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991.
992.
Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps: during the first step detection of a face is done by finding exact position of it in a complex background (various lightning condition), and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used: color based method, mean eye template and SVM (Support Vector Machine) technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position. 相似文献
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994.
运动机构可靠性仿真试验系统体系结构研究 总被引:7,自引:0,他引:7
针对工程实际复杂运动机构系统成本高、研制周期长、试验量少等特点,提出基于机液协同仿真模型的运动机构可靠性仿真分析方法。从工程实际运动机构系统的运动规律和系统性能出发,给出系统的主要失效模式并定量确定主要失效模式的失效判据,通过整合运动机构系统仿真模型可靠性分析的数据来源,建立并不断修正运动机构系统可靠性仿真模型,同时综合集成三维辅助建模软件、多体分析软件、有限元软件和控制系统建模软件等商用软件并结合经典可靠性分析方法,建立一个协同的运动机构可靠性仿真试验平台集成系统,从而对运动机构系统进行可靠性仿真试验分析与评估,为今后可靠性设计提供大量的有用信息。通过起落架落震可靠性仿真试验研究,表明该系统能够实现工程实际复杂运动机构可靠性仿真试验。 相似文献
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997.
The sparsity driven classification technologies have attracted much attention in recent years, due to their capability of providing more compressive representations and clear interpretation. Two most popular classification approaches are support vector machines (SVMs) and kernel logistic regression (KLR), each having its own advantages. The sparsification of SVM has been well studied, and many sparse versions of 2-norm SVM, such as 1-norm SVM (1-SVM), have been developed. But, the sparsification of KLR has been less studied. The existing sparsification of KLR is mainly based on L 1 norm and L 2 norm penalties, which leads to the sparse versions that yield solutions not so sparse as it should be. A very recent study on L 1/2 regularization theory in compressive sensing shows that L 1/2 sparse modeling can yield solutions more sparse than those of 1 norm and 2 norm, and, furthermore, the model can be efficiently solved by a simple iterative thresholding procedure. The objective function dealt with in L 1/2 regularization theory is, however, of square form, the gradient of which is linear in its variables (such an objective function is the so-called linear gradient function). In this paper, through extending the linear gradient function of L 1/2 regularization framework to the logistic function, we propose a novel sparse version of KLR, the 1/2 quasi-norm kernel logistic regression (1/2-KLR). The version integrates advantages of KLR and L 1/2 regularization, and defines an efficient implementation scheme of sparse KLR. We suggest a fast iterative thresholding algorithm for 1/2-KLR and prove its convergence. We provide a series of simulations to demonstrate that 1/2-KLR can often obtain more sparse solutions than the existing sparsity driven versions of KLR, at the same or better accuracy level. The conclusion is also true even in comparison with sparse SVMs (1-SVM and 2-SVM). We show an exclusive advantage of 1/2-KLR that the regularization parameter in the algorithm can be adaptively set whenever the sparsity (correspondingly, the number of support vectors) is given, which suggests a methodology of comparing sparsity promotion capability of different sparsity driven classifiers. As an illustration of benefits of 1/2-KLR, we give two applications of 1/2-KLR in semi-supervised learning, showing that 1/2-KLR can be successfully applied to the classification tasks in which only a few data are labeled. 相似文献
998.
Nonlinear model predictive control with relevance vector regression and particle swarm optimization 总被引:1,自引:0,他引:1
In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression (RVR) and particle swarm optimization with controllable random exploration velocity (PSO-CREV) is applied to a catalytic continuous stirred tank reactor (CSTR) process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function (RBF) kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control (MPC) using a deterministic sparse kernel learning technique called Least squares support vector machines (LS-SVM) regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control. 相似文献
999.
论文综合利用主题搜索技术、深网信息集成技术和Wiki技术,设计并实现了一个面向数学领域的教育平台.此平台是一个集数学资源共享和用户交流的开放平台,用户在此平台可以利用数学Wiki添加、编辑、评论数学领域信息,提升自主学习能力,实现广泛交流.同时,还能利用数学搜索引擎搜索到专业的数学知识,定制URL检索,扩大检索范围,提高了平台的个性化服务水平及搜索的准确率.实验结果表明,文中基于SVM分类算法的数学主题网页分类器具有较高的准确率和召回率. 相似文献
1000.
根据计算机-多单片机系统的多级分层结构,采用异步串行多机通信和主从呼叫响应的通信模式,对表决通信软件的系统功能进行规划.选择和约定了通信模式、通信协议.对下位机通信软件、上位机通信软件和上下位机间的通信过程进行了设计.引入了多机通信的差错控制.系统构建的多机网络模型和上下位机软件模块具有一定的借鉴价值. 相似文献