Finite element model updating of a rod-type ultrasonicmotor based on response surface method

The conventional finite element model （FEM） of a rod-type ultrasonic motor is usually simplified by means of continuous composite structure. Because the actual contact characteristics between the parts of the ultrasonic motor is ignored, there is bigger error between the calculated values and experimental results. Aiming at solving problem, a new modeling method of a rod-type ultrasonic motor is presented to obtain a high-accuracy FEM. The bolt pretension and the normal contact stiffness and friction coefficient of the contact surface of ultrasonic motor are all considered in this method, and the significant parameters of working mode of the motor are selected by the response surface method, and the goal of calculating the structural response rapidly is realized by building the response surface model to replace the FEM. The result of finite element model updating shows that the average error of modal frequencies of updated model drops to 0.21% from 1.20%. The accuracy of FEM is obviously improved, which indicates that the FEM updating based on response surface method is of great application value on the design for a rod-type ultrasonic motor.

A new speech synthesis method based on the LMA vocal tract model

A new speech synthesis algorithm based on the LMA filter in Chinese text-to-speech systern is introduced. Using this method, the system can not only generate speech with higher quality, but also have a more powerful ability to modify the prosodic parameters, which ensures a far more natural and intelligible synthesized speech than ever before. First, the fundamental principles of the LMA filter and the construction of the synthesizer are presented, then, how to modify the acoustic parameters with this synthesizer is described; finally, the quantitative evaluation of the system's performance is shown while compared with a relatively successful PSOLA synthesizer KDTALK_1

A life-prediction method for lithium-ion batteries based on a fusion model and an attention mechanism

The current life-prediction models for lithium-ion batteries have several problems, such as the construction of complex feature structures, a high number of feature dimensions, and inaccurate prediction results. To overcome these problems, this paper proposes a deep-learning model combining an autoencoder network and a long short-term memory network. First, this model applies the characteristics of the autoencoder to reduce the dimensionality of the high-dimensional features extracted from the battery data set and realize the fusion of complex time-domain features, which overcomes the problems of redundant model information and low computational efficiency. This model then uses a long short-term memory network that is sensitive to time-series data to solve the long-path dependence problem in the prediction of battery life. Lastly, the attention mechanism is used to give greater weight to features that have a greater impact on the target value, which enhances the learning effect of the model on the long input sequence. To verify the efficacy of the proposed model, this paper uses NASA's lithium-ion battery cycle life data set.

A synthesis method based on speech production and articulatory model

A method to synthesize formant targeted sounds based on speech production model and Reflection-Type Line Analog (RTLA) articulatory synthesis model is presented. The synthesis model is implemented with scattering process derived from a RTLA of vocal tract system according to the acoustic mechanism of speech production. The vocal-tract area function which controls the synthesis model is derived from the first three formant trajectories by using the inverse solution of speech production. The proposed method not only gives good naturalness and dynamic smoothness, but also is capable to control or modify speech timbres easily and flexibly. Further and mores it needs less number of control parameters and very low update rate of the parameters.