Teknik Bilimler Meslek Yüksekokulu / Vocational School of Technical Sciences
Permanent URI for this collectionhttps://hdl.handle.net/11727/2031
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Item A Novel Approach for Estimating Heat Transfer Coefficients of Ethylene Glycol-Water Mixtures(2014) Bulut, Murat; Ankishan, Haydar; Demircioglu, Erdem; Ari, Seckin; Sengul, Orhan; https://orcid.org/0000-0002-6240-2545; AAH-4421-2019Ethylene glycol-water mixtures (EGWM) are vital for cooling engines in automotive industry. Scarce information is available in the literature for estimating the heat transfer coefficients (HTC) of EGWM using knowledge-based estimation techniques such as adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN) which offer nonlinear input-output mapping. In this paper, the supervised learning methods of ANFIS and ANN are exploited for estimating the experimentally determined HTC. This original research fulfills the preceding modeling efforts on thermal properties of EGWM and HTC applications in the literature. An experimental test setup is designed to compute HTC of mixture over a small circular aluminum heater surface, 9.5 mm in diameter, placed at the bottom 40-mm-wide wall of a rectangular channel 3 mm x 40 mm in cross section. Measurement data are utilized as the train and test data sets of the estimation process. Prediction results have shown that ANFIS provide more accurate and reliable approximations compared to ANN. ANFIS present correlation factor of 98.81 %, whereas ANN estimate 87.83 % accuracy for test samples.Item Optimization of Waiting and Journey Time in Group Elevator System Using Genetic Algorithm(2014) Tartan, Emre Oner; Erdem, Hamit; Berkol, AliEfficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control system's performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm.Item A New Portable Device for the Snore/Non-Snore Classification(2017) Ankishan, Haydar; Tuncer, A. Turgut; 0000-0002-6240-2545; AAH-4421-2019Snoring is widely known as a disease. The aim of this paper is to introduce and validate our newly developed snoring detection device to identify automatically snore and non-snore sounds using a nonlinear analysis technique. The developed device can analyze chaotic features of a snore related sounds such as entropy, Largest Lyapunov Exponents (LLEs) and also has the data classification ability depending on the feature values. We report that the developed snoring detection device with proposed automatic classification method could achieve an accuracy of 94.38% for experiment I and 82.02 for experiment II when analyzing snore and non-snore sounds from 22 subjects. This study revealed the efficacy of our newly developed snoring detection device and indicated that it may be used at home an alternative to diagnose snore related sounds. It is anticipated that our findings will contribute to the development of an automated snore analysis system to be used in sleep studies.Item A New Approach for the Acoustic Analysis of the Speech Pathology(2017) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019Voice disorders are a common physical problem that can be encountered today and can cause serious problems in the long term. It is necessary to analyze the voice and extract its characteristics correctly so that it can be treated. In some cases, due to their sound characteristics, they do not differ from each other characteristics exactly, and today's systems do not yet have the ability to make correct decisions. This study has taken into account those evident which from voice disturbances and tries to the analysis of these disorders by means of previously unused attributes with the help of classifier (SVMs). In this study, after the sounds are modeled with LPC and MFCC, disorder analysis is performed on the obtained signals. In the results obtained from experimental studies, it has been determined that 100% of the patients with four different diseases can be decomposed together with the used nonlinear features.Item A Simple Population Based Hybrid Harmonic Estimation Algorithm(2016) Tartan, Emre Oner; Erdem, HamitThis paper presents a new hybrid algorithm for harmonic estimation. The algorithm combines a simple fast population based search algorithm with Least Squares Method. It is based on the structural property of the harmonic estimation problem which implies that the signal model is linear in amplitude and nonlinear in phase. The hybrid algorithm uses the search algorithm for phase estimation and LS for amplitude estimation, iteratively. Exploiting the objective function defined according to the error of single harmonic's phase estimation, the proposed search algorithm distributes the population through equal intervals and simply narrows the search space sequentially in every generation. Unlike the other heuristic optimization algorithms that uses random distribution in initialization stage, the proposed method provides more robust convergence in the limits determined by the generation number. Simulation results show that the proposed hybrid algorithm not only gives accurate results but also significantly improves the computation time when compared with other heuristic optimization algorithms. Moreover this approach can be used to reduce the search duration when involved in other evolutionary optimization algorithms in a hybrid way and then can deal with frequency deviation and subharmonic estimation which are pitfalls for DFT based algorithms.Item An Android Application for Geolocation Based