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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
Exploring the effects of car ownership and commuting on subjective well-being::a nationwide questionnaire study
How and to what extent household car ownership and commuting behavior affect individual subjective well-being (SWB) is of great interest for urban and transportation planning. Increasing attention has been paid to the associations between car ownership, commuting and SWB. However, only a limited number of studies examined the effects of travel-related factors on both cognitive and affective SWB aspects. This research empirically investigated the relationships from the two SWB aspects. Furthermore, we extend the modeling of generic cognitive SWB to several specific measures (e.g., satisfaction with life compared to a specific group of people, degree of free choice, social position, and social equality) to explore how car ownership and commuting behavior contribute to individual SWB. Drawing on the data derived from the 2014 China Labor-Force Dynamics Survey, a set of ordered probit models based on Bayesian inference are estimated. The findings point out that household car ownership has a significant effect on cognitive SWB but a limited influence on affective SWB. It appears that commuting time is significantly and negatively associated with individuals’ cognitive and affective well-being, whereas a positive correlation is found between the commuting by bicycle and affective SWB. The effects of commuting time and transportation modes on different measured satisfactions with life have no big differences. Finally, results of the Wald tests indicate that incorporating household car ownership and commuting behavior into the modeling framework can significantly improve the prediction accuracy of individual SWB
Pyrosequencing based analysis of soil bacterial communities under long-term soil tillage and crop residue management in semiarid environments
Non-Peer Reviewe
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
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