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实时自动化可溶性选方法使用深度神经网络与手工制作的功能.
Minwoo Jeon1, Geunhyeok Yu1, Hyundo Choi2
1Department of Software Convergence, Kyunghee University, Yongin 17104, Republic of Korea.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一种计算机视觉方法,用于实时,使用手工制作的功能和深度神经网络 (DNN) 分类器进行自动溶解度测量. 这使得溶液溶解状态的自动选成为可能,这对于工业应用至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算机视觉 计算机视觉
背景情况:
- 自动化和实时的溶解度测量在研究和工业中越来越重要.
- 传统方法往往缺乏实时功能,阻碍了流程自动化.
- 在工业环境中,有限的标记图像数据集需要对端到端学习采取替代方法.
研究的目的:
- 开发一种基于计算机视觉的方法,用于解决方案溶解状态的自动分类.
- 为了利用手工制作的功能,在有限的标记数据下提高准确性.
- 为了实现实时,自动化可溶性选.
主要方法:
- 利用计算机视觉算法从解决方案图像中提取九个手工制作的特征.
- 使用这些手工制作的功能训练了一个深度神经网络 (DNN) 分类器.
- 构建了各种溶液图像的数据集,从部分溶解到完全溶解的状态.
主要成果:
- 提出的方法成功地根据溶解状态对溶液进行分类.
- 使用平板电脑或手机等常见设备实现了可溶性状态的自动实时选.
- 证明了手工制作的功能与DNN一起用于此特定任务的有效性.
结论:
- 开发的方法为溶解度测量提供了一种高效和自动化的方法.
- 与自动可溶性改变系统的集成可以导致完全自动化的流程.
- 这种技术对于需要实时监控的工业应用具有重大潜力.
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