Aircrafts type recognition based on shortwave communication

Preparation and artificial neural networks analysis of ultrafine β-Sialon powders by microwave-assisted carbothermal reduction nitridation of sol–gel derived powder precursors

The additional method of fire detection is not reliable and accurate as be influenced by random environmental disturbance. In this paper, first, a novel identification method for fire based on the process information of feature and the neural network of fire detection is discussed. CO and CO2 gas concentration ratio, the ratio of increase in rates and characteristics of the acceleration are chosen as characteristic parameters of the process of fire and input of three layers back-propagation (BP) artificial neural network, which is extracted by extrapolation method and least square method. Second, the true and false fire source can be identified effectively by the trained network based on improved BP arithmetic-LM arithmetic. The tests prove that fire detection system based on BP artificial neural network can detected the process signal of fire and identify the true fire effectively. The reliability and accuracy of fire detection makes better improvement compared with traditional method.

In this study, we present a global database of ten parameters, which include measurements of rock index properties, strength stiffness and dynamic properties. Hoek–Brown constant mi, is included, and was estimated, using Hoek and Brown proposed guidelines for determining mi values for different rock types that can be used for preliminary design when triaxial tests are not available. This broad database is compiled from 96 studies and is labelled as “ROCK/10/4025”, to describe the type of geomaterial, the number of the parameters, and the number of the data samples included. It consists of 35.4 % igneous, 54.8 % sedimentary, and 9.2% metamorphic rocks. The purpose of this paper is to propose a generic soft computing model applicable to multiple lithologies, that can become more reliable and perhaps more suitable for a specific site study when used in order to densify often limited similar site-specific data. To this end four broad samples of data were selected, and served as training data sets for developed machine learning models, to develop a generic compression strength prediction model applicable to multiple lithologies. The suggested algorithms in this study are Back-Propagation Artificial Neural Networks, Artificial Neuro-Fuzzy Inference Systems, Support Vector Machines, Nearest Neighbour classifiers and Ensemble Bagged Trees. According to the findings of this study, Artificial Neuro-Fuzzy Inference Systems model performance was found to be marginally superior, while Back Propagation Artificial Neural Networks, Support Vector Machines and Ensemble Bagged Trees models were found to have good performance. Constant mi seems to be an important training parameter when training predictive models centred on data from multiple lithologies. As a result, we can suggest that these models are powerful tools that allow for a reliable estimation of compressive strength, based on the performance indicators. The performance was found to be 70%–82% when the problem of compressive strength prediction was approached as a classification problem (that is successful prediction of class from very weak to very strong), and 80%–96% when solved as a function approximation problem.

Variants of feedforward backpropagation (BP) artificial neural network (ANNs) such as pseudo Mac Laurin power series order 1 (McL1BP), order 2 (McL2BP), order 3 (McL3BP) and order 4 (McL4BP) models are used to fill in mean annual streamflows. The baseline of modeling process is the standard feedforward backpropagation ANN (StandBP). The sigmoid function is used as activation function. Performance comparisons of data infilling models (ANNs) are conducted using the Root Mean Square Error of Predictions (RMSEp) and graphical plots. To test the performance of the data infilling techniques, selected streamflow gauges (i.e. the Diepkloof (control) gauge on the Wonderboomspruit River and the Molteno (target) gauge on Stormbergspruit River) of the Orange drainage river systems of South Africa have been used. The results demonstrated that relatively higher order ANN models; i.e. McL3BP, McL4BP can still outperform the rest of techniques for 7 % (except StandBP), 20 % and 30 % missing data proportions at Molteno gauge. At the same time they have been outperformed by the rest of ANN models at 13 %. Generally higher and low order Pseudo MacLaurin ANNs are acceptable to fill in missing mean annual flows at Molteno gauge. The accuracy of estimated mean annual values at Molteno is substantially negatively affected beyond 20 % gap size for all ANNs. Hence gap size beyond 20 % yields to relatively higher value of RMSEp for both high and low MacLaurin feedforward ANN techniques. A linear relationship could describe accuracy of estimated values and gap size at Molteno. Further work should include other data regimes as well as other South African catchment areas. Other activation functions should also be tested.

