Özmen, Atilla

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Profile URL
https://hdl.handle.net/20.500.12469/6969
Name Variants
Özmen, Atilla
A.,Özmen
A. Özmen
Atilla, Özmen
Ozmen, Atilla
A.,Ozmen
A. Ozmen
Atilla, Ozmen
Özmen, A.
Ozmen, A.
Özmen, Atilla
Job Title
Dr. Öğr. Üyesi
Email Address
[email protected]
Main Affiliation
Electrical-Electronics Engineering
Electrical-Electronics Engineering
05. Faculty of Engineering and Natural Sciences
01. Kadir Has University
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

15

LIFE ON LAND
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0

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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0

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14

LIFE BELOW WATER
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6

CLEAN WATER AND SANITATION
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3

GOOD HEALTH AND WELL-BEING
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3

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17

PARTNERSHIPS FOR THE GOALS
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1

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4

QUALITY EDUCATION
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2

ZERO HUNGER
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10

REDUCED INEQUALITIES
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7

AFFORDABLE AND CLEAN ENERGY
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1

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13

CLIMATE ACTION
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1

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1

NO POVERTY
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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8

DECENT WORK AND ECONOMIC GROWTH
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11

SUSTAINABLE CITIES AND COMMUNITIES
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2

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5

GENDER EQUALITY
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This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

46

Articles

13

Views / Downloads

329/5100

Supervised MSc Theses

8

Supervised PhD Theses

1

WoS Citation Count

141

Scopus Citation Count

202

WoS h-index

5

Scopus h-index

6

Patents

0

Projects

0

WoS Citations per Publication

3.07

Scopus Citations per Publication

4.39

Open Access Source

22

Supervised Theses

9

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JournalCount
2012 International Symposium on Innovations in Intelligent Systems and Applications2
2007 IEEE 15th Signal Processing and Communications Applications, SIU1
2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 20111
2015 9th International Conference on Electrical and Electronics Engineering (ELECO)1
2018 26th Signal Processing and Communications Applications Conference (SIU)1
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Scholarly Output Search Results

Now showing 1 - 10 of 46
  • Thumbnail Image
    Article
    Citation - WoS: 4
    Citation - Scopus: 6
    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.
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    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Bayesian Estimation of Discrete-Time Cellular Neural Network Coefficients
    (TUBITAK Scientific & Technical Research Council Turkey, 2017) Özer, Hakan Metin; 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.
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    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Deep Learning Based Combining Rule for the Estimation of Vapor-Liquid Equilibrium
    (Springer Heidelberg, 2023) Bekri, Sezin; 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.
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    Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Application of Deep Neural Network (dnn) for Experimental Liquid-Liquid Equilibrium Data of Water + Butyric Acid + 5-Methyl Ternary Systems
    (Elsevier B.V., 2021) Bekri, Sezin; 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.
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    Conference Object
    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.
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    Conference Object
    A Numerical Method for Frequency Determination in the Astable Cellular Neural Networks With Opposite-Sign Templates
    (IEEE, 2006) Özmen, Atilla; Tander, Baran
    In this study a numerical method is proposed to determine the oscillation frequencies in the astable cellular neural networks with opposite-sign templates [1]. This method depends on the training of a multilayer perceptron that uses various template coefficients and the correspondant frequency values as inputs and outputs. First of all a frequency surface is obtained from templates and then training samples are picked from this surface in order to apply to multilayer perceptron. The effects of the template coefficients to the oscillation frequencies are also investigated. Furthermore an oscillator design is carried out for simulation and the performance as well as the advantages of the proposed method are evaluated.
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    Conference Object
    Unifac Application To Water-1 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.
  • Thumbnail Image
    Conference Object
    Rgb Color Based Occupancy Rate Detection of Indoor Spaces
    (IEEE, 2018) Gilik, Ayşenur; 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.
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    Master Thesis
    Autonomous Vehicle Control Using Reinforcement Learning
    (Kadir Has Üniversitesi, 2020) Bozkurt, Hüma; 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.
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    Conference Object
    Citation - Scopus: 3
    Smart Stethoscope
    (IEEE, 2020) Türker, Mehmet Nasuhcan; Çagan, Yagiz Can; Yıldırım, Batuhan; Demirel, Mücahit; Özmen, Atilla; Tander, Baran; Çevik, Mesut
    In this study, a device named smart stethoscope that uses digital sensor technology for sound capture, active acoustics for noise cancellation and artificial intelligence (AI) for diagnosis of heart and lung diseases is developed to help the health workers to make accurate diagnoses. Furthermore, the respiratory diseases are classified by using Deep Learning and Long Short-Term Memory (LSTM) techniques whereas the probability of these diseases are obtained.