Learning Based Methods for Industrial Applications

Dear Colleagues,

Industrial big data is a general term for the data sets related to the industrial manufacturing process, which is the core of the industrial Internet and an important foundation for the development of industrial intelligence. In recent years, mining industrial big data based on machine learning methods to achieve optimization of industrial production and management has received more and more attention. As a result, many successful applications have been achieved in data-driven production process modeling, production process operation optimization, production scheduling, etc.

The aim of this Special Issue is to attract world-leading researchers in the area of learning-based methods for industrial applications in an effort to highlight the latest exciting developments, discuss the new methods of data analysitcs and optimization, and promote specific applications of learning-based methods in various industries. The accepted contributions will include learning-based production process modeling, learning-based product quality prediction, deep-learning-based industrial image analytics, learning-based production process fault diagnosis, learning-based production scheduling and management, etc.

Prof. Dr. Xianpeng Wang
Prof. Dr. Danyu Bai
Prof. Dr. Peng Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.