The Review of Laser Engineering
Online ISSN : 1349-6603
Print ISSN : 0387-0200
ISSN-L : 0387-0200
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Special Issues on Recent Progress of Deep Learning and Automation in Laser Science
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Special Issues on Recent Progress of Deep Learning and Automation in Laser Science
Special Issue
Laser Review
  • Preface to Special Issues on Recent Progress of Deep Learning and Automation in Laser Science
    Shinsuke FUJIOKA
    2022 Volume 50 Issue 12 Pages 659-
    Published: 2022
    Released on J-STAGE: October 08, 2024
    DOIhttps://doi.org/10.2184/lsj.50.12_659
    JOURNAL OPEN ACCESS
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    This paper introduces special issue on the recent progress of deep learning and automation in laser science. The rapid development of the user-friendly machine learning and deep learning technologies is stimulating digital transformation in the laser science and other related communities. This special issue consist of the articles written by specialist of both laser science/technology and information science/ technology. The synesis of these two technologies is expected to change the style of research, development, and operation of the laser system.
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  • Digital Transformation of Operation and Usage of Power Laser GEKKO-XII and Laser for Fast Ignition Experiment (LFEX)
    Hiroyuki OKU, Keisuke TAKAHASHI, Hideo NAGATOMO, Mizuho NAGATA, Yoshin ...
    2022 Volume 50 Issue 12 Pages 661-
    Published: 2022
    Released on J-STAGE: October 08, 2024
    DOIhttps://doi.org/10.2184/lsj.50.12_661
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    World class large-energy and high-power laser facilities are operated for decades in the Institute of Laser Engineering, Osaka University. These laser facilities are used for the research on high energy density science such as the laser fusion, the relativistic laser-plasma interaction and the laser processing. Along with the development of the high-power laser system, operational technology has been improved. Recent development in laser operation, remote control, experimental databases and networks are discussed.
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  • Development of Integrated Systems for Dynamic Laser Compression Experimental Platform at SACLA
    Kohei MIYANISHI, Keiichi SUEDA, Toshinori YABUUCHI
    2022 Volume 50 Issue 12 Pages 668-
    Published: 2022
    Released on J-STAGE: October 08, 2024
    DOIhttps://doi.org/10.2184/lsj.50.12_668
    JOURNAL OPEN ACCESS
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    Dynamic laser compression has played a key role in exploring matters in high-pressure states related to various scientific fields, such as high energy density science, planetary science, and materials science. The advent of X-ray free-electron lasers (XFELs), which are an ultrabright and ultrashort X-ray light source, has brought innovations to these fields. The utilization of XFEL in dynamic compression experiments using high-power optical lasers allows the states of dynamically compressed matters to be captured with unprecedented fidelity. In addition to expanding experimental capabilities, the use of a high-repetition rate XFEL has promoted improvements in the research environment in order to produce results more efficiently. In this article, we report the current status of the experimental platform for laser compression with an XFEL at SACLA, including its remote capability and smartification of the platform.
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  • High-Intense Laser System for Remote Experiments
    Masaki HASHIDA, Shunsuke INOUE, Shin-ichiro MASUNO, Shigeki TOKITA
    2022 Volume 50 Issue 12 Pages 673-
    Published: 2022
    Released on J-STAGE: October 08, 2024
    DOIhttps://doi.org/10.2184/lsj.50.12_673
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    High intense laser systems have been used for an intense laser science at Institute for Chemical Research, Kyoto University since 2003. Our laser system has stably been used for demonstrating the experiments related to high-intense laser-matters interactions. Our laser system facility recently installed a real time and three dimentional viewing system for remote experiments. In this review, we report its performance and its interaction experiments for exploring the high-intense laser science. We also introduce for our system remote experiments.
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  • Remote and Automation Initiative of the J-KAREN-P Petawatt Laser System and Its Research Applications
    Hiromitsu KIRIYAMA, Yuji MASHIBA, Yasuhiro MIYASAKA, Nobuhiko NAKANII, ...
    2022 Volume 50 Issue 12 Pages 678-
    Published: 2022
    Released on J-STAGE: October 08, 2024
    DOIhttps://doi.org/10.2184/lsj.50.12_678
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    The Kansai Photon Science Institute of the National Institutes for Quantum Science and Technology (KPSI, QST) has been developing an ultrahigh-intensity Ti:sapphire chirped-pulse amplification laser system (named J-KAREN-P laser system) with petawatt (PW = 10 15 W) peak power and laser-driven quantum beam sources with J-KAREN-P. Here, we describe the configuration and output characteristics of the J-KAREN-P laser system and briefly discuss its application aimed at opening up a new research area of laser-driven quantum beam science and introduce the remote and automated operation of the J-KAREN-P laser system that started since in the last fiscal year.
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  • Smart Laser Manufacturing with Cyber-Physical System
    Yohei KOBAYASHI, Keiichi BAMOTO, Kohei SHIMAHARA, Tsubasa ENDO, Hiroha ...
    2022 Volume 50 Issue 12 Pages 683-
    Published: 2022
    Released on J-STAGE: October 08, 2024
    DOIhttps://doi.org/10.2184/lsj.50.12_683
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    Cyber-physical systems (CPS) are strongly required for next-generation production systems. This paper reports what a CPS in laser processing is and how it should be constructed.
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  • Utilization of Deep Learning Starting with Neural Network Console and Embedded Board SPRESENSE
    Yoshiyuki KOBAYASHI
    2022 Volume 50 Issue 12 Pages 687-
    Published: 2022
    Released on J-STAGE: October 08, 2024
    DOIhttps://doi.org/10.2184/lsj.50.12_687
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    This paper introduces the recent development and expansion of applications of deep learning, and explains how to utilize deep learning using the GUI-based deep learning development tool Neural Network Console and the embedded board computer SPRESENSE. Neural Network Console can be used for various purposes such as classification of measured signals, abnormality detection, numerical value prediction, and control. Models developed using Neural Network Console can easily run on SPRESENSE for practical use.
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  • Introduction to Deep Learning
    Yuta NAKASHIMA
    2022 Volume 50 Issue 12 Pages 691-
    Published: 2022
    Released on J-STAGE: October 08, 2024
    DOIhttps://doi.org/10.2184/lsj.50.12_691
    JOURNAL OPEN ACCESS
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    Deep learning has replaced conventional machine learning due to its superior performance when exposed to an enormous amount of data. Researchers in many disciplines are drawn to deep learning, including physics, medicine, humanities, etc. Since we expect that readers would like to apply this popular technique in their projects on laser technologies, this paper introduces the fundamentals of deep learning with some mathematics. We also provide the first step in coding a neural network in PyTorch Lightning over Python, which comes with ready-to-go code on Google Colab, allowing novices to immediately get started.
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