Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 4Citation - Scopus: 3A new hybrid CVT design: CVPSTs(Inderscience Enterprises Ltd., 2002) Özdemir, Serhan; Schueller, JohnAutomotive transmissions match the speed and the torque of the power source to the speed and torque requirements of the load. Properly designed continuously variable transmissions (CVTs) have shown promise to improve efficiency and performance. This work discusses some existing CVTs and proposes a new hybrid continuously variable power split transmission (CVPST). The speed relationships are analysed in power split and power recirculation mechanisms, the problem of geared neutral phenomenon with recirculating transmissions are identified and these two separate issues are left out as a topic for another paper. A new 'all-in-one' CVPST transmission incorporating power split and power recirculation is proposed.Conference Object Citation - WoS: 1Citation - Scopus: 6The Use of Chipless Sensors With Rfid for Condition Monitoring(Institute of Electrical and Electronics Engineers Inc., 2018) Biliç, H. Gökay; Büyüköztekin, Tarık; Özdemir, SerhanThis paper presents the development phases and overview of developing research in the area of RFID condition monitoring, focusing on chipless sensors especially use in strain and temperature sensing applications. Classification of RFID sensors and smart material fundamentals are reviewed. The compact and feasible design of RFID sensors will be considered, as well as with the effect of different material usage. Finally, the use of chipless sensors with different condition monitoring applications and their challenges are investigated.Conference Object The Influence of the Surface Topography of Distributed Sensor Networks on Perception(CSREA Press, 2012) Özkan, Özün Beyhan; Tosun, Öykü Ece; Arslan, Arda; Gençer, İsmail Cenk; Özçetin, Mustafa; Serindağ, Yelda; Memiş, Korhan; Özdemir, SerhanThis work investigates the effects of surface topography of the distributed sensor networks on perception through the differences in sensor readings. Compound eyes are found in some insects and crustaceans. Lateral inhibition is a biological signal processing which can increase contrast, enhancing perception. It is known that eye convexity helps increase field of view (FOV). A series of experiments were carried out to understand the effect of surface topography on local contrast gradient. Two sets of sensor networks of 5 × 5 were constructed. In the first network the board holding the sensors was a flat circuit board, whereas the second one was given a radius of curvature of roughly 30 cm. All readings were recorded in a dark chamber. Sensor networks were illuminated by a light source whose coordinates could be adjusted. Results are tabulated. It is seen that eye convexity in compound eyes improves perception, as well as FOV.Article Citation - WoS: 2Citation - Scopus: 2Trait-based heterogeneous populations plus (TbHP+) genetic algorithm(Elsevier Ltd., 2009) Tayfur, Gökmen; Sevil, Hakkı Erhan; Gezgin, Erkin; Özdemir, SerhanThis study developed a variant of genetic algorithm (GA) model called the trait-based heterogeneous populations plus (TbHP+). The developed TbHP+ model employs a memory concept in the form of immunity and instinct to provide the populations with a more efficient guidance. Also, it has an ability to vary the number of individuals during the search process, thus allowing an automatic determination of the size of the population based on the individual qualities such as character fitness and credit for immunity. The algorithm was tested against the classical GA model in convergence and minimum error performance. For this purpose, 5 different mathematical functions from the literature were employed. The selected functions have different topological characteristics, ranging from simple convex curves with 2 variables to complex trigonometric ones having several hilly shapes with more than 2 variables. The developed model and the classical GA model were applied to finding the global minima of the functions. The comparison of the results revealed that the developed TbHP+ model outperformed the classical GA in faster convergence and minimum errors, which may be explained by the adaptive nature of the new paradigm.Conference Object Citation - WoS: 4Citation - Scopus: 4A distributed behavioral model for landmine detection robots(International Association of Engineers, 2007) Bayram, Çağdaş; Sevil, Hakkı Erhan; Özdemir, SerhanThis paper presents a distributed navigation, detection and swarming model for a group of minimalist identical robotic agents. Decision making process of agents is weight based in contrast to widely used precedence based rules. The group is indirectly controlled by an alpha agent that has more sophisticated systems. Computer simulations of the proposed behavioral model generated promising results.Conference Object Citation - Scopus: 2Characterization of Swarm Behavior Through Pair-Wise Interactions by Tsallis Entropy(CSREA Press, 2005) Can, Fatih Cemal; Bayram, Çağdaş; Toksoy, Ahmet Kaan; Avşar, Hakan; Özdemir, SerhanThis paper tries to look at the interactions of a swarm of two at an elementary level. The change in the swarm entropy during the interactions is investigated. The characterization of swarm behavior has been subsumed in four modes, i.e. normal-free, normal-swarm, feeding and obstacle modes. Based on these modes, an entropy based algorithm is constructed to observe pair-wise interactions for each mode. For these modes, individuals of swarm are taken into account as self-driven interacting particles in the mathematical model. Statistical entropy definitions are used to control individual's behavior in feeding and obstacle modes. Individuals lose interactions enabling swarm behavior in feeding mode because of the priority of feeding for individuals as in nature. On the other hand, when swarm confronts an obstacle, individuals interact as much as they can. However they may lose interaction, depending on the size of the obstacle and position of the individuals. For feeding and obstacle modes, it is observed that Tsallis Entropy fits in the simulation better than other entropy definitions such as Shannon and Renyi.Conference Object Citation - Scopus: 1The Effects of Bias, Population Migration and Credit Assignment in Optimizing Trait-Based Heterogeneous Populations(CSREA Press, 2005) Gezgin, Erkin; Sevil, Hakkı Erhan; Özdemir, SerhanPopulation based search algorithms are becoming the mainstay in nonlinear problems with discontinuous search domains. The generic name of genetic algorithms (GAs) basicly applies to all population based methods. GAs have spawned many versions to suit new applications. Some of these alterations have reached such points that the algorithms may no longer be called GAs. One similar study may be found in [1], in which a perturbation based search algorithm was proposed, called Responsive Perturbation Algorithm (RPA). In a later work [2], instead of a population of homogenous individuals, as is the case for generic GAs, a population of heterogeneous individuals has been set to compete. Replacing the set of winner parents, the fittest individual is made the parent to yield offspring. The current work is now called, with the supplements, trait-based heterogeneous populations plus (TbHP+). Credit assignment and bias concepts in the form of immunity and instinct has been added to provide the populations with a more efficient guidance. Simulations were made through an RBF neural network training, as it was carried out in earlier works, mentioned above, for comparison. Results were prsented at the end as network testing errors which showed further improvement with TbHP+.Conference Object Citation - WoS: 1Citation - Scopus: 6Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues(Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Tayfur, GökmenA multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.