A Comparative Study of Impact Localisation in Composite Structures Using Neural Networks under Environmental and Operational Variations

2019 ◽  
Vol 827 ◽  
pp. 410-415
Author(s):  
Aldyandra Hami Seno ◽  
Ferri M.H. Aliabadi
Keyword(s):  
Composite Structures ◽  
Real Life ◽  
Training Data ◽  
Data Set ◽  
Operational Conditions ◽  
Feature Extraction Method ◽  
Set Size ◽  
Artificial Neural Network Ann ◽  
Training Impact ◽  
And Training

In this study we compare the effectiveness of the Normalised Smoothed Envelope (NSET) method for Artificial Neural Network (ANN) based impact localisation under simulated environmental and operational conditions with respect to other ANN based localisation methods developed by other studies. It is shown that when the testing and training impact case is the same, most studies give comparably good accuracy of localisation irrespective of feature extraction method or structure geometry. However, when the testing and training impact cases are not the same, only the NSET method is able to negate the variations caused by various impact cases and provide good localisation accuracy for an ANN trained using only a single impact case thus allowing for smaller training data set size requirements and increasing feasibility for real life application.

An Introduction to Deep Convolutional Neural Networks With Keras

2020 ◽  
pp. 231-272
Author(s):  
Wazir Muhammad ◽  
Irfan Ullah ◽  
Mohammad Ashfaq
Keyword(s):  
Graphics Processing Units ◽  
Real Life ◽  
Research Area ◽  
Training Data ◽  
Training Procedure ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Life Problems ◽  
Graphics Processing ◽  
And Training

Deep learning (DL) is the new buzzword for researchers in the research area of computer vision that unlocked the doors to solving complex problems. With the assistance of Keras library, machine learning (ML)-based DL and various complicated or unresolved issues such as face recognition and voice recognition might be resolved easily. This chapter focuses on the basic concept of Keras-based framework DL library to handle the different real-life problems. The authors discuss the codes of previous libraries and same code run on Keras library and assess the performance on Google Colab Cloud Graphics Processing Units (GPUs). The goal of this chapter is to provide you with the newer concept, algorithm, and technology to solve the real-life problems with the help of Keras framework. Moreover, they discuss how to write the code of standard convolutional neural network (CNN) architectures using Keras libraries. Finally, the codes of validation and training data set to start the training procedure are explored.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

scholarly journals Bootstrapping a Neural Morphological Generator from Morphological Analyzer Output for Inuktitut

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jeffrey Micher
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Data Set ◽  
Set Size ◽  
The Neural Network ◽  
Surface Character ◽  
Finite State ◽  
Character Sequences ◽  
Finite State Transducer

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.

scholarly journals Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques

2014 ◽  
Vol 17 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Gurjeet Singh ◽  
Rabindra K. Panda ◽  
Marc Lamers
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Agricultural Watershed ◽  
Training Data ◽  
Optimum Number ◽  
Training Process ◽  
Data Set ◽  
Reported Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

scholarly journals Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3659 ◽  
Author(s):  
Seno ◽  
Aliabadi
Keyword(s):  
Composite Structures ◽  
Lamb Waves ◽  
Real Life ◽  
Composite Plates ◽  
Information Criterion ◽  
Reference Database ◽  
Time Of Arrival ◽  
Operational Conditions ◽  
Contact Behaviour ◽  
Using Data

A parametric investigation of the effect of impactor stiffness as well as environmental and operational conditions on impact contact behaviour and the subsequently generated lamb waves in composite structures is presented. It is shown that differing impactor stiffness generates the most significant changes in contact area and lamb wave characteristics (waveform, frequency, and amplitude). A novel impact localisation method was developed based on the above observations that allows for variations due to differences in impactor stiffness based on modifications of the reference database method and the Akaike Information Criterion (AIC) time of arrival (ToA) picker. The proposed method was compared against a benchmark method based on artificial neural networks (ANNS) and the normalised smoothed envelope threshold (NSET) ToA extraction method. The results indicate that the proposed method had comparable accuracy to the benchmark method for hard impacts under various environmental and operational conditions when trained only using a single hard impact case. However, when tested with soft impacts, the benchmark method had very low accuracy, whilst the proposed method was able to maintain its accuracy at an acceptable level. Thus, the proposed method is capable of detecting the location of impacts of varying stiffness under various environmental and operational conditions using data from only a single impact case, which brings it closer to the application of data driven impact detection systems in real life structures.

