chance 发表于 2025-3-26 21:44:12
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熔岩 发表于 2025-3-27 03:01:02
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. TRODE 发表于 2025-3-27 08:54:58
http://reply.papertrans.cn/15/1466/146591/146591_33.png烦人 发表于 2025-3-27 12:47:45
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失望昨天 发表于 2025-3-27 13:36:01
http://reply.papertrans.cn/15/1466/146591/146591_35.pngDecimate 发表于 2025-3-27 18:18:27
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 networLATHE 发表于 2025-3-28 00:13:50
http://reply.papertrans.cn/15/1466/146591/146591_37.pngadduction 发表于 2025-3-28 04:12:26
http://reply.papertrans.cn/15/1466/146591/146591_38.png哑剧 发表于 2025-3-28 07:10:50
http://reply.papertrans.cn/15/1466/146591/146591_39.png洞察力 发表于 2025-3-28 13:40:39
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