scholarly journals Artificial neural networks for retrieving absorption and reduced scattering spectra from frequency-domain diffuse reflectance spectroscopy at short source-detector separation

2016 ◽  
Vol 7 (4) ◽  
pp. 1496 ◽  
Author(s):  
Yu-Wen Chen ◽  
Chien-Chih Chen ◽  
Po-Jung Huang ◽  
Sheng-Hao Tseng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Frequency Domain ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Scattering Spectra ◽  
Artificial Neural

scholarly journals Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10672
Author(s):  
Philipp Lechner ◽  
Philipp Heinle ◽  
Christoph Hartmann ◽  
Constantin Bauer ◽  
Benedikt Kirchebner ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Frequency Domain ◽  
Typical Problem ◽  
Print Head ◽  
Acoustic Data ◽  
Artificial Neural ◽  
Binder Jetting

The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an Artificial Neural Network to classify whether the nozzles are functional or clogged. The acoustic data are studied in the frequency domain and utilized as input for an Artificial Neural Network. We found that it is possible to successfully classify individual nozzles well enough to implement a print head monitoring, which automatically determines whether the print head needs maintenance.

scholarly journals Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions

2017 ◽  
Vol 10 (01) ◽  
pp. 1650027 ◽  
Author(s):  
Mahmut Ozan Gökkan ◽  
Mehmet Engin
Keyword(s):  
Neural Networks ◽  
Optical Properties ◽  
Artificial Neural Networks ◽  
Biological Tissue ◽  
Diffuse Reflectance ◽  
Optical Parameters ◽  
Scattering Coefficients ◽  
Surface Image ◽  
Artificial Neural

Optical parameters (properties) of tissue-mimicking phantoms are determined through noninvasive optical imaging. Objective of this study is to decompose obtained diffuse reflectance into these optical properties such as absorption and scattering coefficients. To do so, transmission spectroscopy is firstly used to measure the coefficients via an experimental setup. Next, the optical properties of each characterized phantom are input for Monte Carlo (MC) simulations to get diffuse reflectance. Also, a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 5[Formula: see text] angle to the phantoms. For the illumination of light, a laser light source at 633[Formula: see text]nm wavelength is preferred, because optical properties of different components in a biological tissue on that wavelength are nonoverlapped. During in vitro measurements, we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion (CILE) and evans blue (EB) dye into a distilled water. Finally, all obtained diffuse reflectance values are used to estimate the optical coefficients by artificial neural networks (ANNs) in inverse modeling. For a biological tissue it is found that the simulated and measured values in our results are in good agreement.

scholarly journals Nondestructive Evaluation of Apple Fruit Quality by Frequency-Domain Diffuse Reflectance Spectroscopy: Variations in Apple Skin and Flesh

2019 ◽  
Vol 9 (11) ◽  
pp. 2355 ◽  
Author(s):  
Nan-Yu Cheng ◽  
Chien-Chih Chen ◽  
Bo-Jian Liang ◽  
Sheng-Hao Tseng
Keyword(s):  
Optical Properties ◽  
Nondestructive Evaluation ◽  
Frequency Domain ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Biological Tissues ◽  
Reflectance Spectroscopy ◽  
Apple Fruit ◽  
Scattering Coefficients

The optical properties of fruits, such as light absorption and scattering characteristics, change with biochemical activities during storage. Diffuse reflectance spectroscopy (DRS) systems have been widely applied for noninvasively observing biological tissues. In this study, we used a frequency-domain DRS system to measure the optical properties of apples. Results showed that variations in the chlorophyll, water, and flesh-texture of apples could be noninvasively monitored over time. We also observed substantial differences in the absorption and reduced scattering coefficients between injured and normal apples. The DRS techniques could be used for apple grading, and, by extension, for monitoring the quality of other fruits.

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