Applied Mechanics and Materials Vols. 433-435

Abstract: Ultrasonic standing wave (USW) manipulation of suspension microparticles separation has attracted wide attention due to its non-direct contact, being harmless to the particles, easy to manufacture, low energy consumption and high separation efficiency. USW is widely used in industrial processes, environmental assessment, biochemical analysis, clinical diagnosis and other fields. In this article, particles stress and movement process is analyzed, meanwhile, aggregation and separation of the particles is discussed respectively and the future direction of development is pointed out.

Abstract: Along with continuous increase of capacity of PV(photovoltaic) power station, techniques for power prediction of PV power station play an important role in reducing impact of stochastic fluctuation of PV power stations energy output on power system. The paper proposes a method for power prediction of PV power station based on LMS adaptive filter, a FIR approach model of PV station power prediction model based on LMS adaptive filter is established with history runtime value of PV station as the input value of filter and current value as the expected value. The advantage of using LMS filter to power prediction of PV power station is that a real-time, explicit identification result can be obtained as well as that the algorithm is simple. A test has been made with runtime data of one PV power station and the result showed that the prediction method in the paper has good accuracy in terms of super-short term power prediction.

Abstract: The empirical mode decomposition (EMD) is a good time-frequency analysis method, which can deal with nonlinear and non-stationary signals. Aiming at improving modal aliasing problem brought by the traditional EMD, white noise is introduced into the improved aided analysis algorithm namely ensemble empirical mode decomposition (EEMD), instantaneous amplitude and frequency can be obtained by using teager energy operator (TEO), which is adopted to identify the type of power quality disturbance. The anti-aliasing of EEMD and real-time detection of TEO are verified by the signal simulation in Matlab. Simulation and experimental results show that the proposed algorithm can detect and locate power quality disturbances accurately and quickly, with excellent detection effects.

Abstract: In order to extract the weak signal from strong background signal characteristics, a feature extraction method combined of the singular value decomposition (SVD), empirical mode decomposition (EMD) and mathematical morphology was proposed. The signal got through the singular value decomposition first. Next took the average value of the decomposed main components. And carried on the empirical mode decomposition and selected the main component to summate and refactor. Then morphological difference filter was used to extract the frequency characteristics of the fault signal. The results of numerical simulation test and gear fault simulation experiments show that the proposed method can clearly extract the frequency characteristics of weak signal from strong background signal and noise. Comparison has been done with the results of singular value decomposition (SVD) and morphological filtering method and empirical mode decomposition form of filtering method. It proves the effectiveness of the proposed method.

Abstract: In the motor fault diagnosis technology, vibration signals can fully reflect the motor operation conditions. In this paper, a linear motor fault diagnosis method based on wavelet packet and neural network was presented. The improved neural network system was designed with variable hidden layer neurons. The network chosen different numerical values depending on different situations to reach the requirements that improving diagnostic accuracy and shortening the diagnosis time. The linear motor’s normal and two common faults vibration signals were analyzed and the vibration signals energy characteristics were extracted through wavelet packet, then identified fault through neural network. The experimental results show that this method can effectively improve the motor fault diagnosis accuracy.

Abstract: Attention is put on FDTD algorithm to simulate electromagnetic travelling underground with XFDTD software in this paper to get echo data of underground plant rhizome. A reasonable simulation model is established with some permittivity and conductivity, and choice of grid size and absorbing boundary is supposed to simulate detecting ability for plant rhizome. Adding noise model is supported and wavelet threshold de-noising algorithm is used to process echo signal with noise, and root mean square error (RMSE) is obtained with different SNR. At the same SNR, the relationship of target echo and object size is presented.

Abstract: For Rolling of the mine key equipment is damaged easily the problem which is machinery fault diagnosis,through the failure mechanism of the reload / variable load conditions and the weak fault signal characteristics of coal mine electrical equipment bearing are analyzed, a more refined analysis of the vibration signal and achieve coal mining equipment online monitoring and Intelligent Fault Diagnosis system is constructed directly by scale adaptive lifting wavelet transform

Abstract: Modular exponentiation of large number is widely applied in public-key cryptosystem, also the bottleneck in the computation of public-key algorithm. Modular multiplication is the key calculation in modular exponentiation. An improved Montgomery algorithm is utilized to achieve modular multiplication and converted into systolic array to increase the running frequency. A high efficiency fast modular exponentiation structure is developed on FPGA to bring the best out of the modular multiplication module and enhance the ability of defending timing attacks and power attacks. For 1024 bit key operands, the design can be run at 170MHz.

Abstract: This paper represents a new multiple sensor information fusion algorithm in distributed sensor networks using an additive divided difference information filter for nonlinear estimation and tracking applications. The newly proposed multi-sensor fusion algorithm is derived by utilizing an efficient new additive divided difference filtering algorithm with embedding statistical error propagation method into an information filtering architecture. The new additive divided difference information filter achieves not only the accurate nonlinear estimation solution, but also the flexibility of multiple information fusion in distributed sensor networks. Performance comparison of the proposed filter with the nonlinear information filters is demonstrated through a target-tracking application.

Abstract: The electrocardiogram (ECG) signal is an important basis of diagnosing cardiopathy. It is very important to remove the noises of the signal before the use. Matching pursuit (MP) algorithm uses overcomplete dictionary to decompose the signal, so it can reflect the properties of the signal. In this paper we denoise the ECG signal using MP algorithm,whose dictionary is composed of Gabor atoms.The experiment simulation results show that the proposed method has a good denosing effect.