we will present our methods and some results from several experiments on the real world data set.
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