There have been some algorithms that use image processing for power quality disturbances identification. This algorithms firstly converts 1-D power signal to 2-D image, then use image algorithms to classify power qual...
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ISBN:
(纸本)9781538604328
There have been some algorithms that use image processing for power quality disturbances identification. This algorithms firstly converts 1-D power signal to 2-D image, then use image algorithms to classify power quality disturbances. Different from these methods, a new method is presented here. Converts 2-D texture image algorithms to 1-D waveform algorithms, then use a classifier(here is SVM) for recognize power quality disturbances. These 1-D features include level Co-occurrence Matrix, Markov random field, voltage values and gradient firstly. The experiment show that the algorithm can reach a good result.
Digital image processing technology has gone through rapid development and is extensively applied in daily life and production, with the rapid development of modern information technology. It plays an inestimable role...
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Digital image processing technology has gone through rapid development and is extensively applied in daily life and production, with the rapid development of modern information technology. It plays an inestimable role in remote sensing, medicine, recognition and other fields. This paper briefly introduces the basic concept of digital image processing,summarizes and analyses the commonly used digital image processing technology and the latest scientific research achievements from four aspects, and puts forward the future development direction of digital image processing. In the future, it will pay more attention to artificial intelligence algorithms and achieve better processing results by optimizing the logical *** using the simplified image algorithm, the application scope of digital image processing will gradually expand, and will develop in the direction of miniaturization, intelligence, and convenience.
Temperature is a basic physical parameter in living organisms that directly relates to the physiological state of the body. The demand for in vivo temperature detection is expected to obtain accurate temperature signa...
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Temperature is a basic physical parameter in living organisms that directly relates to the physiological state of the body. The demand for in vivo temperature detection is expected to obtain accurate temperature signals with high spatial resolution. We propose a strategy of constructing and encapsulating the temperature probe (NaNdF4:7%Yb,33%Y) and high-resolution imaging probe (NaYbF4:2%Er,2%Ce) in identical rare earth nanoparticles to attain in vivo temperature detection with high spatial resolution. The temperature probe acquires temperature feedback based on the luminescence lifetime signal which is used for accurate temperature acquisition with a thermal sensitivity of 1.94% K-1 and uncertainty of 0.05 K at 25.8 degrees C. The intensity-based imaging probe with emission wavelength in NIR-IIb is introduced to attain a high-resolution image with a signal-to-noise ratio of 2.5 times that of the temperature probe in NIR-I. Hence, the high-resolution image serves as the luminescence location image for the temperature distribution image attained by the temperature probe. On the basis of obtaining the temperature signal and high-resolution imaging signal, the image algorithm is designed for the superposition of the temperature image and high-resolution image. Ultimately, the dual-dimensional signals acquired by optical detection are superimposed by the image algorithm to obtain high-resolution temperature mapping.
The aim of this paper is to develop a comprehensive modeling strategy for creating a realistic representative volume element (RVE) of 2.5D woven composites. The strategy consists of two main parts: the extraction of g...
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The aim of this paper is to develop a comprehensive modeling strategy for creating a realistic representative volume element (RVE) of 2.5D woven composites. The strategy consists of two main parts: the extraction of geometric feature parameters and the establishment of a parametric voxel-mesh full-cell model (VFM). Firstly, a neural network model is constructed to achieve an accurate segmentation of yarn cross-sections from X-ray computed tomography (XCT) images. Secondly, geometric feature parameters are then extracted from the segmentation results using image algorithms. Finally, a parametric modeling method is proposed to establish the VFM of the material. To evaluate the performance of the VFM, its structural sizes, overall fiber volume fraction (FVF), and stiffness prediction accuracy are assessed. The comparison results indicate that the VFM achieves a fine mesoscale characterization and a high stiffness prediction accuracy.
There have been some algorithms that use image processing for power quality disturbances *** algorithms firstly converts 1-D power signal to 2-D image,then use image algorithms to classify power quality *** from these...
详细信息
There have been some algorithms that use image processing for power quality disturbances *** algorithms firstly converts 1-D power signal to 2-D image,then use image algorithms to classify power quality *** from these methods,a new method is presented *** 2-D texture image algorithms to 1-D waveform algorithms,then use a classifier(here is SVM) for recognize power quality *** 1-D features include level Co-occurrence Matrix,Markov random field,voltage values and gradient *** experiment show that the algorithm can reach a good result.
In recent years, there has been a rapid development and application in the use of ultrasonic phased arrays for non-destructive examination, it's a relatively mature technology to detect over the location and gener...
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In recent years, there has been a rapid development and application in the use of ultrasonic phased arrays for non-destructive examination, it's a relatively mature technology to detect over the location and general size of defect. For a better visualization and evaluation, a few researchers have done some work on three-dimensional reconstruction of defects. This paper proposed a 3 D reconstruction and calculation methodology, which using a linear phased array probe detection system to collect scanning data, combining with image algorithm on MATLAB platform, then realized the 3 D reconstruction and calculation of defects. To verify the reliability of the method, we made two contrast test blocks, including different hole defects. The optimal characterization performance of the methodology will be achieved when the reconstruction model can better characterize the actual defect and the volume calculation results are close to the true values. The experimental results shows the reconstructed 3 D models are continuous and smooth, which can reflect the types of defects. The calculated values of volumes are close to the true values, whose calculation errors were all less than 10%, and the maximum error was less than 2% after error compensation. Besides, the speed of defect reconstruction is more faster than traditional methods. For three-dimensional reconstruction and quantitative analysis of defect, it has important significance and practical values.
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