Advanced Artificial Intelligence Technologies for Smart Manufacturing

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Advanced Artificial Intelligence Technologies for Smart Manufacturing.

As the world enters a new phase of industrialization (Industry 4.0, or the fourth industrial revolution), smart manufacturing has become crucial. Industry 4.0 refers to an industrial transformation aided by smart manufacturing and data exchange, such as high-level factory automation and Internet of Things applications. Artificial intelligence and smart machinery have also become integral research areas in manipulation. Researchers from academia and various industries are now working to develop the next generation of intelligent smart manufacturing applications. With the application of advanced artificial intelligence technologies, the revolution of the smart manufacturing industry can beadvanced more quickly.

To build a competitive advantage, keep up with the “Industry 4.0” trend, and to attract and filter high quality academic contributions, we have organized this Special Section in IEEE Access on “Advanced Artificial Intelligence Technologies for Smart Manufacturing.” High quality articles within the field are highly encouraged and considered in this Special Section.

The topics of interest include, but are not limited to:

  • Artificial Intelligence, Embedded Systems and Cloud Computing in Manufacturing
  • Smart Actuators and Adaptive Control of Machine Tools
  • Man-machine interface and integration
  • Intelligent machinery equipment
  • Intelligent Automation
  • Intelligent manufacturing
  • Advanced signal processing and machine perception of mechanical systems
  • Machine learning techniques for smart manufacturing
  • M2M technology
  • Big Data Analytics in Manufacturing

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor: Her-terng Yau, National Chin-Yi University of Technology, Taiwan

Guest Editors:

    1. Stephen D. Prior, University of Southampton, UK
    2. Yang Wang, Georgia Institute of Technology, USA
    3. Yunhua Li, BeiHang University, China

 

Relevant IEEE Access Special Sections:

  1. Artificial Intelligence Technologies for Electric Power Systems
  2. Big Data Technology and Applications in Intelligent Transportation
  3. Advances in Machine Learning and Cognitive Computing for Industry Applications

 

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: pan1012@ms52.hinet.net; htyau@ncut.edu.tw.

Uncertainty Quantification in Robotic Applications

Submission Deadline: 01 May 2020

IEEE Access invites manuscript submissions in the area of Uncertainty Quantification in Robotic Applications.

Uncertainty in engineering systems comes from a variety of sources such as manufacturing imprecision, assembly errors, model variation and stochastic operating conditions. Hence the actual performance of an engineering system may deviate from the design target, resulting in a quality loss, customer dissatisfaction, or even catastrophic failures. To ensure robust and reliable system operations, it is imperative to quantify and reduce the uncertainty effects during the system design, manufacturing and field operation.

For the robotic systems, the dynamic and highly nonlinear performance is significantly affected by operating conditions, time-varying load, and other random stresses. This creates new challenges in measuring and characterizing the performance uncertainty with dynamic performance. Uncertainty quantification methods, such as reliability modeling, reliability analysis, reliability-based design optimization, model validation, sensitivity analysis, and robust design are deemed essential in improving the reliability of robotic systems.

This Special Section in IEEE Access invites academic scholars and industry practitioners to submit full-length articles that report the recent advances in theoretical, numerical, and experimental development in uncertainty quantification. The articles are expected to describe original findings or innovative concepts that address different aspects of uncertainty quantification challenges arising for robotic systems. New uncertainty quantification methods are anticipated to address the safety, reliability and quality issues of emerging robotic technologies, and thus move the robotic industry forward.

The topics of interest include, but are not limited to:

  • Reliability modeling, analysis and design optimization of robotic systems
  • Model verification and validation of robotic systems
  • Sensitivity analysis of robotic systems
  • Robust design of robotic systems
  • Performance reconstruction under uncertainty of robotic systems
  • Big data and machine learning in robotic systems
  • Internet of Things for robotic systems under uncertainty

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Zhonglai Wang, University of Electronic Science and Technology of China, China

Guest Editors:

  1. Tongdan Jin, Texas State University, USA
  2. Om Prakash Yadav, North Dakota State University, USA
  3. Ningcong Xiao, University of Electronic Science and Technology of China, China
  4. Yi (Leo) Chen, Glasgow Caledonian University, UK

 

Relevant IEEE Access Special Sections:

  1. Advances in Prognostics and System Health Management
  2. Additive Manufacturing Security
  3. Artificial Intelligence in Cyber Security


IEEE Access Editor-in-Chief:
  Prof. Derek Abbott, University of Adelaide

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: wzhonglai@uestc.edu.cn.

Advanced modeling and control of complex mechatronic systems with nonlinearity and uncertainty

Submission Deadline: 28 February 2018

IEEE Access invites manuscript submissions in the area of Advanced modeling and control of complex mechatronic systems with nonlinearity and uncertainty.

Various complex mechatronic systems are widely applied in industries such as robotics, micro-electro-mechanical systems (MEMS), motor or hydraulic driven equipment. And the control technology is the key issue for mechatronic systems to achieve the good performance. However, for those complex mechatronic systems, mechanism nonlinearities and uncertainties (e.g., internal uncertainties, external unstructured environments, and undesired disturbances) are much more obvious and lead to the significant negative effects. Thus, the effective modeling, identification and dynamic analysis are necessary for complex mechatronic systems with nonlinearity and uncertainty. And the model-based advanced control designs such as adaptive control, robust control, sliding-mode control, backstepping control, H-infinite control, etc. are the corresponding solutions to improve the system performance. Contributions from industry applications are particularly encouraged, and both theoretical and experimental works are welcome.

The main objective of this Special Section in IEEE Access is to report the recent theoretical and technological achievements on advanced modeling and control of various complex mechatronic systems with nonlinearity and uncertainty. Potential topics include, but are not limited to:

  • Modeling and identification of mechatronic (e.g., robotics, MEMS, motor actuation, hydraulic actuation) systems
  • Nonlinear dynamic analysis of complex mechatronic systems
  • Model-based advanced control of complex mechatronic systems, such as adaptive control, robust control, sliding-mode control, backstepping control, H-infinite control, etc.
  • Nonlinear observer design and observer-based control for complex mechatronic systems
  • Precision motion control of mechatronic systems with nonlinearity and uncertainty
  • Robust fault diagnosis, fault isolation and fault tolerant control in complex mechatronic systems
  • Active vibration control in complex mechatronic systems

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Zheng Chen, Zhejiang University, China

Guest Editors:

  1. Ya-Jun Pan, Dalhousie University, Canada
  2. Weichao Sun, Harbin Institute of Technology, China
  3. Tao Wang, National University of Singapore, Singapore
  4. Cong Wang, New Jersey Institute of Technology, USA

 

Relevant IEEE Access Special Sections:

  1. Learning Systems Based Control and Optimization of Complex Nonlinear Systems
  2. Advanced Control and Health Management for Aircraft and its Propulsion System
  3. Trends and Advances for Ambient Intelligence with Internet of Things Systems

 

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: specialsections@ieee.org