Neural Network Processing of Optical Fiber Sensor Signals for Impact Location

1994 ◽  
Vol 360 ◽  
Author(s):  
Paul M. Schindler ◽  
John K. Shaw ◽  
Russell G. May ◽  
Richard O. Claus
Keyword(s):  
Neural Network ◽  
Optical Fiber Sensor ◽  
Back Propagation ◽  
Strain Sensors ◽  
Time Data ◽  
Impact Location ◽  
Fabry Perot ◽  
Sensor Signals ◽  
Propagation Network ◽  
The Impact

AbstractA system to detect and locate impacts by foreign bodies on a surface was developed and tested. Fiber optic extrinsic Fabry-Perot interferometer (EFPI) strain sensors were attached to or embedded in the surface, so that stress waves emanating from an impact could be detected. By employing an artificial neural network to process the sensor outputs, the impact location could be inferred to centimeter range accuracy directly from the arrival time data. In particular, the network could be trained to determine impact location regardless of material anisotropy. Results demonstrate that a back-propagation network identifiesimpact location for an anisotropic graphite/bismaleimide plate with the same accuracy as that for an isotropic aluminum plate.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Determination of the Impact Location and Damage Characterization Based on Guided Waves

2016 ◽  
Vol 713 ◽  
pp. 10-13 ◽  
Author(s):  
A. de Luca ◽  
Zahra Sharif Khodaei ◽  
Francesco Caputo
Keyword(s):  
Neural Network ◽  
Guided Waves ◽  
Training Phase ◽  
Damage Mode ◽  
Impact Location ◽  
Damage Characterization ◽  
Artificial Neural Network Ann ◽  
The Impact ◽  
Damage Criteria

The aim of this paper is to understand the effects of the damage criteria modelling on the training phase (performed by means of Finite Element simulations) of an artificial neural network (ANN) enabled to locate impacts onto a CFRP laminate. The developed FE models have been also used to investigate the intra-laminar damage mode, which, among different ones, has the most effects on the residual strength of the panel.

An intelligent system for selection of grinding wheels

1997 ◽  
Vol 211 (8) ◽  
pp. 635-641 ◽  
Author(s):  
Y Li ◽  
B Mills ◽  
W B Rowe
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Back Propagation ◽  
Grinding Wheel ◽  
Neural Network System ◽  
Grinding Conditions ◽  
Training Examples ◽  
Using Data ◽  
Hidden Layer ◽  
Propagation Network

This paper describes the development of a neural network system for grinding wheel selection. The system employs a back-propagation network with one hidden layer and was trained using data from reference handbooks. It is shown that a neural network is capable of learning the relationship between the wheel and the grinding process without a requirement for rules or equations. It was further found that a relatively small number of training examples allows the system to produce reliable recommendations for a much greater number of combinations of grinding conditions. The system was developed on a PC using the C++ programming language.

Comparison of BP and GRNN Algorithm for Factory Monitoring

2011 ◽  
Vol 52-54 ◽  
pp. 2105-2110 ◽  
Author(s):  
Ing Jiunn Su ◽  
Chia Chih Tsai ◽  
Wen Tsai Sung
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Predictive Ability ◽  
Network Models ◽  
General Regression Neural Network ◽  
Data Sets ◽  
Artificial Neural ◽  
Propagation Network ◽  
General Regression ◽  
Monitoring Area

Artificial neural networks (ANNs) are one of the most recently explored advanced technologies which show promise in the factory monitoring area. This paper focuses on two particular network models, back-propagation network (BPN) and general regression neural network (GRNN). The prediction accuracy of these two models is evaluated using a practical application situation in a monitor factory. GRNN emerged as a variant of the artificial neural network. Its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. According the simulation results we can show that GRNN is an effective way to considerably improve the predictive ability of BPN.

scholarly journals Monitoring Method of Total Seed Mass in a Vibrating Tray Using Artificial Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3659 ◽  
Author(s):  
Zhan Zhao ◽  
Fang Qin ◽  
Chun-Jie Tian ◽  
Simon Yang
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Back Propagation ◽  
Seed Mass ◽  
Force Sensor ◽  
Monitoring Method ◽  
Four Corners ◽  
Measurement Results ◽  
Total Seed ◽  
The Impact

