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	Wavelet-Based Image Fusion Method Applied in the Terahertz Nondestructive Evaluation 期刊论文 OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 卷号: 37, 期号: 12, 页码: 3683-3688 作者: Zhang Jin; Wang Jie; Shen Yan; Zhang Jin-bo; Cui Hong-liang \| 收藏 \| 浏览/下载：229/0 \| 提交时间：2018/03/05 Wavelet-based image fusion Terahertz imaging Nondestructive detection Glass fiber-reinforced polymer composites
	Forecasting container throughput of Qingdao port with a hybrid model 期刊论文 OAI收割 JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, 2015, 卷号: 28, 期号: 1, 页码: 105-121 作者: Huang Anqiang; Lai Kinkeung; Li Yinhua; Wang Shouyang \| 收藏 \| 浏览/下载：11/0 \| 提交时间：2021/01/14 PROJECTION PURSUIT REGRESSION WAVELET-BASED DETECTION FINANCIAL TIME-SERIES OUTLIER DETECTION GENETIC ALGORITHMS NOVELTY DETECTION NEURAL-NETWORKS PREDICTION Container throughput forecast genetic programming algorithm outlier processing projection pursuit regression
	Directional multiscale edge detection using the contourlet transform (EI CONFERENCE) 会议论文 OAI收割 2010 IEEE International Conference on Advanced Computer Control, ICACC 2010, March 27, 2010 - March 29, 2010, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States 作者: Jin L.-X.; Han S.-L.; Zhang R.-F. 收藏 \| 浏览/下载：34/0 \| 提交时间：2013/03/25 Wavelet multiresolution analysis allows us to detect edges at different scales also to obtain other important aspects of the extracted edges. However due to the usual two-dimensional tensor product wavelet transform is not optimal for representing images. The main problem in edge detection using wavelet transform is that it can only capture point-singularities and the extracted edges are not continuous. In order to solve that problem we propose a new image edge detection method based on the contourlet transform. The directional multiresolution representation Contourlet takes advantages of the intrinsic geometrical structure of images and is appropriate for the analysis of the image edges. Using the modulus maxima detection an image edge detection method based on contourlet transform is proposed. To suppress the image noise effect on edge detection the scale multiplication in contourlet domain is also proposed. Through real images experiments the proposed edge detection method's performance for the extracted edges is analyzed and compared with other two edge detection methods. The experiment result proves that the proposed edge detection method improves over wavelet-based techniques and Canny detector and also works well for noisy images. 2010 IEEE.
	Real-time matching algorithm of navigation image based on corner detection (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Imaging Detectors and Applications, June 17, 2009 - June 19, 2009, Beijing, China 作者: Zhang T. 收藏 \| 浏览/下载：44/0 \| 提交时间：2013/03/25 In order to meet requirement of real-time and high accuracy in image matching aided navigation SSDA algorithm is used to match remote sensing image and template image coarsely a fast and effective algorithm of remote sensing image matching based on corner detection is put forward. With the combination of rough and fine match when the matching result is bigger than one to count absolute value sum of energy difference of characteristic point energy to realize fine match of remote sensing image and template image to locate the position of template image in remote sensing image accurately. Simulation experiment proves that the matching of a remote sensing image resolution of 10181530 and a template image resolution of 15090 can be fulfilled within 2.392 second wavelet transform is used to acquire low frequency component to realize image compression to decrease calculation work and increase matching speed. Harris corner detection algorithm is used to detect corner of remote sensing image and template image and energy of every corner is calculated the algorithm is robust and effective real time image navigation can be achieved. 2009 SPIE.
	Detection of low contrast targets based on lifting scheme wavelet transform (EI CONFERENCE) 会议论文 OAI收割 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009, August 9, 2009 - August 12, 2009, Changchun, China 作者: Chen X.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：16/0 \| 提交时间：2013/03/25 This paper present a fast algorithm for detection of low contrast objects by using wavelet filters based on lifting scheme. The advantage is robust to noise. According Swelden's lifting wavelet filters are biorthogonal wavelet filters containing free parameters. We use reference image of targets to train the lifting terms so that the learnt wavelet filters have the features of targets. Then applying such filters to the images including targets taken from camera system. We can detect the locations where the high frequency components are almost the same as those of the target image. 2009 IEEE.
