In the aviation industry, many faults may occur frequently during the maintenance processes and a... more In the aviation industry, many faults may occur frequently during the maintenance processes and assembly operations of complex structured aircrafts because of their high dependencies of components. These faults affect the quality of aircraft parts or developed modules adversely. Technical employee requires long time and high labor force while checking the correctness of each component. In addition, the person must be trained regularly because of the ever-growing and changing technology. Generally, the cost of this training is very high. Augmented Reality (AR) technology reduces the cost of training radically and improves the effectiveness of the training. In this study, the usage of AR technology in the aviation industry has been investigated and the effectiveness of AR with heads-up display glasses has been examined. An application has been developed for comparison of production process with AR and manual one.
Gazi Universitesi Muhendislik Mimarlık Fakultesi Dergisi, 2006
... Eğer hem ortalamadan sapma (Sapmai) hem de or-talama (OrtalamaBoşluki) aynı değere sahipse en... more ... Eğer hem ortalamadan sapma (Sapmai) hem de or-talama (OrtalamaBoşluki) aynı değere sahipse en soldaki yani i değeri en küçük olan sütun seçilir. Bu çalışmada kullanılan BT algoritmasına ait JavaScript betiğinin kaba kodu (pseudo-code) aşağı-daki gibidir; ...
ABSTRACT This paper proposes an artificial neural network (ANN) to obtain the electron energy dis... more ABSTRACT This paper proposes an artificial neural network (ANN) to obtain the electron energy distribution functions (EEDFs) in SF6 and argon from the following: 1) mean energies; 2) the drift velocities; and 3) other related swarm data. In order to obtain the required swarm data, the electron swarm behavior in SF6 and argon is analyzed over the range of the density-reduced electric field strength E/N from 50 to 800 Td from a Boltzmann equation analysis based on the finite difference method under a steady-state Townsend condition. A comparison between the EEDFs calculated by the Boltzmann equation and by ANN for various values of E/N suggests that the proposed ANN yields good agreement of EEDFs with those of the Boltzmann equation solution results.
In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been presented to speed contr... more In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been presented to speed control of a switched reluctance motor (SRM). SRMs have become an attractive alternative in variable speed drives due to their advantages such as structural simplicity, high reliability, high efficiency and low cost. But, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter variations. In this study, to tackle these problems, an adaptive neurofuzzy controller is proposed. Heuristic rules are derived with the membership functions then the parameters of membership functions are tuned by ANFIS. The algorithm has been implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the proposed ANFIS controller under different operating conditions of the SRM.
Engineering Applications of Artificial Intelligence, 2005
Switched reluctance motor (SRM) is increasingly employed in industrial applications where variabl... more Switched reluctance motor (SRM) is increasingly employed in industrial applications where variable speed is required because of their simple construction, ease of maintenance, low cost and high efficiency. However, the SRM performance often degrades for the machine parameter ...
World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 2016
Abstract:- Injection pressure in diesel engines has an important effect on the engine performance... more Abstract:- Injection pressure in diesel engines has an important effect on the engine performance and soot formation. During performing the experimental work, the measurement of the torque, power, specific fuel consumption (SFC) and soot formation values in the diesel engines is a time consuming work and it also requires specific tools, an expert. In addition to the difficulties mentioned earlier, some of the operating points can be only investigated and evaluated because of difficulties of measuring the parameters at the operating conditions. In this study, to overcome these difficulties, an artificial neural network (ANN) is used for prediction of performance and soot formation in diesel engines. The training data for ANN is obtained from experimental measurements. In comparison of performance analysis of ANN, the deviation coefficients of torque, power, SFC, and soot formation for the test pressure conditions are less than 1.66, 3.2, 2.89, and 3.47, respectively. The statistical ...
2021 8th International Conference on Electrical and Electronics Engineering (ICEEE)
Day by day the usage of infrared cameras has been increasing in the world. With the increasing us... more Day by day the usage of infrared cameras has been increasing in the world. With the increasing use of thermal infrared cameras and images, especially in military, security and medicine, the need for coloring thermal infrared images to visible spectrum has arisen. In this study, a deep based model has been developed to generate visible spectrum images (RGB - Red Green Blue) from thermal infrared (TIR) images. In the proposed model, an autoencoder architecture with skip connections has been used to generate RGB images. KAIST-MS (Korea Advanced Institute of Science and Technology-Multispectral) dataset used for training and test the developed model. The experimental results extensively tested using Peak Signal-to-Noise Ratio (PSNR), Least Absolute Deviations (L1), Root Mean Squared Error (RMSE) and Structural Similarity Index Measure (SSIM).
