Cumulative sum and neural network approach to the detection and identification of hazardous chemical agents from ion mobility spectra

2008 ◽  
Author(s):  
Marja Ruotsalainen ◽  
Juho Vihonen ◽  
Juha Jylhä ◽  
Jarmo Kauppila ◽  
Osmo Anttalainen ◽  
...  
Keyword(s):  
Neural Network ◽  
Ion Mobility ◽  
Network Approach ◽  
Cumulative Sum ◽  
Neural Network Approach ◽  
Hazardous Chemical ◽  
Chemical Agents ◽  
Detection And Identification ◽  
Ion Mobility Spectra

Human-robot interaction detection: a wrist and base force/torque sensors approach

Robotica ◽  
2006 ◽  
Vol 24 (4) ◽  
pp. 419-427 ◽  
Author(s):  
Shujun Lu ◽  
Jae H. Chung ◽  
Stevens A. Velinsky
Keyword(s):  
Neural Network ◽  
Collision Detection ◽  
Human Robot Interaction ◽  
Impact Model ◽  
Network Approach ◽  
Robot Interaction ◽  
Neural Network Approach ◽  
Identification Scheme ◽  
Interaction Detection ◽  
Detection And Identification

In this paper, a collision detection and identification method of a manipulator, using wrist and base force/torque sensors, is presented. An impact model is used to simulate the interaction between the manipulator and the human or environment. A neural network approach and a model based method are developed to detect the collision forces and disturbance torques on the joints of the manipulator. The experimental results illustrate the validity of the developed collision detection and identification scheme.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach
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