	A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Y.; Li Y.; Li Y.; Li Y. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light and indeed we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm the tracing of WTMM is not just tedious procedure computationally algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.
	Abrupt sensor fault diagnosis based on wavelet network (EI CONFERENCE) 会议论文 OAI收割 2006 IEEE International Conference on Information Acquisition, ICIA 2006, August 20, 2006 - August 23, 2006, Weihai, Shandong, China 作者: Li W.; Li W.; Zhang H.; Zhang H. 收藏 \| 浏览/下载：17/0 \| 提交时间：2013/03/25 The possible faults of a sensor may be classified as abrupt (sudden) faults and incipient (slowly developing) faults. This paper focuses on the abrupt faults of a sensor. Due to the limited number of scales a single wavelet amplitude map has not enough scales to describe all details of the signal. The sampling grid in the scale direction is rather sparse Some of the fault information will be leaked under such sparse grid. To make up for the deficiency of scalar orthogonal wavelet transform in the application of abrupt fault diagnosis multiwavelet packets transform was introduced into the field of abrupt fault diagnosis. The distribution differences of the signal energy on decomposed multiwavelet scales of the signal before and after the fault occurring are extracted as the fault feature and used as the input of multi-dimensional wavelet network. A new model-free diagnostic method for isolating abrupt sensor faults is developed based on a proposed algorithm of multi-dimensional wavelet network constructing. The method has been proved to be quite effective in the detection of sensor abrupt fault. 2006 IEEE.
	Multiwavelet based multispectral image fusion for corona detection (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang X.; Yang H.-J. 收藏 \| 浏览/下载：29/0 \| 提交时间：2013/03/25 Image fusion refers to the integration of complementary information provided by various sensors such that the new images are more useful for human or machine perception. Multiwavelet transform has simultaneous orthogonality symmetry compact support and vanishing moment which are not possible with scalar wavelet transform. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. In this paper a new image fusion algorithm based on discrete multiwavelet transform (DMWT) to fuse the dual-spectral images generated from the corona detection system is presented. The dual-spectrum detection system is used to detect the corona and indicate its exact location. The system combines a solar-blind UV ICCD with a visible camera where the UV image is useful for detecting UV emission from corona and the visible image shows the position of the corona. The developed fusion algorithm is proposed considering the feature of the UV and visible images adequately. The source images are performed at the pixel level. First a decomposition step is taken with the DMWT. After the decomposition step a pyramid for each source image in each level can be obtained. Then an optimized coefficient fusion rule consisting of activity level measurement coefficient combining and consistency verification is used to acquire the fused coefficients. This process reduces the impulse noise of UV image. Finally a new fused image is obtained by reconstructing the fused coefficients using inverse DMWT. This image fusion algorithm has been applied to process the multispectral UV/visible images. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach.
	Arc fault signatures detection on aircraft wiring system (EI CONFERENCE) 会议论文 OAI收割 6th World Congress on Intelligent Control and Automation, WCICA 2006, June 21, 2006 - June 23, 2006, Dalian, China Hongkun Z.; Tao C.; Wenjun L. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 In this paper an arc fault detection method Is proposed based on characteristics of the fault current of an electric arc. A localized signal processing method Is developed using wavelet analysis to decompose the differential current signal Into a series of wavelet components each of which Is a time-domain signal that covers a specific frequency band. Thus more distinctive signal features that represent arc faults and other fault transient phenomena are extracted. As a result by quantifying the extracted features an arc faults are distinguished from phenomena similar to arc and other fault transients using the differences In the quantified features. Simulation studies have demonstrated that the proposed method Is reliable and simple. 2006 IEEE.