Artificial neural networks in diagnosis of thyroid function from in vitro laboratory tests

1993 ◽  
Vol 39 (11) ◽  
pp. 2248-2253 ◽  
Author(s):  
P K Sharpe ◽  
H E Solberg ◽  
K Rootwelt ◽  
M Yearworth
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thyroid Function ◽  
Laboratory Tests ◽  
Extreme Values ◽  
Real Life ◽  
Training Data ◽  
Data Set ◽  
Artificial Neural

Abstract We studied the potential benefit of using artificial neural networks (ANNs) for the diagnosis of thyroid function. We examined two types of ANN architecture and assessed their robustness in the face of diagnostic noise. The thyroid function data we used had previously been studied by multivariate statistical methods and a variety of pattern-recognition techniques. The total data set comprised 392 cases that had been classified according to both thyroid function and 19 clinical categories. All cases had a complete set of results of six laboratory tests (total thyroxine, free thyroxine, triiodothyronine, triiodothyronine uptake test, thyrotropin, and thyroxine-binding globulin). This data set was divided into subsets used for training the networks and for testing their performance; the test subsets contained various proportions of cases with diagnostic noise to mimic real-life diagnostic situations. The networks studied were a multilayer perceptron trained by back-propagation, and a learning vector quantization network. The training data subsets were selected according to two strategies: either training data based on cases with extreme values for the laboratory tests with randomly selected nonextreme cases added, or training cases from very pure functional groups. Both network architectures were efficient irrespective of the type of training data. The correct allocation of cases in test data subsets was 96.4-99.7% when extreme values were used for training and 92.7-98.8% when only pure cases were used.

scholarly journals Zero-Shot Human Activity Recognition Using Non-Visual Sensors

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 825 ◽  
Author(s):  
Fadi Al Machot ◽  
Mohammed R. Elkobaisi ◽  
Kyandoghere Kyamakya
Keyword(s):  
Activity Recognition ◽  
High Performance ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Sensor Data ◽  
Sensor Technology ◽  
Training Dataset ◽  
Data Sets ◽  
Data Set

Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose instances have been already seen during training. Activity recognition methods based on real-life settings should cover a growing number of activities in various domains, whereby a significant part of instances will not be present in the training data set. However, to cover all possible activities in advance is a complex and expensive task. Concretely, we need a method that can extend the learning model to detect unseen activities without prior knowledge regarding sensor readings about those previously unseen activities. In this paper, we introduce an approach to leverage sensor data in discovering new unseen activities which were not present in the training set. We show that sensor readings can lead to promising results for zero-shot learning, whereby the necessary knowledge can be transferred from seen to unseen activities by using semantic similarity. The evaluation conducted on two data sets extracted from the well-known CASAS datasets show that the proposed zero-shot learning approach achieves a high performance in recognizing unseen (i.e., not present in the training dataset) new activities.

Selection of optimal external filter for colorimetric camera

2021 ◽  
Vol 2021 (29) ◽  
pp. 141-147
Author(s):  
Michael J. Vrhel ◽  
H. Joel Trussell
Keyword(s):  
Figure Of Merit ◽  
Band System ◽  
Image Data ◽  
Optimal Filter ◽  
Training Data ◽  
Noise Model ◽  
Data Set ◽  
Testing Data ◽  
And Training ◽  
Selection Of

A database of realizable filters is created and searched to obtain the best filter that, when placed in front of an existing camera, results in improved colorimetric capabilities for the system. The image data with the external filter is combined with image data without the filter to provide a six-band system. The colorimetric accuracy of the system is quantified using simulations that include a realistic signal-dependent noise model. Using a training data set, we selected the optimal filter based on four criteria: Vora Value, Figure of Merit, training average ΔE, and training maximum ΔE. Each selected filter was used on testing data. The filters chosen using the training ΔE criteria consistently outperformed the theoretical criteria.

