The Asymptotic Waveform Evaluation (AWE) technique is an extrapolation method that provides a reduced-order model of linear system and has already been successfully used to analyze wideband electromagnetic scattering ...The Asymptotic Waveform Evaluation (AWE) technique is an extrapolation method that provides a reduced-order model of linear system and has already been successfully used to analyze wideband electromagnetic scattering problems. As the number of unknowns increases, the size of Method Of Moments (MOM) impedance matrix grows very rapidly, so it is a prohibitive task for the computation of wideband Radar Cross Section (RCS) from electrically large object or multi-objects using the traditional AWE technique that needs to solve directly matrix inversion. In this paper, an AWE technique based on the Characteristic Basis Function (CBF) method, which can reduce the matrix size to a manageable size for direct matrix inversion, is proposed to analyze electromagnetic scattering from multi-objects over a given frequency band. Numerical examples are presented to il-lustrate the computational accuracy and efficiency of the proposed method.展开更多
Based on auditory peripheral simulation model,a new Sound Quality Objective Evaluation(SQOE)method is presented,which can be used to model and analyze the impacts of head,shoulder and other parts of human body on soun...Based on auditory peripheral simulation model,a new Sound Quality Objective Evaluation(SQOE)method is presented,which can be used to model and analyze the impacts of head,shoulder and other parts of human body on sound wave trans-mission.This method employs the artificial head technique,in which the head related transfer function was taken into account tothe outer ear simulation phase.First,a bionic artificial head was designed as the outer ear model with considering the outersound field in view of theory and physical explanations.Then the auditory peripheral simulation model was built,which mimicsthe physiological functions of the human hearing,simulating the acoustic signal transfer process and conversion mechanismsfrom the free field to the peripheral auditory system.Finally,performance comparison was made between the proposed SQOEmethod and ArtemiS software,and the verifications of subjective and objective related analysis were made.Results show thatthe proposed method was economical,simple,and with good evaluation quality.展开更多
Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (...Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60771034 )the 211 Project of Anhui University
文摘The Asymptotic Waveform Evaluation (AWE) technique is an extrapolation method that provides a reduced-order model of linear system and has already been successfully used to analyze wideband electromagnetic scattering problems. As the number of unknowns increases, the size of Method Of Moments (MOM) impedance matrix grows very rapidly, so it is a prohibitive task for the computation of wideband Radar Cross Section (RCS) from electrically large object or multi-objects using the traditional AWE technique that needs to solve directly matrix inversion. In this paper, an AWE technique based on the Characteristic Basis Function (CBF) method, which can reduce the matrix size to a manageable size for direct matrix inversion, is proposed to analyze electromagnetic scattering from multi-objects over a given frequency band. Numerical examples are presented to il-lustrate the computational accuracy and efficiency of the proposed method.
文摘Based on auditory peripheral simulation model,a new Sound Quality Objective Evaluation(SQOE)method is presented,which can be used to model and analyze the impacts of head,shoulder and other parts of human body on sound wave trans-mission.This method employs the artificial head technique,in which the head related transfer function was taken into account tothe outer ear simulation phase.First,a bionic artificial head was designed as the outer ear model with considering the outersound field in view of theory and physical explanations.Then the auditory peripheral simulation model was built,which mimicsthe physiological functions of the human hearing,simulating the acoustic signal transfer process and conversion mechanismsfrom the free field to the peripheral auditory system.Finally,performance comparison was made between the proposed SQOEmethod and ArtemiS software,and the verifications of subjective and objective related analysis were made.Results show thatthe proposed method was economical,simple,and with good evaluation quality.
文摘Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.