To maintain the continuous working performance of a vacuum plate seeder, it is important to monitor the total seed mass in the seed tray in real time and accurately control the pickup position of the suction plate accordingly. Under the excitation of reciprocating vibration varying with time and interference by direction angle, the motion of seeds in a rectangular tray was simulated using the discrete element method (DEM). A measurement method for seed mass in a small area was proposed based on the impulse theorem. The impact force of seeds was monitored with a cantilever force sensor, and the corresponding signal processing circuit was designed. Calibration results indicated that the relative nonlinear error was less than 2.3% with an average seeds-mass-per-unit-area (SMA) of 0.3–2.4 g/cm2. Then, four sets of force sensors were installed symmetrically near the four corners of the vibrating tray which were used to measure the SMA respectively. Back propagation (BP) neural networks which take four SMA measurement results as input parameters were developed to monitor the total seed mass in the tray. Monitoring results using DEM simulation data showed that the general relative error was 3.0%. Experiments were carried out on a test-rig and the results validated that the relative error was reduced to 5.0% by using the BP neural network method.

Prediction of Shear Strength for Subgrade Soil under Freezing-Thawing Based on Back Propagation Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 610-614 ◽  
Author(s):  
Jing Long Chen ◽  
Pei Feng Cheng ◽  
Chuan Jun Yin
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Transfer Function ◽  
Sandy Soil ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Friction Angle ◽  
Back Propagation Neural Network ◽  
Linear Transfer Function ◽  
Propagation Network

Soil samples are taken from two experimental roads in Heilongjiang province for the test. Then a prediction of shear strength is carried out, basing on a three-layer BP (back propagation) network in Matlab, the hidden layer, output layer and training function of which adopt non-linear transfer function tansig, linear transfer function purelin, and trainbfg function respectively. It is found workable to predict factors influencing shearing strength using BP neural network with given soil properties. Prediction results of cohesion strength for clay show a better performance than those for sandy soil, while results of friction angle for sandy soil are better than those for clay. It is indicated that BP neural network does a better work in predicting the friction angle than that of cohesion.

Mechanics Analysis of Long Jump by Means of the Theory of Artificial Neural Networks

2014 ◽  
Vol 1014 ◽  
pp. 106-109
Author(s):  
Qun Long Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Excellent Performance ◽  
Mechanics Analysis ◽  
Long Jump ◽  
Artificial Neural ◽  
Run Up ◽  
Propagation Network ◽  
Mechanics Characteristics

Takeoff is extremely important to long jump. The paper analyzes the mechanics characteristics of takeoff for long jump by means of the theory of neural network. It firstly discusses some importantly influencing factors for long jump in theory. On the basis of description of the theory of Artificial Neural Network, the back propagation network is applied to model the long jump. The results show that an excellent performance of long jump is depend on a rapid run-up speed and the rhythm of the final two steps.

scholarly journals Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shoujing Zhang ◽  
Xiaofan Qin ◽  
Sheng Hu ◽  
Qing Zhang ◽  
Bochao Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Inventory Management ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Spare Part ◽  
Back Propagation Neural Network ◽  
Spare Parts ◽  
Training Data ◽  
Importance Value ◽  
The Impact

The quantitative evaluation of the importance degree of spare parts is essential as spare parts’ maintenance is critical for inventory management. Most of the methods used in previous research are subjective. For this reason, an accurate method for the evaluation of the importance degree combining an improved clustering algorithm with a back-propagation neural network (BPNN) is proposed in the present paper. First, we classified the spare parts by analyzing their historical maintenance and inventory data. Second, we evaluated the effectiveness of classification using the Davies–Bouldin index and the Calinski–Harabasz indicator and verified it using the training data. Finally, we used BPNN to determine the training data necessary for an accurate assessment of the importance degree of spare parts. The previous importance evaluation methods were susceptible to subjective factors during the evaluation process. The model established in this paper used the actual data of the company for machine learning and used the improved clustering algorithm to implement training and classification of spare parts data. The importance value of each spare part was output, which additionally reduced the impact of subjective factors on the importance evaluation. At the same time, the use of less data to evaluate the importance of spare parts was achieved, which improved the evaluation efficiency.

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