Civril A.,Meliksah University |
Magdon-Ismail M.,Rensselaer Polytechnic Institute
Theoretical Computer Science | Year: 2012
Celik T.,Agency for Science, Technology and Research Singapore |
Celik T.,Meliksah University
Pattern Recognition | Year: 2012
In this paper, we propose a two-dimensional histogram equalization (2DHE) algorithm which utilizes contextual information around each pixel to enhance the contrast of an input image. The algorithm is based on the observation that the contrast in an image can be improved by increasing the grey-level differences between each pixel and its neighbouring pixels. The image equalization is achieved by assuming that for a given image, the modulus of the grey-level differences between pixels and their neighbouring pixels are equally distributed. The well-known global histogram equalization algorithm is a special case of 2DHE when contextual information is not utilized. 2DHE is easy to implement requiring only a small number of simple arithmetic operations and is thus suitable for real-time contrast enhancement applications. Experimental results show that 2DHE produces better or comparable enhanced images than several state-of-the-art algorithms. The only parameter in 2DHE which requires tuning is the size of the spatial neighbourhood support which provides the contextual information for a given dynamic range of the enhanced image. An automated parameter selection algorithm is also presented. The algorithm can be applied to a wide range of image types. © 2012 Elsevier Ltd.
Sevkat E.,Meliksah University |
Liaw B.,City College of New York |
Delale F.,City College of New York
Materials and Design | Year: 2013
Drop-weight impact response of hybrid woven composite plates was studied. Hybrid S2 glass-IM7 graphite fibers/toughened epoxy composites with two lay-up arrangements were impacted using spherical, flat-ended cylindrical and straight-line Charpy impactors. The time-histories of impact-induced dynamic strains and impact forces were recorded. The damaged specimens were inspected using ultrasonic C-Scan methods. Experimental results exhibited that hybrid composites with glass outer skins had higher resistance to impact compared to second type. It also delaminated more than hybrid composites with graphite outer skins. The 3-D dynamic finite element software, LS-DYNA, was then used to simulate the experimental result of drop-weight tests. Good agreement between experimental and FE results was achieved when comparing dynamic force, strain histories and damage patterns between experimental measurements and finite element simulations. © 2013 Elsevier Ltd.
Ozturk I.,Cag University |
Aslan A.,University of Nevsehir |
Kalyoncu H.,Meliksah University
Energy Policy | Year: 2010
This paper uses the panel data of energy consumption (EC) and economic growth (GDP) for 51 countries from 1971 to 2005. These countries are divided into three groups: low income group, lower middle income group and upper middle income group countries. Firstly, a relationship between energy consumption and economic growth is investigated by employing Pedroni (1999) panel cointegration method. Secondly, panel causality test is applied to investigate the way of causality between the energy consumption and economic growth. Finally, we test whether there is a strong or weak relationship between these variables by using Pedroni (2001) method. The empirical results of this study are as follows: i) Energy consumption and GDP are cointegrated for all three income group countries. ii) The panel causality test results reveal that there is long-run Granger causality running from GDP to EC for low income countries and there is bidirectional causality between EC and GDP for middle income countries. iii) The estimated cointegration factor, Β, is not close to 1. In other words, no strong relation is found between energy consumption and economic growth for all income groups considered in this study. The findings of this study have important policy implications and it shows that this issue still deserves further attention in future research. © 2010 Elsevier Ltd.
Camci F.,Meliksah University |
Chinnam R.B.,Wayne State University
IEEE Transactions on Automation Science and Engineering | Year: 2010
Failure mechanisms of electromechanical systems usually involve several degraded health-states. Tracking and forecasting the evolution of health-states and impending failures, in the form of remaining-useful-life (RUL), is a critical challenge and regarded as the Achilles' heel of condition-based- maintenance (CBM). This paper demonstrates how this difficult problem can be addressed through Hidden Markov models (HMMs) that are able to estimate unobservable health-states using observable sensor signals. In particular, implementation of HMM based models as dynamic Bayesian networks (DBNs) facilitates compact representation as well as additional flexibility with regard to model structure. Both regular HMM pools and hierarchical HMMs are employed here to estimate online the health-state of drill-bits as they deteriorate with use on a CNC drilling machine. Hierarchical HMM is composed of sub-HMMs in a pyramid structure, providing functionality beyond an HMM for modeling complex systems. In the case of regular HMMs, each HMM within the pool competes to represent a distinct health-state and adapts through competitive learning. In the case of hierarchical HMMs, health-states are represented as distinct nodes at the top of the hierarchy. Monte Carlo simulation, with state transition probabilities derived from a hierarchical HMM, is employed for RUL estimation. Detailed results on health-state and RUL estimation are very promising and are reported in this paper. Hierarchical HMMs seem to be particularly effective and efficient and outperform other HMM methods from literature. © 2010 IEEE.