PostgreSQL存储过程移植方法

在对PostgreSQL存储过程处理机制进行研究的基础上,分析了Oracle存储过程移植到PostgreSQL时的语法、参数默认值、out参数、事务回滚、游标和内置函数等问题,并从成本、收益和技术难度的角度比较了两种存储过程移植方法--改写存储过程方法和扩展PostgreSQL的存储过程功能方法.深入研究了通过扩展PL/pgSQL源码来调整PostgreSQL存储过程功能的PLSE方法.     

基于双链量子遗传算法的过程神经元网络训练

基于函数正交基展开的过程神经元网络训练,由于参数较多BP算法不易收敛。针对这一问题,本文提出了一种基于双链量子遗传算法的解决方案。首先按权值参数的个数确定染色体上的基因数,完成种群编码,然后通过染色体评估获得当前最优染色体,以该染色体为目标,用量子旋转门完成种群中个体的更新,用量子非门实现个体变异增加种群多样性。在该方法中,每条染色体携带两条基因链,因此可扩展对解空间的遍历性,加速优化进程。以两组二维三角函数的模式分类问题为例,仿真结果表明该方法不仅收敛速度快,而且寻优能力强。     

Visual quality recognition of nonwovens using generalized Gaussian density model and robust Bayesian neural network

This work is dedicated to develop an algorithm for the visual quality recognition of nonwoven materials, in which image analysis and neural network are involved in feature extraction and pattern recognition stage, respectively. During the feature extraction stage, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. Then the wavelet coefficients in each subband are independently modeled by the generalized Gaussian density (GGD) model to calculate the scale and shape parameters with maximum likelihood (ML) estimator as texture features. While for the recognition stage, the robust Bayesian neural network is employed to classify the 625 nonwoven samples into five visual quality grades, i.e., 125 samples for each grade. Finally, we carry out the outlier detection of the training set using the outlier probability and select the most suitable model structure and parameters from 40 Bayesian neural networks using the Occam's razor. When 18 relevant textural features are extracted for each sample based on the GGD model, the average recognition accuracy of the test set arranges from 88% to 98.4% according to the different number of the hidden neurons in the Bayesian neural network.     

一种融合多种定位精度改进策略的实时定位中间件

无论是普适计算还是物联网,实时定位均是其关键技术之一。在建筑物、洞室等复杂环境中,由于无线信号的不规则衰减、非视距传播,使得常用的一些定位算法(如接收信号强度、到达角度等)无法达到较高的定位精度。为了解决复杂环境中定位精度不足的问题,设计并实现了一个定位精度改进中间件。它集成了平滑、传感器融合、卡尔曼滤波、粒子滤波等多种精度改进策略,可以根据用户对定位精度的需求动态地选择相应的策略。通过实验分析了不同精度改进策略的性能,结果表明,该中间件能够较好地满足用户对不同精度的需求。     

Remoting技术在工业远程测控系统中的应用研究

在工业分布式测控通信系统中,Microsoft.NET Remoting技术可以在应用程序内部或应用程序域之间传输数据.采用Remoting技术结合二进制流的方法,通过远程对象和数据接口的建立,以及服务器端和客户端的设置,可以在软件各个结构层之间进行快速高效的远程对象访问,达到实时控制的目的.该方案使测控仪表具有更好的开放性和可集成能力,有一定的参考和实用价值.     

基于P2P的语义Web服务发现机制

分析集中式语义Web服务发现机制存在的问题,利用P2P和本体技术,提出一种基于P2P的语义Web服务发现机制。将服务的部署与发布无缝地绑定在一起,按应用领域和服务目标将整个P2P网络组织成3层结构,基于该结构建立3阶段服务发现算法,达到优化网络结构、提高服务发现效率的目的。     

Discovery of probabilistic nearest neighbors in traffic-aware spatial networks

Travel planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of discovering probabilistic nearest neighbors and planning the corresponding travel routes in traffic-aware spatial networks (TANN queries) to avoid potential time delay/traffic congestions. We propose and study four novel probabilistic TANN queries. Thereinto two queries target at minimizing the travel time, including a congestion-probability threshold query, and a time-delay threshold query, while another two travel-time threshold queries target at minimizing the potential time delay/traffic congestion. We believe that TANN queries are useful in many real applications, such as discovering nearby points of interest and planning convenient travel routes for users, and location based services in general. The TANN queries are challenged by two difficulties: (1) how to define probabilistic metrics for nearest neighbor queries in traffic-aware spatial networks, and (2) how to process these TANN queries efficiently under different query settings. To overcome these challenges, we define a series of new probabilistic metrics and develop four efficient algorithms to compute the TANN queries. The performances of TANN queries are verified by extensive experiments on real and synthetic spatial data.     

Continuous-variable quantum homomorphic signature

Quantum cryptography is believed to be unconditionally secure because its security is ensured by physical laws rather than computational complexity. According to spectrum characteristic, quantum information can be classified into two categories, namely discrete variables and continuous variables. Continuous-variable quantum protocols have gained much attention for their ability to transmit more information with lower cost. To verify the identities of different data sources in a quantum network, we propose a continuous-variable quantum homomorphic signature scheme. It is based on continuous-variable entanglement swapping and provides additive and subtractive homomorphism. Security analysis shows the proposed scheme is secure against replay, forgery and repudiation. Even under nonideal conditions, it supports effective verification within a certain verification threshold.     

人群运动中的视觉显著性研究

在公共场所中人们都倾向于以分组的形式进行运动,本文把这种以分组形式运动的若干个行人称为运动群组,具有视觉显著性的人群运动群组是场景理解的重点,其对人群的整体运动也影响最大。本文对运动群组的视觉显著性展开了研究,分别从规模、速度、组内紧致度和变化度４个方面来对运动群组的视觉显著性进行度量,并基于该度量给出了视觉显著性运动群组检测方法。首先,利用光流法对运动人群进行分析得到光流向量;然后通过层次聚类算法对运动人群进行分组;最后,基于本文所给出的度量计算每个群组的视觉显著性,以检测出视觉显著性最高的运动群组。实验表明该方法能够有效地对视觉显 著性运动群组进行检测,该研究成果可应用于人群场景理解、人群运动分析和人群场景分类等计算机视觉研究领域。     

A shape prior constraint for implicit active contours

We present a shape prior constraint to guide the evolution of implicit active contours. Our method includes three core techniques. Firstly, a rigid registration is introduced, using a line search method within a level set framework. The method automatically finds the time step for the iterative optimization processes. The order for finding the optimal translation, rotation and scale is derived experimentally. Secondly, a single reconstructed shape is created from a shape distribution of a previously acquired learning set. The reconstructed shape is applied to guide the active contour evolution. Thirdly, our method balances the impact of the shape prior versus the image guidance of the active contour. A mixed stopping condition is defined based on the stationarity of the evolving curve and the shape prior constraint. Our method is completely non-parametric and avoids taking linear combinations of non-linear signed distance functions, which would cause problems because distance functions are not closed under linear operations. Experimental results show that our method is able to extract the desired objects in several circumstances, namely when noise is present in the image, when the objects are in slightly different poses and when parts of the object are invisible in the image.