Objectives: Evaluating daily functioning of people with mental illnesses with questionaires is time-consuming. Additionally, patients' familiarity with the context of function tests compromises test accuracy. Applying a computer-assisted support assessment system in this study, we intended to predict the daily functioning of the mentally ill objectively and conveniently. Methods: We collected 54 patients attending a psychiatric daycare ward at a medical center in the Taipei city. A five-fold cross-validation scheme was applied to minimize possible bias and to provide reliable estimates. We used discriminant analysis (DA) and a back-propagation neural network (BPN), to predict patients' daily functioning, according to gender, educational background, diagnosis, and age, on Chu's daily function scale. Results: Both models achieved high average overall accuracy of more than 70%. The BPN model had a high overall classification accuracy of 92.55%, 16.55% better than that of the DA model. Additionally, the discriminant function showed that young males not diagnosed with schizophrenia had better daily function. Conclusion: This study was found that the BPN as a computer-assisted assessment support system predicted daily functioning more effectively than DA. To predict daily functioning relatively more precisely, we suggest that future research need to expand the sample population and to use additional variables, such as patients' personality, family support, and living status.

Nano α-Al2O3 powders were prepared by thermal decomposition of ammonium aluminium carbonate hydroxide (AACH). The effects of α-Al2O3 crystal seeds, reactant concentration, content of active agent (PEG), annealing temperature and ZnF2 addition on the preparation of nano α-Al2O3 powder were studied. The results showed that nano α-Al2O3 powder with apparent crystallite size ∼40 nm could be prepared, and the complete transformation temperature of α-Al2O3 could be reduced from 1250 to 1050°C as α-Al2O3 seeds and ZnF2 were added. The prepared nano α-Al2O3 powders were characterised by X-ray diffraction (XRD), differential thermal analysis and thermogravimetric (TG-DTA) and scanning electron microscopy (SEM). A back propagation (BP) artificial neural network (ANN) has been used to establish models between the relative content of the nano α-Al2O3 powders and the preparation parameters. The results of checking experiments were in accordance with the calculated values.

With the development of data collection and storage capabilities in recent decades, abundant data have been accumulated in geotechnical engineering fields, providing opportunities for the usage of machine learning approaches. Thus, a rising number of scholars are adopting machine learning techniques to settle geotechnical issues. In this paper, the application of three popular machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT), as well as other representative algorithms in geotechnical engineering, is reviewed. Meanwhile, the applicability of diverse machine learning algorithms in settling specific geotechnical engineering issues is compared. The main findings are as follows: ANN, SVM, and DT have been widely adopted to solve a variety of geotechnical engineering issues, such as the classification of soil and rock types, predicting the properties of geotechnical materials, etc. Based on the collected relevant research, the performance of random forest (RF) in sorting soil types and assessing landslide susceptibility is satisfying; SVM has high precision in classifying rock types and forecasting rock deformation; and backpropagation ANNs and Hopfield ANNs are recommended to forecast rock compressive strength and soil settlement, respectively.

Temporary storage for collection of municipal solid waste is designed with various parameters, such as size of bin, collection frequency, average filling rate, number of bins, volume of material needed for bin production, bin density, and catchment area. The environmental impact of temporary storage is evaluated with emissions generated due to the production of net material used for bin production. An artificial neural network-back propagation method is proposed for modeling the temporary storage. In this proposed artificial neural network-back propagation network, the number of bins and global warming potential (GWP) based on the quantity of generated wastes are predicted. Thus, a set of different structures of artificial neural network-back propagation are investigated and then the best model is developed for temporary storage of municipal solid waste. The artificial neural network-back propagation model is trained using various parameters, such as number of neurons in the hidden layers, training tolerance, momentum, and learning rate. This model is helpful for the decision makers in choosing the environmentally sound options in the design of temporary storage for municipal solid waste management. Artificial neural network-back propagation can be successfully used to predict number of bins, GWP from waste generation rate of the city.

Artificial neural networks (ANN) consider classification as one of the most dynamic research and application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by back propagation algorithm [1]. A neural network represent a mathematical models of information processing, benefited from the human biological systems (i.e. a brain or nerve cell), where the neutral network can be trained and learned the same as a human brain does. The learning will be done by changing the weight during the training process and by using certain formula. One of the most known neural networks is the Back Propagation Network. This net has been used in variety of application areas. One of the, the classification of certain objects by known only a portion of information of the object to be classified. In this paper we shall use the back propagation network to classify the human blood groups, also we shall use the same program to be same analysis to find the best number of neurons in hidden layer that gives lower number of iteration.

