In cellular-connected unmanned aerial vehicle (UAV) network, a minimization problem on the weighted sum of time cost and expected outage duration is considered. Taking advantage UAV’s adjustable mobility, UAV navigation approach formulated to achieve aforementioned optimization goal. Conventional offline techniques suffer from inefficiency in accomplishing task due practical consideration local...

background: intraoral radiographs are believed to deliver low doses to patients, thus little work has been done in this regards. considering the increment in the number of patients reporting for the examination and the probability of delayed somatic effects for accumulated low doses of x-irradiation, it is expedient to determine the doses to three critical organs eye, thyroid and parotid that a...

We replicated and extended treatment procedures described by Lennox, Miltenberger, and Donnelly (1987) designed to reduce rapid eating. The participant was a 17-year-old girl with developmental disabilities who engaged in dangerously high rates of food ingestion. The procedure involved an adjusting differential-reinforcement-of-low-rate-responding (DRL) schedule, response blocking, and prompts....

In datacenter networks, bandwidth-demanding elephant flows without deadline and delay-sensitive mice with strict coexist. They compete each other for limited network resources, the effective scheduling of such mix-flows is extremely challenging. We propose a deep reinforcement learning private link approach (DRL-PLink), which combines software-defined (DRL) to schedule mix-flows. DRL-PLink divi...

Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things (IoT) ecosystem due to their ability collect and transmit data from remote hard-to-reach areas. Ensuring collision-free navigation for these UAVs is crucial achieving this goal. However, existing UAV collision-avoidance methods face two challenges: conventional path-planning are energy-intensive computationa...

Current research on Deep ReinforcementLearning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics. This work considers a power-split Plug-In Hybrid Electric Vehicle (PHEV), the 2015 Toyota Prius Plug-In, using DRL. The control PHEV energy management are co-optimized such that DRL policy directly outputs power split between engine electric motor. testing results show ca...

Routing optimization has long been a problem in the networking field. With rapid development of user applications, network traffic is continuously increasing dynamicity, making routing NP-hard. Traditional algorithms cannot ensure both accuracy and efficiency. Deep reinforcement learning (DRL) recently shown great potential solving problems. However, existing DRL-based solutions process graph-l...