Papers by Keyword: Wavelet Transform (WT)

Abstract: Textiles are necessaries of human life. The fiber content is index of textile quality and how to measure it has important meaning. A method for testing fiber contents in mixture textiles by near infrared spectroscopy (NIR) was researched. The near infrared Spectra of samples in the range of 4000 cm^-1 - 10000 cm^-1 were obtained. Noise reduction and compression of spectra data was done by wavelet transform (WT). The reconstructed spectral signals were established based on WT and the correction models based on back propagation (BP) neural network were built. Comparisons between the BP neural network models at different analysis scale and the model of partial least square method (PLS) were given. When the structure of neural network is 11-9-2 for cotton/ terylene mixture samples and 21-13-2 for cotton/wool mixture samples, the best accuracy and fastest convergence speed is achieved. Experimental results have shown that this approach by Fourier transform NIR based on the BP neural network to predict the fiber content of textile mixture can satisfy the requirement of quantitative analysis and is also suitable for other fiber contents measurement of mixture textiles.

Abstract: In the fault detection process for large-scale circuit communication systems, the traditional method needs to exam each node to determine whether a failure exists. It is complex and needs long time which causes a certain lagging. To avoid the defects mentioned above, this paper proposes a fault detection method based on wavelet transformation that calculates the changes of coefficients of the wavelet transform and the similarity between wavelet function and the signal. This kind of fault detection can pre-process the failure might occur and effectively improve the efficiency. The experimental results approve that the proposed method can predicate the fault location and reach satisfied effects.

Abstract: The 3D model retrieval technology has been widely used in many fields, then the 3D model retrieval based on content is proposed, feature extraction becomes the key to retrieve accurate or not, using wavelet transform combined with wave frequency method of feature extraction can partly solve the problem of ambiguity, the experiment proved that the algorithm improve the efficiency and accuracy of retrieval.

Abstract: We presented 3D fusion technique based on wavelet transform for analyzing 3D dataset of gravity and magnetic inversion intuitively and comprehensively. The technique expands the conventional 2D image fusion technique based on wavelet transform to 3D case, including using 3D wavelet decomposition and reconstruction to replace 2D ones and reforming the fusion rules of high and low frequency components in 3D field. The disciplines of some crucial parameters related to the 3D fusion technique were provided, so that bring some convenient to use this techinique. The synthetic data test showed that the 3D fusion technique is effetive and reliable.

Abstract: An adaptive fault detection algorithm based on wavelet and SVM (Support Vector Machine) is proposed for LRE(Liquid Rocket Engine) turbopump real-time fault detection. The algorithm firstly divides the historical signals into some segments by reasonable step length. Then for each segment it gets M-layer detail signals through Daubechies wavelet transform. Thirdly it divides every layer into K average segments and calculates there RMS values, gets M RMS sequences of detail signals. After that it constructs M-dimensional RMS vector as fault feature by extracting RMS values at the same position in every RMS sequence, and extracts all the fault feature vectors of historical signal to construct SVM training sample set and then obtains SVM classifier. At last the classifier will be real-time updated by a reasonable method in the testing process to improve the classification accuracy. To validate the algorithm, a track of the vibration acceleration signal of a certain type of turbopump was chosen as the test object. The test results showed that the algorithm met its demands of accuracy and real-time performance.

Abstract: Radar Level Meter is being more and more widely used in the industry field. However, due to the complex radar system, the noise contained in the clutter echo signal, and the interference within the system and hardware, it is quietly significant to study the research of radar echo reduction technology. Based on the introduction of radar level meter ‘working theory, This paper presents a wavelet transform analysis of the echo signal de-noising method. Studies have shown that the radar echo signal processing method of using wavelet transform can effectively suppress noise, remove interference and achieve echo signal de-noising processing.

Abstract: How to identify and remove the noise of the dam monitoring data is an important work of dam safety-monitoring. The article makes use of a new compromise thres-holding method to de-noise the dam horizontal dam crack width. Afterwards, the article uses the Markov correction model to predict the crack width of the dam body based on the de-nosing data. In order to identify the effectiveness of the model, the article makes a comparison between the results of the above method with that of the multiple regression method. And the results show that the method of the article is of effective.

Abstract: Based on wavelet transform of strain mode, the damage identification of plane frame was studied in this paper. After solving strain modal parameters of plane frames with cracks by means of the finite element theory, applying the gauss2 wavelet transform to analyze the stain mode of plane frame, and denoising the wavelet coefficients of the strain mode by db3 wavelet, the crack location of the plane frame could be identified by the maximum of wavelet coefficients after denoising. Taking one-story plane frame for example, three finite element models, including a frame beam with a crack, each frame column with a crack, and each frame beam and column with a crack, were set up separately, then the above-mentioned method was applied to identify the location of the crack. The results show the method is effective, and it may be useful in damage identification and diagnosis in structures.

Abstract: Gas valves are very important parts in compressors. If a valve fault in a compressor takes place, the expulsion of the compressor will decrease. In some critical cases, abnormal gas transmission will lead to a serious accident. This article proposes a method of fixed time basis in multi-resolution analysis and wavelet transform. This method can thoroughly resolve the problems occurred in present valve fault diagnosis caused by the fact that the wavelet transform does not have time shift invariance. By the analysis of experimental data, it shows that the valve fault can be preferably and accurately detected by using the method.

Abstract: Aiming at the problems of complicated convolution process of traditional wavelet transform and the unsatisfied effect of SPIHT algorithm for texture image compression, an improved SPIHT algorithm for texture image compression is proposed. At first, the texture image is decomposed into N order with the help of the lifting wavelet and the first-order high frequency sub-bands are decomposed into N-1 order by the lifting wavelet, and then the wavelet coefficients are encoded by the improved SPIHT algorithm. The improved SPIHT algorithm improved the process method of the wavelet coefficients in the low-frequency sub-bands and the detection method of some important coefficient in the L collection of the original SPIHT algorithm. Experiments show that the improved algorithm can retain the texture information of texture image more effectively and the quality of reconstructed image and peak signal to noise ratio are better than the original algorithm at the same rate. The effect is better especially at low rate, so the improved algorithm is an efficient compression method for texture image.