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首页> 外文期刊>Journal of Software Engineering and Applications >Single-Phase Velocity Determination Based in Video and Sub-Images Processing: An Optical Flow Method Implemented with Support of a Programmed MatLab Structured Script
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Single-Phase Velocity Determination Based in Video and Sub-Images Processing: An Optical Flow Method Implemented with Support of a Programmed MatLab Structured Script

机译：基于视频和子图像处理的单相速度确定：一种通过编程的MatLab结构化脚本实现的光流方法

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Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent); the raw source code with comments had almost 3000 (three thousand) characters; and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.
机译：在行业的许多不同领域中，重要的是，由于需要测量精度，确定流速的重要性变得越来越重要，为了确定正确的生产率，确定不需要的流体的体积产量，基于这些测量值建立自动化控制避免困难的是确定嵌入在某些流体中的特定流体的速度，例如，确定在整个液相中流动的气泡流速。尽管已经在行业内研究并实施了不同且已经适用的方法，但是提供这些流速度的非侵入式自动化方法仍然很重要，并且可能会对项目预算产生巨大影响。知道其确定的重要性，此开发的脚本使用一种将实时视频媒体分解为帧图像的方法，通过像素相关性分析可能的叠加匹配，以进一步估计气泡流速度。从原始意义上讲，该脚本本身基于MatLab中已经可用的功能和过程，可以用于图像处理和处理，从而可以实现该方法。运行测试后，其准确性约为97％（百分之九十七）;带注释的原始源代码将近3000（三千）个字符;运行该代码的硬件是支持Intel Core Duo 2.13 [Ghz]和2 [Gb] RAM内存的工作站。即使显示出良好的结果，也可以说只有端点相关才真正到达了最终解决方案。这样一来，利用自学习功能或神经网络，就可以肯定地增强应用程序实时运行的能力，而不会被迭代循环所累。

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