基于子搜索算法优化的多气体污染物检测ALSTM-FCNN
Xueying Kou1, Xingchi Luo1, Wei Chu1
1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China.
本研究介绍了一种基于注意力的长短记忆全卷积网络 (ALSTM-FCN),用于危险气体检测. 与Sparrow Search Algorithm (SSA) 优化的ALSTM-FCN模型,与传统方法相比,实现了更高的准确性.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 计算机科学 计算机科学
背景情况:
- 准确检测有害气体对于工业安全和医学诊断至关重要.
- 现有的方法在可靠地识别和分类广泛的危险气体方面面临挑战.
研究的目的:
- 提出和评估用于危险气体检测和分类的先进深度学习模型.
- 将拟议模型的性能与已建立的深度学习和传统机器学习方法进行比较.
主要方法:
- 开发一种基于注意力的长期短期记忆全卷积网络 (ALSTM-FCN).
- 使用Sparrow搜索算法 (SSA) 优化ALSTM-FCN网络参数.
- 使用加利福尼亚大学-伊尔文 (UCI) 数据集进行评估,并与LSTM,FCN和各种机器学习模型 (AdaBoost,LR,ET,DT,RF,KNN) 进行比较.
主要成果:
- ALSTM-FCN模型的可靠性测试准确率达到99.461%,明显超过LSTM (89.471%) 和FCN (96.083%).
- 与传统的机器学习模型相比,建议的SSA优化的ALSTM-FCN显示出更高的气体分类准确性.
- 在参数调整方面,SSA优化证明比PSO,GA,GWO和CS算法更有效.
结论:
- ALSTM-FCN混合型号为危险气体检测提供了高度准确和可靠的解决方案.
- 该研究强调了SSA优化的深度学习在广泛的污染气体检测应用中的潜力.
更多相关视频
09:04Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
Published on: April 18, 2019
07:32Detection of 3-Nitrotyrosine in Atmospheric Environments via a High-performance Liquid Chromatography-electrochemical Detector System
Published on: January 30, 2019
相关概念视频
Gas Chromatography: Types of Detectors-II
Gas Chromatography: Types of Detectors-I
TCD is the earliest and most widely used detector that operates by measuring the changes in the thermal conductivity of the carrier gas. When a sample compound enters the detector,...
Atomic Emission Spectroscopy: Interference
Gas Chromatography: Overview of Detectors
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
Atomic Fluorescence Spectroscopy
Gas Chromatography–Mass Spectrometry (GC–MS)
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....
