Modelling and Simulation of Cognitive Electronic Attack under the Condition of System of systems Combat

From the height of system-of-systems combat and operational perspective, the operations of cognitive electronic warfare (CEW) was analysed, and its main process and links were described. Secondly, the jamming effectiveness evaluation (JEE) model of cognitive electronic attack (CEA) operations was established based on the interference side, in which the change of threat degree was used as the measure index of jamming effectiveness. Then, based on the Q-learning model, an intelligent countermeasure strategy generation (ICSG) model was established, and the main steps in the model were given. Finally, on the basis the JEE model and the ICSG model, the simulation experiment was carried out for CEA operations. The result showed that combining the JEE model with the ICSG model can express the main process of the operations of CEW, as well as proved the validity of these models

Surface deformation law of mining under thick loose layer and thin bedrock: taking the southern Shandong Mining Area as an example

The surface subsidence in the thick loose layer and thin bedrock mining area in the east of China has the characteristics of large subsidence value, wide movement range and long settling time. Taking a coal mine in Southern Shandong Mining Area as an example,this paper discusses the variation rules of coal seam mining surface deformation parameters under different loose layer and bedrock thickness ratio conditions, on the basis of field measurements, using FLAC3D, and establishes a surface deformation calculation model for coal seam mining under the conditions of different loose layer bedrock thickness ratios (0.25−5.00), studies the characteristics of surface deformation, analyzed the influence of ratio of loose layer thickness to bedrock thickness on the parameters of probability integral method, and quantitatively analyzed and discussed the conditions of thick loose layer and thin bedrock from the perspective of mining subsidence. Research shows: ①Under the same mining thickness conditions,when the ratio of loose layer thickness to bedrock thickness increases, the surface deformation amount obviously increases first and then decrease, when the ratio reaches a certain limit, the ground surface deformation tends to be stabilized; ②The subsidence coefficient, the horizontal movement coefficient and the tangent of the main influence angle all increase first and then decreases, and the inflection point is 1.75,1.25 and 1.25, respectively; ③The proportion of loose bed thickness in the average mining depth has great influence on the angle of draw and boundary angle. The boundary angle and the angle of draw gradually decrease with the increase of the ratio. Based on the above research, it is proposed that the ratio of 1.25−1.75 is the critical value for the condition of thick loose bedding and thin bedrock, which provides a theoretical basis and technical reference for the prediction of surface deformation and the prevention and control of mining subsidence disasters in typical thick loose layer thin bedrock mining areas in eastern China

A novel definition of generalized synchronization on networks and a numerical simulation example

AbstractThis paper develops a novel definition of generalized synchronization on complex networks consisting of systems evolving in a chaotic or regular fashion. With two usual methods for detecting generalized synchronization, two criteria for generalized synchronization on networks are advanced. Some complex dynamical behaviors are discussed briefly on the basis of numerical simulations of a real network example

The chaotic dynamics of the social behavior selection networks in crowd simulation

National High-Tech Research and Development Plan of China [2007AA01Z323]; Natural Science Foundation of China [60774088]; Foundation of the Application Base and Frontier Technology Research Project of Tianjin [08JCZDJC21900]This paper researches the nonlinear dynamics of the behavior selection networks (BSN) model by virtue of which we can understand the origin of flocking behaviors in social networks. To commentate the notion of BSN, this article introduces a social behavior selection model for evolutionary dynamics of behaviors in social networks that exhibits a rich set of emergent behaviors of evolution. For behavioral networks with different complex networks topology, we analyze the nonlinear dynamics including the chaotic dynamics by the numerical simulation tools. With changing the topological structure, the behavioral networks behave affluent dynamical phenomena. Lastly, we draw the conclusion and paste the prospection about the networks model