Shock wave boundary layer interaction studied by high-speed schlieren

Shock wave boundary layer interactions at compression ramps have been examined by high-speed schlieren. A total of six ramps with angles ranging from 20 deg to 30 deg, the ramp angle effect on the SWBLI is thus studied. The present high-speed schlieren with a frame rate of 20 kHz generates a large ensemble of 9000 images, which secures the convergence of the statistics of the schlieren intensity. The rms of the schlieren intensity is of great interest, as it enables visualisation of the flow features that are not observable in the raw schlieren images, such as the corner separation/low momentum region, the spot of strong flow unsteadiness right after the shock wave and the location of the peak fluctuation over the ramp. Through the present systematic experimental investigation of SWBLI, the highspeed schlieren is demonstrated to be of great capability for SWBLI study

Demographic structure and overlapping generations: A simpler proof with more general conditions

d'Albis (2007) considers a continuous-time general equilibrium overlapping-generations model with age-specific mortality rates. His proof of the existence and uniqueness of the steady-state equilibrium, which can be extended to other overlapping-generations models, relies on the shape of a function that appears in the equation defining the equilibrium. By focusing on the mean age as a function of the stable population growth rate instead of the function used in d'Albis (2007), we provide a simpler proof with more general conditions. We also obtain useful properties about the first and second derivatives of the mean age function that can be applied in future work. © 2009 Elsevier B.V.postprin

UAV flight control method based on deep reinforcement learning

Aiming at the intelligent perception and obstacle avoidance of UAV for the environment, an obstacle-avoidance flight decision method of UAV based on image information is proposed in this paper. Add Gate Recurrent Unit (GRU) to the neural network, and use the deep reinforcement learning algorithm DDPG to train the model. The special gates structure of GRU is utilized to memorize historical information, and acquire the variation law of the environment of UAV from the time sequential data including image information and UAV position and speed information to realize the dynamic perception of obstacles. Moreover, the basic framework and training method of the model are introduced, and the generalization ability of the model is tested. The experimental results show that the proposed method has better generalization ability and better adaptability to the environment

User Selection in Reconfigurable Intelligent Surface Assisted Communication Systems

This paper presents a detailed investigation on the performance of reconfigurable intelligent surface (RIS)-assisted communication system with user scheduling. Depending on the availability of channel state information (CSI) at the RIS, two separate scenarios are considered, namely without CSI and with CSI. Closed-form expressions are derived for the ergodic capacity of the system in both scenarios. It is found that CSI has a significant impact on the performance of the system. Without CSI, the RIS provides an array gain of N, where N is the number of reflecting elements, and user scheduling provides an multi-user gain of log logM, where M is the number of users. With CSI, the RIS provides an array gain of N2, while no multi-user diversity gain can be obtained

UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning

This paper combines Deep Reinforcement Learning (DRL) with Meta-learning and proposes a novel approach, named Meta Twin Delayed Deep Deterministic policy gradient (Meta-TD3), to realize the control of Unmanned Aerial Vehicle (UAV), allowing a UAV to quickly track a target in an environment where the motion of a target is uncertain. This approach can be applied to a variety of scenarios, such as wildlife protection, emergency aid, and remote sensing. We consider multi-tasks experience replay buffer to provide data for multi-tasks learning of DRL algorithm, and we combine Meta-learning to develop a multi-task reinforcement learning update method to ensure the generalization capability of reinforcement learning. Compared with the state-of-the-art algorithms, Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic policy gradient (TD3), experimental results show that the Meta-TD3 algorithm has achieved a great improvement in terms of both convergence value and convergence rate. In a UAV target tracking problem, Meta-TD3 only requires a few steps to train to enable a UAV to adapt quickly to a new target movement mode more and maintain a better tracking effectiveness

Design and simulation of a testing fixture for planar magnetic levitation system control using switched reluctance actuator

Author name used in this publication: Norbert C. CheungRefereed conference paper2008-2009 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Integral sliding mode control with integral switching gain for magnetic levitation apparatus

Author name used in this publication: Norbert C. CheungRefereed conference paper2008-2009 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Possible Way to Synthesize Superheavy Element Z=117

Within the framework of the dinuclear system model, the production of superheavy element Z=117 in possible projectile-target combinations is analyzed systematically. The calculated results show that the production cross sections are strongly dependent on the reaction systems. Optimal combinations, corresponding excitation energies and evaporation channels are proposed in this letter, such as the isotopes ^{248,249}Bk in ^{48}Ca induced reactions in 3n evaporation channels and the reactions ^{45}Sc+^{246,248}Cm in 3n and 4n channels, and the system ^{51}V+^{244}Pu in 3n channel.Comment: 10 pages, 4 figures, 1 tabl