Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Regression Analysis of Material Properties and Hardness of Dense Boron Carbide(Wiley, 2025) Toksoy, M. Fatih; Haber, Richard A.Material properties directly affect the final performance of the produced articles. This study aims to establish a correlation between materials properties and hardness of boron carbide samples. Ten different boron carbide powders were sintered to high densities by spark plasma sintering, and material properties (grain size, density, stoichiometry, and free carbon) were analyzed. Hardness tests were conducted on these samples. All experimental procedures were completed by a single operator, and the same instruments were used for all the samples. Multiple linear regressions using the ordinary least squares method in SPSS were carried out to identify the relationship between hardness and material properties. Analyses showed density is the most dominant property, surpassing any other parameter. Grain size became more dominant at higher densities (>99%) and affected hardness results. Both grain size and density are the result of the starting powder and the densification procedure. This study showed that 80% of the hardness variation can be explained by this model.Article Citation - WoS: 3Citation - Scopus: 3Regression-Based Prediction for Task-Based Program Performance(World Scientific Publishing, 2019) Öz, Işıl; Bhatti, Muhammad Khurram; Popov, Konstantin; Brorsson, MatsAs multicore systems evolve by increasing the number of parallel execution units, parallel programming models have been released to exploit parallelism in the applications. Task-based programming model uses task abstractions to specify parallel tasks and schedules tasks onto processors at runtime. In order to increase the efficiency and get the highest performance, it is required to identify which runtime configuration is needed and how processor cores must be shared among tasks. Exploring design space for all possible scheduling and runtime options, especially for large input data, becomes infeasible and requires statistical modeling. Regression-based modeling determines the effects of multiple factors on a response variable, and makes predictions based on statistical analysis. In this work, we propose a regression-based modeling approach to predict the task-based program performance for different scheduling parameters with variable data size. We execute a set of task-based programs by varying the runtime parameters, and conduct a systematic measurement for influencing factors on execution time. Our approach uses executions with different configurations for a set of input data, and derives different regression models to predict execution time for larger input data. Our results show that regression models provide accurate predictions for validation inputs with mean error rate as low as 6.3%, and 14% on average among four task-based programs.Article Citation - WoS: 28Citation - Scopus: 18Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey(John Wiley and Sons Inc., 2010) İnal, FikretTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross-validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 μg/m3, 11.15μg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. Tropospheric ozone has adverse effects on human health and environment. Here, the next-day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron type artificial neural networks (MLP-ANNs). The MLP-ANNs were compared to multiple linear and multiple non-linear regression models. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.