Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Optimization of university course scheduling problem with a hybrid artificial bee colony algorithm

Abstract: Course scheduling problem (CSP) is concerned with developing a timetable that illustrates a number of courses assigned to the classrooms. In this study, a hybrid algorithm composed of a heuristic graph node coloring (GNC) algorithm and artificial bee colony (ABC) algorithm is proposed to solve CSP. The study is one of the few applications of ABC on discrete optimization problems and to our best knowledge it is the first application on CSP. A basic heuristic algorithm of node coloring problem takes part initially to develop some feasible solutions of CSP. Those feasible solutions correspond to the food sources in ABC algorithm. The ABC is then is used to improve the feasible solutions. The employed and onlooker bees are directed or controlled in a specific manner in order to avoid the conflicts in the course timetable. Proposed solution procedure is tested using real data from a university in Turkey. The experimental results demonstrate that the proposed hybrid algorithm yields efficient solutions.

Hierarchical Reconstruction and Structural Waveform Analysis for Target Classification

Abstract: Classification of objects from scattered electromagnetic waves is a difficult problem, as it heavily depends on aspect angle. To minimize this dependency, distinguishable features can be used. In this paper, we propose a target identification method in the resonance scattering region using a novel structural feature set based on the scattered signal waveform. To obtain robustness at low signal-to-noise ratio (SNR), a multiscale approximation is used for distortion correction prior to the feature extraction. This is achieved by an overlapping grid hierarchical radial basis function (HRBF $_{\mathrm {\mathrm {OG}}})$ network topology, which is demonstrated to outperform existing HRBF techniques. The results obtained from the simulations and the measurements performed for various targets show high accuracy for classification with the proposed feature set, robustness through the use of HRBF at low SNR, and efficient computation in real time.

Torsional Characteristics of Carbon Nanotubes: Micropolar Elasticity Models and Molecular Dynamics Simulation.

Abstract: Efficient application of carbon nanotubes (CNTs) in nano-devices and nano-materials requires comprehensive understanding of their mechanical properties. As observations suggest size dependent behaviour, non-classical theories preserving the memory of body’s internal structure via additional material parameters offer great potential when a continuum modelling is to be preferred. In the present study, micropolar theory of elasticity is adopted due to its peculiar character allowing for incorporation of scale effects through additional kinematic descriptors and work-conjugated stress measures. An optimisation approach is presented to provide unified material parameters for two specific class of single-walled carbon nanotubes (e.g., armchair and zigzag) by minimizing the difference between the apparent shear modulus obtained from molecular dynamics (MD) simulation and micropolar beam model considering both solid and tubular cross-sections. The results clearly reveal that micropolar theory is more suitable compared to internally constraint couple stress theory, due to the essentiality of having skew-symmetric stress and strain measures, as well as to the classical local theory (Cauchy of Grade 1), which cannot accounts for scale effects. To the best of authors’ knowledge, this is the first time that unified material parameters of CNTs are derived through a combined MD-micropolar continuum theory.