Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

Low thermal resistance high-speed top-emitting 980-nm VCSELs

Increasing copper plated heatsink radii from 0 to 4 /spl mu/m greater than the mesa in vertical-cavity surface-emitting lasers (VCSELs) reduced the measured thermal resistance for a range of device sizes to values 50% lower than previously reported over a range of device sizes. For a 9-/spl mu/m diameter oxide aperture, the larger heatsink increases output power and bandwidth by 131% and 40%, respectively. The lasers exhibit a 3-dB modulation frequency bandwidth up to 9.8 GHz at 10.5 kA/cm/sup 2/. The functional dependence of thermal resistance on oxide aperture diameter indicates the importance of lateral heat flow to mesa sidewalls.     

Query by image and video content: a colored-based stochastic model approach

For efficient image retrieval, the image database should be processed to extract a representing feature vector for each member image in the database. A reliable and robust statistical image indexing technique based on a stochastic model of an image color content has been developed. Based on the developed stochastic model, a compact 12-dimensional feature vector was defined to tag images in the database system. The entries of the defined feature vector are the mean, variance, and skewness of the image color histogram distributions as well as correlation factors between color components of the RGB color space. It was shown using statistical analysis that the feature vector provides sufficient knowledge about the histogram distribution. The reliability and robustness of the proposed technique against common intensity artifacts and noise was validated through several experiments conducted for that purpose. The proposed technique outperforms traditional and other histogram based techniques in terms of feature vector size and properties, as well as performance.     

The Robust Regression Methods for Estimating of Finite Population Mean Based on SRSWOR in Case of Outliers

The ordinary least square (OLS) method is commonly used in regression analysis. But in the presence of outlier in the data, its results are unreliable. Hence, the robust regression methods have been suggested for a long time as alternatives to the OLS to solve the outliers problem. In the present study, new ratio type estimators of finite population mean are suggested using simple random sampling without replacement (SRSWOR) utilizing the supplementary information in Bowley’s coefficient of skewness with quartiles. For these proposed estimators, we have used the OLS, Huber-M, Mallows GM-estimate, Schweppe GM-estimate, and SIS GM-estimate methods for estimating the population parameters. Theoretically, the mean square error (MSE) equations of various estimators are obtained and compared with the OLS competitor. Simulations for skewed distributions as the Gamma distribution support the results, and an application of real data set containing outliers is considered for illustration.     

Acceptance Sampling Plans with Truncated Life Tests for the Length-Biased Weighted Lomax Distribution

In this paper, we considered the Length-biased weighted Lomax distribution and constructed new acceptance sampling plans (ASPs) where the life test is assumed to be truncated at a pre-assigned time. For the new suggested ASPs, the tables of the minimum samples sizes needed to assert a specific mean life of the test units are obtained. In addition, the values of the corresponding operating characteristic function and the associated producer’s risks are calculated. Analyses of two real data sets are presented to investigate the applicability of the proposed acceptance sampling plans; one data set contains the first failure of 20 small electric carts, and the other data set contains the failure times of the air conditioning system of an airplane. Comparisons are made between the proposed acceptance sampling plans and some existing acceptance sampling plans considered in this study based on the minimum sample sizes. It is observed that the samples sizes based on the proposed acceptance sampling plans are less than their competitors considered in this study. The suggested acceptance sampling plans are recommended for practitioners in the field.     

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.     

Extended Rama Distribution: Properties and Applications

In this paper, the Rama distribution (RD) is considered, and a new model called extended Rama distribution (ERD) is suggested. The new model involves the sum of two independent Rama distributed random variables. The probability density function (pdf) and cumulative distribution function (cdf) are obtained and analyzed. It is found that the new model is skewed to the right. Several mathematical and statistical properties are derived and proved. The properties studied include moments, coefficient of variation, coefficient of skewness, coefficient of kurtosis and moment generating function. Some simulations are undertaken to illustrate the behavior of these properties. In addition, the reliability analysis of the distribution is investigated through the hazard rate function, reversed hazard rate function and odds function. The parameter of the distribution is estimated based on the maximum likelihood method. The distributions of order statistics for ERD are also presented. The performance of the suggested model is compared with several other lifetime distributions based on some goodness of fit tests on a real dataset. It turns out that the suggested model is more flexible than its competitors considered in this study, for modeling real lifetime data.     

Structural,magnetic and 151Eu Mössbauer studies of mechanosynthesized nanocrystalline EuCr1−xFexO3 particles

We report on the mechanosynthesis of single-phased EuCr_1?xFe_xO₃ (x = 0.0, 0.1, 0.3, 0.5, 0.7, 0.9 and 1.0) nanocrystalline particles (～20–50 nm) at temperatures that are significantly lower than those at which the corresponding bulk materials are conventionally synthesized, and their structural and magnetic characterization. It was found that the average crystallite size decreased, the lattice parameters increased and the unit cell gets more distorted with increasing x. All samples showed weak ferromagnetism due to spin canting. Although Curie temperatures increase with increasing x, they are significantly lower than those of the corresponding bulk materials. Simple Curie–Weiss fits for the paramagnetic susceptibilities were found to be more physically plausible relative to fits where Vleck-type contributions to the paramagnetic susceptibilities due to Eu³⁺ ionic sublattices, which are separate from of the Cr³⁺/Fe³⁺ ones, were assumed. The non-existence of such separate Eu³⁺ and Cr³⁺/Fe³⁺ sublattices was further supported by ¹⁵¹Eu Mössbauer spectra of the nanoparticles which favor a non-equilibrium cation distribution wherein a considerable amount of Eu³⁺ and Cr³⁺/Fe³⁺ ions exchange their normal dodecahedral and octahedral perovskite-related sites, respectively. The supertransferred hyperfine magnetic field at the site of the ¹⁵¹Eu nuclei, induced by neighboring Cr³⁺/Fe³⁺ ions, was found to be considerably larger for B-site nuclei than for A-site ones. The antisite behavior concluded in this study is consistent with ⁵⁷Fe Mössbauer spectral analysis of the nanoparticles that favor the presence of the majority of the Fe³⁺ ions at the usual B-sites and minority Fe³⁺ ions either at the A-sites or at B-sites with Eu³⁺/Cr³⁺ nearest B-site neighbors. The relative site occupancies associated with the cationic site exchange inferred from both the ¹⁵¹Eu and ⁵⁷Fe Mössbauer spectra are remarkably consistent.     

Enhanced Modeling and Solution of Layered Queueing Networks

Layered queues are a canonical form of extended queueing network for systems with nested multiple resource possession, in which successive depths of nesting define the layers. The model has been applied to most modern distributed systems, which use different kinds of client-server and master-slave relationships, and scales up well. The layered queueing network (LQN) model is described here in a unified fashion, including its many more extensions to match the semantics of sophisticated practical distributed and parallel systems. These include efficient representation of replicated services, parallel and quorum execution, and dependability analysis under failure and reconfiguration. The full LQN model is defined here and its solver is described. A substantial case study to an air traffic control system shows errors (compared to simulation) of a few percent. The LQN model is compared to other models and solutions, and is shown to cover all their features.