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Article
Peer-Review Record

Defect Detection for Wear Debris Based on Few-Shot Contrastive Learning

Appl. Sci. 2022, 12(23), 11893; https://doi.org/10.3390/app122311893
by Hang Li, Li Li * and Hongbing Wang
Reviewer 2:
Appl. Sci. 2022, 12(23), 11893; https://doi.org/10.3390/app122311893
Submission received: 26 October 2022 / Revised: 11 November 2022 / Accepted: 12 November 2022 / Published: 22 November 2022

Round 1

Reviewer 1 Report

Authors made good attempt to predict defect detection of wear debris, and given sound explanation regarding the same

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

I think it would be possible to reduce the number of tables or make it clearer with the help of graphs. (For example, combine tables 1 and 2.)

In equation 4, the quantities T, Q, K, d are not clearly described to me.

I think that the methods are described too generally.

What programming language did the authors use and why?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

What further work is foreseen on the methods? What can the results be, for example, with a 50 shot? Is there any interest in these methods by future users in the industry?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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