渐进网格简化模型的改进算法

渐进网格模型可以用于层次细节的实现、三维网格的简化及重建等.改进并实现了一种渐进网格生成算法,针对渐进网格在简化和重建过程中各边权值计算中出现的误差,给出一种改进的权值公式,以便更精确地确定各边的优先级.同时针对简化过程中,各个块之间会出现裂缝,提出裂缝消除的方法.实验结果表明,该方法可以有效的简化模型,以满足用户的需求并提高效率.     

PEJA: Progressive Energy-Efficient Join Processing for Sensor Networks

Sensor networks are widely used in many applications to collaboratively collect information from the physical environment. In these applications,the exploration of the relationship and linkage of sensing data within multiple regions can be naturally expressed by joining tuples in these regions. However,the highly distributed and resource-constraint nature of the network makes join a challenging query. In this paper,we address the problem of processing join query among different regions progressively and energy-efficiently in sensor networks. The proposed algorithm PEJA(Progressive Energy-efficient Join Algorithm) adopts an event-driven strategy to output the joining results as soon as possible,and alleviates the storage shortage problem in the in-network nodes. It also installs filters in the joining regions to prune unmatchable tuples in the early processing phase,saving lots of unnecessary transmissions. Extensive experiments on both synthetic and real world data sets indicate that the PEJA scheme outperforms other join algorithms,and it is effective in reducing the number of transmissions and the delay of query results during the join processing.     

支持外观属性保持的三维网格模型简化

对已有的三维网格简化技术进行分析,利用半边折叠操作对QEM(quadric error metric)算法进行改进,提出了一种基于二次误差测度(QEM)的网格简化算法,解决了非连续外观属性在简化过程中的畸变问题.通过分析顶点与非连续外观接缝的关系,得出了一个新的边折叠代价公式,使得外观畸变在简化过程中尽可能地推迟;并且在执行半边折叠时给受影响的三角形找到了合适的替换wedge,避免外观畸变的发生.实验结果表明,该算法保持了QEM 算法的高效性,同时在几何属性和外观属性上都取得了令人满意的简化效果.     

Dynamic Terrain Visualization Based on ROAM and OGRE

Terrain Visualization is an important part of visualization systems of battlefield, and the visualization of dynamic terrain is also important for dynamic battle environment. In this paper, special attention has been paid on real-time optimally adapting meshes （ROAM） algorithm, which is a candidate for dynamic terrain, and its mesh representation, mesh continuity algorithm and error metrics are discussed. The DEXTER-ROAM algorithm is discussed and analyzed. By revising the mesh representation of ROAM, a dynamic ROAM algorithm based on partial-regular grid is established. By introducing transition region, mesh discontinuity of dynamic partial-regular grid is resolved. Error metric blocks are removed for computation complexity and culling blocks are introduced to accelerate view frustum culling. The algorithm is implemented in a 3D rendering engine called OGRE. In the end, an example of dynamic crater is given to examine the dynamic ROAM algorithm.     

一种基于二次误差测度的三维累进网格生成算法

研究了一类三维累进网格生成算法,在网格分辨率固定的前提下,以期获得较高质量的三维模型;基于二次误差模型,提出了基于累进网格生成的改进算法;引入了边界约束条件,提出了二阶邻域指标。实验证明,该算法输出的模型质量优于文献中的算法。     

基于EZW的牙颌模型三维数据压缩

针对牙颌模型深度数据的压缩处理问题,阐述了嵌入零树小波(Embedded Zerotree Wavelet,EZW)编码算法的基本原理和算法实现,讨论了基于感兴趣区域的EZW算法及其实现,并在实验室进行了深度数据和三维重构图像的基于感兴趣区域的EZW编码实验.实验表明,EZW编码既能实现图像的有损压缩也可实现图像的无损压缩,并具有很大的压缩比.     

按曲率选取基点的多分辨率表示重构算法

通过曲率引导选取一组基面来完成Eck等提出的任意拓扑三角网格多分辨率表示重构算法中的Voronoi划分．在提高效率的同时,可在相同网格规模下取得更好的重构质量;在重采样过程中以粗网格的Loop细分来指导参数域的细分,减轻了原算法因线性细分而产生的块状分界现象．最后提出一种自适应细分重采样技术,以减少数据冗余．     

源荷双边参与的高比例新能源电力系统能量与备用市场联合出清方法

风光出力的随机性与反调峰特性增加了电网对新能源的消纳难度。利用源侧、荷侧的灵活可调节特性,设计源荷双边参与规则,引导源荷进行跨时间的双向功率协调以支撑清洁能源高效消纳。首先,梳理源荷双边参与的基本原则与原理,明确交易主体、品种以及模式并设计其参与规则。然后,基于源荷双边参与规则,构建计及源荷双边参与的日前能量与备用联合优化调度模型,在促进新能源消纳的同时降低工业用户用电成本。其次,针对多场景混合整数规划的求解难题,采用改进的渐进式对冲分解算法进行求解,通过启发式的变量“固定-筛选-松弛”,保证模型的收敛性。最后,基于IEEE 118系统进行算例分析,验证了所提能量与备用联合出清模型在促进新能源消纳方面的有效性。     

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image Classification Algorithm (PICA) system is introduced to implement an image processing technique, detect disasters, and determine results with the help of the PICA, which allows disaster analysis to be extracted more effectively. The PICA is essential to overcoming strong shadows, for proper access to disaster characteristics to false positives by operators, and to false predictions that affect the impact of the disaster. The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. Two types of proposed PICA systems detect disasters faster and more accurately (95.6%).     

基于混合模型的钢轨检测识别方法

为了改善我国现有钢轨检测识别方法准确性和鲁棒性不高以及弯轨拟合较差等问题,提出一种基于直线-曲线混合模型的钢轨检测识别算法。首先对图像进行预处理,调整滞后阈值进行Canny边缘检测。采用累计概率Hough变换对直轨检测并完成近远视场的划分以及消失点的确定。对近视场直轨采用直线模型拟合,根据其检测结果对远视场进行循环线性近似获取钢轨特征点,并根据钢轨灰度特征进行验证,采用最小二乘法完成曲线拟合。直线-曲线模型的切换根据制定的规则完成。实验结果表明,提出的算法检测正确率为90.1%,适用于不同环境的场景,具有较好的鲁棒性。