Intelligence against complexity: Machine learning for nonuniform temperature-field measurements through laser absorption

Ruiyuan Kang; Dimitrios C. Kyritsis; Panos Liatsis

doi:10.1371/journal.pone.0278885

Peer Review History

Original SubmissionAugust 29, 2022
4 Nov 2022 Decision Letter - Jianguo Wang, Editor PONE-D-22-24085Intelligence Against Complexity: Machine Learning for Nonuniform Temperature-field Measurements Through Laser AbsorptionPLOS ONE Dear Dr. Liatsis, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Dec 19 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Revision 1
9 Nov 2022 Author Response Response to the comments of the reviewer R1.C1: This an outstanding paper, written with high quality style and good English language. The authors have compared between sixteen models for uniform-profile spectra and the best three models, i.e., GPR, VGG and BRF were further compared for non-uniform-profile data. The paper is well-documented and well strutured and presented in a clear manner. The results are deeply analyzed. I have no comment about the paper, except one minor 1. I wonder if the authors can provide a shoot discussion, more precisely, a comparison of the present paper with other reported studies in the literature before the conclusion. Response: We are sincerely grateful to the reviewer for his encouraging comments and for recognizing the contributions of our work. We fully agree with the reviewer’s suggestion and in this respect, we incorporated a new section entitled “Discussion” as follows: “In this work, we provided quantitative results and associated discussions in terms of the effect of spatial non-uniformity with respect to the parameters of temperature and concentration distribution on the accuracy of the average temperature measurement estimate using laser absorption spectroscopy. The focus of the research was to explore the feasibility whether machine learning methods could provide reliable parameter estimates by overcoming this kind of effect. Indeed, some earlier studies [11], [13], [15] provided the means to measure average temperature from absorption spectra subject to the presence of non-uniformity. However, they neither provided a systematic study of the effects of the non-uniformity, nor attempted to define its impact on the accuracy of the mean temperature measurements. For instance, in [12], there was an attempt to formally analyze and explain the mechanism of the effect of non-uniformities in temperature and distribution concentration. The outcomes of this work have merit, however, due to the complexities of the associated theoretical analysis in the simultaneous presence of non-uniformities in both temperature and concentration, the associated conclusions were not clear. In this research, we pursue a fundamentally different approach. The essence of the underlying concept is to use multiple machine-learning-based surrogate models, where each model was trained on uniform-profile data, to estimate the average temperature from nonuniform-profile data, and next, apply data analysis tools to quantify the effect of non-uniformity. Based on quantitative statistics, we confirmed that using temperature measurement tools for uniform profiles directly on spectra generated from non-uniform profiles introduces significant errors. Lastly, to the best of our knowledge, this is the first work, which attempts to overcome non-uniformity effects in laser absorption spectra by machine-learning-based solutions. The results demonstrate that some machine-learning-based solutions, based on global information, demonstrate robustness to changes in the magnitude and style of non-uniformity in temperature and concentration distributions, which could potentially solve the problem of measuring average temperature from laser absorption spectra, in the case of nonuniform profiles.” [11] Tudor Palaghita and Jerry Seitzman. “Control of Temperature Nonuniformity Based on Line-of-Sight Absorption”. In: 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. 2004, p. 4163. doi: 10.2514/6.2004-4163. [12] Christopher S. Goldenstein et al. “Two-Color Absorption Spectroscopy Strategy for Measuring the Column Density and Path Average Temperature of the Absorbing Species in Nonuniform Gases”. In: Applied Optics 52.33 (2013), pp. 7950–7962. doi: 10.1364/AO.52.007950. [13] Xiang Liu, Jay B. Jeffries, and Ronald K. Hanson. “Measurement of Nonuniform Temperature Distributions Using Line-of-Sight Absorption Spectroscopy”. In: AIAA Journal 45.2 (2007), pp. 411–419. doi: 10.2514/1.26708. [15] Scott T. Sanders et al. “Diode-Laser Absorption Sensor for Line-of-Sight Gas Temperature Distributions”. In: Applied Optics 40.24 (2001), pp. 4404–4404. doi: 10.1364/ao.40.004404. Attachments Attachment Submitted filename: Response to Reviewers-PONE-D-22-24085.pdf https://doi.org/10.1371/journal.pone.0278885.r002
28 Nov 2022 Decision Letter - Jianguo Wang, Editor Intelligence Against Complexity: Machine Learning for Nonuniform Temperature-field Measurements Through Laser Absorption PONE-D-22-24085R1 Dear Dr. Liatsis, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Jianguo Wang, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't Know ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors have provided the necessary section (Discussion) and the paper is now ready for publication. No further revision is necessary. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: Pr. Salim Heddam ******** https://doi.org/10.1371/journal.pone.0278885.r003
Formally Accepted
1 Dec 2022 Acceptance Letter - Jianguo Wang, Editor PONE-D-22-24085R1 Intelligence Against Complexity: Machine Learning for Nonuniform Temperature-field Measurements Through Laser Absorption Dear Dr. Liatsis: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Jianguo Wang Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0278885.r004

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