Although the integral quantities of atmospheric turbulence are conveniently measured using sonic anemometers, obtaining relevant finescale variables such as the kinetic energy dissipation using conventional hot-film/wire techniques remains a challenge because of two main difficulties. The first difficulty is the mean wind variability, which causes violation of the requirement that mean winds have a specific alignment with the hot-film/wire probe. To circumvent this problem, a combination of collocated sonic and hot-film anemometers, with the former measuring mean winds and aligning the latter in the appropriate wind direction via an automated platform, is successfully designed and implemented. The second difficulty is the necessity of frequent and onerous calibrations akin to hot-film anemometry that lead to logistical difficulties during outdoor (field) measurements. This is addressed by employing sonic measurements to calibrate the hot films in the same combination, with the output (velocity) to input (voltage) transfer function for the hot film derived using a neural network (NN) model. The NN is trained using low-pass-filtered hot-film and sonic data taken in situ. This new hot-film calibration procedure is compared with the standard calibration method based on an external calibrator. It is inferred that the sonic-based NN method offers great potential as an alternative to laborious standard calibration techniques, particularly in the laboratory and in stable atmospheric boundary layer settings. The NN approximation technique is found to be superior to the conventionally used polynomial fitting methods when used in conjunction with unevenly spaced calibration velocity data generated by sonic anemometers.Corresponding author address: E. Kit, School of Mechanical Engineering, Tel-Aviv University, Ramat Aviv, Tel Aviv 69978,
This paper presents a study of nonwoven fabric irregularity. The method of two- dimensional random fields has been used to investigate longitudinal, transverse, and area irregularities, and micro- and macro-scale irregularities are determined. The effects of inherent random irregularity and transverse nonhomogeneity are described, and a method for separating these effects is suggested. Computer-aided simulation is used to verify and illustrate algorithms for the nonhomogeneous and irregularity analysis as applied to nonwovens. The influence of an average cross-profile of random field irreg ularities on the coefficient of variation in the transverse direction is determined. A macro-scale irregularity analysis of chemically bonded nonwoven fabrics yields esti mates of the longitudinal, transverse, and area irregularities, the value of the "degree of influence," estimates of two-dimensional correlation functions, and a set of corre lation functions in the longitudinal direction.
Surface defects in fabrics are one of the most serious problems in the textile and apparel industries. In most cases we can define two types of these defects: defects concerned with a structural failure of the fabric, and defects concerned with protruding separate fibers or group of fibers from the surface of the fabric.The most common types of fabric surface defect are pilling, fuzziness, snagging, and hairiness. In general, these defects are formed during the daily use of the fabric as a garment or textile fabric. In order to predict how vulnerable a fabric is to wear and tear culminating in protrusions, accelerated tests have been devised. Several tests are aimed at evaluating the ability of the fabric to withstand rubbing and abrasion [1][2][3][4][5][6][7][8][9]. These standards determine the instruments and the procedures of inducing protrusions onto the fabric. An expert grades the protrusions either by a comparison with photos, holograms, or a written description. Some standards demand grading according to the relative change of the protrusions after being tested compared with the original samples before the tests have been applied.In general it is fair to say that the expert grading of tested fabric is subjective and in some cases it is difficult to carry out. The experts have a differing wide range of visual abilities as well as opinions on the severity of the defects.Hence, a substantial variation between the grading results of different experts is common.The problem of surface quality evaluation for textiles has been widely discussed in the literature [10-16] during the last 10-15 years. A number of sources mention the use of image processing techniques for fabric defect evaluation [13,14,[17][18][19][20][21][22]. The different methods have been used for fabric defect localization and evaluation including Fourier transform [23,24], artificial neural networks [25][26][27][28], fuzzy logic method [10,12,23], wavelet analysis [29][30][31][32][33], and others. Common to all of these works is the use of 2D images of fabric as the starting point of analysis. A number of publications are devoted to pilling detection and evaluation [12][13][14][15][16][17][18]25]. The above-mentioned methods were used in the majority of these publications. The original approach is based on the Gaussian fit theory and threshold grading for linear convolution of the pills parameters is used in [18].The method of 3D representation of fabric surface is used in works [34,35]. However, most of the works are based on 2D monocular images of fabric surface in contrast to 3D stereoscopic expert vision. Generally speaking, Abstract In this paper we present a new approach to the problem of textile surface quality evaluation. The approach is based on the capture and analysis of the profile of protrusions. The specific methods of image processing for protrusion detection and parameterization have been elaborated. New algorithms for the grading of four types of fabric defect -pilling, fuzziness, snagging, and hairiness -have been suggested, ...
A two-dimensional mathematical model of the carding process considers the longitudinal transport of fibers on the surface of the main cylinder and fiber diffusion in the transverse direction. The weight functions of the dynamic model in the longitudinal and transverse directions are derived, and the two-dimensional transfer function is
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