A Bidirectional Context Propagation Network for Urine Sediment Particle Detection in Microscopic Images

2020 ◽  
Author(s):  
Meng Yan ◽  
Qing Liu ◽  
Zhihua Yin ◽  
Du Wang ◽  
Yixiong Liang
Keyword(s):  
Sediment Particle ◽  
Particle Detection ◽  
Urine Sediment ◽  
Microscopic Images ◽  
Propagation Network

Comparative study of sediment particle mixing in a Zostera noltei meadow and a bare sediment mudflat

2014 ◽  
Vol 514 ◽  
pp. 71-86 ◽  
Author(s):  
G Bernard ◽  
ML Delgard ◽  
O Maire ◽  
A Ciutat ◽  
P Lecroart ◽  
...  
Keyword(s):  
Comparative Study ◽  
Sediment Particle ◽  
Bare Sediment ◽  
Zostera Noltei ◽  
Particle Mixing

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Electromagnetic waves

2018 ◽  
Author(s):  
Nathalie Deruelle ◽  
Jean-Philippe Uzan
Keyword(s):  
Plane Wave ◽  
Electromagnetic Waves ◽  
Maxwell Equations ◽  
Plane Waves ◽  
Geometrical Optics ◽  
Particle Detection ◽  
Spatial Coordinates ◽  
A Charge

This chapter examines solutions to the Maxwell equations in a vacuum: monochromatic plane waves and their polarizations, plane waves, and the motion of a charge in the field of a wave (which is the principle upon which particle detection is based). A plane wave is a solution of the vacuum Maxwell equations which depends on only one of the Cartesian spatial coordinates. The monochromatic plane waves form a basis (in the sense of distributions, because they are not square-integrable) in which any solution of the vacuum Maxwell equations can be expanded. The chapter concludes by giving the conditions for the geometrical optics limit. It also establishes the connection between electromagnetic waves and the kinematic description of light discussed in Book 1.

scholarly journals Interaction of Very Large Scale Motion of Coherent Structures with Sediment Particle Exposure

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 248
Author(s):  
Sencer Yücesan ◽  
Daniel Wildt ◽  
Philipp Gmeiner ◽  
Johannes Schobesberger ◽  
Christoph Hauer ◽  
...  
Keyword(s):  
Coherent Structures ◽  
Large Scale ◽  
Numerical Study ◽  
Local Maximum ◽  
Wave Number ◽  
Exposure Level ◽  
Sediment Particle ◽  
High Wave Number ◽  
Large Scale Motion ◽  
Scale Motion

A systematic variation of the exposure level of a spherical particle in an array of multiple spheres in a high Reynolds number turbulent open-channel flow regime was investigated while using the Large Eddy Simulation method. Our numerical study analysed hydrodynamic conditions of a sediment particle based on three different channel configurations, from full exposure to zero exposure level. Premultiplied spectrum analysis revealed that the effect of very-large-scale motion of coherent structures on the lift force on a fully exposed particle resulted in a bi-modal distribution with a weak low wave number and a local maximum of a high wave number. Lower exposure levels were found to exhibit a uni-modal distribution.

The study of needle type influence on woven fabric surface area during the sewing process

2021 ◽  
pp. 004051752110191
Author(s):  
Beti Rogina-Car ◽  
Stana Kovačević
Keyword(s):  
Computer Program ◽  
Tensile Properties ◽  
Surface Area ◽  
Cotton Fabrics ◽  
Woven Fabric ◽  
Microscopic Images ◽  
Needle Type ◽  
Fabric Surface ◽  
Needle Point

The aim of this study was to investigate the damage to cotton fabrics (ticking and damask) caused by stitching with three types of needle point shapes (R, SES and SUK) and four needle sizes (70, 80, 90 and 100 Nm). Damage to the yarn and the surface area of the hole were investigated. Based on the results, it can be concluded that two types of damage occur during sewing: the needle passes through the warp/weft (it displaces the yarn) and the needle damages the warp/weft. An analysis and comparison of the surface area of the holes was carried out, obtained by a computer program based on microscopic images. The results show greater damage to the yarn at the needle piercing point in the ticking due to higher density, friction and low yarn migration. The largest surface area of the holes was produced when sewing with SUK-designated needles on ticking and damask. When sewing damask, R-designated needles cause the least damage to the piercing point, whereas SES-designated needles give the best results when sewing the ticking. Thread damage was further confirmed by testing the tensile properties of the yarn at the needle piercing points.

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