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.

A Copula-Based BRDF Model

Abstract: In this paper, we introduce a novel approach for modeling surface reflection. We focus on using a family of probability distributions called Archimedean copulas as BRDF models. The Archimedean representation has an attractive property in that the multivariate distributions are characterized by their marginal distributions through a single univariate function only. It is shown that the proposed model meets the reciprocity property of reflection. Based on measured BRDF data, we demonstrate that the proposed approach provides a good approximation to BRDF. Empirical comparisons are made with some classically used BRDF models.

Does the Association between Illness-Related and Religious Searches on the Internet Depend on the Level of Religiosity?

Abstract: Recent research suggested that illness-related search predicts religious search on Google. In the current research, I aimed to replicate this finding and investigate whether such association depend...

New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration

Abstract: As the environments that human live are complex and uncontrolled, the object manipulation with humanoid robots is regarded as one of the most challenging tasks. Learning a manipulation skill from human Demonstration (LfD) is one of the popular methods in the artificial intelligence and robotics community. This paper introduces a deep learning based teleoperation system for humanoid robots that imitate the human operator’s object manipulation behavior. One of the fundamental problems in LfD is to approximate the robot trajectories obtained by means of human demonstrations with high accuracy. The work introduces novel models based on Convolutional Neural Networks (CNNs), CNNs-Long Short-Term Memory (LSTM) models combining the CNN LSTM models, and their scaled variants for object manipulation with humanoid robots by using LfD. In the proposed LfD system, six models are employed to estimate the shoulder roll position of the humanoid robot. The data are first collected in terms of teleoperation of a real Robotis-Op3 humanoid robot and the models are trained. The trajectory estimation is then carried out by the trained CNNs and CNN-LSTM models on the humanoid robot in an autonomous way. All trajectories relating the joint positions are finally generated by the model outputs. The results relating to the six models are compared to each other and the real ones in terms of the training and validation loss, the parameter number, and the training and testing time. Extensive experimental results show that the proposed CNN models are well learned the joint positions and especially the hybrid CNN-LSTM models in the proposed teleoperation system exhibit a more accuracy and stable results.

The Sphere of Consensus in a Polarized Media System: The Case of Turkey During the Catastrophic Coup Attempt

Abstract: How does a highly polarized media system respond to a catastrophic event? The July 2016 coup attempt in Turkey provides fertile ground to examine how a catastrophic event has shaped the editorial policies of news media outlets in a highly polarized media system. This article hypothesizes that, mainly due to the peculiarities of the Turkish media system, even at the time of a catastrophic event, the framing strategies of media outlets converge only to a limited degree on a sphere of consensus. Adopting a content analysis methodology, we analyze the framing strategies of four national newspapers affiliated with specific sociopolitical camps (the pro-government Sabah , the moderate Hurriyet , and the oppositional Sozcu and Cumhuriyet ). We reach the counterintuitive conclusion that these news outlets used different framing strategies in the immediate aftermath of the coup attempt and that the gap between them widened over the period of analysis.

Assessment of Maisotsenko Combustion Turbine Cycle with Compressor Inlet Cooler

Abstract: In this study, a Maisotsenko combustion turbine cycle (MCTC) with compressor inlet cooling system is proposed and studied through energy, exergy and exergoeconomic analysis methods. The present system consists of a Maisotsenko air cooler, a compressor, a turbine, a generator, a combustor, and a compressed air saturator. The results show that an exergy efficiency of 58.27 % is higher than the corresponding energy efficiency of 51.55 % for the MCTC system, due to the fact that the exergy content of the fuel fed into the combustion chamber is lower than its energy content. Also, the maximum exergy destruction rates occur in the compressor and turbine with the values of 166.964 kW and 150.864 kW, respectively. Furthermore, the exergoeconomic results indicate that the highest exergetic cost factor defined as the destruction in the component per cost is determined to be 0.013148 kW/$ for the turbine, while the Maisotsenko cycle air cooler has a minimum rate of 0.000006 kW/$. The better optimization of this component may be considered. It is concluded that Maisotsenko cycle systems can be effectively integrated to turbine cycle systems. Also, energy, exergy and exergoeconomic analyses give more useful information together about assessing the MCTC system and minimizing the thermodynamic inefficiencies.