En kisa yol problemi icin cok sayida arastirma yapilmakta ve ortaya konulan cozumler basta bilgis... more En kisa yol problemi icin cok sayida arastirma yapilmakta ve ortaya konulan cozumler basta bilgisayarmuhendisligi ve endustri muhendisligi olmak uzere cok farkli alanlarda uygulanmaktadir. Bu makalede yolmaliyetleri zamana bagli dinamik olarak degisen en kisa yol probleminin cozumu icin genetik algoritmakullanilarak yeni bir algoritma gelistirilmistir. Onerilen algoritma ile literaturde yer alan algoritmalarinkarsilastirilmasi icin ornek bir uygulama gelistirilmistir. Benzetim sonuclari gelistirilen algoritmanin dahabasarili oldugunu gostermistir.
Reverse logistics has received growing attention throughout this decade because of the increasing... more Reverse logistics has received growing attention throughout this decade because of the increasing environmental concern, government regulations and economical reasons. The design of reverse logistics network is one of the most important and challenging problems in the field of reverse logistics. This paper proposes a capacitated, multi-echelon, multi-product mixed integer linear programming model for generic integrated logistics network design. The problem includes the decision of the number and location of forward and reverse plants and the distribution network design to satisfy the demands of customers with minimum cost. Because of the complexity of the model, a solution methodology based on the genetic algorithm which hybridizes the heuristic approach with LP is developed. Results obtained by GAMS-CPLEX and proposed solution methodology are compared for different sized test problems.
Day by day the usage of infrared cameras has been increasing in the world. With the increasing us... more Day by day the usage of infrared cameras has been increasing in the world. With the increasing use of thermal infrared cameras and images, especially in military, security and medicine, the need for coloring thermal infrared images to visible spectrum has arisen. In this study, a deep based model has been developed to generate visible spectrum images (RGB - Red Green Blue) from thermal infrared (TIR) images. In the proposed model, an autoencoder architecture with skip connections has been used to generate RGB images. KAIST-MS (Korea Advanced Institute of Science and Technology-Multispectral) dataset used for training and test the developed model. The experimental results extensively tested using Peak Signal-to-Noise Ratio (PSNR), Least Absolute Deviations (L1), Root Mean Squared Error (RMSE) and Structural Similarity Index Measure (SSIM).
Abstract With the widespread use of social networks, blogs, forums and e-commerce web sites, the ... more Abstract With the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and providers of the products and services. Therefore, sentiment analysis and opinion mining have become important research areas. In user reviews mining, topic modeling based approaches and Latent Dirichlet Allocation (LDA) are significant methods that are used in extracting product aspects in aspect based sentiment analysis. However, LDA cannot be directly applied on user reviews and on other short texts because of data sparsity problem and lack of co-occurrence patterns. Several studies have been published for the adaptation of LDA for short texts. In this study, a novel method for aspect based sentiment analysis, Sentence Segment LDA (SS-LDA) is proposed. SS-LDA is a novel adaptation of LDA algorithm for product aspect extraction. The experimental results reveal that SS-LDA is quite competitive in extracting products aspects.
2021 6th International Conference on Computer Science and Engineering (UBMK)
With the rapid development of information technologies and the widespread use of the internet, th... more With the rapid development of information technologies and the widespread use of the internet, the volume and diversity of data has also increased. Meaningful information and important results can be obtained by processing this data, which is expressed with the concept of big data. In this study, a machine learning platform that can automatically learn from data sets with different data types and dimensions has been developed. When the dataset of any field is given as an input to the developed automatic machine learning platform, the most appropriate machine learning model is determined. With this platform, which has a Web interface that can be easily used by people who are experts in their field but do not have sufficient knowledge in the field of machine learning and data science, the most suitable machine learning model for the data set is suggested to the users and training and test results for different models can be obtained and compared. The experimental studies have shown that the developed platform is successful in knitting a machine learning model suitable for the dataset.
