基于改进FAHP的电力通信网可靠性评估方法研究

论文阐述了可靠性的一般评估方法,在借鉴TMN管理模型的基础上,对电力通信网可靠性进行了分层,构建了可靠性综合评估指标体系.为减少指标体系权重的主观性,提高评估结果的客观性和可信性,提出了一种改进的FAHP综合可靠性评估方法.该方法基于对数最小二乘法(LLSM)进行模糊权重计算,利用限位系数法来实现评估结果的解模糊过程,实例评估验证了该方法的有效性和可行性.最后对电力通信网可靠性的研究方向给出了建议.     

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。     

蒸汽发电系统的优化

介绍蒸汽发电系统的优化方法,以及时侦测、剔除和校正误差数据,有效保障发电机组正常运行.     

Poisson image fusion based on Markov random field fusion model

In this paper, we present a gradient domain image fusion framework based on the Markov Random Field (MRF) fusion model. In this framework, the salient structures of the input images are fused in the gradient domain, then the final fused image is reconstructed by solving a Poisson equation which forces the gradients of the fused image to be close to the fused gradients. To fuse the structures in the gradient domain, an effective MRF-based fusion model is designed based on both the per-pixel fusion rule defined by the local saliency and also the smoothness constraints over the fusion weights, which is optimized by graph cut algorithm. This MRF-based fusion model enables the accurate estimation of region-based fusion weights for the salient objects or structures. We apply this method to the applications of multi-sensor image fusion, including infrared and visible image fusion, multi-focus image fusion and medical image fusion. Extensive experiments and comparisons show that the proposed fusion model is able to better fuse the multi-sensor images and produces high-quality fusion results compared with the other state-of-the-art methods.     

方向波合成孔径雷达图像噪声抑制

关于提高孔径雷达图像质量,应研究雷达图像去噪问题,针对目前小波对高维信息的方向性不敏感,在合成孔径雷达图像斑点噪声抑制中不能很好的保留图像的边缘、轮廓与纹理信息的缺点,为提高图像质量,提出一种方向波的 SAR 图像斑点噪声抑制方法.根据数字化线段理论和整数栅格理论的方向渡不仅继承了小波变换维数可分性的特点,而且通过选择变换方向和队列方向来获得灵活的多方向性进行仿真,从而能够更好的捕获图像方向信息,经实验证明,所提出的方法能够克服小波去噪中"振铃效应"和边缘模糊等现象,证明了方法有效性.     

浅谈政府公文交换系统的发展

在社会信息化的不断推进过程中,各级政府的办公自动化需求也在不断升级,公文交换系统多样化且技术更新加速,随着改革的深入和我国加入WTO,公文交换系统的建设已经成为当前我国信息化建设的首要任务.     

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.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.