We would like to invite you to contribute to a special issue of the journal Control Engineering Practice dedicated to "Machine Learning and Advanced Data Analytics in Control Engineering Practice" (Submission deadline: *May 1, 2019*)
We are currently at the cusp of the fourth industrial revolution (4IR) or Industry 4.0 that is poised to reshape all the sectors of economy and society with an unprecedented depth and breadth. Emerging technologies including complex organization and systems, smart sensing, industrial robotics, industrial wireless communications, industrial Internet-of-Things (IIoT), Internet-of-Moving-Things (IoMT), industrial cloud, industrial big data and cyber-physical systems (CPS) have become the hotspots of research and innovation globally. Industry 4.0 is driven by the advancements in digitalization, artificial intelligence and advanced analytics, massive computing power, inexpensive memory and the gigantic volumes of data that are being collected. The process industries are in a unique position to benefit from Industry 4.0, as they have the right infrastructure, and are in possession of massive amounts of heterogeneous industrial data. Industry 4.0 is poised to provide economic and competitive advantages in the face of ever-increasing demands on energy, environment and quality by providing a level of automation and efficiency never seen before. Process industries have been using data analytics in various forms for more than three decades. In particular, statistical techniques, such as principal component analysis (PCA), partial least squares (PLS), canonical variate analysis (CVA); and time-series methods for modelling, such as maximum-likelihood and prediction-error methods have been successfully applied on industrial data. Recent developments in artificial intelligence, machine learning and advanced analytics provide a new opening for leveraging industrial data for solving complex systems engineering problems.
This special issue on Machine Learning and Advanced Data Analytics in Control Engineering Practice intend to curate novel advances in the development and application of machine learning techniques to address ever-present challenges of dealing with complex and heterogeneous industrial data in process systems engineering. Practical contributions are invited on topics that include, but are not limited to:
(i) Data analytics machine learning methods for modelling, control and optimization;
(ii) Reinforcement-learning/deep-learning methods for modelling and control;
(iii) Advanced methods for process data visualization;
(iv) Natural language processing/computer-vision/speech-recognition in the process industries;
(v) Adaptive methods for autonomous learning in the process industries;
(vi) Video and image-based soft-sensors;
(vii) Mobile and cloud computing in the industry; and
(viii) Information-theoretic methods for routine and predictive maintenance.
Through this special issue, we hope to attract academic researchers and industrial practitioners into working and shaping this fascinating and important area.
Instructions for submission: Papers are due by *May 1, 2019*, but early submissions are highly encouraged. Please use Elsevier electronic submission system at https://ees.elsevier.com/conengprac/ to submit your paper, and make sure to select "VSI: Machine Learning in CEP" when you reach the "Article Type" step in the submission process.
We look forward to receiving your contributions.
Aditya Tulsyan (Guest Editor), Amgen Inc., USA, [log in to unmask]
Manabu Kano (Guest Editor), Kyoto University, Japan, [log in to unmask]
Margret Bauer (Guest Editor), University of Pretoria, South Africa, [log in to unmask]
Zhiqiang Ge (Guest Editor), Zhejiang University, China, [log in to unmask]
Biao Huang (Editor-in-Chief), University of Alberta, Canada, [log in to unmask]