Quantifying the thickness of each color material in multilayer transparent specimen based on transmission image

Combing the color transmission image and the Beer–Lambert law shows a great application prospect in quantifying each material in multilayer specimen. Here, a novel, low-cost, and efficient optical algorithm is proposed to predict the thickness of each color material in a multilayer specimen from the color transmission image based on the Beer–Lambert Law. In this work, a normal scanner is employed to achieve the color transmission image of the monochrome transparent films. RGB values represent the transmitted intensity. A linear relationship between the optical depth and physical thickness is observed under different monochromatic lights. It is supposed that for a multilayer transparent film which consisted of different monochrome transparent films, the optical depth is related to the physical thickness of each monochrome transparent component. Therefore, an estimating equation is proposed to predict the thickness of each color material in the multilayer specimen. According to the result, the standard deviation of predicted thickness and practical thickness of each color film in the multilayer specimen is 0.93%. Fairly good agreement and high accuracy are obtained between the practical and predicted values, and the validity of this method is confirmed.

Design and Construction of an Imaging beamline at the Nagoya University Neutron Source

The Nagoya University Accelerator driven Neutron Source (NUANS) is constructed at the main campus of the Nagoya University. The electrostatic accelerator is used with the maximum proton energy and intensity of 2.8MeV, 15mA(42kW) respectively. Two neutron beamlines are designed at NUANS. The BL1 is dedicated to BNCT development. The BL2 is designed for research and development for neutron devices and neutron imaging. The neutrons used for the BL2 are generated by using the (p, n) reaction from a thin beryllium target. We constructed a compact target station for the BL2 and measured the neutron transmission image.

Algorithm for predicting the length of each color fiber in mixed-wool fiber assemblies based on the transmission image

A color-separation algorithm was proposed to predict the length of each color fiber in mixed-wool fiber assemblies based on a red, green, and blue transmission image. In this work, mixed-wool fiber assemblies consisted of different color wool fibers and a digital color image was obtained by a scanner. The relative thickness of the fiber assemblies was measured based on the Beer-Lambert theory. The color-separation formula was constructed to calculate the quantity of each color fiber at every point of the mixed-wool top to achieve the relative linear density curve and the average length. A series of systematic experiments demonstrated high consistency with the reference relative linear density curve and average length and confirmed the validity of the color-separation formula. This algorithm could be used for quality detection and control of mixed-wool tops. It could be also extended to uniformity detection of other mixed-color fiber assemblies.

Extracting fiber length distributions from dual-beard fibrographs with the Levenberg–Marquardt algorithm

Fiber length is a critical cotton fiber property that impacts yarn strength, yarn evenness, and ultimately fabric strength and appearance. In this paper, a new fiber fibrograph method was presented for accurate measurements of fiber length distributions (FLDs). The method, called the dual-beard fibrograph (DBF), was based on the transmission image of a combed sample with two tapered fiber ends/beards, and the approximation of the fibrograph with a series of triangular base functions. A desktop scanner was used to generate the transmission image of a dual-beard sample, and essential image-processing algorithms were utilized to mitigate image differences originating from variations in the scanning condition (e.g., brightness, resolution) and the sample condition (e.g., weight, orientation). The fibrograph approximation was implemented by minimizing a cost function that contains the sum of squared errors between the DBF and the ensemble of the weighted triangular base functions, and the regularization term that stabilizes the optimization with the Levenberg–Marquardt algorithm. The minimization eventually determined the optimal weights of the triangular base functions, which defined the FLDs of the scanned image. Important length measurements currently used in the industry can be easily calculated from the FLD. It was found that the DBF could correctly detect fiber lengths cut from 5 to 10 mm, respectively, and it could measure the short fiber content change after a known number of short fibers was added to an existing sample. When compared with an existing fiber testing instrument, the DBF was able to output more reasonable cotton length distributions.