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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING投稿须知(官网信息)

2021/11/16 13:52:43 来源:官网信息 阅读:536 发布者:
编者按:以下内容由万维书刊网根据期刊官网信息整理发布,仅供投稿参考!

INSTRUCTIONS FOR AUTHORS

AIMS AND SCOPE

AI EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing is a journal intended to reach two audiences: engineers and designers who see AI technologies as powerful means for solving difficult engineering problems and researchers in AI and computer science who are interested in engineering applications of AI and in the theoretical issues that arise from such applications. The journal publishes significant, original articles about AI theory and applications based on the most up to date research in all branches and phases of engineering. Suitable topics include analysis and evaluation, selection, configuration and design, manufacturing and assembly, and concurrent engineering. Specific subareas include cognitive modelling; creativity; learning; qualitative reasoning; spatial reasoning; graphics and modelling; constraints and preferences; style and brands; human–computer interaction; multimodal interaction; computational linguistics; design and process planning; scheduling; simulation; optimization; dis- tributed teams and systems; multiagent applications; design rationale and histories; functional, behavioral, and structural reasoning; knowledge management; and ontologies. AI EDAM is also interested in comprehensive review papers, as well as in practicum papers that describe original, major applications of state-of-the-art AI techniques to important engineering problems, with enough details to help others build similar sys- tems. In addition to the rapid publication and dissemination of unsolicited research papers, AI EDAM is committed to producing special issues on important, timely topics. AI EDAM is indexed in Compendex Plus, SciSearch, Research Alert, and CompuMath Citation Index.

AI EDAM ISSUES

All issues of AI EDAM now include a Thematic Collection section, and a general section for papers not part of a Thematic Collection. Papers for the general section can be submitted at any time, and if accepted will be published in the next available issue.

ORIGINALITY AND COPYRIGHT

To be considered for publication in AI EDAM a manuscript cannot have been published previously or be under review for publication elsewhere. Papers with multiple authors are reviewed with the assumption that all authors have approved the submitted manuscript and concur about its submission to AI EDAM.

The policy of AI EDAM is that authors (or in some cases their employers) retain copyright and grant Cambridge University Press a licence to publish their work. In the case of gold open access articles this is a non-exclusive licence. Authors must complete and return an author publishing agreement form as soon as their article has been accepted for publication; the journal is unable to publish without this. Please download the appropriate publishing agreement here.

For open access articles, the form also sets out the Creative Commons licence under which the article is made available to end users: a fundamental principle of open access is that content should not simply be accessible but should also be freely re-usable. Articles will be published under a Creative Commons Attribution license (CC-BY) by default. This means that the article is freely available to read, copy and redistribute, and can also be adapted (users can “remix, transform, and build upon” the work) for any commercial or non-commercial purpose, as long as proper attribution is given. Authors can, in the publishing agreement form, choose a different kind of Creative Commons license (including those prohibiting non-commercial and derivative use) if they prefer.

Authors are responsible for obtaining written permission from the copyright owners (authors, editors, and publisher) to reprint or adapt any previously published material included in their article. These permissions must be provided before an article can be published.

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更多详情:

https://www.cambridge.org/core/journals/ai-edam/information/instructions-contributors


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