Özmen, Atilla

Özmen, Atilla

Name Variants

Özmen, Atilla
A.,Özmen
A. Özmen
Atilla, Özmen
Ozmen, Atilla
A.,Ozmen
A. Ozmen
Atilla, Ozmen
Özmen, A.

Job Title

Dr. Öğr. Üyesi

Email Address

Aozmen@khas.edu.tr

Full item page

Scholarly Output

43

Articles

13

Citation Count

0

Supervised Theses

7 Scholarly Output Search Results

Now showing 1 - 10 of 42

Citation Count: 6
Application of deep neural network (DNN) for experimental liquid-liquid equilibrium data of water + butyric acid + 5-methyl-2-hexanone ternary systems
(Elsevier B.V., 2021) Özmen, Atilla; Dilek, Özmen; Aykut, Türkmenoğlu; Atilla, Özmen
LLE data are important for simulation and design of extraction equipment. In this study, deep neural network (DNN) structure was proposed for modelling of the ternary liquid-liquid equilibrium (LLE). LLE data of (water + butyric acid + 5-methyl-2-hexanone) ternaries defined at three different temperatures of 298.2, 308.2, and 318.2 K and P = 101.3 kPa, were obtained experimentally and then correlated with nonrandom two-liquid (NRTL) and universal quasi-chemical (UNIQUAC) models. The performance of the proposed DNN model was compared with that of NRTL and UNIQUAC in terms of the root mean square errors (RMSE). RMSE values were obtained between 0.02-0.06 for NRTL and UNIQUAC, respectively. For DNN, the error values were obtained between 0.00005-0.01 for all temperatures. According to the calculated RMSE values, it was shown that proposed DNN structure can be better choice for the modelling of LLE system. Othmer-Tobias and Hand correlations were also used for the experimental tie-lines. Distribution coefficient and separation factors were calculated from the experimental data.
Citation Count: 0
UNIFAC application to water-1-propanol-n-amyl alcohol and n-amyl acetate ternaries
(2006) Özmen, Atilla; Çehreli, Süheyla
Liquid-liquid equilibrium (LLE) data for water-1-propanol-n-amyl alcohol and water-1-propanol-n-amyl acetate ternaries were measured at T=298.2 K. The UNIFAC model was used to correlate the experimental data. A comparison of the extracting capabilities of the solvents was made with respect to distribution coefficients and separation factors.
Citation Count: 1
Bayesian estimation of discrete-time cellular neural network coefficients
(TUBITAK Scientific & Technical Research Council Turkey, 2017) Şenol, Habib; Özmen, Atilla; Şenol, Habib
A new method for finding the network coefficients of a discrete-time cellular neural network (DTCNN) is proposed. This new method uses a probabilistic approach that itself uses Bayesian learning to estimate the network coefficients. A posterior probability density function (PDF) is composed using the likelihood and prior PDFs derived from the system model and prior information respectively. This posterior PDF is used to draw samples with the help of the Metropolis algorithm a special case of the Metropolis--Hastings algorithm where the proposal distribution function is symmetric and resulting samples are then averaged to find the minimum mean square error (MMSE) estimate of the network coefficients. A couple of image processing applications are performed using these estimated parameters and the results are compared with those of some well-known methods.
Citation Count: 0
Deep learning based combining rule for the estimation of vapor-liquid equilibrium
(Springer Heidelberg, 2023) Özmen, Atilla; Ozmen, Dilek; Ozmen, Atilla
Vapor-liquid equilibrium (VLE) data plays a vital role in the design, modeling and control of process equipment. In this study, to estimate the VLE data of binary systems, a deep neural network (DNN)-based combining rule was proposed based on the cross-term parameter (a(ij)) in the two-parameter Peng-Robinson cubic equation of state (PR-EoS) combined with the one-parameter classical van der Waals mixing and combining rule (1PVDW). Experimental VLE data of alternative binary refrigerant systems selected from the literature were calculated using both the PR + 1PVDW and the DNN-based model. Vapor phase mole fractions (y(i)) and equilibrium pressures (P) obtained from the proposed DNN-based and PR + 1PVDW models were compared in the terms of average percent deviations. For the DNN-based model, the vapor phase mole fractions give at least as good results as the models in the literature, and also it has been shown that a much better estimate of the equilibrium pressure (P) is obtained when compared with that of the literature. Results obtained using the proposed DNN-based model are presented with tables and graphs. For the equilibrium pressure, while the average percent deviation errors (Delta P/P%) calculated in the literature are less than 7.739, the errors obtained with the proposed DNN-based model are smaller than 3.455. And also, for vapor phase mole fractions, while the maximum error (Delta(y1)/(y1) %) in the literature is obtained as 6.142, the largest error calculated with DNN-based model is 3.545. It has been seen that the proposed DNN-based model makes more practical and less error-prone estimations than the methods in the literature.
Autonomous vehicle control using reinforcement learning
(Kadir Has Üniversitesi, 2020) Özmen, Atilla; Özmen, Atilla
