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The purpose of this paper is to provide a way to analyze satellite images using various clustering algorithms and refined bitplane methods with other supporting techniques to prove the superiority of RIFCM.

A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems. Four images were selected dealing with hills, freshwater, freshwatervally and drought satellite images.

The superiority of the proposed algorithm, RIFCM with refined bitplane towards other clustering techniques with other supporting methods clustering, has been found and as such the comparison, has been made by applying four metrics (Otsu (Max-Min), PSNR and RMSE (40%-60%-Min-Max), histogram analysis (Max-Max), DB index and D index (Max-Min)) and proved that the RIFCM algorithm with refined bitplane yielded robust results with efficient performance, reduction in the metrics and time complexity of depth computation of satellite images for further process of an image.

For better clustering of satellite images like lands, hills, freshwater, freshwatervalley, drought, etc. of satellite images is an achievement.

The existing system extends the novel framework to provide a more explicit way to analyze an image by removing distortions with refined bitplane slicing using the proposed algorithm of rough intuitionistic fuzzy c-means to show the superiority of RIFCM.

This article considers Inverse Gaussian distribution as the basic lifetime model for the test units. The unknown model parameters are estimated using the method of moments, the method of maximum likelihood and Bayesian methods. As part of maximum likelihood analysis, this article employs an expectation-maximization algorithm to simplify numerical computation. Subsequently, Bayesian estimates are obtained using the Metropolis–Hastings algorithm. This article then presents the design of optimal censoring schemes using a design criterion that deals with the precision of a particular system lifetime quantile. The optimal censoring schemes are obtained after taking into account budget constraints.

This article first presents classical and Bayesian statistical inference for Progressive Type-I Interval censored data. Subsequently, this article considers the design of optimal Progressive Type-I Interval censoring schemes after incorporating budget constraints.

A real dataset is analyzed to demonstrate the methods developed in this article. The adequacy of the lifetime model is ensured using a simulation-based goodness-of-fit test. Furthermore, the performance of various estimators is studied using a detailed simulation experiment. It is observed that the maximum likelihood estimator relatively outperforms the method of moment estimator. Furthermore, the posterior median fares better among Bayesian estimators even in the absence of any subjective information. Furthermore, it is observed that the budget constraints have real implications on the optimal design of censoring schemes.

The proposed methodology may be used for analyzing any Progressive Type-I Interval Censored data for any lifetime model. The methodology adopted to obtain the optimal censoring schemes may be particularly useful for reliability engineers in real-life applications.

Homogeneous charge compression ignition (HCCI) engine is an advanced combustion method to use alternate fuel with higher fuel economy and, reduce NOX and soot emissions. This paper aims to investigate the influence of ethanol fraction (ethanol plus gasoline) on dual fuel HCCI engine performance.

In this study, the existing CI engine is modified into dual fuel HCCI engine by attaching the carburetor to the inlet manifold for the supply of ethanol blend (E40/E60/E80/E100). The mixture of ethanol blend and the air is ignited by diesel through a fuel injector into the combustion chamber at the end of the compression stroke. The experiments are conducted for high load conditions on the engine i.e. 2.8 kW and 3.5 kW maximum output power for 1,500 constant rpm.

It is noticed from the experimental results that, with an increase of ethanol in the blends, ignition delay (ID) increases and the start of combustion is retarded. It is noticed that E100 shows the highest ID and low in-cylinder pressure; however, E40 shows the lowest ID compared to higher fractions of ethanol blends. An increase in ethanol proportion reduces NOX and smoke opacity but, HC and CO emissions increase compared to pure diesel mode engine. E100 plus diesel dual-fuel HCCI engine shows the highest brake thermal efficiency compared to remaining ethanol blends and baseline diesel engine.

This experimental study concluded that E100 plus diesel and E80 plus diesel gave optimum dual fuel HCCI engine performance for 2.8 kW and 3.5 kW rated power, respectively.