We are currently at the cusp of the fourth industrial revolution (4IR) or Industry 4.0, which is poised to reshape the economy and society with 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, big data and cyber-physical systems (CPS) have become the hotspots of research and innovation globally. In the last few years, these emerging technologies have become mainstream and industrially-relevant due to continuous advancements in digitalization, artificial intelligence (AI), advanced analytics, massive computing power, inexpensive memory, and the gigantic volumes of data collected.
The process industries are in a unique position to benefit from Industry 4.0, as they have the right infrastructure and own 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 automation and efficiency never seen before. Process industries have been using data analytics (e.g., principal component analysis (PCA), partial least squares (PLS), canonical variate analysis (CVA), and time-series methods for modeling) in various forms for more than three decades. Recent developments in AI, machine learning, and advanced analytics provide a new opening for leveraging industrial data for solving complex systems engineering problems.
Building upon the success of the first special issue on Machine learning and Advanced Data Analytics in Control Engineering Practice, we are happy to release the Call-for-Papers (CfP) for the second special issue on the same topic. The second special issue intends to continue 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 and beyond. Practical contributions are invited on topics that include, but are not limited to:
Data analytics and machine learning methods for modeling, control, and optimization;
Reinforcement-learning/deep-learning methods for modeling and control;
Advanced methods for process data visualization;
Natural language processing/computer-vision/speech-recognition in the process industries;
Adaptive methods for autonomous learning in the process industries;
Video and image-based soft-sensors;
Mobile and cloud computing in the industry; and
Routine and predictive maintenance.
Control Engineering Practice is a premier journal that publishes papers with direct applications of profound control theory and its supporting tools in all possible areas of automation. Through this special issue, we hope to attract more academic researchers and industrial practitioners to work and shape this new, fascinating and vital area.
Aditya Tulsyan, Amgen Inc., USA, firstname.lastname@example.org
Jong Min Lee, Seoul National University, South Korea, email@example.com
Zhiqiang Ge, Zhejiang University, China, firstname.lastname@example.org
Zhongliang Li, Aix-Marseille University, France, email@example.com
Biao Huang, University of Alberta, Canada, firstname.lastname@example.org
Submission opens: Immediately
Submission deadline: Dec 31, 2020
Target final acceptance notification: August 1, 2021