ResNet14注意网络用于识别二酸的定位终点
Siwen Liang1, Linfei Yin1, Dashui Zhang2
1Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning, Guangxi, 530004, China.
Heliyon
|August 23, 2023
概括
一个新的ResNet14Attention网络准确地识别了用于测试水质的二氧化定位终点. 这种人工智能模型在比较研究中实现了100%的准确性和更高的速度.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 计算机科学 计算机科学
背景情况:
- 工业发展增加了污水排放,使得水质检测至关重要.
- 二酸盐定位对于水质分析至关重要,但终点识别具有挑战性.
- 准确和快速检测定位终点是一个重要的研究障碍.
研究的目的:
- 开发一种准确和快速的方法来识别二酸盐定位终点.
- 引入一种新的深度学习模型,用于增强水质分析.
主要方法:
- 设计了一个ResNet14Attention网络,集成剩余模块和注意力机制.
- 拟议的网络与其他12个卷积神经网络进行了基准测试,包括VGG和GoogLeNet.
- 绩效是根据训练/测试准确性和速度进行评估的.
主要成果:
- ResNet14Attention网络实现了100%的培训和测试准确性,超过了所有比较模型.
- 在网络中,它展示了最快的培训速度,准确度超过90%.
- 该模型有效地关注相关的图像信息和分类目标.
结论:
- ResNet14Attention网络为二酸盐定位终点检测提供了一个高度准确和高效的解决方案.
- 这种人工智能驱动的方法显著提升了水质监测能力.
- 这项研究强调了环境分析中专用深度学习模型的潜力.
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