To establish a practical and effective model in predicting the premature birth, using the Back-Propagation Artificial Neural Network (BPANN). This was a prospective cohort study. Data was gathered from pregnant women selected by cluster sampling method from 2010 to 2012 in Liuyang city, Hunan province and was randomly divided into training sample (to establish the prediction models), validation sample (to select the optimal network) and testing sample (to evaluate the prediction models) by ratio of 2:1:1. BPANN and logistic regression analysis were used to establish models while ROC was applied to evaluate the 'prediction models'. Among the 6 270 pregnant women, 265 premature births were seen, with the premature incidence as 4.22%. The 7 variables which entered into the forecasting model would include abnormal uterine or uterine deformity, parity, number of pregnancies, gestational hypertension, placenta previa, premature rupture of membrane and regular prenatal examination. Sensitivity, specificity, agreement rate and area under the ROC curve of BPANN were 67.65% , 84.87%, 84.12% and 0.795, respectively. However, the sensitivity, specificity, agreement rate and area under the ROC curve of logistic regression were 64.71%, 85.60%, 84.69% and 0.783, respectively. The newly established BPANN model was practical and reliable, which proved that this model was slightly better than the logistic regression in the prediction of premature birth.

Aiming at the fact that the current method of designing elastic constrained mechanism in swashplate engine is deficient and the vibration noise is very large, a new method for optimization design of constrained mechanism is put forward based on back propagation (BP) artificial neural network and genetic algorithm (GA). Firstly the dynamics model of constrained mechanism-shafting in swashplate engine is set up, and the samples are gained by numerical simulation; then the non-linear mapping relationship of elastic constrained mechanism's designed parameters and the objective function is established with BP neural network; finally the trained network is called back by GA to make global optimization. The optimization results show that the global optimal resolution can be searched rapidly and correctly with the method, besides, the method is precise, and the vibration damping effect of the optimized elastic constrained mechanism is very conspicuous.

This manuscript considers a new architecture to handwritten characters recognition based on simulation of the behavior of one type of artificial neural network, called the Error Back Propagation Artificial Neural Network (EBPANN). We present an overview of our neural network to be optimized and tested on 12 offline isolated Arabic handwritten characters (???????????????? ?,?,???) because the similarity of some Arabic characters and the location of the points in the character. Accuracy of 93.61% is achieved using EBPANN which is the highest accuracy achieved during Offline Handwritten Arabic Character Recognition. It is noted that the EBPANN in general generates an optimized comparison between the input samples and database samples which improves the final recognition rate. Experimental results show that the EBPANN is convergent and more accurate in solutions that minimize the error recognition rate.

Nonstationary flow conditions can introduce complexities and nonlinear characteristics to pumping systems. This paper presents comparative studies of impeller fault detection techniques combined with artificial neural networks (ANNs) to propose the most appropriate diagnosis system. An experimental study, including seven impeller conditions, is performed to further explore the phenomena. Statistical parameters, frequency peaks, and wavelet packet energy present data feature sets, and a three-layer back-propagation ANN is used for fault recognition. The verification of the results proves that the detectability of the wavelet packet transform (WPT)-ANN model is considerably improved by using the energy of the decomposed vibration from WPT. This model can save computational time and provide superior diagnostic information. This study provides two key contributions. First, the feasibility and effectiveness of common monitoring techniques are compared. Second, the results demonstrate the accuracy of the proposed models for impellers operating under variable working conditions, which has not been previously addressed in the literature.

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations.

To monitor third-party damage (TPD) activities on oil transmission pipeline such as man-made drilling, hammering and excavating on metallic pipe, acoustic method is proposed based on wavelet packet energy feature extraction and least square support vector machine (LS-SVM). To effectively detect and classify pipe TPD signals with small sampling, multi-class LS-SVM classifier algorithm and a novel feature extraction method is presented. Original TPD signal is divided into third level with wavelet transform, then approximation signal which covers main information of TPD signal is extracted to be decomposed into third level with wavelet packet decomposition. Wavelet packet energy is selected as feature to LS-SVMs. Feature extraction method reduces computation cost of on-line implement. When detection spacing is 600m, four TPD signals: normal, drilling, hammering and excavating conditions, classification success rate is more than 85%. The monitoring system can effectively detect and classify pipe acoustic TPD signal