三个素数猜想的证明

采用新的构造方法,把不规则分布的素数进行分类,即把单个素数a都同时表示成等距的区间[a,a＋h]（a为任意素数）,在[a,a＋h]中的元素为自然数,将[a,a＋h]中对应位置有相同情况,或都是素数,或都不是素数的为同一等价类,其中每个[a,a＋h]中至少有一个素数a,h取不同自然数时,分类方法不同,显然[a,a＋h]的等价类的个数在h有限时是有限的,小于2h/2。     

在制品库存优化控制

分析了在制品库存的作用、应设立的位置及最佳容量.建立了离散制造系统在制品库存之比例积分控制模型.再以摩托车曲轴箱体生产线为例评价了控制效果,表明在需求动态变化时PI控制较之传统的正负反馈控制能更好地使在制品库存平稳化.     

Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas

Urban land cover information extraction is a hot topic within urban studies. Heterogeneous spectra of high resolution imagery—caused by the inner complexity of urban areas—make it difficult. In this paper a hierarchical object oriented classification method over an urban area is presented. Combining QuickBird imagery and light detection and ranging (LIDAR) data, nine kinds of land cover objects were extracted. The Spectral Shape Index (SSI) method is used to distinguish water and shadow from black body mask, with 100% classification accuracy for water and 95.56% for shadow. Vegetation was extracted by using a Normalized Difference Vegetation Index (NDVI) image at first, and then a more accurate classification result of shrub and grassland is obtained by integrating the height information from LIDAR data. The classification accuracy of shrub was improved from 85.25% to 92.09% and from 82.86% to 97.06% for grassland. More granularity of this classification can be obtained by using this method. High buildings and low buildings can, for example, be distinguished from the original building class. Road class can also be further classified into roads and crossroads. The comparison of the classification accuracy between this method and the traditional pixel-based method indicates that the total accuracy is improved from 69.12% to 89.40%.     

Examining LiDAR – WorldView-3 data synergy to generate a detailed stand map in a mixed forest in the north-west of Turkey

Although stand delineation approach based on aerial photographs and field survey produces high accuracy maps, it is labour-intensive and time consuming. Furthermore, conventional forest stand maps may have some uncertainties that can hardly be verified due to the experiments and skills of photo-interpreters. Therefore, researchers have been seeking more objective and cost-effective methods for forest mapping. LiDAR (Light Detection and Ranging) data have a high potential to automatically delineate forest stands. Unlike optical sensors, LiDAR height data provides information about both the horizontal and vertical structural characteristics of forest stands. However, it deprives of spectral data that may be successfully used in separating tree species. In this study, we investigate the potential of LiDAR – WorldView-3 data synergy for the automatic generation of a detailed forest stand map which can be used for a tactical forest management plan. Firstly, image segmentation was applied to LiDAR data alone and LiDAR/WorldView-3 data set in order to obtain the most suitable image objects representing forest stands. Visual inspection of the segmentation results showed that image objects based on the LiDAR/WorldView-3 data set were more compatible with the reference forest stand boundaries. After the segmentation process, the LiDAR and LiDAR/WorldView-3 data sets were independently classified using object-based classification method. We tested two levels of classification. The first was a detailed classification with 14 classes considering reference stand types. The second was the rough classification with 9 classes where some stand types were combined. The mean, standard deviation and texture features of LiDAR metrics and spectral information were used in the classification. The accuracy assessment results of LiDAR data showed that the Overall Accuracy (OA) was calculated as 0.31 and 0.43, and the Kappa Index (KIA) was calculated as 0.26 and 0.32 for the detailed and rough classifications, respectively. For the LiDAR/WorldView-3 data set, the OA values were calculated as 0.50 and 0.61, and the KIA were calculated as 0.46 and 0.55 for the detailed and rough classifications, respectively. These results showed that the forest stand map derived from the LiDAR/WorldView-3 data synergy is more compatible with the reference forest stand map. In conclusion, it can be said that the forest stand maps produced in this study may provide strategic forest planning needs, but it is not sufficient for tactical forest management plans.     

