Material properties identification of a piezoelectric beam using inverse method

2019 ◽  
Vol 234 (7) ◽  
pp. 1351-1365 ◽  
Author(s):  
Mohammad Amin Nematollahi ◽  
Behzad Hasanshahi ◽  
Malihe Eftekhari ◽  
Ali Akbar Safavi
Keyword(s):  
Neural Network ◽  
Material Properties ◽  
Input Data ◽  
Inverse Method ◽  
Identification Problem ◽  
Mode Analysis ◽  
Network Training ◽  
Piezoelectric Beam ◽  
Actuation Mode ◽  
Identification Analysis

This paper presents an inverse method for material properties identification of a piezoelectric beam (piezoelectric charge and relative dielectric coefficients) using a wavelet-based neural network as an inverse tool. The identification analysis is carried out by using two approaches. In the first approach, i.e. sensor mode analysis, the input data for wavelet-based neural network training are measured voltages at several specific points on the beam's top surface resulting from the applied beam tip deflection. In the second approach, i.e. actuation mode analysis, the input data are values of the beam tip deflection caused by applying voltage on the beam's top surface. In this study, the input parameters employed to train the wavelet-based neural network are obtained using the finite element method. The identification results are compared with those of some conventional neural networks including radial basis function and multilayer perceptron. The results show that the proposed neural network is an efficient tool in the material properties identification problem.

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

scholarly journals Image Pattern Recognition in Spatial Data using Artificial Neural Network

2021 ◽  
Vol 884 (1) ◽  
pp. 012050
Author(s):  
Nursida Arif ◽  
Edi Nursantosa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Input Data ◽  
Neural Network Training ◽  
Network Training ◽  
Input Dataset ◽  
Artificial Neural ◽  
Artificial Neural Network Training ◽  
Image Pattern

Abstract This study predicts erosion based on the image patterns as the input data by using an ANN approach. Several simulations had been carried out to get the ANN parameter combination in producing the best accuracy through trials and errors. The results show that the accuracy of artificial neural network training is not influenced by the number of channels, namely the input dataset (erosion factors) and the dimensions of the data, but it is determined by changes in the network parameters. The best combination of parameters is 2 hidden layers, learning rate 0.001, Momentum 0.9, and RMS 0.0001 with an accuracy of 98.55%

scholarly journals Artificial Neural Network as a Virtual Sensor of Nitrate Nitrogen (V) Concentration in an Activated Sludge Reactor

2020 ◽  
Vol 30 (4) ◽  
pp. 188-200
Author(s):  
Lesław Płonka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Activated Sludge ◽  
Input Data ◽  
Nitrate Nitrogen ◽  
Wastewater Treatment Plants ◽  
Network Training ◽  
Activated Sludge Reactor ◽  
Artificial Neural

Abstract The paper discusses the use of an artificial neural network to control the operation of wastewater treatment plants with activated sludge. The task of the neural network in this case is to calculate (predict) the readings of the probe measuring the concentration of nitrate nitrogen (V) in one of the biological reactor tanks. Neural networks are known for their ability to universal approximation of virtually any relationship, including the function of many variables, but the process of “training” the network requires the presentation of many sets of input data and corresponding expected results. This is a difficulty in the case of wastewater treatment plants, because some key process parameters are usually not measured online (samples are taken and measurements are taken in the laboratory), and even if they are, the time intervals are large. Bearing in mind the aforementioned difficulty, this work uses a set of input data consisting only of information that can be measured with measuring probes. As a result of the conducted experiments a high compliance of the probe’s prediction with the expected values was obtained. The paper also presents data preparation and the network “training” process.

scholarly journals OPTIMIZATION OF THE INPUT DATA VECTOR TO IMPROVE THE NEURAL NETWORK TRAINING FOR OPC

2021 ◽  
Author(s):  
Georgiy Teplov ◽  
Almira Galeeva ◽  
Aleksey Kuzovkov
Keyword(s):  
Neural Network ◽  
Data Structure ◽  
Input Data ◽  
Input Vector ◽  
Neural Network Training ◽  
Network Training ◽  
The Neural Network ◽  
Data Vector ◽  
The Impact

This work explored the impact of input data structure to improve the neural network training. The impact of two variants of the input data vector on the training accuracy of the network was studied. The first version of the input vector included the intensity of the exposure radiation map. The second version of the input vector included the intensity of the exposure radiation map and IC topology.

scholarly journals Influence of Data Scaling and Normalization on Overall Neural Network Performances in Photoacoustics

2021 ◽  
Author(s):  
Miroslava Ivko Jordovic Pavlovic ◽  
Katarina Djordjevic ◽  
Zarko Cojbasic ◽  
Slobodanka Galovic ◽  
Marica Popovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Photoacoustic Signal ◽  
Output Data ◽  
Network Training ◽  
Input And Output ◽  
The Neural Network ◽  
Data Scaling ◽  
Logarithmic Scaling ◽  
Network Prediction

Abstract In this paper, the influence of the input and output data scaling and normalization on the neural network overall performances is investigated aimed at inverse problem-solving in photoacoustics of semiconductors. The logarithmic scaling of the photoacoustic signal amplitudes as input data and numerical scaling of the sample thermal parameters as output data are presented as useful tools trying to reach maximal network precision. Max and min-max normalizations to the input data are presented to change their numerical values in the dataset to common scales, without distorting differences. It was demonstrated in theory that the largest network prediction error of all targeted parameters is obtained by a network with non-scaled output data. Also, it was found out that the best network prediction was achieved with min-max normalization of the input data and network predicted output data scale within the range of [110]. Network training and prediction performances analyzed with experimental input data show that the benefits and improvements of input and output scaling and normalization are not guaranteed but are strongly dependent on a specific problem to be solved.

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.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

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