2018 26th Signal Processing and Communications Applications Conference (SIU)
Number of mobile devices that are an important part of everyday life and the users who are intere... more Number of mobile devices that are an important part of everyday life and the users who are interested in this technology are increasing. Increasing mobile applications and malwares are leading the privacy of personal data. Existing security approaches are not enough because malwares are quickly modified and malware detection becomes difficult. In this work, a new malware detection system based on multilayer perceptron for detection of Android malware has been developed. In the developed system, a dataset consisting of 7210 applications including malicious applications in Drebin dataset and normal applications obtained through the Google Play Store were used. The analysis results show that the developed system performs malware detection with 95.658% success rate.
2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)
Sentiment analysis is one of the most popular research topics in last years. There are lots of da... more Sentiment analysis is one of the most popular research topics in last years. There are lots of data on web which require analysis in order for them to become useful. Many researchers have focused on making sense of these data. Therefore, sentiment analysis concept is proposed. Sentiment analysis methods try to emerge any opinions, feelings, and subjectivity behind the text. Machine learning algorithms and vocabulary based methods are used to perform sentiment analysis. In this research, (i) recently studied researches on machine learning based sentiment analysis are investigated to give background; (ii) they are classified according to their tasks on extracting information; (iii) the encountered and potential challenges on this research topic are revisited and discussed.
In the aviation industry, many faults may occur frequently during the maintenance processes and a... more In the aviation industry, many faults may occur frequently during the maintenance processes and assembly operations of complex structured aircrafts because of their high dependencies of components. These faults affect the quality of aircraft parts or developed modules adversely. Technical employee requires long time and high labor force while checking the correctness of each component. In addition, the person must be trained regularly because of the ever-growing and changing technology. Generally, the cost of this training is very high. Augmented Reality (AR) technology reduces the cost of training radically and improves the effectiveness of the training. In this study, the usage of AR technology in the aviation industry has been investigated and the effectiveness of AR with heads-up display glasses has been examined. An application has been developed for comparison of production process with AR and manual one.
Gazi Universitesi Muhendislik Mimarlık Fakultesi Dergisi, 2006
... Eğer hem ortalamadan sapma (Sapmai) hem de or-talama (OrtalamaBoşluki) aynı değere sahipse en... more ... Eğer hem ortalamadan sapma (Sapmai) hem de or-talama (OrtalamaBoşluki) aynı değere sahipse en soldaki yani i değeri en küçük olan sütun seçilir. Bu çalışmada kullanılan BT algoritmasına ait JavaScript betiğinin kaba kodu (pseudo-code) aşağı-daki gibidir; ...
ABSTRACT This paper proposes an artificial neural network (ANN) to obtain the electron energy dis... more ABSTRACT This paper proposes an artificial neural network (ANN) to obtain the electron energy distribution functions (EEDFs) in SF6 and argon from the following: 1) mean energies; 2) the drift velocities; and 3) other related swarm data. In order to obtain the required swarm data, the electron swarm behavior in SF6 and argon is analyzed over the range of the density-reduced electric field strength E/N from 50 to 800 Td from a Boltzmann equation analysis based on the finite difference method under a steady-state Townsend condition. A comparison between the EEDFs calculated by the Boltzmann equation and by ANN for various values of E/N suggests that the proposed ANN yields good agreement of EEDFs with those of the Boltzmann equation solution results.
In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been presented to speed contr... more In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been presented to speed control of a switched reluctance motor (SRM). SRMs have become an attractive alternative in variable speed drives due to their advantages such as structural simplicity, high reliability, high efficiency and low cost. But, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter variations. In this study, to tackle these problems, an adaptive neurofuzzy controller is proposed. Heuristic rules are derived with the membership functions then the parameters of membership functions are tuned by ANFIS. The algorithm has been implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the proposed ANFIS controller under different operating conditions of the SRM.
Engineering Applications of Artificial Intelligence, 2005
Switched reluctance motor (SRM) is increasingly employed in industrial applications where variabl... more Switched reluctance motor (SRM) is increasingly employed in industrial applications where variable speed is required because of their simple construction, ease of maintenance, low cost and high efficiency. However, the SRM performance often degrades for the machine parameter ...
World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 2016
Abstract:- Injection pressure in diesel engines has an important effect on the engine performance... more Abstract:- Injection pressure in diesel engines has an important effect on the engine performance and soot formation. During performing the experimental work, the measurement of the torque, power, specific fuel consumption (SFC) and soot formation values in the diesel engines is a time consuming work and it also requires specific tools, an expert. In addition to the difficulties mentioned earlier, some of the operating points can be only investigated and evaluated because of difficulties of measuring the parameters at the operating conditions. In this study, to overcome these difficulties, an artificial neural network (ANN) is used for prediction of performance and soot formation in diesel engines. The training data for ANN is obtained from experimental measurements. In comparison of performance analysis of ANN, the deviation coefficients of torque, power, SFC, and soot formation for the test pressure conditions are less than 1.66, 3.2, 2.89, and 3.47, respectively. The statistical ...
