Mekatronik Mühendisliği Bölümü

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Citation - WoS: 2
Citation - Scopus: 4
A new systematic and exible method for developing hierarchical decision-making models
(Tubitak Scientific & Technological Research Council Turkey, 2015) Beldek, Ulas; Leblebicioglu, Mehmet Kemal; 59950
The common practice in multilevel decision-making (DM) systems is to achieve the final decision by going through a finite number of DM levels. In this study, a new multilevel DM model is proposed. This model is called the hierarchical DM (HDM) model and it is supposed to provide a flexible way of interaction and information flow between the consecutive levels that allows policy changes in DM procedures if necessary. In the model, in the early levels, there are primary agents that perform DM tasks. As the levels increase, the information associated with these agents is combined through suitable processes and agents with higher complexity are formed to carry out the DM tasks more elegantly. The HDM model is applied to the case study 'Fault degree classification in a 4-tank water circulation system'. For this case study, the processes that connect the lower levels to the higher levels are agent development processes where a special decision fusion technique is its integral part. This decision fusion technique combines the previous level's decisions and their performance indicator suitably to contribute to the improvement of new agents in higher levels. Additionally, the proposed agent development process provides flexibility both in the training and validation phases, and less computational effort is required in the training phase compared to a single-agent development simulation carried out for the same DM task under similar circumstances. Hence, the HDM model puts forward an enhanced performance compared to a single agent with a more sophisticated structure. Finally, model validation and efficiency in the presence of noise are also simulated. The adaptability of the agent development process due to the flexible structure of the model also accounts for improved performance, as seen in the results.
Citation - WoS: 8
Citation - Scopus: 11
Developing and Implementation of an Optimization Technique for Solar Chimney Power Plant With Machine Learning
(Asme, 2021) Ulucak, Oguzhan; Beldek, Ulaş; Kocak, Eyup; Bayer, Ozgur; Beldek, Ulas; Yapic, Ekin Ozgirgin; Ayli, Ece; 59950; 31329; 265836; Mekatronik Mühendisliği
Green energy has seen a huge surge of interest recently due to various environmental and financial reasons. To extract the most out of a renewable system and to go greener, new approaches are evolving. In this paper, the capability of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System in geometrical optimization of a solar chimney power plant (SCPP) to enhance generated power is investigated to reduce the time cost and errors when optimization is performed with numerical or experimental methods. It is seen that both properly constructed artificial neural networks (ANN) and adaptive-network-based fuzzy inference system (ANFIS) optimized geometries give higher performance than the numerical results. Also, to validate the accuracy of the ANN and ANFIS predictions, the obtained results are compared with the numerical results. Both soft computing methods over predict the power output values with MRE values of 12.36% and 7.25% for ANN and ANFIS, respectively. It is seen that by utilizing ANN and ANFIS algorithms, more power can be extracted from the SCPP system compared to conventional computational fluid dynamics (CFD) optimized geometry with trying a lot more geometries in a notably less time when it is compared with the numerical technique. It is worth mentioning that the optimization method that is developed can be implemented to all engineering problems that need geometric optimization to maximize or minimize the objective function.
Kromozom seçim metriğinin yinelemeli iki-aşamalı evrimsel programlamada performansa katkısı
(Çankaya Üniversitesi, 2013) Beldek, Ulaş; Beldek, Ulaş; 59950; Mekatronik Mühendisliği
Bu çalışmada yeni bir evrimsel eniyileştirme algoritması olan yinelemeli iki-aşamalı evrimsel programlama algoritmasında kromozom seçmek için kullanılan metriğin farklılaştırlmasının eniyileştirme problemlerinin çözümlemedeki katkısı incelenmiştir. Gerçekleştirlen metrik değişikliği , önceden uygulanan metriğe göre test edilen eniyileştirme problemlerinde karşılaştırılabilecek sonuçlar sunmaktadır. Elde edilen sonuçlar bu çalışmada gösterilmektedir.
Model Based PI Controller Design and Test of a DC Motor Using Root Locus
(2019) Beldek, Ulaş; Mahmood, Ahmed Imad; 59950; Mekatronik Mühendisliği
:In this paper the mathematical model of an experimental DC motor control system is constructed in a simulative environment for speed (angular velocity) control process and a PI controller is designed using root locus technique. The designed controller is then tested in different scenarios with varying reference signalsand changing disturbance load conditions. The designed controller demonstrated satisfactory results in simulations.

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