scholarly journals HTTP Traffic Analysis based on Multiple Deep Convolution Network Model Generation Algorithms

2021 ◽  
Vol 2037 (1) ◽  
pp. 012048
Author(s):  
Bocheng Liu ◽  
Fan Yang
2021 ◽  
Author(s):  
Davendu Y. Kulkarni ◽  
Luca di Mare

Abstract The design and analysis of the secondary air system (SAS) of gas turbine engine is a complex and time-consuming process because of its complicated geometry topology. The conventional SAS design-analysis model generation process is quite tedious, time consuming. It is still heavily dependent on human expertise and thus incurs high time-cost. This paper presents an automated, whole-engine SAS flow network model generation methodology. During the SAS preprocessing step, the method accesses a pre-built whole-engine geometry model created using a novel, in-house, feature-based geometry modelling environment. It then transforms the engine geometry features into the features suitable for SAS flow network analysis. The proposed method not only extracts the geometric information from the computational geometry but also retrieves additional non-geometric attributes such as, rotational frames, boundary types, materials and boundary conditions etc. Apart from ensuring geometric consistency, this methodology also establishes a bi-directional information exchange protocol between engine geometry model and SAS flow network model, which enables making engine geometry modifications based on SAS analysis results. The application of this feature mapping methodology is demonstrated by generating the secondary air system (SAS) flow network model of a modern three-shaft gas turbine engine. This capability is particularly useful for the integration of geometry modeler with the simulation framework. The present SAS model is generated within a few minutes, without any human intervention, which significantly reduces the SAS design-analysis time-cost. The proposed method allows performing a large number of whole-engine SAS simulations, design optimisations and fast re-design activities.


Author(s):  
M. Nakagawa ◽  
R. Nozaki

<p><strong>Abstract.</strong> Three-dimensional indoor navigation requires various functions, such as the shortest path retrieval, obstacle avoidance, and secure path retrieval, for optimal path finding using a geometrical network model. Although the geometrical network model can be prepared manually, the model should be automatically generated using images and point clouds to represent changing indoor environments. Thus, we propose a methodology for generating a geometrical network model for indoor navigation using point clouds through object classification, navigable area estimation, and navigable path estimation. Our proposed methodology was evaluated through experiments using the benchmark of the International Society for Photogrammetry and Remote Sensing for indoor modeling. In our experiments, we confirmed that our methodology can generate a geometrical network model automatically.</p>


Author(s):  
Davendu Kulkarni ◽  
Luca di Mare

Abstract The design and analysis of the secondary air system (SAS) of gas turbine engine is a complex and time-consuming process because of the complicated topology and iterative nature of SAS design. The conventional SAS design-analysis model generation process is quite tedious and inefficient. It is still largely dependent on human expertise and thus incurs high time-cost. This paper presents an automated, whole-engine SAS flow network model generation methodology. This method accesses a pre-built feature-based whole-engine geometry model and transforms the geometry features into the features suitable for SAS flow network analysis. The proposed method extracts both the geometric and non-geometric information from the engine geometry model such as, rotational frames, materials and boundary conditions etc. Apart from ensuring geometric consistency, this methodology also establishes a bi-directional information exchange protocol between the engine geometry model and the SAS flow network model, which enables to make engine geometry modifications based on SAS analysis results. The application of this feature mapping methodology is demonstrated by generating the secondary air system (SAS) flow network model of a modern three-shaft gas turbine engine. This flow network model is generated within a few minutes, without any human intervention, which significantly reduces the SAS design-analysis time-cost. The proposed methodology seamlessly links the geometry and the air system modellers of Virtual Gas Turbines simulation framework and thus allows performing a large number of whole-engine SAS simulations, design optimisations and fast re-design activities.


1991 ◽  
Vol 8 (1) ◽  
pp. 77-90
Author(s):  
W. Steven Demmy ◽  
Lawrence Briskin
Keyword(s):  

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