Papers by Keyword: K-S Test

Abstract: Metals distribution (Cu, Zn, Pb, Mn and Ni) in campus wastewater was examined by nonparametric tests (Kolmogorov-Smirnov (K S) and Friedman One-Way ANOVA). Based on the K-S test, all metals except zinc followed normal distribution. Based on the Friedman One-Way ANOVA test, nickel concentration posed substantial difference between three sampling stations under study (p < 0.05). All metals posed under the critical values on the basis of Environmental Quality (Sewage and Industrial Effluents) Regulations (1979). The water parameters (BOD, COD, TSS, pH and temperature) were also implicated to inform the chemical background of wastewater under study in relation to metals distribution.

Abstract: In order to solve the problem that empirical mode decomposition (EMD) will cause false components in the process of signal decomposition, a method of false component discriminant of EMD based on Kolmogorov-Smirnov test was put forward. First, the original signal was decomposed into several intrinsic mode functions (IMFs) by EMD. Then the K-S test was used to calculate the similarity between each IMF and the original signal. The reasonable similarity threshold was selected for judging the authenticity of the IMFs. The IMFs of which the similarity values were less than the threshold value were determined to be the false components. The others of which the similarity values were greater than the threshold value were determined to be the real components. As a result, the false components were removed and the real components were remained. The vibration signal of bearing experiment indicated that the method of K-S test could discriminate the real components and the false components obviously. Then the false components were removed quickly and accurately and the real components of the original signal were obtained.

Abstract: In data analysis of reliability, the traditional goodness-of-fit test is not applicable for grouped data under some circumstances. In this paper, a Kolmogorov-Smirnov test based on survival ratio method is proposed to determine the distribution type of grouped data. The power of the proposed test and other well-known goodness-of-fit tests are compared by Monte Carlo simulation, and the results show that the proposed test method is more powerful.

Abstract: Statistical techniques applying to multi-feature similarity comparison belong to the type of goodness-of-fit test which include chi-square test, rank correlation test and Kolmogorov-Smirnov test (K-S test). Experiments show that both chi-square independence test and rank correlation test are subject to the variation of sample size. With the expansion of sample size, the former test achieves the results of significant difference and the latter achieves the results of significant correlation easily. However, both results fail to reveal the actual situation of multi-feature similarity comparison between corpora. Only K-S test, which quantifies a distance between the empirical distribution functions of two samples, can achieve the highest statistical effectiveness.

Abstract: B610CF steel is a newly steel, and is widely used. The test data of tensile strength for B610CF steel is fitted several kinds of probability statistical models. In this paper, the correlation coefficient method and K-S method were used to test the fitting effects. It is concluded that three-parameter Weibull distribution is the optimal distribution for the test data, and the security tensile strength of 48mm B610CF steel is 627.54MPa under the condition of 99.9% reliability. Through the study of tensile strength confidence interval for B610CF steel, it is obtained that confidence interval is [601.47MPa, 644.86MPa] under the condition of 99.9% reliability and 80% confidence level. The results are important for the structural reliability analysis of B610CF steel.