Special Issue on: Machine Learning and Advanced Data Analytics in Control Engineering Practice
摘要截稿:
全文截稿: 2019-05-01
影响因子: 3.193
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 计算机科学 - 2区
• 小类 : 自动化与控制系统 - 2区
• 小类 : 工程:电子与电气 - 2区
Overview
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 4.0is driven by the advancements in digitalization, artificial intelligence and advanced analytics, massive computing power, inexpensivememory and thegigantic 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 andcompetitive advantages in the face of ever-increasing demands onenergy, environmentand 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 threedecades. In particular, statistical techniques, such as principal component analysis(PCA), partial least squares (PLS), canonical variate analysis (CVA); and time-seriesmethods for modeling, such as maximum-likelihood and prediction-error methodshave beensuccessfully applied on industrial data. Recent developments in artificial intelligence, machinelearning andadvanced analytics provide a new opening for leveraging industrial data forsolving complex systems engineering problems. This special issue on Machine Learning and Advanced Data Analytics in Control Engineering Practice intends 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:
Data analytics machine learning methods formodeling, control and optimization;
Reinforcement-learning/deep-learning methods for modeling and control;
Advanced methods forprocessdata visualization;
Natural languageprocessing/computer-vision/speech-recognition in the process industries;
Adaptive methods forautonomous learning in the process industries;
Videoand image-based soft-sensors;
Mobile and cloud computing in industry; and
Information-theoretic methods for routine and predictive maintenance.