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Titlebook: Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology; Methods and Algorith Roumen Kountchev,Sri

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31#
發(fā)表于 2025-3-26 21:44:12 | 只看該作者
Crack patching: design aspects,ch of bacterial foraging optimization algorithm (BFO), this paper introduces the chemotaxis and dispersal operation of the bacterial foraging algorithm into the PSO algorithm to obtain hybrid algorithm. This paper applies the hybrid algorithm to 3D path planning. The simulation results show that the
32#
發(fā)表于 2025-3-27 03:01:02 | 只看該作者
https://doi.org/10.1007/978-3-658-25921-1r predicting and reconstructing missing k-space signal. Without sampling full k-space data, MRI speed is therefore accelerated and clinical scan cost can be reduced. However, due to noise and outliers existing multiple coil data, reconstructed image is deteriorated by noise and aliasing artifacts. T
33#
發(fā)表于 2025-3-27 08:54:58 | 只看該作者
34#
發(fā)表于 2025-3-27 12:47:45 | 只看該作者
https://doi.org/10.1007/978-981-16-3899-2tral reconstruction accuracy. In order to solve the problems of the traditional neural network spectral reconstruction algorithms, this paper proposes appropriate improvements. The polynomial regression method is used to extend the camera response. The Bayesian regularization is used to improve the
35#
發(fā)表于 2025-3-27 13:36:01 | 只看該作者
36#
發(fā)表于 2025-3-27 18:18:27 | 只看該作者
https://doi.org/10.1007/978-981-16-3899-2. Firstly, the NSST is performed on each source image and the NSST contrast of the image is calculated according to the high-frequency and low-frequency coefficients. Then the NSST contrast of the partial region of the source image is selected as the training sample for the feedforward neural networ
37#
發(fā)表于 2025-3-28 00:13:50 | 只看該作者
38#
發(fā)表于 2025-3-28 04:12:26 | 只看該作者
39#
發(fā)表于 2025-3-28 07:10:50 | 只看該作者
40#
發(fā)表于 2025-3-28 13:40:39 | 只看該作者
https://doi.org/10.1007/978-3-658-32298-4ta in the power system. In terms of feature extraction, based on the Alexnet model, two independent CNN models are proposed to extract the characteristics of power equipment. In terms of recognition algorithm, the advantages of traditional machine learning methods are combined with the advantages of
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