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In this paper, we propose an optimized modeling method based on hierarchical reconstruction for 3D building point cloud. In contrast to existing approaches, this method can make full use of the geometric information of the original building facade point cloud and roof point cloud, which will provide a more complete description for the model of buildings. Firstly, we segment and extract the facade and the roof point cloud of the building based on the difference in planar slices. Further, we use the P-Linkage algorithm to segment the facade point cloud into planar patches, and cluster the planar patches to layer the facade point cloud. Therefore, we reduce the complexity of reconstruction from three-dimensional to two-dimensional. Then we reconstruct the building facade based on the hierarchical reconstruction method and the building roof based on the block reconstruction method. Finally, we merge the facade model and roof model and clip unnecessary polygons, leading to the final model. We experiment with different data and the results compared with the commercial modeling software show that the method is feasible and can improve the three-dimensional building modeling effect.

Automatic recognition methods for non-stationary EEG data collected from EEG sensors play an essential role in neurological detection. The integrative approaches proposed in this study consists of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-level classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets were adopted to decompose the EEG data into five time-frequency sub-bands, whose statistical features were computed and used as classification features. The grid search optimizer was used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a support vector machine and a random forest classifier constructed according to previous descriptions. Multiple-index were used to evaluate the Symlet wavelet transform-gradient boosting machine-grid search optimizer classification scheme, which provided better classification accuracy and detection effectiveness than has recently reported in other work on three-level classification of EEG data.

The development process of a complex project involves many tasks, organizations and persons. Therefore, how to assign tasks reasonably to the corresponding organizations or persons will substantially affect the project development results. Based on the summary of the main factors that influence the decision-making behaviors, this paper analyzes the characteristics of tasks and persons. Firstly, the importance of tasks is defined and prioritized. Secondly, this paper identified the cooperative inertia factors. Then the person cooperation model is established. Furthermore, the task assignment algorithm based on the organizational evolution model is proposed. Finally, the simulation results demonstrate the rationality of the proposed task assignment model. It can be employed to perform the quantitative analysis of tasks assignment and help managers make decisions.

In this paper, Computer Generated Forces (CGF) behavior modeling was studied from the viewpoint of associate data mining, for the large quantity of data, rules and models in its process. Because CGF behavior models data source was the combination of staticDB and dynamic data stream, the paper advanced the methods of item truncation and aim-pattern restriction. Through pretreatment, coding, searching frequent pattern, generating associate rules of the CGF behavior modeling data, then decision could be made according as these rules. Application of the two methods improves on the classical aprior algorithm, also improves efficiency of searching frequent items and credibility of CGF's decision. Finally, the application of associate rules mining in air-combat is studied in detail. As the simulation shows, comparing with the traditional matching-rule decision, associate rule mining has higher efficiency on condition with guaranteeing reliability of decision.

Ant colony optimization(ACO) provides an effective way to solve combinatorial optimization problem. However, with the complexity of the problem increasing, the ACO algorithm needs considerable computational time and resources to improve the good quality of solution, and this rarely satisfies the requirement of real-time computing in M&S (Modeling and Simulation) area. Parallel implementation of ACO can reduce the computational time obviously for the large scale combinatorial optimization problem, and much of the previous work in this field focuses on parallel implementation using MPI which is executed on clusters. Meanwhile, great emphasis is placed on multi-core computing technology with the development of multi-processor architecture and multi-core architecture. A new parallel ant colony optimization (PACO) algorithm is proposed, which applies two kinds of typical multi-core computing technologies, the well-known OpenMP and the recently introduced TBB (Threading Building Blocks) library by Intel Corporation, to solve target assignment problem(TAP). Effectiveness and efficiency of proposed algorithm is validated by studying the convergence speed, problem size scalability and thread size scalability of it.

The Model Driven Architecture (MDA) concept and key technology were discussed, including modeling technology, model transformation technology and model template. A MDA framework using UML for Platform Independent Model (PIM) and XML for Platform Specific Model (PSM) was proposed for simulation system development. The main technology of the MDA framework was introduced, which included PIM modeling guideline, PIM validity checking, PSM template, corresponding relation, symbol dictionary, CODE template and other key technologies. A MDA tool oriented simulation system was built based on the MDA framework. The tool has realized MDA process, where models in simulation system representing static and dynamic behaviors could be transformed from PIM to codes of different platforms. The MDA tool has been used in the transportation system simulation, the simulation models of different MDA periods are given, and the feasibility of applying MDA tool in the simulation system model development is verified.

A dasiageneralized optimal game theorypsila (GOGT) is proposed for the first time to establish special modeling and nonlinear decision-making, some principles and algorithms are also deduced from GOGT model. Junior and senior models derived from the main principles are respectively used to simulate models with multi-granularities and complicated decision-making problems. Crucial equation appending random disturbance holds the uniform scenario of Junior. A virtual 3D system of dynamic stars, moon and their mirror images in water is presented according to junior model. An armor CGF with multi-granularities is provided to testify the feasibility of senior model. Damage-summarizing and dynamic algorithm of aggregation is discussed using generalized mathematics expectation and fuzzy mathematics. Finally, an intelligent observer in large-scale 3D battlefield was designed using synthetic model. Quantitative parameters of nature, sense and complexity were presented The weighted 9-observational mode decision-making equation and observational target competition method are all calculated by advanced GOG models combining virtual and acoustical weights.