Research on the Problems in the Collecting Land

Along with our country urbanization advancement quickening, the scale of construction land expands, but there are many problems in collecting land. These questions have directly restricted the land collection work to develop smoothly. This paper analyzes the main questions which collection work is up against and puts forward the improved proposal aiming at the peasant losing territory being short of participating in the process of pricing compensation, social security vacancy and so on.

The Problems of Industrial Land Use in Chinese Cities and Counter—Measures

The concentration of Industries In cities is a commonphenomenon In the course of urhanlzatlon.The reason isIballhe concenlralbn orsndustrles wsuob重alnlhe‘乞concen-traied conomlc returns.” The concentration ofindustriesincities has occupied more land for indutrial use ifthe industri-al land use makes up a very low proportion in the total landuse In cities,the concentrated e门Dciency can not be broughtinto play.Ifthe Proportion is too big,land for other func-nons will be squeezed out,thus affecting the full play of theoverall functions of＊theons urban land.The unreasonable indus-trial land used now exists in Chinese cities.

Real-time control for fuel-optimal Moon landing based on an interactive deep reinforcement learning algorithm

In this study,a real-time optimal control approach is proposed using an interactive deep reinforcement learning algorithm for the Moon fuel-optimal landing problem.Considering the remote communication restrictions and environmental uncertainties,advanced landing control techniques are demanded to meet the high requirements of real-time performance and autonomy in the Moon landing missions.Deep reinforcement learning(DRL)algorithms have been recently developed for real-time optimal control but suffer the obstacles of slow convergence and difficult reward function design.To address these problems,a DRL algorithm is developed using an actor-indirect method architecture to achieve the optimal control of the Moon landing mission.In this DRL algorithm,an indirect method is employed to generate the optimal control actions for the deep neural network(DNN)learning,while the trained DNNs provide good initial guesses for the indirect method to promote the efficiency of training data generation.Through sufficient learning of the state-action relationship,the trained DNNs can approximate the optimal actions and steer the spacecraft to the target in real time.Additionally,a nonlinear feedback controller is developed to improve the terminal landing accuracy.Numerical simulations are given to verify the effectiveness of the proposed DRL algorithm and demonstrate the performance of the developed optimal landing controller.