Mathematical engineering modeling and optimization of convolutional neural networks for common data areas in multitasking systems

Abstract

Abstract This paper presents an in-depth study and analysis of modeling optimization in mathematical engineering for common data areas in multitasking systems using convolutional neural networks. A study of multi-convolutional neural network task computation is developed, and a multi-convolutional neural network task switching architecture is designed based on FPGA. To address the phenomenon of failure correlation among different functional types of components in complex systems, a basic model of the functionally related voting system is defined, and the component functional dependence mechanism is used to guide the construction of the system's dynamic Bayesian network topology and the generation of node conditional probability tables. On this basis, the state probability distribution of system nodes is calculated based on dynamic Bayesian network inference to realize the dynamic assessment of system reliability with online state data. The results of the algorithm show that the method can effectively realize both function-related voting system reliability modeling and dynamic updating of the complex system reliability index using online state data. Meanwhile, the method applies a deep learning technique from a 3D tree model library to learn the 2-dimensional shape of 3D shape mapping, which results in more natural and reliable depth information. Then, a complete 3D tree model is generated by combining a procedural tree modeling approach under the constraint of 3D shapes, where 3D tree models with different levels of detail can be created by different semantic strokes. Finally, this study experimentally verifies the efficiency and effectiveness of the method in terms of recovering 3D tree models from a single image.