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Bar Coding and Other Marking Systems
Published in Jack Arabian, Computer Integrated Electronics Manufacturing and Testing, 2020
Optical character reading or recognition attempts to combine machine readability with human readability. There are both similarities to and contrasts with the bar coding technique, although OCR requires a much more sophisticated technology than bar code reading (see Reference 5-02). Each human-readable character to be read is placed on a grid which is divided into smaller elements called pixels (Reference 5-10). Each pixel is then illuminated for the presence or absence of light depending on the presence of light-absorbing ink or a light-reflective background. A portion of the character to be read is in each pixel. The scanned pixels are then electronically translated to the printed character and the recognition is made. As in bar codes, the quality of printing is vital to the accuracy of the interpretation. Special fonts have been developed by the National Retail Merchants Association (NRMA) in order to make elite character shapes which are electronically and optically efficient to read and still able to maintain their appearance for human readability. Character font sets known as OCR-A and OCR-B have been generated. Pattern defects in OCR technology are known as “scobs” and, if present, can cause erroneous readings. Once again, standards and specifications are needed to identify the requirements for OCR systems. For example, for an OCR-A character set read by a silicon diode detector, specifications require that the background medium reflect at least 70% of the light to which it is exposed, and the printing ink should absorb more than 50% of the same illumination (Reference 5-10).
Technologies Supporting Supply Chain Safety Management
Published in Andrzej Szymonik, Robert Stanisławski, Supply Chain Security, 2023
Andrzej Szymonik, Robert Stanisławski
Optical character recognition (OCR) is a technology that allows the conversion of scanned text into digital form (Berchmans & Kumar, 2014). OCR is developing in two directions. On the one hand, better and better text recognition methods are being developed, including handwritten texts. Intelligent character recognition (ICR) deals with the latter. On the other hand, special fonts are being created to facilitate reading, i.e. OCR-A and OCR-B. Programs that work on images sent from scanners and digital cameras are used for character recognition. Special fonts are usually used when there is a need for frequent, fast and easy reading of information. The OCR systems employ barcode, typewriter, magnetic type, block type.
On constitutive modelling of anisotropic viscous and non-viscous soft soils
Published in António S. Cardoso, José L. Borges, Pedro A. Costa, António T. Gomes, José C. Marques, Castorina S. Vieira, Numerical Methods in Geotechnical Engineering IX, 2018
M. Tafili, Th. Triantafyllidis
The compression λ and swelling index κ, are calibrated with isotropic or oedometric compression tests in the e vs. ln(p) space upon loading and unloading paths, respectively. The Poisson ratio ν and the anisotropic coefficient pha can be determined by measuring the shear modulus G for small strain amplitudes and using the relations given in Eq. 9 and the transverse isotropic stiffness H from Eq. 11. The parameter Mc is adjusted to the slope of the critical state line CSL within the p vs. q space under triaxial compression. The parameter fb0 controls approximately the maximum stress ratio for triaxial compression fb0 = ηmax/Mc and can therefore be adjusted to highly overconsolidated samples OCR > 2. When data is scarce, a recommended value of fb0 = 1.3 may be carefully used according to our experience with some soft soils. The reference void ratio ei0 corresponds to the maximum void ratio at the reference pressure pref = 1 kPa. Due to the decomposition of the strain rate into three parts, what differs this model to other models, the ei0 should be calibrated at the isotach with infinite strain rate || ε˙ ||= ∞. To simplify the calibration (Fuentes et al. 2017) deduced the relation ei0 = eri − λ ln(OCRri). For the viscosity index two isotropic compression tests with different strain rates, || ε˙a and ||ε˙b||, respectively, are required. Iv can then be computed through the relation Iv = ln(OCRb/OCRa/ln(|| ε˙a/|| ε˙b||) (Niemunis 2003). The parameter nocr controls the shape of the OCR surface. We recommend to calibrate this parameter by trial and error given some undrained tests.
Quasi-site-specific multivariate probability distribution model for sparse, incomplete, and three-dimensional spatially varying soil data
Published in Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2022
Jianye Ching, Kok-Kwang Phoon, Zhiyong Yang, Armin W. Stuedlein
The HBM is adopted to learn the information of the (μs, Cs) values for the generic sites in the CLAY/10/7490 database for subsequent analyses in 3D site characterisation. This clay database contains (LL, PI, LI, , OCR, , Bq, qt1) data from 200+ sites. The data for each site are also sparse and incomplete, but the HBM can handle sparse and incomplete data. The learning outcome is the hyper-parameter samples {(μ0,i,C0,i,Σ0,i,ν0,i): i = 1, … , NHBM}, where NHBM is taken to be 1,000. Some of the learning outcomes for the CLAY/10/7490 database have been presented in Ching, Wu, and Phoon (2021).