With the gradual expansion of high buildings and underground spaces, deep foundation pits have been widely used in these engineering projects, but if they are not well-designed, safety problems occur. Proper deep foundation pit design requires proper exit distribution. However, calculating an adequate number of exit distributions for evaluation is difficult due to the numerous influential factors existing in the deep foundation pit environment. To this end, this paper presents a prototype of a decision-making system that uses agent-based modeling to simulate deep foundation pit evacuation in the presence of collapse disaster. By modeling the collapse occurrence process and agent escape process, an agent-based evacuation model is built, and a modified simulation-based particle swarm optimization algorithm is used to solve the optimization problem of exit distribution. Extensive experiments are conducted to verify the system, and the results show that the system provides a feasible framework for deep foundation pit evacuation.

Perception model is an important research field of CGF behavior modeling. As the input of the reasoning module, whether the information obtained from the perception model is precise is of great significance for the simulation fidelity. This paper first investigates the related researches on CGF perception model, and focuses on analyzing the attention mechanism, action time, and action way. Besides, a virtual human perception framework based on attention mechanism is proposed by constructing the knowledge base of the attention and modeling of information feedback mechanism. Compared to the traditional perception model, the proposed framework is more effective to capture and deal with the information from simulation environment.

In this paper, we propose a K‐nearest neighbor manifold filter on the Riemannian manifold and apply it to signal detection within clutter. In particular, the correlation and power of sample data in each cell are modeled as an Hermitian positive definite (HPD) matrix. A K‐nearest neighbor filter that performs the weight average of the set of K‐nearest neighbor HPD matrices of each HPD matrix is proposed to reduce the clutter power. Then, the clutter covariance matrix is estimated as the Riemannian mean of a set of secondary HPD matrices. Signal detection is considered as distinguishing the matrices of clutter and target signal on the Riemannian manifold. Moreover, to speed up the convergence of matrix equation of Riemannian mean, we exploit a strategy to choose the initial input matrix and step size of this equation. Numerical results show that the proposed detector achieves a detection performance improvement over the conventional detector as well as its state‐of‐the‐art counterpart in nonhomogeneous clutter.

In order to master the aggregated load characteristics of the grid-connected microgrid to serve the planning and operation of the distribution network, the operation strategies and aggregated load characteristics of the grid-connected microgrid under the differential power price are studied. This paper investigates the market mechanism of domestic and foreign microgrids and establishes power configuration modes for different types of microgrids. Under the current differentiated distributed power tariff, considering various power tariffs and generation costs, the output strategies and state change paths for different power scenarios are proposed. Based on this, a solution method based on adaptive immune particle swarm algorithm is presented. Through a large number of simulations, the aggregated load characteristics of the grid-connected microgrid are extracted, which provides a reference for the planning, operation and price mechanism of the microgrid and distribution network.