Journal Information
Advanced Engineering Informatics (AEI)
Impact Factor:

Call For Papers
Advanced computing methods and related technologies are changing the way engineers interact with the information infrastructure. Explicit knowledge representation formalisms and new reasoning techniques are no longer the sole territory of computer science. For knowledge-intensive tasks in engineering, a new philosophy and body of knowledge called Engineering Informatics is emerging.

Advanced Engineering Informatics solicits research papers with particular emphases both on 'knowledge' and 'engineering applications'. As an international Journal, original papers typically:

• Report progress in the engineering discipline of applying methods of engineering informatics.
• Have engineering relevance and help provide the scientific base to make engineering decision-making more reliable, spontaneous and creative.
• Contain novel research that demonstrates the science of supporting knowledge-intensive engineering tasks.
• Validate the generality, power and scalability of new methods through vigorous evaluation, preferably both qualitatively and quantitatively.

In addition, the Journal welcomes high quality review articles that summarise, compare, and evaluate methodologies and representations that are proposed for the field of engineering informatics. Similarly, summaries and comparisons of full-scale applications are welcomed, particularly those where scientific shortcomings have hindered success. Typically, such papers have expanded literature reviews and discussion of findings that reflect mastery of the current body of knowledge and propose novel additions to contemporary research.

Papers missing explicit representation and use of knowledge, such as those describing soft computing techniques, mathematical optimization methods, pattern recognition techniques, and numerical computation methods, do not normally qualify for publication in the Journal. Papers must illustrate contributions using examples of automating and supporting knowledge intensive tasks in artifacts-centered engineering fields such as mechanical, manufacturing, architecture, civil, electrical, transportation, environmental, and chemical engineering. Papers that report application of an established method to a new engineering subdomain will qualify only if they convincingly demonstrate noteworthy new power, generality or scalability in comparison with previously reported validation results. Finally, papers that discuss software engineering issues only are not in the scope of this journal.
Last updated by Dou Sun in 2018-07-15
Special Issues
Special Issue on Data-Driven Collaborative Engineering (EVISE Article Type: “Data-Driven Collab Eng”)
Submission Date: 2019-10-31

Collaborative Engineering (CE) involves the research and development of collaboration technologies and their applications to the design of processes, products, systems, and services in industries and societies with objectives for better product quality, shorter lead-time, more competitive cost and higher customer satisfaction. Collaboration technologies include theories, methods, mechanisms, protocols, software tools, platforms, and services that support communication, coordination and collaboration among people, software and hardware systems. New generation of ICT technologies, including Cloud/Fog/Edge computing, Internet of Things (IoT), Big Data and Artificial Intelligence/Deep Learning, can substantially expand the frontiers of what is possible in the CE area. Data is the new oil. In the real-world, the pervasive sensing ability of IoT systems gives rise to the generation of huge and diverse volumes of data, which can be utilized to assist optimal decision-making for collaboration technologies and their applications. The data sets are still growing rapidly because the density of sensing and actuation coverage is still at early stages of development and much more IoT devices will be deployed. Cloud/Fog/Edge computing provides different but complementary paradigms for data storage, integration and processing. Big Data and Artificial Intelligence/Deep Learning enable the efficient data analytics for useful information, insights and knowledge. With the help of ICT, physical objects are virtualized and represented as twin models (Avatars) seamlessly and closely integrated in both the physical and cyber spaces. The simulation systems which comprise twin models and other digital models will operate as an essential part of the corresponding physical system to assist decision-making, training, etc. As a result, what has been described as a data-driven revolution has reshaped both CE and scientific landscapes. Specific topics of interest include, but are not limited to the following: Collaboration theory, ontology and methodology Architecture, interoperability and standard for CE Emerging sensing technologies of IoT for CE IoT-enabled collaborative process monitoring and control Resource composition and reliable networked application Cloud based collaborative Big Data and Artificial Intelligence Human-centered pervasive CE environment Augmented reality and wearable computing for CE Collective design and social manufacturing in CE Cybersecurity and privacy for CE systems Cyber physical production systems and industry 4.0
Last updated by Dou Sun in 2019-07-21
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