Under the guidance of the theories of complex Adaptive System and Artificial Societies, modeling and simulation of international politics evolution in crisis was studied. The Model of International Politics Evolution ...
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The effectiveness evaluation of C4ISR is playing a more and more important role in its designing, developing and using. According to its characters, such as complexity and intelligence, the traditional evaluation meth...
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Visualization of highway traffic environments in virtual reality plays a key role on fidelity of simulation and validity of driving behaviors in driving simulator studies. This paper describes a procedure to replicate...
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Visualization of highway traffic environments in virtual reality plays a key role on fidelity of simulation and validity of driving behaviors in driving simulator studies. This paper describes a procedure to replicate a signalized intersection with many complex features into a driving simulator's 3-D databases through building a graphical visual database, constructing a road motion database, and creating simulated traffic. The validity of the visualization was assessed via the comparison of the drivers' speeds in the simulator and the speed data at the real intersection. It was found that both speed data follow normal distributions and have equal means for each intersection approach;however, that the speeds measured in the driving simulator have a larger variability than those measured in the field. The users' subjective evaluation results indicated that 92% subjects could recognize the simulated intersection in the driving simulator experiment. Therefore, the strategy of geo-specific environment modeling would be useful for studying driving behaviors in virtual environments.
A rapid reentry trajectory planning method for a common aero vehicle (CAV) subject to all path constraints is developed. The reentry trajectory is divided into initial descent phase and quasi-equilibrium glide phase w...
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ISBN:
(纸本)9781424460434
A rapid reentry trajectory planning method for a common aero vehicle (CAV) subject to all path constraints is developed. The reentry trajectory is divided into initial descent phase and quasi-equilibrium glide phase which possesses a majority of the reentry period. An improved quasi-equilibrium glide condition is utilized to convert reentry corridor constraints to the control variable constraints and get a simple relation between the range-to-go and velocity. Accordingly the longitudinal reference trajectory is computed through the one-parameter search of bank angle model and the corresponding tracking law is designed using linear quadratic regulator theory. To enhance the maneuvering capability, a geometrical control approach considering no-fly zone constraints is applied to the lateral guidance. Finally, the performance of this method is verified by computer simulation.
The effectiveness evaluation of C4ISR is playing a more and more important role in its designing, developing and using. According to its characters, such as complexity and intelligence, the traditional evaluation meth...
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The effectiveness evaluation of C4ISR is playing a more and more important role in its designing, developing and using. According to its characters, such as complexity and intelligence, the traditional evaluation methods and systems are not feasible and effective enough. In this paper, a multi-agent based simulation and evaluation framework of C4ISR is advanced, which includes the architecture and the working procedure. The agent-based simulation model of C4ISR is put forward, according to which the C2 Agent, Armament Agent and the Evaluation Agent are founded. The interaction model between each agent is also designed. At final, based on the Swarm, a multi-agent based C4ISR effectiveness simulation and evaluation prototype is developed, through which NATO C2 effectiveness evaluation is thoroughly analyzed.
Pre-trained language models learn informative word representations on a large-scale text corpus through self-supervised learning, which has achieved promising performance in fields of natural language processing (NLP)...
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Pre-trained language models learn informative word representations on a large-scale text corpus through self-supervised learning, which has achieved promising performance in fields of natural language processing (NLP) after fine-tuning. These models, however, suffer from poor robustness and lack of interpretability. We refer to pre-trained language models with knowledge injection as knowledge-enhanced pre-trained language models (KEPLMs). These models demonstrate deep understanding and logical reasoning and introduce interpretability. In this survey, we provide a comprehensive overview of KEPLMs in NLP. We first discuss the advancements in pre-trained language models and knowledge representation learning. Then we systematically categorize existing KEPLMs from three different perspectives. Finally, we outline some potential directions of KEPLMs for future research.
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