Abstract Machine learning (ML) methods has recently contributed very well in the
advancement of the prediction models used for energy consumption. Such models highly …

In modern manufacturing industry, dynamic scheduling methods are urgently needed with
the sharp increase of uncertainty and complexity in production process. To this end, this …

Due to the recent advancements in the Internet of Things and data science techniques, a
wide range of studies have investigated the use of data mining (DM) and machine learning …

Smart manufacturing refers to an advanced mode of manufacturing, which incorporates
computer-integrated manufacturing (CIM) and artificial intelligence (AI) for data-enabled …

With the ever-accelerating development of information and sensor technology, plenty of data-
driven fault diagnosis algorithms have shown impressive performance. However, in practical …

With the development of artificial intelligence and the Internet of things, the prospects of
cloud computing applications have become broader, and the number of users and cloud …

Finding a vacant parking slot in densely populated areas leads to excessive emission of
Carbon Dioxide, fuel, and time wastage. Recently, the Industrial Internet of Things (IIoT) has …

In this article, the Nash equilibrium strategy is used to solve the multiobjective optimization
problems (MOPs) with the aid of an integrated algorithm combining the particle swarm …

Abstract Space-based moving targets tracking and observation facilitates target recognition
and analysis of target characteristics, but the ability of satellite attitude tracking control needs …

Advances in deep learning significantly affect reinforcement learning, which results in the
emergence of Deep RL (DRL). DRL does not need a data set and has the potential beyond …