BeiDou-3 navigation satellite system was officially opened in 2020.While bringing high-performance services to people around the world,the navigation system requires well-designed BeiDou antennas.In this paper,we prop...BeiDou-3 navigation satellite system was officially opened in 2020.While bringing high-performance services to people around the world,the navigation system requires well-designed BeiDou antennas.In this paper,we propose a wideband circularly polarized high-performance BeiDou antenna.The antenna realizes wideband circularly polarized radiation through a four-port sequential feed network,and the phase imbalance of the feed network from 1.05 to 1.80 GHz is less than 7°.The manufactured antenna demonstrates a return loss of more than 13 dB and an axial ratio<3 dB over the entire global navigation satellite system(GNSS)frequency band.The right-handed circular polarization(RHCP)gain of the proposed antenna is greater than 4 dB in the GNSS low-frequency band and can reach more than 7.1 dB in the high-frequency band.Dimension of the proposed antenna is 120 mm×120 mm×20 mm,i.e.,0.54λo×0.54λo×0.09λo,whereλo is the wavelength of the center frequency.The proposed antenna connected to a GNSS receiver has tracked 12 BeiDou satellites with C/N0 ratios of GNSS signals greater than 30 dB.Such a high-performance antenna provides a basis for high-quality positioning services.展开更多
In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data ...In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.展开更多
This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Sp...This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.展开更多
A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluct...A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.展开更多
In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other...In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model’s primary goal is to identify potential malfunctions at an early stage to ensure the production process’ continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.展开更多
This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in th...This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.展开更多
In this study, an application of artificial neural network （ANN） has been presented in modeling and studying the effect of compounding variables on abrasion behavior of rubber formulations. Three case studies were c...In this study, an application of artificial neural network （ANN） has been presented in modeling and studying the effect of compounding variables on abrasion behavior of rubber formulations. Three case studies were carried out in which the experiment data were collected according to classical response surface designs. Besides developing the ANN models, we developed response surface methodology （RSM） to confirm the ANN predictions. A simple relation was employed for determination of relative importance of each variable according to ANN models. It was shown through these case studies that ANN models delivered very good data fitting and their simulating curves could help the researchers to better understand the abrasion behavior.展开更多
Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been dev...Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.展开更多
Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied cat...Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> and Pd/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (<i><span style="white-space:nowrap;"><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">ø</span></span></i>) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners w<span style="white-space:normal;font-family:;" "="">as</span><span style="white-space:normal;font-family:;" "=""> scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (<i>r</i>), the radial distance from the center line of the flame, and (<i>x</i>), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three</span><span style="white-space:normal;font-family:;" "="">-</span><span style="white-space:normal;font-family:;" "="">layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate </span><span style="white-space:normal;font-family:;" "="">the </span><span style="white-space:normal;font-family:;" "="">effects of coded <i>R</i> and <i>X</i> input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of & F_Ratio are 0.9181</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9809 & 634.5</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 3528.8 for RSM method compared to 0.9857</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9951 & 7636.4</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 24</span><span style="white-space:normal;font-family:;" "="">,</span><span style="white-space:normal;font-family:;" "="">028.4 for ANN method beside lower values </span><span style="white-space:normal;font-family:;" "="">for error analysis terms.</span>展开更多
文摘BeiDou-3 navigation satellite system was officially opened in 2020.While bringing high-performance services to people around the world,the navigation system requires well-designed BeiDou antennas.In this paper,we propose a wideband circularly polarized high-performance BeiDou antenna.The antenna realizes wideband circularly polarized radiation through a four-port sequential feed network,and the phase imbalance of the feed network from 1.05 to 1.80 GHz is less than 7°.The manufactured antenna demonstrates a return loss of more than 13 dB and an axial ratio<3 dB over the entire global navigation satellite system(GNSS)frequency band.The right-handed circular polarization(RHCP)gain of the proposed antenna is greater than 4 dB in the GNSS low-frequency band and can reach more than 7.1 dB in the high-frequency band.Dimension of the proposed antenna is 120 mm×120 mm×20 mm,i.e.,0.54λo×0.54λo×0.09λo,whereλo is the wavelength of the center frequency.The proposed antenna connected to a GNSS receiver has tracked 12 BeiDou satellites with C/N0 ratios of GNSS signals greater than 30 dB.Such a high-performance antenna provides a basis for high-quality positioning services.
基金Heilongjiang Natural Science Foundation (F0318).
文摘In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.
文摘This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.
文摘A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.
文摘In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model’s primary goal is to identify potential malfunctions at an early stage to ensure the production process’ continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.
文摘This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.
文摘In this study, an application of artificial neural network （ANN） has been presented in modeling and studying the effect of compounding variables on abrasion behavior of rubber formulations. Three case studies were carried out in which the experiment data were collected according to classical response surface designs. Besides developing the ANN models, we developed response surface methodology （RSM） to confirm the ANN predictions. A simple relation was employed for determination of relative importance of each variable according to ANN models. It was shown through these case studies that ANN models delivered very good data fitting and their simulating curves could help the researchers to better understand the abrasion behavior.
文摘Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.
文摘Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> and Pd/<i>γ</i>Al<sub>2</sub>O<sub>3</sub> disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (<i><span style="white-space:nowrap;"><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">ø</span></span></i>) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners w<span style="white-space:normal;font-family:;" "="">as</span><span style="white-space:normal;font-family:;" "=""> scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (<i>r</i>), the radial distance from the center line of the flame, and (<i>x</i>), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three</span><span style="white-space:normal;font-family:;" "="">-</span><span style="white-space:normal;font-family:;" "="">layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate </span><span style="white-space:normal;font-family:;" "="">the </span><span style="white-space:normal;font-family:;" "="">effects of coded <i>R</i> and <i>X</i> input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of & F_Ratio are 0.9181</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9809 & 634.5</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 3528.8 for RSM method compared to 0.9857</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 0.9951 & 7636.4</span><span style="white-space:normal;font-family:;" "=""> </span><span style="white-space:normal;font-family:;" "="">- 24</span><span style="white-space:normal;font-family:;" "="">,</span><span style="white-space:normal;font-family:;" "="">028.4 for ANN method beside lower values </span><span style="white-space:normal;font-family:;" "="">for error analysis terms.</span>
