利用深度机器学习鲁棒识别和恢复被遮挡的 CO₂ 吸收信号

IF 2.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Photonics Technology Letters Pub Date : 2024-08-28 DOI:10.1109/LPT.2024.3451014

Faezeh Abbasi;Zahra Shabani;Mohammad Sadegh Yazdani;Alireza Khorsandi

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引用次数: 0

摘要

我们介绍了深度学习（DL）在从噪声光谱中识别和恢复二氧化碳吸收线方面的功效。在对 DL 方法的能力进行模拟评估之后，我们将其应用于利用锁相放大器和机械斩波器作为相敏检测单元的实验中。结果表明，DL 方法完全可以取代传统的降噪系统，在简化光谱分析的同时确保可靠性和智能性。

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Robust Identification and Recovery of Obscured CO₂ Absorption Signals Using Deep Machine Learning

We present the efficacy of deep learning (DL) in identifying and recovering the CO2 absorption line from a noisy spectrum. Following a simulation-based assessment of the DL method’s capabilities, it was applied in an experiment utilizing a lock-in amplifier and mechanical chopper as a phase-sensitive detection unit. The results demonstrate that the DL method is fully qualified to replace traditional noise-reduction systems, simplifying spectroscopy while ensuring reliability and intelligence.

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来源期刊

IEEE Photonics Technology Letters 工程技术-工程：电子与电气

CiteScore

5.00

自引率

3.80%

发文量

404

审稿时长

2.0 months

期刊介绍： IEEE Photonics Technology Letters addresses all aspects of the IEEE Photonics Society Constitutional Field of Interest with emphasis on photonic/lightwave components and applications, laser physics and systems and laser/electro-optics technology. Examples of subject areas for the above areas of concentration are integrated optic and optoelectronic devices, high-power laser arrays (e.g. diode, CO2), free electron lasers, solid, state lasers, laser materials'' interactions and femtosecond laser techniques. The letters journal publishes engineering, applied physics and physics oriented papers. Emphasis is on rapid publication of timely manuscripts. A goal is to provide a focal point of quality engineering-oriented papers in the electro-optics field not found in other rapid-publication journals.

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