机上无线局域网用户行为分析

随着无线网络的普及,用户的上网行为分析已经成为各单位提高服务质量、增长收益的重要途径。概述了Web日志挖掘的相关概念、方法和步骤。针对机上无线局域网的用户访问行为,对用户访问日志进行预处理,由日志中的旅客座位信息所关联到的旅客基本信息来确定旅客类别,通过计算分析用户的访问兴趣度,最终得出不同类别旅客的访问兴趣分布。     

Using S-band dual polarized radar for convective/stratiform rain indexing and the correspondence with AMSR-E GSFC profiling algorithm

The separation of rain types in convective and stratiform regimes has long been a goal in microwave remote sensing of precipitation research. In this essence, a dual polarized radar based indexing scheme that provides information on convective and stratiform (C/S) rain regimes has been presented in correspondence with advanced microwave scanning radiometer – earth observing system (AMSR-E) GSFC profiling algorithm estimate of convective rain percentage. The dual polarized radar based C/S indexing scheme first retrieves the normalized gamma drop size distribution parameters, median volume drop diameter (D₀) and concentration parameter (N_w), from dual polarized radar measurements Z_H and Z_DR, representing reflectivity and differential reflectivity respectively, by means of the genetic programming approach. Next, the C/S rain index is calculated based on the formulation of an empirical relation in N_w–D₀ domain. The scheme has been inspected and applied on measurements from the S-band Chilbolton dual polarized radar. A considerable number of “coincident” cases from the radar and the AMSR-E observations are investigated. It has been revealed that the dual polarized radar based C/S rain indexing is in a similar pattern with the AMSR-E GSFC profiling algorithm estimate of convective rain percentage. Generally, as C/S rain index value increases, which signifies a stratiform to convective trend, the AMSR-E convective rain percentage also increases.     

模糊聚类分析与库存重点管理

建立模糊聚类分析模型，以某厂总装车间配套库为研究对象，选择具有实际意义且有较强分辨性和代表性的统计指标，利用模糊集聚类分析方法对库存物资进行ＡＢＣ分析，得出最具实际经济意义的分类方案，同时根据最优分类结果，针对配套库库存行物资的特性及不合理管理实行重点管理，为以最小的管理精力，取得最大的管理效果和经济效益提供了有铲的方法。     

"需求拉动式"航空器材需求预测与控制模式

提出了"需求拉动式"航空器材库存控制模式,建立了基于可靠性分析的航空器材需求预测模型.该模型通过对航空器材使用信息进行可靠性分析,得出零件可靠性寿命函数,并根据给定可靠度下的零件寿命预测航空器材需求时间.最后对视情维护下的周转件需求预测与库存控制进行了案例分析.     

收益管理中的网络优化研究

介绍航空公司收益管理研究中针对航线网络进行座位优化的基本理论与研究现状,分析比较了概率数学规划、确定性数学规划、随机数学规划等常用方法,并对座位控制的嵌套策略与竞价机制研究现状进行了讨论,指出了研究中存在的问题。针对网络座位优化这一NP难问题,探讨了采用智能仿生算法与博弈技术的可行性。     

Softmax分类器深度学习图像分类方法应用综述

基于深度学习的人工智能图像分类方法研究是当前计算机视觉领域的研究热点。面向深度学习中的Softmax图像分类方法,首先回顾了图像分类技术的发展历程,接着介绍了图像识别技术中的分类器,并解释了Softmax回归函数的分类实现原理。基于Softmax回归分类器的应用,详细阐述了多种图像分类技术,具体包括浅层神经网络、深度置信网络、深度自编码器和卷积神经网络。同时,对比介绍了各种级联模型的具体结构、训练方法、实际应用、分类效果以及优缺点。最后,从Softmax回归分类器、深度学习网络模型和高维数据分类三个方面对基于Softmax回归分类器的深度学习模型在图像分类方面的发展与应用前景进行了展望。