Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures

This study deals with usage of linear regression (LR) and artificial neural network (ANN) modeling to predict engine performance; torque and exhaust emissions; and carbon monoxide, oxides of nitrogen (CO, NOx) of a naturally aspirated diesel engine fueled with standard diesel, peanut biodiesel (PME) and biodiesel-alcohol (EME, MME, PME) mixtures. Experimental work was conducted to obtain data to train and test the models. Backpropagation algorithm was used as a learning algorithm of ANN in the multilayered feedforward networks. Engine speed (rpm) and fuel properties, cetane number (CN), lower heating value (LHV) and density (?) were used as input parameters in order to predict performance and emission parameters. It was shown that while linear regression modeling approach was deficient to predict desired parameters, more accurate results were obtained with the usage of ANN. © 2016 Faculty of Engineering, Alexandria Universit

Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures

This study deals with usage of linear regression (LR) and artificial neural network (ANN) modeling to predict engine performance; torque and exhaust emissions; and carbon monoxide, oxides of nitrogen (CO, NOx) of a naturally aspirated diesel engine fueled with standard diesel, peanut biodiesel (PME) and biodiesel-alcohol (EME, MME, PME) mixtures. Experimental work was conducted to obtain data to train and test the models. Backpropagation algorithm was used as a learning algorithm of ANN in the multilayered feedforward networks. Engine speed (rpm) and fuel properties, cetane number (CN), lower heating value (LHV) and density (ρ) were used as input parameters in order to predict performance and emission parameters. It was shown that while linear regression modeling approach was deficient to predict desired parameters, more accurate results were obtained with the usage of ANN

A General BRDF Representation Based on Tensor Decomposition

WOS: 000297317200021Generating photo-realistic images through Monte Carlo rendering requires efficient representation of lightsurface interaction and techniques for importance sampling. Various models with good representation abilities have been developed but only a few of them have their importance sampling procedure. In this paper, we propose a method which provides a good bidirectional reflectance distribution function (BRDF) representation and efficient importance sampling procedure. Our method is based on representing BRDF as a function of tensor products. Four-dimensional measured BRDF tensor data are factorized using Tucker decomposition. A large data set is used for comparing the proposed BRDF model with a number of well-known BRDF models. It is shown that the underlying model provides good approximation to BRDFs.Scientific and Technical Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108E007]The authors thank anonymous reviewers for their comments and Wojciech Matusik et al. [MPBM03] for using their measured BRDF data. This work was supported by a grant from the Scientific and Technical Research Council of Turkey (Project No: 108E007)

Breast Cancer Detection with Reduced Feature Set

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity

Determining the behaviour of high-rise structures with geodetic hybrid sensors

Observing the normal oscillations and the behaviours of high-engineering structures has become almost a necessity in terms of construction security and human health. For this purpose, an real-time kinematic GPS (NovAtel 400) and a tilt sensor (Leica Nivel20) were installed in a TV tower (220 m high) located in Istanbul, Turkey. The observation serials were recorded over a period of 9 days. All data-sets in X and Y directions were examined in the time domain and were analyzed using FFT€. The dominant frequency values (significant frequencies) were determined by comparing at the high- and low-frequency values. These dominant frequencies showed that the tower made 4- and 6-second short-period oscillations and 24- and 12-hour long-period oscillations. All the observation signals were re-created by the significant low-frequency values using the inverse Fourier transform. Thus, the motion model of the tower was determined over 9 days. In this study, the 24-hour and 12-hour periodic oscillations were defined that represent the behaviour of the tower in relation to the effect of the sun's radiation and the temperature changes

Breast Cancer Detection with Reduced Feature Set

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%–40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden’s index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity

Exergy analysis of an inter-city bus air-conditioning system

In this paper, exergy analysis was implemented to improve the intercity bus air-conditioning (AC) system design. Firstly, an inter-city bus with a passenger capacity of 56 people was selected and then its hourly cooling load capacity was determined with the help of cooling load hourly analysis program (HAP). Analyses were performed for different air mixing ratios (MRs) and seasons. Coefficient of performance (COP), exergy efficiency (?) and exergy destructions (Exdest) of whole system and its each sub-units were evaluated. Results demonstrated that the atmospheric air temperature and air MR substantially affect the inter-city bus AC systems' performance. According to the results obtained for July, when air MR is 0.5, maximum exergy destruction value was calculated as 6.96 kW at compressor. Exergy destruction values were computed as 2.78, 2.61 and 0.99 kW for evaporator, condenser and expansion valve, respectively. © 2016 Inderscience Enterprises Ltd