粗糙集在海量数据库分解中的应用

首先介绍粗糙集理论,在属性选择量度的基础上进行数据库分解,并且描述分解的流程,从而得到不同的规则集。最后本文算法通过与ID3算法和RSES算法进行比较,从实验结果可知,虽然本文算法在分辨率上略低于其他2种算法,但规则条数远少于其他2种方法,说明本文算法分类模型简单,决策分析性高。

边防浅水运输船的技术设计分析

近年来额尔古纳河严重枯水 ,2 70 3工厂和驻哈船艇军代室受总装备部陆装科订部车船局的委托 ,实地调查了当地超浅水航区的特点 ,以及正在服役中的 1 0t内河运输船。在此基础上 ,深入研究了该船艇为适应这种航区所必须采取的技术措施 ,介绍了边防浅水运输船在主要量度选择、总体性能和动力装置方面的技术设计方案。

Experimental study on mechanism of influence of laser energy density on surface quality of Ti-6Al-4V alloy in selective laser melting

This experiment obtained different laser energy density(LED) by changing SLM molding process parameters.The surface morphology, surface quality, and microstructure of as-fabricated samples were studied. The effects of scanning speed, hatching space, and laser power on surface quality were analyzed, and the optimal LED range for surface quality was determined. The results show that pores and spherical particles appear on the sample’s surface when low LED is applied, while there are lamellar structures on the sides of the samples. Cracks appear on the sample’s surface,and the splash phenomenon increases when a high LED is taken. At the same time, a large amount of unmelted powder adhered to the side of the sample. The surface quality is the best when the LED is 150-170 J/mm^(3). The preferred hatch space is currently 0.05-0.09 mm, the laser power is 200-350 W, and the average surface roughness value is(15.1±3) μm.The average surface hardness reaches HV404±HV3, higher than the forging standard range of HV340-HV395.Increasing the LED within the experiment range can increase the surface hardness, yet an excessively high LED will not further increase the surface hardness. The microstructure is composed of needle-like α’-phases with a length of about 20μm, in a crisscross ‘N’ shape, when the LED is low. The β-phase grain boundary is not obvious, and the secondaryphase volume fraction is high;when the LED is high, the α’-phase of the microstructure is in the form of coarse slats, and the secondary-phase is composed of a small amount of secondary α’-phase, the tertiary α’-phase and the fourth α’-phase disappear, and the volume fraction of the secondary-phase becomes low.

Variable Selection Procedures in Linear Regression Models with Screening Consistency Property

There are two fundamental goals in statistical learning： identifying relevant predictors and ensuring high prediction accuracy. The first goal, by means of variable selection, is of particular importance when the true underlying model has a sparse representation. Discovering relevant predictors can enhance the performance of the prediction for the fitted model. Usually an estimate is considered desirable if it is consistent in terms of both coefficient estimate and variable selection. Hence, before we try to estimate the regression coefficients β , it is preferable that we have a set of useful predictors m hand. The emphasis of our task in this paper is to propose a method, in the aim of identifying relevant predictors to ensure screening consistency in variable selection. The primary interest is on Orthogonal Matching Pursuit（OMP）.

Effect of promoter MgO on CeO2/Al2O3 oxygen carrier used in partial oxidation of methane to syngas

Promoter MgO on 10% CeO2/Al2O3 oxygen carrier was investigated for direct partial oxidation of methane to syngas in molten salt. The MgO content of 0.5%, 1%, 2%, 3% and 4% on the 10%CeO2/Al2O3 oxygen carriers in experiments were prepared at the temperature of 750℃, respectively. The methane conversion, H2 and CO selectivity was measured on these prepared oxygen carriers at different reaction temperature. The results showed that the 3% MgO on 10%CeO2/Al2O3 had the best activity, and the CH4 conversion and CO selectivity reached 92.58% and 87.64% at 875℃, respectively. The effect of different calcination temperature on 3% MgO as promoter on 10% CeO2/Al2O3 oxygen carrier was investigated. The results of BET indicated that oxygen carrier had the largest surface area at 750℃. When the calcined temperature was too high there would be a negative effect on oxygen carrier activity.

Multistatic radar analysis based on ambiguity function and Cramér-Rao lower bounds

The primary goal of this work is to characterize the impact of weighting selection strategy and multistatic geometry on the multistatic radar performance. With the relationship between the multistatic ambiguity function （AF） and the multistatie Cram6r-Rao lower bound （CRLB）, the problem of calculating the multistatic AF and the multistatic CRLB as a performance metric for multistatic radar system is studied. Exactly, based on the proper selection of the system parameters, the multistatic radar performance can be significantly improved. The simulation results illustrate that the multistatic AF and the multistatic CRLB can serve as guidelines for future multistatic fusion rule development and multistatic radars deployment.

A routing algorithm for industrial wireless network based on ISA100.11a

ISA100.11 a industrial wireless network standard is based on a deterministic scheduling mechanism.For the timeslot delay caused by deterministic scheduling,a routing algorithm is presented for industrial environments.According to timeslot,superframe,links,channel and data retransmission of deterministic scheduling mechanisms that affect the design of the routing algorithm,the algorithm selects the link quality,timeslot delay and retransmission delay as the routing criteria and finds the optimum communication path by k shortest paths algorithm.Theoretical analysis and experimental verification show that the optimal paths selected by the algorithm not only have high link quality and low retransmission delay,but also meet the requirements of the deterministic scheduling.The algorithm can effectively solve the problem of packet loss and transmission delay during data transmission,and provide a valuable solution for efficient data transmission based on determinacy.

Polistes metricus queens exhibit personality variation and behavioral syndromes

Consistent differences in behavior between individuals, otherwise known as animal personalities, have become a staple in behavioral ecology due to their ability to explain a wide range of phenomena. Social organisms are especially serviceable to animal personality techniques because they can be used to explore behavioral variation at both the individual and group level. Despite the suc- cess of personality research in social organisms generally, and social Hymenoptera in particular, social wasps （Vespidae） have received little to no attention in the personality literature. In the pre- sent study, we test Polistes metricus （Vespidae; Polistinae） paper wasp queens for the presence of repeatable variation in, and correlations （＂behavioral syndromes＂） between, several commonly used personality metrics： boldness, aggressiveness, exploration, and activity. Our results indicate that P. metricus queens exhibit personalities for all measured traits and correlations between differ- ent behavioral measures. Given that paper wasps have served as a model organism for a wide range of phenomena such as kin selection, dominance hierarchies, mate choice, facial recognition, social parasitism, and chemical recognition, we hope that our results will motivate researchers to explore whether, or to what degree, queen personality is important in their research programs.

The Continuous Selections for the Metric Projection

Some results concerning weakly continuous selection for set-valued mappingare given and, applied to metric projection. Let Y be a subspace of a Banach space X.If Y is a separable reflexive Banach space,reinoved a first category subset, the metricprojection is weakly lower semicontinnous and admits a weakly continuous selection.

A feature selection approach based on a similarity measure for software defect prediction

Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class （defective or non-defective）, but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure （SM） for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor （KNN） model and measured by an area under curve （AUC） metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration （NASA） datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.