Age and Gender Classification Using Backpropagation and Bagging Algorithms

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

FORCE RIPPLE SUPPRESSION TECHNOLOGY FOR LINEAR MOTORS BASED ON BACK PROPAGATION NEURAL NETWORK

Various force disturbances influence the thrust force of linear motors when a linear motor （LM） is running. Among all of force disturbances, the force ripple is the dominant while a linear motor runs in low speed. In order to suppress the force ripple, back propagation（BP） neural network is proposed to learn the function of the force ripple of linear motors, and the acquisition method of training samples is proposed based on a disturbance observer. An off-line BP neural network is used mainly because of its high running efficiency and the real-time requirement of the servo control system of a linear motor. By using the function, the force ripple is on-line compensated according to the position of the LM. The experimental results show that the force ripple is effectively suppressed by the compensation of the BP neural network.

Aeromagnetic Compensation Algorithm Based on Levenberg-Marquard Neural Network

The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.

Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.

Robust Control Strategy for the Speed Control of Brushless DC Motor

Brushless DC motor ( BLDCM) speed servo system is multivariable,nonlinear and strong coupling. The parameter variation, the cogging torque and the load disturbance easily influence its performance. Therefore,it is difficult to achieve superior performance by using the conventional PID controller. To solve the deficiency,the paper represents the algorithm of active-disturbance rejection control ( ADRC) based on back-propagation ( BP) neural network. The ADRC is independent on accurate system and its extended-state observer can estimate the disturbance of the system accurately. However,the parameters of Nonlinear Feedback ( NF) in ADRC are difficult to obtain. So in this paper,these parameters are self-turned by the BP neural network. The simulation and experiment results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity,control accuracy,adaptability and robustness.

Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar

Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.

Artificial neural network approach to assess selective flocculation on hematite and kaolinite

Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared （FTIR） spectroscopy. A three-layer artificial neural network （ANN） model （4？4？3） was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values.

Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these security issues,the safety protection technology of the in-vehicle system has become a focus of research.Using the advanced autoencoder network and recurrent neural network in deep learning,we investigated the intrusion detection system based on the in-vehicle system.We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior.In order to verify the accuracy and efficiency of the proposed model,it was evaluated using real vehicle data.The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior.The parameters of the model are self-iteratively updated using the time-based back propagation algorithm.We verified that the model proposed in this study can reach a nearly 96%accurate detection rate.

Hybrid Image Compression-Encryption Scheme Based on Multilayer Stacked Autoencoder and Logistic Map

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic map.The proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

New pattern recognition system in the e-nose for Chinese spirit identification

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.

Fast Quantification of the Mixture of Polycyclic Aromatic Hydrocarbons Using Surface-Enhanced Raman Spectroscopy Combined with PLS-GA-BP Network

To realize the fast and accurate quantitative analysis of the mixture of polycyclic aromatic hydrocarbons(PAHs),surface-enhanced Raman spectroscopy(SERS)coupled with multivariate calibrations were employed.In this study,three kinds of calibration algorithms were used to quantitative analysis of the mixture of naphthalene(Nap),phenanthrene(Phe),and pyrene(Pyr).Firstly,partial least squares(PLS)algorithm was used to select characteristic variables,then the global search capability of genetic algorithm(GA)was used for the determining of the initial weights and thresholds of back propagation(BP)neural network so that local minima was avoided.The PLS-GA-BP model exhibited superiority to quantify PAHs mixture,which achieved R2=0.9975,0.9710,0.9643,ARE=10.07%,19.28%,16.72%and RMSE=13.10,5.40,5.10 nmol L−1 for Nap,Phe,Pyr(in the PAHs mixture)concentration prediction respectively.The forecast error,ARE and RMSE have been reduced more than 50%and 60%respectively compared with the whole spectral BP model.The study indicates that accurate quantitative spectroscopic analysis of the mixture of PAHs samples can be achieved through the combination of SERS technique and PLS-GA-BP algorithm.

A Method for the Environmental Quality Assessment of Surface Water Based on Artificial Neural Networks

An assessment method for the environmental quality of surface water was established based on artificial neural networks (ANN), in which different classification values were trained as learning samples. The assessment results from the major river section in Datong city indicated that the ANN model have characteristics of simple operation and distinct and quantitative expression of assessment results in comparison with the standard index method for the environmental quality assessment of surface water.