SBS APP复合改性沥青的研究

采用SBS、APP复合改性渣油,可以得到性能优良的改性沥青材料。其最佳配料和共混工艺条件为:SBS/(SBS+APP)之比0.4、外掺量20%、转速1930rPm、共混时间1.0 hr,共混温度20℃。     

采用AT89c52的乱序键盘设计

The password keyboard as a kind of input device that is essential to bank and other financial departments,it must have high credibility,safety,and the function of keeping secret.Disorder keyboard is a developed kind on the basis of the traditional password keyboard,which increased some new functions and characteristics,for example,there is LED on the surface of the keys,and the numbers displayed on the keys are random,also a certain angle of view is restricted.Outside of a certain angle,the number LED displayed on the keyboard is impossible to be seen.Even other people can see the keys that the customer pressed,but because when the customer finished inputting the password,the key numbers will be re-arranged again,so,the customer unnecessarily worried about that other people would take his password.     

Optimal predictor for a singular random process

An optimal predictor is developed for a singular random process generated by a known system driven by a white noise sequence. The properties of the predictor are compared with those of the optimal predictor for a nonsingular random process.     

后桥壳的可靠性优化设计

该文讨论了后桥壳的可靠性优化设计问题.在基本随机变量的概率特性已知的情况下,应用随机摄动法和约束随机方向法对后桥壳进行可靠性优化设计,并编制了实用的计算机程序,可以迅速准确地得到后桥壳的可靠性设计参数.     

铜基催化剂孔隙率对气体有效扩散系数的影响

研究了18种具有不同孔结构参数的粒状铜基甲醇合成催化剂孔隙率对有效扩散系数的影响。结果发现，催化剂微孔孔隙率增大，曲节因子亦增大，扩散阻力增加。将孔隙率与催化剂曲节因子关联，得到了半经验方程，用它可估算催化剂的曲节因子。     

山区水库溢洪道和放水洞工程布置优化组合及型式选择

山区河流上修水库与平原水库不同的是，地形复杂、地质条件各异。要充分利用坝址处的地物地貌及地质条件，如何趋利避害，合理布置水库的土坝走向，放水洞与溢洪道的位置及型式，以取得安全、经济、美观三者协调统一。     

变权组合预测模型在大坝安全监测中的应用

简要介绍了组合预测模型的基本原理，该模型改进了单一模型考虑因素的片面性，但常规的组合预测模型的权系数是固定的。考虑到各种模型的预测精度的变化，以及大坝安全监测数据存在的突变、时效等因素，固定权系数的组合预测模型有待进一步改进。文中提出了变权系数组合预测模型，并将其用于实际的大坝安全监测工程。结果表明，变权组合预测模型的预报精度高于其他模型。是一种值得推广的方法。     

Approximating the problem, not the solution: An alternative view of point set matching

This work discusses the issue of approximation in point set matching. In general, one may have two classes of approximations when tackling a matching problem: (1) an algorithmic approximation which consists in using suboptimal procedures to infer the assignment, and (2), a representational approximation which involves a simplified and suboptimal model for the original data. Matching techniques have typically relied on the first approach by retaining the complete model and using suboptimal techniques to solve it. In this paper, we show how a technique based on using exact inference in simple Graphical Models, an instance of the second class, can significantly outperform instances of techniques from the first class. We experimentally compare this method with well-known Spectral and Relaxation methods, which are exemplars of the first class. We have performed experiments with synthetic and real-world data sets which reveal significant performance improvement in a wide operating range.     

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.     

Inference for pth‐order random coefficient integer‐valued autoregressive processes

Abstract. A pth‐order random coefficient integer‐valued autoregressive [RCINAR(p)] model is proposed for count data. Stationarity and ergodicity properties are established. Maximum likelihood, conditional least squares, modified quasi‐likelihood and generalized method of moments are used to estimate the model parameters. Asymptotic properties of the estimators are derived. Simulation results on the comparison of the estimators are reported. The models are applied to two real data sets.