According to dynamics of coupled galvanic anode with carbon steel,the integral of galvanic current vs.time is approximately equal to actual current capacity of galvanic anode.Galvanic current of cast aluminum galvanic...According to dynamics of coupled galvanic anode with carbon steel,the integral of galvanic current vs.time is approximately equal to actual current capacity of galvanic anode.Galvanic current of cast aluminum galvanic anode coupled with carbon steel is tested in3.5%NaCl solution and ambient temperature.Rapid evaluation the performance of galvanic anode using galvanic current is feasible,and the test time is20min.The galvanic current is used to select aluminum galvanic anodes in oil brine,and then test the galvanic anodes with impressed current test method.The result shows,the performance of galvanic anodes degrads in oil brine,but has not much difference in the two media to the preferable anodes,and the optimal galvanic anode is gained.展开更多
Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features ma...Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.展开更多
通过仿真对比研究了基于特征匹配的目标识别算法快速性及鲁棒性问题.采用目前常用的STAR、FAST、SIFT(scale invariant feature transform)、SURF(speeded up robust features)、ORB(oriented FAST and rotated BRIEF)、BRISK(binary ro...通过仿真对比研究了基于特征匹配的目标识别算法快速性及鲁棒性问题.采用目前常用的STAR、FAST、SIFT(scale invariant feature transform)、SURF(speeded up robust features)、ORB(oriented FAST and rotated BRIEF)、BRISK(binary robust invariant scalable keypoint)和FREAK(fast retina keypoint)等算法,对算法快速性和鲁棒性进行比较,并通过不同检测子与描述子的相互结合,找出最佳组合方式,提出了一种运用匹配点数与总耗时的比值来衡量算法综合性能好坏的新方法.仿真对比证明,FAST检测子、BRISK描述子以及STAR与BRISK的组合具有较好的性能.展开更多
基金National Natural Science Foundation of China(Nos.51204147,51274175,51574206,51574207)Program for International S&T Cooperation Projects of China(No.2014DFA50320)+1 种基金Program for International S&T Cooperation Projects of Shanxi Province(No.201381017)Technological Projects of Shanxi Province(No.20150313002-3)
文摘According to dynamics of coupled galvanic anode with carbon steel,the integral of galvanic current vs.time is approximately equal to actual current capacity of galvanic anode.Galvanic current of cast aluminum galvanic anode coupled with carbon steel is tested in3.5%NaCl solution and ambient temperature.Rapid evaluation the performance of galvanic anode using galvanic current is feasible,and the test time is20min.The galvanic current is used to select aluminum galvanic anodes in oil brine,and then test the galvanic anodes with impressed current test method.The result shows,the performance of galvanic anodes degrads in oil brine,but has not much difference in the two media to the preferable anodes,and the optimal galvanic anode is gained.
基金supported by the UGC, SERO, Hyderabad under FDP during XI plan periodthe UGC, New Delhi for financial assistance under major research project Grant No. F-34-105/2008
文摘Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.
