دانلود مقاله کاربرد یادگیری ماشین در حسگرهای فیبر نوری
خلاصه
1. معرفی
2. سیستم نوری هیبریدی مبتنی بر حسگرهای فیبر نوری
3. نمونه هایی از الگوریتم های ML
4. کاربرد ML در حسگرهای فیبر نوری
5. نتیجه گیری و چشم اندازهای آینده
بیانیه مشارکت نویسنده CRediT
اعلامیه منافع رقابتی
در دسترس بودن داده ها
قدردانی
منابع
Abstract
1. Introduction
2. Hybrid optical system based on optical fiber sensors
3. Examples of ML algorithms
4. Application of ML in optical fiber sensors
5. Conclusion and future perspectives
CRediT authorship contribution statement
Declaration of competing interest
Data availability
Acknowledgments
References
چکیده
در سال های اخیر، با افزایش تقاضا برای جامعه هوشمند، فوتونیک هوشمند به سرعت توسعه یافته است. یادگیری ماشینی (ML)، به عنوان زیر مجموعه ای از هوش مصنوعی (AI)، نقش مهمی در تکامل هوشمند حسگرهای فیبر نوری ایفا کرده است. تأثیر آن فراتر از افزایش عملکرد حسگر با معرفی رویکردهای نوآورانه حل مسئله است. به طور خاص، الگوریتم‌های ML در دمدولاسیون سیگنال و افزایش کارایی حسگرهای گسسته و توزیع شده ابزاری هستند و همچنین توسعه پردازش الگوی لکه‌ای فیبر نوری را به میزان زیادی ارتقا داده‌اند. این مقاله آخرین پیشرفت‌ها را در حسگرهای فیبر نوری مبتنی بر ML ارائه می‌کند، مشکلاتی را که با روش‌های دمدولاسیون مرسوم و الگوریتم‌های رایج ML اعمال می‌شود در حسگرهای فیبر نوری با آن مواجه می‌شوند، و بر کاربردهای کلیدی تأکید می‌کند. علاوه بر این، این مقاله به چالش ها و توسعه آینده این جهت تحقیقاتی در حال ظهور می پردازد.
Abstract
In recent years, with the increasing demand for intelligent society, intelligent photonics has developed rapidly. Machine learning (ML), as a subset of artificial intelligence (AI), has played an important role in the intelligent evolution of optical fiber sensors. Its impact extends beyond enhancing sensor performance by introducing innovative problem-solving approaches. Specifically, ML algorithms have become instrumental in signal demodulation and elevating the efficacy of discrete and distributed sensors, and have also greatly promoted the development of optical fiber speckle pattern processing. This paper presents the latest advancements in ML-based optical fiber sensors, outlines the problems faced by conventional demodulation methods and the common ML algorithms applied in optical fiber sensors, and emphasizes key applications. Additionally, this paper delves into the challenges and future development of this emerging research direction.
Introduction
Due to advancements in powerful computing tools, hardware, widespread use of cloud data, and the evolution of the Internet of Things, the accessibility of large datasets has significantly improved. This has promoted the emergence of many efficient machine learning (ML) algorithms. These algorithms, aided by continuous advancements in data analysis technology, have demonstrated effectiveness in training models and solving specific problems. If ML algorithms are complex and deep enough in terms of layers, then ML can become so-called deep learning (DL) [1]. Regardless of the name, ML seems to have a wide range of applications in modern society, because the achievements of artificial intelligence (AI) often exceed those achieved by manual control design.
In 2021, Nature Photonics further advanced its exploration of the hot topic of optics/photonics through a focused issue called “Machine learning of Light”. As an important type of microphysics, optics seems to have similarities in complexity with some of the “black box” processes in ML. In the Q&A session, David Pille and Aydogan Ozcan discussed the two primary drivers behind the current application of ML in photonics [2]. One direction is to use AI methods on hardware to design optical structures and devices with specific performance. Another direction is to use optical systems as fully optical/hybrid statistical inference models to achieve AI calculations.
Optics and photonics applied to ML computing offer unique advantages, allowing for ultrafast calculations at extreme frame rates and low energy consumption [3], [4], [5], [6]. For instance, Feldmann et al. [7] proposed a fully optical neural synaptic system. They use wavelength division multiplexing technology to implement a scalable circuit architecture for photonic neural networks (NNs) and use optical pulse signals to regulate the phase change materials that encapsulate neurons. This phase change material can regulate transmittance and reflectivity through a controllable laser. This system can perform both supervised and unsupervised learning. The work successfully demonstrates direct pattern recognition in the full optical system. This photonic NN has the potential to leverage the inherent high-speed and high bandwidth of optical systems, thereby enabling the direct processing of full optical communication and visual data. However, logic gates, computation, and NNs are all linear (passive) systems that cannot address nonlinear (active) issues. The lack of effective nonlinear optical processes as activation functions for many nodes poses challenges for all-optical implementation [8].
Conclusion and future perspectives
This paper reviews the application of the ML algorithm in optical fiber sensors. In recent years, the advent of ML has greatly impacted optical development. Compared with static demodulation algorithms and complex spatial optical paths, the ML algorithm offers better signal processing mechanisms for optics. ML algorithms enable automated processing, analysis, and recognition of spectrums, thereby enhancing the efficiency and accuracy of spectrum analysis. Using ML algorithms to classify and identify the spectrum of different substances, enabling qualitative and quantitative analysis of substances. By learning from known corresponding spectrums and building models, ML algorithms can predict and analyze unknown spectrums. ML algorithms can extract and recognize features from the spectrum, analyze the content and proportion of various components, and reveal the intrinsic nature of substances. With the continuous development of spectroscopy and ML algorithms, the application prospects of ML in spectroscopy will become increasingly widespread. This advancement will contribute to more efficient and accurate analytical methods in chemical analysis, environmental monitoring, food safety, and other fields, promoting progress in scientific research and expanding application fields.