Applied Mechanics and Materials Vols. 300-301

Abstract: For a problem of mode mixing occurs in implementation process of local mean decomposition (LMD) method, an analytical method based on ensemble local mean decomposition (ELMD) and neural network is proposed to apply to fault diagnosis of rolling bearing, the vibrational signal of rolling bearing is decomposed into a series of product functions(PF) by ELMD method. The PF components which contain main fault information are selected to perform a further analysis. The kurtosis coefficient and energy characteristic parameters extracted from these PF components can be used as the input parameters of the neural network to identify the working status and fault types of rolling bearing. Through the analysis of rolling bearing with fault-free, inner-race fault and outer-race fault, the results indicate that the method based on ELMD and neural network has a higher failure recognition rate than the method based on wavelet packet analysis and neural network, and the working status and fault types of rolling bearing can be identified accurately and effectively.

Abstract: P300 speller is a well-known brain-computer interface (BCI), which allows patients with severe motor disabilities to spell words through the recognition on patients’ brain activity measured by electroencephalography (EEG). The brain-activity recognition is essentially a task of detecting of P300 responses in EEG signals. Support vector machine (SVM) has been a widely-used P300 detector in existing works. However, SVM is computationally expensive, greatly reducing the usability of the speller BCI for practical use. To address this issue, we propose in this paper a novel P300 detector, which is based on the kernel principal component analysis (KPCA). The proposed detector has a lower computational complexity, and can measure the belongingness of an input EEG to P300 class by the construction of EEG in nonlinear eigenspaces. Results carried out on subjects show that the proposed method is able to significantly shorten offline training sessions of the speller BCI while achieving high online P300-detection accuracy.

Abstract: Based on the conventional radial feature code extraction, this paper proposes a improved algorithm with the polar coordinate origin variable algorithm. Extracted feature with the improved algorithm proposed by this paper is more stable then the feature extracted by calculate the center of gravity for the overall character dot, and thus contribute to the improvement of the recognition rate. It is confirmed that the feature codes obtained with the improved algorithm are better and more stable by programming experiment.

Abstract: Most existing machine vision processing system is 8-bit or 16-bit processor control system, complex algorithms and multi-tasking of the vision system have been severely constrained. DaVinci DM355 integrated ARM926 RISC processor core and specialized image processor is a programmable DMSoC development platform with digital multimedia codecs, high integration, low-power consumption. The machine vision system based on DaVinci DM355 development goal is to establish a low-power hardware development board based on the DaVinci DM355, transplant Linux operating system based on the hardware board and develop corresponding driver.This will provide the basis for the realization of complex algorithm and multitasking system for machine vision system.

Abstract: Fuzzy C-Means (FCM) clustering algorithm can be used to classify hand gesture images in human-robot interaction application. However, FCM algorithm does not work well on those images in which noises exist. The noises or outliers make all the cluster centers towards to the center of all points. In this paper, a new FCM algorithm is proposed to detect the outliers and then make the outliers have no influence on centers calculation. The experiment shows that the new FCM algorithm can get more accurate centers than the traditional FCM algorithm.

Abstract: LPR (License Plate Recognition) System has been widely used in highway toll collection, parking management, various traffic regulations enforcement and other systems. Currently, most of the existing LPL (license plate localization) systems are with single camera that is limited to recognizing vehicles in one lane. In this paper we design a license plate localization system that simultaneously recognizes license plates of vehicles on multi-lane by using single high-resolution camera. Our approach significantly reduces the hardware cost of LPR system without sacrificing the accuracy of recognition. And our success rate is about 94%.

Abstract: In order to improve the decreasing resolution ability of Propagator Method (PM) algorithm under the environments like low signal noise ratio and small number of snapshots, a new weighted projection PM algorithm is proposed in this paper. This algorithm orthogonalizes noise subspace to get a new one, gains the signal subspace with the relationship between it and noise subspace, and weights the signal subspace and noise subspace with values gained by projecting integral value of steering vector in the field around the signals to each element of subspace. Simulation results show that the proposed method can keep computation simple, and also can decrease signal noise ratio threshold and snapshots threshold, so it has the better resolution ability and higher precision in snapshot deficient and low signal noise ratio scenario.

Abstract: The method of wavelet compression and reconstruction is studied. The wind turbine vibration signal is compressed and reconstructed by wavelet transformation. The compression and reconstruction of wind turbine vibration signal are achieved with different wavelet base. The compression ratio and the compression reconstruction accuracy are analyzed. The method and the result facilitate the choice of wavelet base for transmission of wind turbine vibration signal.

Abstract: In the general reliability analysis of series systems, the components which compose the system are assumed independent of each other. However, for most mechanical series systems, dependent failure is their common feature. If neglecting dependent failures where CCF(Common Cause Failure) plays an important role, it will lead to considerable errors or even misleading conclusions. In this article, the concept of series chain model is put forward in consideration of the CCF. By using the load—strength interference model, the order statistic theory and the knowledge of statistical distribution, the author develops a specific method to evaluate the reliability of series systems under exponential distribution. In the last part, by using Monte Carlo simulation, a group of specific data are given to verify the series chain model and compare the two different series system model. The results show that the reliability calculation under the classic series model is relatively conservative, while the reliability calculation under the series chain model is convincing. And thus, the series chain model is more applicable in engineering.

Abstract: In this paper, the eigenvalue problem that involves uncertain-but-non-random parameters is discussed. The error of dynamical parameters of a system is unavoidable in the course of manufacture and installation. Eigenvalues of the system are hard to obtain by the traditional dynamical theory. A new method based on matrix inequality theory is developed to evaluate the upper and lower bounds of the eigenvalues. In this method, properties of matrix’s spectral radius and norm are used. The illustrative numerical examples are provided to demonstrate the validity of the method. Compared with the other methods, the calculated results show that the proposed method in this paper is effective in evaluating bounds of the eigenvalues of structures with uncertain-but-bounded parameters.