Ultrasonic motor (USM) is a newly developed motor, and it has some excellent performances and useful features, therefore, it has been expected to be of practical use. However, the driving principle of USM is different...Ultrasonic motor (USM) is a newly developed motor, and it has some excellent performances and useful features, therefore, it has been expected to be of practical use. However, the driving principle of USM is different from that of other electromagnetic type motors, and the mathematical model is complex to apply to motor control. Furthermore, the speed characteristics of the motor have heavy nonlinearity and vary with driving conditions. Hence, the precise speed control of USM is generally difficult. This paper proposes a new speed control scheme for USM using an artificial neural network. An accurate tracking response can be obtained by random initialization of the weights of the network owing to the powerful on line learning capability. Two prototype ultrasonic motors of travelling wave type were fabricated, both having 100 mm outer diameters of stator and piezoelectric ceramic. The usefulness and validity of the proposed control scheme are examined in experiments.展开更多
Recently,ultrasonic waves had been introduced as the transmission medium in Body Area Networks(BANs) to reduce the incalculable damage caused by radio waves. However,the communications based on ultrasonic waves suffer...Recently,ultrasonic waves had been introduced as the transmission medium in Body Area Networks(BANs) to reduce the incalculable damage caused by radio waves. However,the communications based on ultrasonic waves suffer from poor propagation of signals in air and consume too much energy. To address these limitations,firstly,we make the theoretical analysis to ensure ultrasonic waves could be used in BANs(UBANs). Then,we propose an error control strategy in UBANs to dynamically adjust the error control scheme and the Max-Retries based on the current channel state,which is called UECS. The UECS is based on IEEE 802.15.6 standards and considering the characteristics of ultrasonic waves in BANs. Simulation results show that UECS achieves better performance in terms of packet delivery ratio and energy consumption compared with the traditional strategies.展开更多
On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative fe...On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative feedback of the neural cell in the output layer, the cell in the input layer excites the corresponding cell in the ontput layer meanwhile it inhibits the lateral cells. The network has its advantage on the processing of sound wave. In addition to filter the noise, it can search the significance frequency segments （Barks）. The ＂channel suppresser＂ feature, the special phenomena of the human ear, is explained based on the model. The learning algorithm of the network model is discussed, too. In the end, an example is introduced about the application of the network.展开更多
In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coa...In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coal types are recognized based on a suitable consideration of ultrasonic speed,an ultrasonic attenuation coefficient,characteristics of ultrasonic transmission and other parameters relating to structural coal types.We have focused on a computational model of ultrasonic speed,attenuation coefficient in coal and differentiation algorithm of structural coal types based on a BP neural network.Experiments demonstrate that the model can distinguish structural coal types effectively.It is important for the improved ultrasonic differentiation model to predict coal and gas outbursts.展开更多
This paper presents a study where artificial neural networks are used as a curve fitting method applying measured data from an axial compressor test rig to predict the compressor map. Emphasis is on models for predict...This paper presents a study where artificial neural networks are used as a curve fitting method applying measured data from an axial compressor test rig to predict the compressor map. Emphasis is on models for prediction of pressure ratio, compressor mass flow and mechanical efficiency. Except for evaluation of interpolation and extrapolation capabilities, this study also investigates the effect of the design parameters such as number of neurons and size of training data. To reduce the effect of noise, the auto associative neural network has been applied for noise filtering of the data from the parameters used to calculate the efficiency. In summary, the results show that artificial neural network can be used for compressor map prediction, but it should be emphasized that the selection of data normalisation scale is crucial for the model where compressor mass flow is predicted. Furthermore, it is shown that the AANN （auto associative neural network） can be used to the reduce noise in measured data and thereby enhance the quality of the data.展开更多
文摘Ultrasonic motor (USM) is a newly developed motor, and it has some excellent performances and useful features, therefore, it has been expected to be of practical use. However, the driving principle of USM is different from that of other electromagnetic type motors, and the mathematical model is complex to apply to motor control. Furthermore, the speed characteristics of the motor have heavy nonlinearity and vary with driving conditions. Hence, the precise speed control of USM is generally difficult. This paper proposes a new speed control scheme for USM using an artificial neural network. An accurate tracking response can be obtained by random initialization of the weights of the network owing to the powerful on line learning capability. Two prototype ultrasonic motors of travelling wave type were fabricated, both having 100 mm outer diameters of stator and piezoelectric ceramic. The usefulness and validity of the proposed control scheme are examined in experiments.
基金partly supported by the National Natural Science Foundation of China(Grant No.61272412)Project 2016194 Supported by Graduate Innovation Fund of Jilin UniversitySpecialized Research Fund for the Doctoral Program of Higher Education under Grant Nos.20120061110044
文摘Recently,ultrasonic waves had been introduced as the transmission medium in Body Area Networks(BANs) to reduce the incalculable damage caused by radio waves. However,the communications based on ultrasonic waves suffer from poor propagation of signals in air and consume too much energy. To address these limitations,firstly,we make the theoretical analysis to ensure ultrasonic waves could be used in BANs(UBANs). Then,we propose an error control strategy in UBANs to dynamically adjust the error control scheme and the Max-Retries based on the current channel state,which is called UECS. The UECS is based on IEEE 802.15.6 standards and considering the characteristics of ultrasonic waves in BANs. Simulation results show that UECS achieves better performance in terms of packet delivery ratio and energy consumption compared with the traditional strategies.
基金Shanghai Natural Research Foundation (No.06dz15003)
文摘On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative feedback of the neural cell in the output layer, the cell in the input layer excites the corresponding cell in the ontput layer meanwhile it inhibits the lateral cells. The network has its advantage on the processing of sound wave. In addition to filter the noise, it can search the significance frequency segments （Barks）. The ＂channel suppresser＂ feature, the special phenomena of the human ear, is explained based on the model. The learning algorithm of the network model is discussed, too. In the end, an example is introduced about the application of the network.
基金Projects 50674093 supported by the National Natural Science Foundation of China20050290010 by the Doctoral Foundation of the Chinese Education Ministry
文摘In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coal types are recognized based on a suitable consideration of ultrasonic speed,an ultrasonic attenuation coefficient,characteristics of ultrasonic transmission and other parameters relating to structural coal types.We have focused on a computational model of ultrasonic speed,attenuation coefficient in coal and differentiation algorithm of structural coal types based on a BP neural network.Experiments demonstrate that the model can distinguish structural coal types effectively.It is important for the improved ultrasonic differentiation model to predict coal and gas outbursts.
文摘This paper presents a study where artificial neural networks are used as a curve fitting method applying measured data from an axial compressor test rig to predict the compressor map. Emphasis is on models for prediction of pressure ratio, compressor mass flow and mechanical efficiency. Except for evaluation of interpolation and extrapolation capabilities, this study also investigates the effect of the design parameters such as number of neurons and size of training data. To reduce the effect of noise, the auto associative neural network has been applied for noise filtering of the data from the parameters used to calculate the efficiency. In summary, the results show that artificial neural network can be used for compressor map prediction, but it should be emphasized that the selection of data normalisation scale is crucial for the model where compressor mass flow is predicted. Furthermore, it is shown that the AANN （auto associative neural network） can be used to the reduce noise in measured data and thereby enhance the quality of the data.