Aerodynamic Prediction on the Off-Design Performance of a S-CO2 Turbine Based on Deep Learning

2021 ◽  
Author(s):  
Yuqi Wang ◽  
Tianyuan Liu ◽  
Di Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Performance Prediction ◽  
Aerodynamic Performance ◽  
Operating Conditions ◽  
Training Data ◽  
Brayton Cycle ◽  
Data Set ◽  
Set Size ◽  
The Mean

Abstract The research on the supercritical carbon dioxide (S-CO2) Brayton cycle has gradually become a hot spot in recent years. The off-design performance of turbine is an important reference for analyzing the variable operating conditions of the cycle. With the development of deep learning technology, the research of surrogate models based on neural network has received extensive attention. In order to improve the inefficiency in traditional off-design analyses, this research establishes a data-driven deep learning off-design aerodynamic prediction model for a S-CO2 centrifugal turbine, which is based on a deep convolutional neural network. The network can rapidly and adaptively provide dynamic aerodynamic performance prediction results for varying blade profiles and operating conditions. Meanwhile, it can illustrate the mechanism based on the field reconstruction results for the generated aerodynamic performance. The training results show that the off-design aerodynamic prediction convolutional neural network (OAP-CNN) has reduced the mean and maximum error of efficiency prediction compared with the traditional Gaussian Process Regression (GPR) and Artificial Neural Network (ANN). Aiming at the off-design conditions, the pressure and temperature distributions with acceptable error can be obtained without a CFD calculation. Besides, the influence of off-design parameters on the efficiency and power can be conveniently acquired, thus providing the reference for an optimized operation strategy. Analyzing the sensitivity of AOP-CNN to training data set size, the prediction accuracy is acceptable when the percentage of training samples exceeds 50%. The minimum error appears when the training data set size is 0.8. The mean and maximum errors are respectively 1.46% and 6.42%. In summary, this research provides a precise and fast aerodynamic performance prediction model in the analyses of off-design conditions for S-CO2 turbomachinery and Brayton cycle.

Simulating Multiphase Flow in Reservoirs with Generative Deep Learning

2021 ◽  
Author(s):  
Abdullah A. Alakeely ◽  
Roland N. Horne
Keyword(s):  
Deep Learning ◽  
Multiphase Flow ◽  
Flow Behavior ◽  
Training Data ◽  
Data Set ◽  
Operational Conditions ◽  
Heterogeneous Reservoirs ◽  
Reservoir Flow ◽  
Latent Space ◽  
Reservoir Simulator

Abstract This study investigated the ability to produce accurate multiphase flow profiles simulating the response of producing reservoirs, using Generative Deep Learning (GDL) methods. Historical production data from numerical simulators were used to train a GDL model that was then used to predict the output of new wells in unseen locations. This work describes a procedure in which data analysis techniques are used to gain insight into reservoir flow behavior at a field level based on existing historical data. The procedure includes clustering, dimensionality reduction, correlation, in addition to novel interpretation methodologies that synthesize the results from reservoir simulation output, characterizing flow conditions. The insight was then used to build and train a GDL algorithm that reproduces the multiphase reservoir behavior for unseen operational conditions with high accuracy. The trained algorithm can be used to further generate new predictions of the reservoir response under operational conditions for which we do not have previous examples in the training data set. We found that the GDL algorithm can be used as a robust multiphase flow simulator. In addition, we showed that the physics of flow can be captured and manipulated in the GDL latent space after training to reproduce different physical effects that did not exist in the original training data set. Applying the methodology to the problem of determining multiphase production rate from new producing wells in undrilled locations showed positive results. The methodology was tested successfully in predicting multiphase production under different scenarios including multiwell channelized and heterogeneous reservoirs. Comparison with other shallow supervised algorithms demonstrated improvements realized by the proposed methodology, compared to existing methods. The study developed a novel methodology to interpret both data and GDL algorithms, geared towards improving reservoir management. The method was able to predict the performance of new wells in previously undrilled locations without using a reservoir simulator.

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