Autonomous vehicles have become an important research topic where artificial intelligence is applied. As the research increases, by means of the applications of artificial intelligence algorithms in different areas, enable the working mechanisms of the systems to become more optimal due to the change of factors such as human power, time, energy and control. It has been observed that deep learning and machine learning algorithms have advantages and disadvantages in different situations and conditions. Since deep learning algorithms require large amounts of data, studies on the reinforcement learning model based on the experience from the environment and based on the reward-punishment system have recently concentrated and some striking results have been obtained. Reinforcement learning is considered a powerful AI paradigm that can be used to teach machines through interaction with the environment and learning from their mistakes. In this thesis, an environment was created based on a two-dimensional vehicle scenario created using a pyglet simulation tool. A comparative simulation study of different reinforcement learning algorithms such as Q-Learning, SARSA and Deep Q-Network (DQN) is presented on this environment. While making this comparison, a certain learning criterion was added, and also, parameters such as epsilon value, step number were changed, and changes in training and test stages were analyzed. For this study, the actors (agent, sensor, obstacles etc.) provided by the simulator program were supported. Through the feedback provided by the sensors, the reinforcement learning agent trains himself on the basis of these algorithms and determines a movement strategy to explore the environment limited to a specific area.
Citation Count: 1
Design and Implementation of a Cellular Neural Network Based Oscillator Circuit
(World Scientific and Engineering Acad and Soc, 2009) Tander, Baran; Özmen, Atilla; Özçelep, Yasin
In this paper, a novel inductorless oscillator circuit with negative feedbacks, based on a simple version of a "Cellular Neural Network" (CNN) called "CNN with an Opposite Sign Template" (CNN-OST) is designed and implemented. The system is capable of generating quasi-sine oscillations with tuneable amplitude and frequency which can't be provided at the same time in the conventional oscillator circuits.
Citation Count: 4
Channel Estimation for Realistic Indoor Optical Wireless Communication in ACO-OFDM Systems
(Springer, 2018) Özmen, Atilla; Şenol, Habib
In this paper channel estimation problem in a visible light communication system is considered. The information data is transmitted using asymmetrical clipped optical orthogonal frequency division multiplexing. Channel estimation and symbol detection are performed by the Maximum Likelihood and the Linear Minimum Mean Square Error detection techniques respectively. The system performance is investigated in realistic environment that is simulated using an indoor channel model. Two different channels are produced using the indoor channel model. Symbol error rate (SER) performance of the system with estimated channels is presented for QPSK and 16-QAM digital modulation types and compared with the perfect channel state information. As a mean square error (MSE) performance benchmark for the channel estimator Cramer-Rao lower bound is also derived. MSE and SER performances of the simulation results are presented.
Citation Count: 0
Analytical approaches for the amplitude and frequency computations in the astable cellular neural networks with opposite sign templates
(IEEE, 2007) Tander, Baran; Özmen, Atilla
In this paper, by using surface fitting methods, analytical approaches for amplitudes and frequencies of the x(1,2)(t) "States" in a simple dynamical neural network called "Cellular Neural Network with Opposite Sign Templates" which was proposed by Zou and Nossek [1], are obtained under oscillation conditions. The mentioned explicit expressions are employed in a cellular neural network based, amplitude and frequency tuneable oscillator design.
Citation Count: 0
RGB Color Based Occupancy Rate Detection of Indoor Spaces
(IEEE, 2018) Özmen, Atilla; Demir, Kubilay; Özmen, Atilla
In this study, a system has been developed to detect the human density of indoor spaces such as libraries, banks, shopping malls. The RGB images used in this work was obtained from the real-life space. First and second order color moments were used as feature extractor.
Air quality prediction using a hybrid deep learning architecture
(Kadir Has Üniversitesi, 2020) Özmen, Atilla; Özmen, Atilla
Air pollution prediction is related to the variables in environmental monitoring data and modeling of the complex relationship between these variables. The objectives of the thesis are to develop a supervised model for the prediction of air pollution by using real sensor data and to transfer the model between cities. A CNN+LSTM deep neural network model was developed to predict the concentration of air pollutants in multiple locations by using a spatial-temporal relationship. The 2D input (univariate) contains the information of one pollutant; the 3D input (multivariate) contains the information of all pollutants and meteorology. There are three methods employed according to the input-output type: Method-1 is based on univariate-input and univariate-output; Method-2 is based on multivariate input and univariate-output; Method-3 is based on multivariate input and multivariate output. The study was carried out for different pollutants which are in publicly available data of the cities of Barcelona, Kocaeli, and İstanbul. The hyperparameters were tuned to determine the architecture that achieved the lowest test RMSE. Comparing the performance of the CNN+LSTM network with a 1-hidden layer LSTM network, the proposed model improved the prediction performance by the rates between 11%-53% for PM10, 20%-31% for O3, 9%-47% for NOX and 18%-46% for SO2. After, the network weights were transferred from the source domains to the target domain. The model has a more reliable prediction performance with the transfer of the network from Kocaeli to İstanbul because of the similarities between those two cities.