Acoustic emission (AE) method of nondestructive check is based on exertion wave radiation and their registration during fast local material structure reorganization. It is used as a means of analysis of materials, constructions, productions control and diagnosis during operating time. In the article, it is applied to structural health monitoring of Wind Turbine Gearbox (WTG). Acoustic emission testing has been used for years to test metallic structures. More recently it has become the primary method of testing WTG; all are present in the failure of WTG. AE has been very successful at detecting all of these failure mechanisms and sometimes identifying them from amplitude analysis of the AE signals. However in large structures, the high acoustic attenuation in WTG precludes amplitude analysis unless the origin of the individual signals can be identified and corrections for the distances traveled applied to the signal amplitudes. The usual method of testing WTG structures has been to apply an array of sensors spaced so that a moderate amplitude AE signal occurring midway between them will just barely trigger each sensor.
In order to achieve lower limb gait rehabilitation of patients, the normal walking gait trajectory was analyzed fully. According to the analysis results, a health hip and knee gait trajectory planning method was presented. The computer data analysis results show that the gait trajectory analytical expressions proposed can represent a normal gait accurately.
As a novel MGA (Multiscale Geometric Analysis) tool, shearlet is equipped with a rich mathematical structure similar to wavelet. In this paper, a novel image fusion method using Non-subsampled Shearlet Transform is proposed. First, the source images are decomposed into low-pass and high-pass subbands using NSST. Second, the high-pass subbands coefficients of the images are fused according to the average gradient. Third, the low-pass subbands coefficients of the images are fused by the weighted regional entropy. Finally, the image is reconstructed by the inverse non-subsampled shearlet transform. In the method, two sets of source images and five objective parameters are used to test the algorithm. The experimental results show that the proposed method is better than the conventional DWT-based and NSCT-based methods.
Aiming at the problem of face tracking under rapid moving process, a fast and robust tracking method is proposed. The possible position of face detected by the Camshift algorithm in the next frame is predicted by the square-root cubature Kalman filte (SCKF). Then, the localization and tracking of face are got frames by frames. The experimental results show that: the use of SCKF to solve the nonlinear effect caused by non-uniform motion of face and overcome the target loss problem of the linear Kalman algorithm. The proposed method greatly improves the tracking accuracy of face in the process of rapid movement.
This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.
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