W-F STRUCTURE: A NEW ALGORITHM ON WIRE-FRAME MODELING

This paper advances a new algorithm oriented to geometry modeling (GM) by using frame model. The elemental data structure of frame model is the vertex. This afeoithm provides a general and rapid method. By this way,we can link the vertices to construct the elemental frame and need not consider the topological relation among vertices which consist of the concrete entity. Then, we can combine the elemental frames to complete frame modeling by using aided-line method referring to concrete entity. We will discuss two keystones in the paper. Then we give a 3D geometry modeling example based on wire-frame model using the new algorithm.

An Improved Functional Hierarchy Frame Model for System Maintainability

By means of analogy, this paper analyses the present functional hierarchyframe model for system maintainability, and presents an improved model. Practical applicationindicates that the improved model is visualized, more convenient and perfected over the perviousmodels.

A MULTIPLE STRUCTURE RECONSTRUCTION ALGORITHM BASED ON WIRE FRAME MODEL

A “LAG & RLC” visualization algorithm has been presented to display a clear three dimensional structure from stereo slice data which has the characteristics of multiple layer structure. This algorithm is based on wire frame model which can do multiple logical operations and has multiple display effects. It has wide use in biomedical science and computer graphics. The practical system is based on PC with a TVGA adapter at least 512k byte display memory and 2M byte extended memory.

Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes

A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence of sudden illumination changes.The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems.To solve this problem,a mixture Gaussian model has been built for each pixel in the video frame,and according to the scene change from the frame difference,the learning rate of GMM can be dynamically adjusted.The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate.The method was tested on a certain dataset,and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.

MRF model and FRAME model-based unsupervised image segmentation

This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.

Numerical simulation of aerodynamic interactions among helicopter rotor,fuselage,engine and body of revolution

Numerical simulations of helicopter aerodynamic interactions among the main rotor,fuselage,engine inlets/outlets and slung loads of specific geometries have been conducted by very few researchers.In this work,the steady-state compressible Reynolds-averaged navier-stokes equations are solved to study the aerodynamic interactions among helicopter rotor,fuselage,engine and body of revolution in three cases,namely MI-171V5,ROBIN and UH-60A.In the first case,the downwash flow provided by the rotor of the uniform actuator disc model induces a significant deflection of the airflow velocity.The vortex-shaped distribution and evolution are discussed in detail.The engine can effectively change the overall flow field.The asymmetry of the flow field is observed by using the non-uniform actuator disc model.Qualitative analysis of ROBIN and quantitative computation of UH-60A show a consistent accuracy of the rotating reference frame model for rotor.The blade tip vortex motion of UH-60A is simulated and its radial position prediction is compared to empirical formulas.While performing flow of UH-60A in hover,both the fuselage normal force and rotor lift decrease because of the impact of the body of revolution.

Finite element model validation of bridge based on structural health monitoring——Part I: Response surface-based finite element model updating

In the engineering practice, merging statistical analysis into structural evaluation and assessment is a tendency in the future. As a combination of mathematical and statistical techniques, response surface （RS） methodology has been successfully applied to design optimization, response prediction and model validation. With the aid of RS methodology, these two serial papers present a finite element （FE） model updating and validation method for bridge structures based on structural health monitoring. The key issues to implement such a model updating are discussed in this paper, such as design of experiment, parameter screening, construction of high-order polynomial response surface model, optimization methods and precision inspection of RS model. The proposed procedure is illustrated by a prestressed concrete continuous rigid-frame bridge monitored under operational conditions. The results from the updated FE model have been compared with those obtained from online health monitoring system. The real application to a full-size bridge has demonstrated that the FE model updating process is efficient and convenient. The updated FE model can relatively reflect the actual condition of Xiabaishi Bridge in the design space of parameters and can be further applied to FE model validation and damage identification.

Story Parsing Grammar

Story understanding is one of the important branches of natural language under-standing research in AI techniques. A new approach to story understanding is proposedin this paper. The so-called Story Parsing Grammar (SPG) is used to represent thestory abstracting processes with different degrees in story understanding, and the storyunderstanding process is converted to the story recognizing process done by the syn-tactic parser of SPG. This kind of story understanding is called story parsing. In thispaper, firstly, a survey of story understanding research is given. Secondly, by the clas-sification of various kinds of story structures, the so-called Case Frame Forest (CFF) isproposed to represent the superficial meaning of story. Based on CFF, a high- dimen-sional grammar, called Forest Grammar (FG), is defined. Furthermore, SPG is definedas a subclass of context-sensitive FG. Considering the context-sensitivity of story con-tent, a type of context-sensitive derivation is defined in the definition of SPG. Lastly,data about runtime efficiency of the syntactic parsing algorithm of weak precedenceSPG, a subclass of SPG, are given and analysed.