Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

In recent years, intelligent sensing has gained significant attention because of its
autonomous decision-making ability to solve complex problems. Today, smart sensors …

In this paper we propose an efficient hardware architecture that implements the Q-Learning
algorithm, suitable for real-time applications. Its main features are low-power, high …

Object tracking is a fundamental computer vision problem that refers to a set of methods
proposed to precisely track the motion trajectory of an object in a video. Multiple Object …

With the wide application of the shield tunneling method in tunnel engineering, the untimely
and incorrect attitude control of shield systems has become an essential factor affecting the …

This survey gives a selective review of recent development of machine learning (ML) for
combinatorial optimization (CO), especially for graph matching. The synergy of these two …

Multi-object tracking (MOT) aims to associate target objects across video frames in order to
obtain entire moving trajectories. With the advancement of deep neural networks and the …

Electric vehicle (EV) technology is emerging as one of the most promising solutions for
green transportation. The same growth occurs in the charging infrastructure development …

Multiple object tracking (MOT) of unmanned aerial vehicle (UAV) systems is essential for
both defense and civilian applications. As drone technology moves towards real-time …

Featured Application Wind Turbine Pitch Control. Abstract In this work, a pitch controller of a
wind turbine (WT) inspired by reinforcement learning (RL) is designed and implemented …