短网址技术在浙江省气象短信业务中的应用

随着3G网络的发展及智能手机的普及,各类气象服务终端的推广和服务途径的拓展,公众获取气象信息途径日趋普及,对近年来发展起来的气象短信业务造成严重的冲击。本文旨在通过对3G网络发展的分析,研究利用短网址技术扩展气象短信服务内容,寻求缓解短信订制用户下滑的办法,并进行相应的尝试。     

Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

Bartels–Stewart algorithm is an effective and widely used method with an O(n ³) time complexity for solving a static Sylvester equation. When applied to time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. Gradient-based recurrent neural network are able to solve the time-varying Sylvester equation in real time but there always exists an estimation error. In contrast, the recently proposed Zhang neural network has been proven to converge to the solution of the Sylvester equation ideally when time goes to infinity. However, this neural network with the suggested activation functions never converges to the desired value in finite time, which may limit its applications in realtime processing. To tackle this problem, a sign-bi-power activation function is proposed in this paper to accelerate Zhang neural network to finite-time convergence. The global convergence and finite-time convergence property are proven in theory. The upper bound of the convergence time is derived analytically. Simulations are performed to evaluate the performance of the neural network with the proposed activation function. In addition, the proposed strategy is applied to online calculating the pseudo-inverse of a matrix and nonlinear control of an inverted pendulum system. Both theoretical analysis and numerical simulations validate the effectiveness of proposed activation function.     

开关跳闸的原因及对策

分析日常生产及供电系统中,空气开关(断路器)跳闸的可能原因,并结合现场实际情况给出相应的处理方法与对策.     

Multiconsensus of second order multiagent systems with directed topologies

A novel multiconsensus problem is introduced in multiagent systems. The states of multiple agents in each subnetwork asymptotically converge to a consistent value with information exchanges among subnetworks. The multiconsensus problem of second order multiagent systems with directed topologies is studied. Three linear protocols are proposed to solve the multiconsensus problem. Necessary and sufficient conditions are derived based on matrix theory. Simulations are provided to demonstrate the effectiveness of the theoretical results.     

网络机房制冷效率研究

根据相关的调查显示,在传统观念和设计标准的影响下,网络机房存在各种各样的设计缺陷,其中的一些缺陷会导致网络机房内部空间形成热点、降低制冷容错能力、制冷效率和制冷量,从而提高运行成本。网络机房的制冷效率和运行成本的大幅提高成为制约发展的重要因素。文章从网络机房面临的实际问题出发,有针对性地分析了机房设计的典型错误,阐述了其原理和影响,并根据实践经验,介绍了一些简单可行的解决方案。     

Google体系结构分析

为了适应大规模广范围高密度的搜索,Google的网络搜索应用允许在不同的处理器上运行不同的查询;同时,通过对全局索引进行分解,Google也允许在多个处理器上运行单一的查询.Google的主旨是,使用市场上流行的具有通用的PC,以达到大型机所具有的高性能.通过对已有资料进行搜集,对Google的体系结构、工作性能等进行分析,目的是通过对Google的调研,给网格建设提供一定的借鉴.     

创设情景,诱发学生的主动性

课堂教学是教师给学生传授知识和技能的全过程,如何让学激发学生的学习兴趣,培养学生学习的主动性非常重要.心理学研究表明,"兴趣是学生最好的老师",兴趣能使学生在愉悦的气氛中学习,唤起学生强烈的求知欲望,是教学成功的关健.     

高职院校计算机教育探索

计算机技能型劳动人员是近阶段社会需求量非常大的实用型人才,社会各个行业都需要既懂得技术又能具体操作的高素质计算机专业劳动者.本文对如何改变部分高中生进入用人单位后,"不能干、干不了、干不好、不会干、干不长"的现象在职业高中教育需要解决的问题进行了探讨.     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

Perceptual image preview

Image preview is a convenient way to browse large or multiple images on small displays. However, current signal-level image resampling algorithms may remove many features of interest in the preview image. In this paper, we propose perceptual image preview which retains more perceptual features such that users can inspect features of interest by viewing the preview image only and without zooming in. This technology has two components, structure enhancement and perceptual feature visualization. Structure enhancement enhances the image structure while suppressing subtle details using a gradient modulation method, thus making the succedent perceptual features more apparent. For perceptual feature visualization, features of interest detected in the picture is visualized on the structure enhanced preview image. We demonstrate with two examples of most commonly used image quality features, image blur and noise. The effectiveness of the proposed method is validated by experimental results.