Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

Most scheduling problems have been demonstrated to be NP-complete problems. The
Hopfield neural network is commonly applied to obtain an optimal solution in various …

This paper deals with self-adaptive real-time embedded systems (RTES). A self-adaptive
system can operate in different modes. Each mode encodes a set of real-time tasks. To be …

In this paper, we study a set of real-time scheduling problems whose objectives can be
expressed as piecewise linear utility functions. This model has very wide applications in …

The Hopfield neural network is extensively applied to obtaining an optimal/feasible solution
in many different applications such as the traveling salesman problem (TSP), a typical …

As machine learning (ML) has been proven effective in solving various problems,
researchers in the real-time systems (RT) community have recently paid increasing attention …

To reduce the latency of time-sensitive flows in Ethernet networks, the IEEE TSN Task Group
introduced the IEEE 802.1 Qbu Standard, which specifies a 1-level preemption scheme for …

The traveling salesman problem (TSP), a typical combinatorial explosion problem, has been
well studied in the AI area, and neural network applications to solve the problem are widely …

Generally, how to satisfy the deadline constraint is the major issue in solving real-time
scheduling. Recently, neural network using competitive learning rule provides a highly …

Consolidating tasks to a smaller number of electronic control units (ECUs) is an important
strategy for optimizing costs and resources in the automotive industry. In our research, we …