scholarly journals Visualization of the process-resource model for workflows

2020 ◽  
Author(s):  
Thomas Hoffman-Walbeck ◽  
◽  
Richard Adams II ◽  
Keyword(s):  
Resource Model ◽  
Fixed Grid ◽  
Input And Output ◽  
Type Process ◽  
Resource Group

We describe the design of a JavaScript prototype that allows the user to visualize an arbitrary production workflow for print production that is based on the Process-Resource Model. The user can create square cards of type “process,” “resource” or “resource group.” For each card, a name might be chosen out of a list and neighboring relations can be set (input and output). Each card should be positioned on a fixed grid. Finally, the user can check the workflow design according to different reasoning.

Resource Constituent of Russian Regional Economy

2007 ◽  
pp. 116-132 ◽  
Author(s):  
S. Kimelman ◽  
S. Andyushin
Keyword(s):  
Economic Policy ◽  
Economic Development ◽  
Russian Federation ◽  
Regional Economy ◽  
Federal District ◽  
Economic Potential ◽  
Resource Model ◽  
The Social ◽  
Regional Economic Policy ◽  
Far Eastern

The article basing upon estimation of the social and economic potential of Russian Federation subjects shows that the resource model of economic development is suitable for nearly half of them. The advantages of this model are described using the example of the Far Eastern Federal District subjects that could be the proof of the necessity of "resource correction" of regional economic policy in Russia.

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

Sign in / Sign up

Export Citation Format

Share Document