Measuring in-plane micro-motion of micro-structure using optical flow

Optical flow method is one of the most important methods of analyzing motion images.Optical flow field is used to analyze characteristics of motion objects.According to motion features of micro-electronic mechanical system(MEMS)micro-structure,the optical algorithm based on label field and neighborhood optimization is presented to analyze the in-plane micro-motion of micro-structure.Firstly,high speed motion states for each frequency segment of micro-structure in cyclic motion are frozen based on stroboscopic principle.Thus a series of image sequences,and can obtain reliable and precise optical field and reduce computing time.As micro-resonator of urement precision of the presented algorithm is high,and measurement repeatability reaches 40 am under the same experiment condition.     

国家自然科学基金资助项目“热油长输管道泄漏检测与定位技术研究”结硕果

现代管道运输起始于19世纪中叶。1865年, 萨谬尔·凡·赛克尔(Samnel Van Sychel)在美国宾夕法尼亚州泰特期绍尔油田内铺设了世界上第一条输油管道(管径50 mm,长8 km)。经过一百多年的发展,目前世界上大型油气管道已超过200万公里。管道运输已成为与铁路、公路、航空、水运并驾齐驱     

Study on Detection Technique for Pipeline Leakage

Pipeline integrity defect is a considerable portion in all the defects.Therefore,the study on detection technique is of great concern to the owners of pipelines.A real-time monitoring system of pipeline defect is established.For unexpected accidents of pipeline leakage,several methods of signal processing,including wavelet analysis are utilized.The leakage can be located accurately in time.This system is applied to many long distance pipelines in China,which has made great contributions to the economy.     

Blind separation of speech signals based on wavelet transform and independent component analysis

Speech signals in frequency domain were separated based on discrete wavelet transform （DWT） and inde- pendent component analysis （ICA）. First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation （BSS） were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were car- ried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.