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。     

普通热轧钢材的合理洁净度及生产工艺探讨

钢凝固前、后生成的非金属夹杂物形貌和在钢中的分布特点不同,凝固前生成的夹杂物尺寸较大,在钢中随机分布,主要影响钢材的疲劳强度和延性等性能。凝固后生成的夹杂物尺寸小,主要在晶界富集析出。硅锰脱氧钢脱氧后钢液仍含有20×10-6以上溶解氧,这部分溶解氧在钢凝固后绝大多数会转变为微细的氧化物或氧硫化物夹杂。钢凝固后生成的夹杂物主要影响钢材的冲击韧性、韧-脆转换温度等性能。 对热轧长型材用普碳钢和低合金钢不必采用铁水脱硫预处理,对热轧钢板用普碳钢可采用铁水轻度脱硫预处理,对热轧钢板用低合金高强度钢可采用铁水脱硫预处理、炼钢过程抑制回硫和出钢过程脱硫的工艺。对超低硫低合金钢,在钢水炉外精炼脱硫处理过程钢-渣间须维持非常低的氧势,炉渣成分应控制在CaO:SiO2:Al2O3=60％～65％:4％-8％:30％～35％范围。 普通热轧长型材用钢脱氧后溶解氧含量可放宽至(30-50)×10-6,对中、厚板钢溶解氧含量必须较严格控制,最好采用铝脱氧将溶解氧控制在较低的水平,并将T[O]含量控制在50×10-6以下。采用降低炼钢终点钢水氧含量、出钢挡渣、CAS和CAB等较简易炉外精炼方法和防止铸坯夹渣等措施,能够生产满足普通热轧钢洁净度要求的高品质钢材。     

Step response classification for model-based autotuning via polygonal curve approximation

A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results.     

Technical Note: Selecting a Classification Method by Cross-Validation

If we lack relevant problem-specific knowledge, cross-validation methods may be used to select a classification method empirically. We examine this idea here to show in what senses cross-validation does and does not solve the selection problem. As illustrated empirically, cross-validation may lead to higher average performance than application of any single classification strategy, and it also cuts the risk of poor performance. On the other hand, cross-validation is no more or less a form of bias than simpler strategies, and applying it appropriately ultimately depends in the same way on prior knowledge. In fact, cross-validation may be seen as a way of applying partial information about the applicability of alternative classification strategies.     

货运调车场减速顶噪声及治理的探讨

铁路货物运输枢纽的调车场中，驼峰调车是主要的调车手段，为提高生产效率和确保安全，沿驼峰下滑道布置的减速器（减速闸，减速顶）的噪声是调车场噪声的主要声源之一，本文通过对昆明东站铁路降噪工程的观测试验项目，探讨了减速顶噪声的特性及治理措施。     

A stability classification method and its application to pipelined solution of linear recurrences

A new method of classification for numerical stability of parallel algorithms is proposed based on the theoretical foundation of forward error analysis. It partitions the algorithms according to their asymptotic stability—a measure introduced to relate the limiting behavior of the stability to the size of the problem. Using this method, the stability aspect of the pipelined solution technique for first-order and second-order linear recurrences—the core of a tridiagonal linear equation solver—is studied. In particular, it shows that the pipelined solution method of the first-order linear recurrences has the same degree of stability as the commonly used sequential evaluation algorithms. The stability problems of sequential and pipelined solution methods of the second-order linear recurrences are also studied.     

航天产品多源可靠性信息的收集与整理--建立可靠性信息数据库

航天产品可靠性信息的来源多样，可靠性信息的时间性、随机性、有价性、时效性和可追溯性等特征，决定了可靠性数据收集应满足真实性、连续性、完整性等基本要求。同时，对多源信息的收集应遵循先进行需求分析，再确定数据收集点并制定数据收集表格的程度进行。给出了信息的适用性准则和信息分类的三维模型。最后结合具体型号航天产品的可靠性工作的要求，给出了可靠性信息数据库系统的整体方案设计。     

On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.     

基于Sql Server BI平台和决策树技术的CET-4成绩分析

文章首先对Sql Server 2008商业智能平台及决策树技术相关理论作了介绍,然后对挖掘数据源数据进行了一系列预处理。利用Sql Server 2008商业智能平台下的决策树技术并采用数据挖掘扩展语言即DMX语言创建了CET-4成绩分析决策树模型。分类矩阵和挖掘提升图分别对该模型的评估结果表明了模型具有较高的可靠性和分类准确度,同时模型对应的一些规则可作为英语教学管理和改革的重要参考依据。     

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types.Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.