Health Monitoring, Consultancy And Alarm System(2018) Tartan, Emre Oner; Ciflikli, CebrailIn the last decade significant progress have been made in smart phone technology as well as in wireless wide area network technologies. Today among a wide population smart phones and mobile applications are considered as indispensable part of daily life. A field that mobile applications have great potential is health monitoring. Health monitoring covers various physiological signals. One of these signals is heart rate which is related to cardiovascular state of the body. Recently producers offer heart rate monitoring with the on board or wearable heart rate sensor. Although the main trend is for individual usage especially in sports, heart rate monitoring can also be benefited in an emergency alarm system for people who have potential risks while doing sports or elderly people. Such a distant monitoring system can be helpful to deliver first aid in emergency cases. Moreover an health expert can monitor states of the patients in real time. In this study we benefit the facilities provided by mobile technology and propose a geolocation-based heart rate monitoring system. The developed mobile application can send alarm message through notification, sms, mail and allows messaging with the health expert for consultancy. Hence if anomalies are observed in heart rate variability during the outdoor activities, emergency information can be delivered in the shortest time and the delays which have crucial affects can be prevented. The same framework can be extended to a more general system including different sensors for monitoring various physiological signals.Item Classification of acoustic signals with new feature: Fibonacci space (FSp)(2019) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019In this study, a new feature and feature space (FSp) are introduced by using the approach of Fibonacci series formation. The results are presented as two experimental studies. The nine groups of acoustic signals and pathological human voices are investigated in the first and second experiments, respectively. Convolutional Neural Network (CNN) and Multi-Class Support Vector Machines (M-SVMs) are used to figure out the effect of the proposed feature and its FSp on the classification accuracy. It is observed that the proposed feature and its formed space yield significant results for the discrimination of those signals. Experimental studies show that the classification accuracy of test data is increased by 5.3% when the proposed feature is used with CNN and M-SVMs. In addition, each acoustic group is significantly discriminated in both experimental studies. It is concluded that the proposed feature and its space can be used as a temporal feature for different purposes such as automatic speech recognition, pattern recognition, and emotional voice discrimination etc. (C) 2018 Elsevier Ltd. All rights reserved.Item Estimation of heartbeat rate from speech recording with hybrid feature vector (HFV)(2019) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019This paper introduces a new hybrid feature vector for revealing the relationship between human voice and heartbeat rate (HBR). Various samples of the sustained vowel /a/ for different HBR have been extracted from a database which is created for this study. A convolutional neural network (CNN)-Regression (R), support vector machines (SVMs)-Regression (R), and multiple linear regression (MLR) are used as regression models. The experimental results show that the percentage of predictions within an acceptable error margin has been obtained as 98.92% for CNN-R, 98.70% for SVMs-R and 96.88% for MLR when Forward Sequential is used as a feature selection algorithm. The results also reveal that the CNN-R (root mean square error (RMSE) =0.3909) has produced better prediction values in estimating HBR than those produced by SVMs-R (RMSE=0.4277) and MLR (RMSE =0.4449). As a result, it is seen that the extracted hybrid feature vector provides a novel relationship between human voice and HBR. (C) 2019 Elsevier Ltd. All rights reserved.Item A hybrid measure for the discrimination of the acoustic signals: Feature matrix (FMx)(2019) Anskishan, Haydar; Inam, Sitki Cagdas; 0000-0002-6240-2545; 0000-0003-0820-9186; AAH-4421-2019We introduce a new feature matrix (FMx) to discriminate the acoustic signals with the help of their hybrid characteristics. The FMx has hybrid domain characteristics consisting of feature values such as distributional area (polygonal area), maximum values of the histogram and fundamental frequency of the difference-difference (d2d) vector. To show the performance of the FMx, three different datasets are used together with quadratic discriminant analysis (QDA), multiclass support vector machines (M-SVMs) and convolutional neural networks (CNN). The simulation results show that FMx provides effective and useful information for the discrimination of the signals into subclasses with the help of ReliefF and forward sequential algorithms. In simulations, the test accuracies with QDA, M-SVMs and CNN were obtained as 94.20%, 100% and 100% respectively. So, the results of the simulations support the effectiveness of the FMx for the acoustic signal classification with three different datasets compared to the previous studies. (C) 2019 Elsevier Ltd. All rights reserved.Item A model for the visualization and analysis of elevator traffic(2019) Ciflikli, Cebrail; Tartan, Emre OnerAnalysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.