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In this study, the geometric process (GP) model is considered in order to calculate the debugging and testing costs of a software product. Under the assumption of the GP model, the debugging and testing costs of the software product are obtained depending on the first and second moment functions of the GP. It is observed that the values of the first and second moment functions of the process must be known in order to calculate the debugging and testing costs. At the same time, the calculation of moment functions also depends on both the distribution of the first interarrival time of the GP and the estimates of the model and distribution parameters. In this study, the proposed debugging and testing costs are calculated for the data set containing 136 failure times of a real-time command and control system. For this dataset, it has been shown in previous studies that the GP with gamma distribution can be proposed as a model. Under gamma distribution assumption, the maximum likelihood estimates of the model parameters are obtained. Using the estimates of the model parameters, the first and second moment functions of the GP are calculated with the help of the numerical methods proposed for these functions. Finally, the debugging and testing costs are obtained for the data set. [ABSTRACT FROM AUTHOR]

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Abstract Nowadays, biodiesel is seen as an alternative fuel to diesel fuel due to its many advantages such as higher density, cetane number and flash point. Although several methods are available for estimating fuel properties of biodiesel-diesel fuel blends, there is still the lack of works on the comparison of regression models and artificial neural networks (ANN) in predicting viscosities of the blends. Therefore, in this work, (1) optimum reaction parameters providing the lowest viscosity were determined for methanolysis of waste cooking oil, (2) waste cooking oil methyl ester was synthesized based on the determined optimum parameters, and it was mixed with diesel fuel on different volume ratios (3) viscosity measurements of the prepared blends were made at the temperature ranges between 273.15 K and 373.15 K , (4) changes in viscosity versus temperature and biodiesel fraction in blend were investigated and the rational model was proposed, finally (5) the predictive capability of rational model was compared to the three-term Vogel model, Bingham model and ANN by fitting to viscosity data measured by the authors and by Geacai et al. According to results, the measured values by the authors and Geacai et al. are the most accurately predicted by the rational model. Highlights • Experimental optimization of transesterification reaction. • Production of waste cooking oil biodiesel having the lowest viscosity. • Effects of biodiesel fraction and temperature on biodiesel-diesel binary blends. • Viscosity prediction capabilities of regression models and ANN. • Rational model for viscosity changes vs. biodiesel fraction and temperature. [ABSTRACT FROM AUTHOR]

Density is one of the most important fuel properties influencing the injection, spray, and combustion characteristics. Since a number of articles have recently been performed investigating the effects of biodiesel–diesel–alcohol ternary blends on combustion characteristics and exhaust emissions, the reliable density data and regression models for the ternary blends become more important in developing accurate spray, combustion and emission models. However, there is lack of studies which focus on (a) the measurement of densities of ternary blends including higher alcohols over wide alcohol blending ratio at different temperatures, (b) development of one‐dimensional regression models to predict densities of ternary blends, and (c) comparison of predictive capabilities of models with artificial neural networks (ANNs). Therefore, in this article, to eliminate the lack of such studies in the existing literature, waste cooking oil biodiesel was produced, and it was mixed with diesel fuel and several alcohols to prepare ternary blends. The density measurements of ternary blends were performed under various temperatures (278.15 K−368.15 K). The exponential equation which can be used in spray or combustion model was derived by fitting the density data. The predictive capability of the exponential model was compared with the linear model and ANN using different density data of ternary blends to determine the best‐fit correlation. According to results, ANN is the most suitable one to estimate density; however, the exponential model is also thought to be an alternative to ANN. [ABSTRACT FROM AUTHOR]

The main idea of brain-computer interfaces (BCIs) is to facilitate the lives of patients having difficulties to move their muscles due to a disorder of their motor nervous systems but healthy cognitive functions. BCIs are usually electroencephalography (EEG)-based, and the success of the BCIs relies on the precision of signal preprocessing, detection of distinctive features, usage of suitable classifiers and selection of effective channels. In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imagery/movement tasks. In the first stage of the channel selection, the channels were combined according to the neurophysiological information about brain functions acquired from the literature, then averaged and a single channel was formed. In the second stage, selective channels were specified with the common spatial pattern-linear discriminant analysis (CSP-LDA)-based sequential channel removal. After the channel selection phase, the feature extraction was carried out with local transformation-based methods (LTBM): local centroid pattern (LCP), one-dimensional-local gradient pattern (1D-LGP), local neighborhood descriptive pattern (LNDP) and one-dimensional-local ternary pattern (1D-LTP). The distinctions and deficiencies of these methods were compared with other methods in the literature and the classification performances of the k-nearest neighbor (k-NN) and the support vector machines (SVM) were evaluated. As a result, the proposed methods yielded the highest average classification accuracies as 99.34%, 95.95%, 98.66% and 99.90% with the LCP, 1D-LGP, LNDP and 1D-LTP when using k-NN, respectively. The two-stage channel selection and 1D-LTP method showed promising results for recognition of motor tasks. The LTBM will contribute to the development of EEG-based BCIs with the advantages of high classification accuracy, easy implementation and low computational complexity.