Nowadays, biodiesel and vegetable oils have received increasing attention as renewable clean alternative fuels to fossil diesel fuel because of decreasing petroleum reserves and increasing environmental concerns. However, the straight use of biodiesel and vegetable oils in pure form results in several operational and durability problems in diesel engines because of their higher viscosity than fossil diesel fuel. One of the most used methods for solving the high viscosity problem is to blend them with fossil diesel fuel or alcohol. The reliable viscosity and density data of various biodiesel-diesel-alcohol ternary blends or biodiesel-diesel binary blends are plentifully available in existing literature, however, there is still the scarcity of dependable measurement values on different biodiesel-diesel-vegetable oil ternary blends at various temperatures. Therefore, in this study, waste cooking oil biodiesel (ethyl ester) was produced, and it was blended with fossil diesel fuel and waste cooking oil at different volume ratios to prepare ternary blends. Viscosities and densities of the ternary blends were determined at different temperatures according to DIN 53015 and ISO 4787 standards, respectively. The variation in viscosity with respect to temperature and oil fraction and the change of density vs. temperature were evaluated, rational and exponential models were proposed for these variations, and these models were tested against the density and viscosity data measured by the authors, Nogueira et al. and Baroutian et al. by comparing them to Gupta et al. model, linear model, Cragoe model and ANN (artificial neural networks) previously recommended in existing literature.
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.
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.
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