Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Subject: search.filters.subject.Kolmogorov-Smirnov test

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    A new nonlocal maximum likelihood estimation method for denoising magnetic resonance images
    (2013) Rajan, J.; den Dekker, A.J.; Juntu, J.; Sijbers, J.
    Denoising of Magnetic Resonance images is important for proper visual analysis, accurate parameter estimation, and for further preprocessing of these images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising Magnetic Resonance (MR) images. Among the ML based methods, the recently proposed Non Local Maximum Likelihood (NLML) approach gained much attention. In the NLML method, the samples for the ML estimation of the true underlying intensity are selected in a non local way based on the intensity similarity of the pixel neighborhoods. This similarity is generally measured using the Euclidean distance. A drawback of this approach is the usage of a fixed sample size for the ML estimation and, as a result, optimal results cannot be achieved because of over- or under-smoothing. In this work, we propose an NLML estimation method for denoising MR images in which the samples are selected in an adaptive way using the Kolmogorov-Smirnov (KS) similarity test. The method has been tested both on simulated and real data, showing its effectiveness. © Springer-Verlag 2013.
  • No Thumbnail Available
    Item
    A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov-Smirnov test
    (Elsevier, 2014) Rajan, J.; den Dekker, A.J.; Sijbers, J.
    Denoising algorithms play an important role in the enhancement of magnetic resonance (MR) images. Effective denoising is vital for proper analysis and accurate quantitative measurements from MR images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising MR images. Among the ML based methods, the recently proposed non-local maximum likelihood (NLML) approach gained much attention. In the NLML method, the samples for the ML estimation of the true underlying intensity are selected in a non-local way based on the intensity similarity of the pixel neighborhoods. This similarity is generally measured using the Euclidean distance. A drawback of this approach is the usage of a fixed sample size for the ML estimation resulting in over- or under-smoothing. In this work, we propose an NLML estimation method for denoising MR images in which the samples are selected in an adaptive and statistically supported way using the Kolmogorov-Smirnov (KS) similarity test. The method has been tested both on simulated and real data, showing its effectiveness. © 2013 Elsevier B.V.
  • No Thumbnail Available
    Item
    Model based reliability analysis of shovel–dumper system’s mechanical failures used in the surface coal mine: a case study
    (Taylor and Francis Ltd., 2020) N. S., N.S.; Choudhary, R.P.; Murthy, C.S.N.
    In a surface coal mine, efficient usage of shovels and dumpers should be most reliable and available to reach their production target. Keeping this in mind, this study was carried out at a surface coal mine, which is located at Open Cast-I, The SCCL, Kothagudem area, Telangana. The mechanical failure data such as time between failures (TBF) and time to repair (TTR) of each sub-system of a shovel-dumper system were collected and analysed statistically and graphically for reliability and availability. Graphical methods such as trend test and series correlation test and Chi-squared test were used to validate the independent, identically distribution of mean time between failures and mean and time to repair for the same a shovel and dumpers. Statistically, the Weibull distribution is selected as it was found the best-fit distribution by the Kolmogorov-Smirnov (K-S) test using Isograph Reliability Workbench. In K-S test, it was found that shovel KS1 has reliability of 0.2682 and dumpers BD1, BD2, KD2 and KD4 had reliability of 0.3731, 0.2850, 0.2897 and 0.2962, respectively for the 1 year of working hours. Also, calculated reliability- based time interval for preventive maintenance to achieve 70, 80 and 90%, respectively of reliability for same shovel-dumper system. © 2020 Safety and Reliability Society.