Both concentrations and emissions of many air pollutants have been decreasing due to implement of control measures in China,in contrast to the fact that an increase in emissions of non-methane hydrocarbons(NMHCs)has b...
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Both concentrations and emissions of many air pollutants have been decreasing due to implement of control measures in China,in contrast to the fact that an increase in emissions of non-methane hydrocarbons(NMHCs)has been *** study employed seven years continuous NMHCsmeasurements and the related activities data of Shanghai,a megacity in China,to explore evolution of emissions and effectiveness of air pollution control *** mixing ratio of NMHCs showed no statistical interannual changes,of which their compositions exhibited marked *** resulted in a decreasing trend of ozone formation potential by 3.8%/year(p<0.05,the same below),which should be beneficial to ozone pollution mitigation as its production in Shanghai is in the NMHCs-limited *** alkanes,aromatics and acetylene changed by+3.7%/year,-5.9%/year and-7.4%/year,respectively,and alkenes showed no apparent *** sources were apportioned by a positive matrix factorization ***,vehicular emissions(-5.9%/year)and petrochemical industry emissions(-7.1%/year)decreased significantly,but the decrease slowed down;significant reduction in solvent usage(-9.0%/year)appeared after 2010;however,emissions of natural gas(+12.6%/year)and fuel evaporation(with an increasing fraction)became more *** inconsistency between observations and inventories was found in interannual trend and speciation as well as source contributions,emphasizing the need for further validation in NMHCs emission *** study confirms the effectiveness of measures targeting mobile and centralized emissions from industrial sources and reveals a need focusing on fugitive emissions,which provided new insights into future air policies in polluted region.
Currently, with the deepening of development and open of China's border, issues of border joint defense become increasingly arduous. Traditional means of cooperation and coordination are far from being able to ali...
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This paper addresses the consistency issue of multi-robot distributed cooperative localization. We introduce a consistent distributed cooperative localization algorithm conducting state estimation in a transformed coo...
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Cross-domain synergy and intelligence will become important features of future warfare. Cross-domain guidance is one of the typical forms of cross-domain synergy. It can significantly improve the ability to respond qu...
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The Industrial Internet of Things (IIoT) is revolutionizing industries through device interconnectivity, enabling real-time data collection and transmission for enhanced monitoring, control, and automation. This has l...
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Machine Learning and Artificial Intelligence technology accelerates technological progress and promotes social development, but also brings many security problems. Machine learning models may be affected, deceived, co...
Machine Learning and Artificial Intelligence technology accelerates technological progress and promotes social development, but also brings many security problems. Machine learning models may be affected, deceived, controlled or destroyed at different stages, e.g. training and inference, which may lead to serious consequences. In view of the main stages of the life cycle of machine learning, we summarizes the related attack and defense technologies based on threat matrix, makes a comprehensive and systematic analysis on the security issues of machine learning, and puts forward several security protection suggestions for intelligent information systems.
Visual relationship detection aims to predict the relationships between detected object pairs. It is well believed that the correlations between image components (i.e., objects and relationships between objects) are s...
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作者:
Zhang, ZhibingSchool of Mathematics and Physics
Key Laboratory of Modeling Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education Institutes Anqing Normal University Anqing246133 China
In this paper, we study Liouville type results for the three-dimensional stationary incompressible MHD equations and Hall-MHD equations. By a new iteration argument, we establish Liouville type theorems if the velocit...
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Aiming at the problem that the traditional deep reinforcement learning algorithm has poor generalization ability of generating strategies when solving complex game problems such as real-time strategy games, an agent s...
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ISBN:
(数字)9798350384185
ISBN:
(纸本)9798350384192
Aiming at the problem that the traditional deep reinforcement learning algorithm has poor generalization ability of generating strategies when solving complex game problems such as real-time strategy games, an agent strategy optimization method based on the combination of expert knowledge and self-play is proposed. Firstly, the opponent strategy of the agent is designed based on expert knowledge, and the corresponding strategy is generated by the proximal policy optimization algorithm (PPO), and the generated strategy is continuously put into the opponent strategy pool to provide a variety of opponent strategies for the agent in the training process. Through continuous iterative confrontation, the agent's autonomous decision-making ability in the face of different opponent strategies is improved. The simulation results show that compared with the traditional deep reinforcement learning algorithm, the introduction of the self-play training framework can significantly improve the average confrontation rate of the agent strategy, improve the robustness and generalization ability of the agent confrontation strategy, guide the agent to learn more effective tactical strategy, and achieve the adversarial advantage of “Priority first enemy discovery, Priority enemy launch”.
A multi-agent decision network based on QMIX is proposed in this paper to cope with the coordination decision problem of multiple UAV air combat missions. To speed up the training process, three improvements are intro...
A multi-agent decision network based on QMIX is proposed in this paper to cope with the coordination decision problem of multiple UAV air combat missions. To speed up the training process, three improvements are introduced: 1) An improved $\epsilon$ -decaying method that enable some tutor to help in action selection at the early stage of the training. This measure greatly improves the exploring efficiency when the network are far from being fully trained; 2) State pruning and action mask measures are applied during the training. The former improves the effectiveness of the input state information, and the latter reduces unnecessary action exploring. 3) A gradually training configuration is used to make the training process more robust, where the combat adversaries are configured as the static targets, the randomly maneuver vehicles, and the Min-Max strategy vehicles respectively. The multi-UAV air combat scenarios are built up and the experiments are conducted. The results shows that these improvements have significantly improved training efficiency.
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