Real Time Identification and Classification of Road Surface with Neural Network

1993 ◽  
Author(s):  
Toshinari Shiotsuka ◽  
Akio Nagamatsu ◽  
Kazuo Yoshida
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Road Surface ◽  
Real Time Identification

scholarly journals Real-Time Identification and Classification of Road Surface with Neural Network.

1993 ◽  
Vol 59 (564) ◽  
pp. 2353-2360
Author(s):  
Toshinari Shiotsuka ◽  
Takahiro Kobayashi ◽  
Kazuo Yoshida ◽  
Akio Nagamatsu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Road Surface ◽  
Real Time Identification

Research on the Key Technologies of Intelligent Control for Cap-Shape Bending of Sheet Metal

2011 ◽  
Vol 239-242 ◽  
pp. 2867-2872
Author(s):  
Hong Lei Sun ◽  
Chun Jian Su ◽  
Rui Xue Zhai
Keyword(s):  
Neural Network ◽  
Control System ◽  
Real Time ◽  
Sheet Metal ◽  
Intelligent Control ◽  
Feed Forward Neural Network ◽  
Forming Process ◽  
Intelligent Control System ◽  
The Neural Network ◽  
Real Time Identification

The blueprint for an intelligent control system of cap-shape bending has been advanced in this paper using neural network technology, aiming at an accurate control of bending springback, the prominent problem during the forming process for the cap-shape bending of sheet metal. The feed-forward neural network of real-time identification for material performance parameters and the friction coefficient have been established. The neural network identifies the parameters for real-time needed material performance, which utilizes the measurability of the physical quantities, and predicts the parameters for optimum technology, so a satisfied accuracy of convergence has been achieved. The intelligent control experimentation system of cap-shape bending has been established, the validity of which has been tested for four kinds of materials. The result of the tests proves the feasibility of the blueprint of the intelligent control system.

An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity

2019 ◽  
Vol 21 (9) ◽  
pp. 681-692
Author(s):  
Luis Francisco Barbosa-Santillán ◽  
María de los Angeles Calixto-Romo ◽  
Juan Jaime Sánchez-Escobar ◽  
Liliana Ibeth Barbosa-Santillán
Keyword(s):  
Neural Network ◽  
Enzymatic Activity ◽  
Real Time ◽  
High Throughput Screening ◽  
Cellulolytic Activity ◽  
Color Patterns ◽  
Cellulolytic Microorganisms ◽  
The Neural Network ◽  
Neural Network Classifiers

Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.

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