EL-CID电磁铁心故障检测试验的应用研究

为了检验发电机定子铁心质量,在制造过程中和安装现场,对发电机定子铁心进行试验是必不可少的.目前国内针对定子铁心更多地是采用常规试验方法,但是常规铁损试验所需的设备容量大、试验线路复杂,而且经济性较差、效率低下.而采用电磁定子铁芯故障探测仪(EL-CID)铁心试验只需提供约4％的额定磁通量,即可完成对铁心故障的诊断,其所需的励磁电源容量较小且容易配备和携带.为了更好地利用EL-CID检测方式进行铁心质量的判定,本文通过模拟多种故障状况下的试验,论证了EL-CID的使用条件和影响因素,为实际工作中EL-CID的应用提供更好的参考依据.     

研究合金定向凝固组织的脉冲磁场设备的研制

介绍了脉冲电磁场发生设备的工作原理 ,设计等。并介绍了其如何与合金的定向凝固设备结合 ,以实现对合金的定向凝固组织在脉冲电磁场作用下的研究。     

Pure spatial rich model features for digital image steganalysis

The SRM (Spatial Rich Model) is a very effective steganalysis method. It uses statistics of neighboring noise residual samples as features to capture the dependency changes caused by embedding. Because the noise residuals are the high-frequency components of image and closely tied to image content, the residuals of different types of image regions have different statistical properties and effectiveness for steganalysis. In this paper, the effectiveness of the residuals is investigated. Then the effectiveness of the statistics collected from different types of neighboring residual samples is investigated from the FLD (Fisher Linear Discriminant) viewpoint, and ineffective, effective and high-effective neighboring residual samples are defined. The ineffective neighboring residual samples are not likely to change during embedding, and if they are counted in statistics, they may mix the features with noise and make the features impure. Pure SRM features are extracted based on neighboring noise residual sample selection strategy. Furthermore, multi-order statistical features are proposed to increase the statistical diversity. Steganalysis performances of the statistical features collected from different types of neighboring residual samples are investigated on three content adaptive steganographic algorithms. Experimental results demonstrate that the proposed method can achieve a more accurate detection than SRM.     

市政工程给水管道施工质量控制

在各个城市市政工程给水管道的施工中,相关部门在质量监测和管理等方面的工作中仍然存在不足之处。因此,如何解决市政工程给水管道施工中出现的各种故障,是相关施工人员值得思考的问题。     

An adaptive discrete-time ILC strategy using fuzzy systems for iteration-varying reference trajectory tracking

In this article, a novel fuzzy systems based on adaptive Iterative Learning Control (ILC) strategy is presented to deal with a class of non-parametric nonlinear discrete-time systems which perform iteration-varying reference trajectory tracking. Using the technique of fuzzy systems to compensate for the non-parametric uncertainty of the discrete-time system dynamics, the proposed adaptive ILC scheme can well track the iteration-varying reference trajectory beyond the initial time points. The convergence of the fuzzy systems based adaptive ILC algorithm is guaranteed by theoretical analysis, and a numerical example is given to illustrate the effectiveness of the adaptive ILC scheme.

     

An update-based step-wise optimal cache replacement for wireless data access

Many network applications requires access to most up-to-date information. An update event makes the corresponding cached data item obsolete, and cache hits due to obsolete data items become simply useless to those applications. Frequently accessed but infrequently updated data items should get higher preference while caching, and infrequently accessed but frequently updated items should have lower preference. Such items may not be cached at all or should be evicted from the cache to accommodate items with higher preference. In wireless networks, remote data access is typically more expensive than in wired networks. Hence, an efficient caching scheme considers both data access and update patterns can better reduce data transmissions in wireless networks. In this paper, we propose a step-wise optimal update-based replacement policy, called the Update-based Step-wise Optimal (USO) policy, for wireless data networks to optimize transmission cost by increasing effective hit ratio. Our cache replacement policy is based on the idea of giving preference to frequently accessed but infrequently updated data, and is supported by an analytical model with quantitative analysis. We also present results from our extensive simulations. We demonstrate that (1) the analytical model is validated by the simulation results and (2) the proposed scheme outperforms the Least Frequently Used (LFU) scheme in terms of effective hit ratio and communication cost.     

Fast and effective color-based object tracking by boosted color distribution

In this paper, we propose a novel tracking algorithm, boosted color distribution (BCD), for tracking color objects. There exist three contributions in this paper. First, we propose a novel online gentle boost (OGB) algorithm for online learning. The essential idea of OGB is composed of two aspects: online updating candidate weak classifiers, and then choosing and combining them in a boosting way. Second, we design a novel weak classifier, log color feature distribution ratio, which focuses on the difference of color distributions rather than individual samples and provides a simple yet effective manner of mining color features for object tracking. Third, by combining our OGB algorithm and our log color features, we develop a fast yet effective color-based object tracking algorithm. Numerous experiments demonstrate that our tracking algorithm is better than or not worse than some state-of-the-art tracking algorithms on some public sequences.Overall, this paper presents a novel BCD algorithm for color object tracking that achieves good results at a fast speed.     

网络数据库安全及加密技术

互联网的发展,给网络数据库数据的保密性、完整性和可用性带来了很大的威胁.本文总结了近些年数据库安全技术的发展过程,重点介绍了数据库加密技术的特点及新型的数据加密模式.