采用信息增益率的混合入侵检测模型设计

针对现有混合入侵检测模型仅定性选取特征而导致检测精度较低的问题，同时为了充分结合误用检测模型和异常检测模型的优势，提出一种采用信息增益率的混合入侵检测模型.首先，利用信息增益率定量地选择特征子集，最大程度地保留样本信息；其次，采用余弦时变粒子群算法确定支持向量机参数构建误用检测模型，使其更好地平衡粒子在全局和局部的搜索能力，然后，选取灰狼算法确定单类支持向量机参数构建异常检测模型，以此来提高对最优参数的搜索效率和精细程度，综合提高混合入侵检测模型对攻击的检测效果；最后，通过两种数据集进行仿真实验，验证了所提混合入侵检测模型具有较好的检测性能.     

Study on metamerism degree evaluation based on wavelength sensitive cone weighting algorithm

Metamerism phenomenon can be used in illuminant detection to ensure the accuracy of light source. A method based on Long‐, Middle‐, Short‐ wavelength cones(LMS) weighting algorithm to evaluate metamerism degree is proposed. The chromatic relationship between the degree of metamerism mismatch and the light source is studied. Herein, the consistency between the metameric indices (MIs) and CIE1976 L*a*b* color difference ranking is analyzed by SRCC, KRCC, PLCC and RMSE. A statistically sampling method to obtain practical LMS cone fundamentals to evaluate metamerism degree is employed. The analysis results obtained show that the method based on LMS weighting algorithm has good evaluation ability and stability in simulation experiments and statistically sampling experiments, which are in line with visual characteristics of human. Proposed method meets the requirements of selecting metameric pairs used in light source detection. The analysis results have certain guiding significance.     

多测井曲线的综合处理合成

测井曲线是地质分层的基础，但在利用单一测井曲线解决复杂地质问题时也存在多解性和局限性。为克服多解性，人们采用多种测井曲线进行地质分层，提高了分层的精度。本文在此基础上提出把多条曲线分别赋予不同的加权因子，然后进行归一化处理，形成一条标准综合参数测井曲线。此综合参数曲线突出了所选的多条测井曲线的公共信息，减少或削弱了单一测井曲线中的偶然误差，提高了综合后测井资料的可信度。该方法可应用于地质分层、岩性分析、含油气预测等诸多领域。应用时要注意，并非所选择的测井曲线种类越多越好，而是要选择那些最能反映地层单元特征信息的测井曲线。     

基于神经网络基础上的两种系统安全综合评价方法

从工业企业生产发展的特点出发,在界定安全评价概念的基础上，从系统论的角度简要介绍了运用三层BP神经网络技术进行安全综合评价的方法和安全模糊综合评价方法。通过对这两种较为流行的安全评价方法进行分析，指出了传统评价方法的不合理性，辨证说明了这两种评价方法的优越性。     

铜杆信息采集系统的研究

阐述了铜杆生产过程中信息采集系统的主要研究内容；概括了硬件的结构和选用的主要设备；阐述了上位机软件的主要模块及实现方法；系统采用面向对象的编程方法，界面友好，操作简单，功能完善；实际运行效果良好，具有较好的应用前景。     

Determination of the Coordinates of the Center of a Gaussian Beam by Means of a Matrix Photodetector Using a Weighting Method

A weighting algorithm to determine the coordinates of the center of a Gaussian laser beam projected onto a matrix photodetector is considered. The influence of the internal noise of the photodetector, the maximum brightness of the signal at the beam maximum, and the beam radius on the precision of the algorithm is investigated. Recommendations on image processing are presented.     

Results of the ASTM field test of tristimulus weighting functions

In 1993, the American Society for Testing and Materials carried out a field test of newly calculated tristimulus weighting factors. These weighting factors had been calculated by a method proposed by Venable. the test also included a method of correction for bandpass dependence put forth by Stearns. the purpose of the trial was to assess the possible reduction in bandpass dependence introduced by each of these sets of weights. A large number of sets of spectral data were gathered from the cooperators in the field test. Results of integration by the various sets of tristimulus weighting factors were calculated. A total of 15 120 color differences were calculated and statistics were derived to test the probable error resulting from each method of correction. Errors attributable to bandpass dependence were on the order of a few tenths to as much as one CIELAB unit when uncorrected weight sets were used. These errors could be reduced to a few hundreths of a CIELAB unit, and in some cases to a few thousandths of a unit, by employing one correcting strategy or the other. an overall mix of strategies was ultimately chosen to minimize the bandpass dependence over the entire range of weight sets. Utilizing this mixed strategy, the median error introduced into 10-nm integration by bandpass dependence was only 0.004 CIELAB units. © 1995 John Wiley & Sons, Inc.     

Class normalization in centroid-based text categorization

Centroid-based categorization is one of the most popular algorithms in text classification. In this approach, normalization is an important factor to improve performance of a centroid-based classifier when documents in text collection have quite different sizes and/or the numbers of documents in classes are unbalanced. In the past, most researchers applied document normalization, e.g., document-length normalization, while some consider a simple kind of class normalization, so-called class-length normalization, to solve the unbalancedness problem. However, there is no intensive work that clarifies how these normalizations affect classification performance and whether there are any other useful normalizations. The purpose of this paper is three folds; (1) to investigate the effectiveness of document- and class-length normalizations on several data sets, (2) to evaluate a number of commonly used normalization functions and (3) to introduce a new type of class normalization, called term-length normalization, which exploits term distribution among documents in the class. The experimental results show that a classifier with weight-merge-normalize approach (class-length normalization) performs better than one with weight-normalize-merge approach (document-length normalization) for the data sets with unbalanced numbers of documents in classes, and is quite competitive for those with balanced numbers of documents. For normalization functions, the normalization based on term weighting performs better than the others on average. For term-length normalization, it is useful for improving classification accuracy. The combination of term- and class-length normalizations outperforms pure class-length normalization and pure term-length normalization as well as unnormalization with the gaps of 4.29%, 11.50%, 30.09%, respectively.     

文本索引词项相对权重计算方法与应用

文本索引词权重计算方法决定了文本分类的准确率。该文提出一种文本索引词项相对权重计算方法,即文本索引词项权重根据索引词项在该文本中的出现频率与在整个文本空间出现的平均频率之间的相对值进行计算。该方法能有效地提高索引词对文本内容识别的准确性。