2021 8th International Conference on Electrical and Electronics Engineering (ICEEE)
Day by day the usage of infrared cameras has been increasing in the world. With the increasing us... more Day by day the usage of infrared cameras has been increasing in the world. With the increasing use of thermal infrared cameras and images, especially in military, security and medicine, the need for coloring thermal infrared images to visible spectrum has arisen. In this study, a deep based model has been developed to generate visible spectrum images (RGB - Red Green Blue) from thermal infrared (TIR) images. In the proposed model, an autoencoder architecture with skip connections has been used to generate RGB images. KAIST-MS (Korea Advanced Institute of Science and Technology-Multispectral) dataset used for training and test the developed model. The experimental results extensively tested using Peak Signal-to-Noise Ratio (PSNR), Least Absolute Deviations (L1), Root Mean Squared Error (RMSE) and Structural Similarity Index Measure (SSIM).
En kisa yol problemi icin cok sayida arastirma yapilmakta ve ortaya konulan cozumler basta bilgis... more En kisa yol problemi icin cok sayida arastirma yapilmakta ve ortaya konulan cozumler basta bilgisayarmuhendisligi ve endustri muhendisligi olmak uzere cok farkli alanlarda uygulanmaktadir. Bu makalede yolmaliyetleri zamana bagli dinamik olarak degisen en kisa yol probleminin cozumu icin genetik algoritmakullanilarak yeni bir algoritma gelistirilmistir. Onerilen algoritma ile literaturde yer alan algoritmalarinkarsilastirilmasi icin ornek bir uygulama gelistirilmistir. Benzetim sonuclari gelistirilen algoritmanin dahabasarili oldugunu gostermistir.
Reverse logistics has received growing attention throughout this decade because of the increasing... more Reverse logistics has received growing attention throughout this decade because of the increasing environmental concern, government regulations and economical reasons. The design of reverse logistics network is one of the most important and challenging problems in the field of reverse logistics. This paper proposes a capacitated, multi-echelon, multi-product mixed integer linear programming model for generic integrated logistics network design. The problem includes the decision of the number and location of forward and reverse plants and the distribution network design to satisfy the demands of customers with minimum cost. Because of the complexity of the model, a solution methodology based on the genetic algorithm which hybridizes the heuristic approach with LP is developed. Results obtained by GAMS-CPLEX and proposed solution methodology are compared for different sized test problems.
Day by day the usage of infrared cameras has been increasing in the world. With the increasing us... more Day by day the usage of infrared cameras has been increasing in the world. With the increasing use of thermal infrared cameras and images, especially in military, security and medicine, the need for coloring thermal infrared images to visible spectrum has arisen. In this study, a deep based model has been developed to generate visible spectrum images (RGB - Red Green Blue) from thermal infrared (TIR) images. In the proposed model, an autoencoder architecture with skip connections has been used to generate RGB images. KAIST-MS (Korea Advanced Institute of Science and Technology-Multispectral) dataset used for training and test the developed model. The experimental results extensively tested using Peak Signal-to-Noise Ratio (PSNR), Least Absolute Deviations (L1), Root Mean Squared Error (RMSE) and Structural Similarity Index Measure (SSIM).
Abstract With the widespread use of social networks, blogs, forums and e-commerce web sites, the ... more Abstract With the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and providers of the products and services. Therefore, sentiment analysis and opinion mining have become important research areas. In user reviews mining, topic modeling based approaches and Latent Dirichlet Allocation (LDA) are significant methods that are used in extracting product aspects in aspect based sentiment analysis. However, LDA cannot be directly applied on user reviews and on other short texts because of data sparsity problem and lack of co-occurrence patterns. Several studies have been published for the adaptation of LDA for short texts. In this study, a novel method for aspect based sentiment analysis, Sentence Segment LDA (SS-LDA) is proposed. SS-LDA is a novel adaptation of LDA algorithm for product aspect extraction. The experimental results reveal that SS-LDA is quite competitive in extracting products aspects.
2021 6th International Conference on Computer Science and Engineering (UBMK)
With the rapid development of information technologies and the widespread use of the internet, th... more With the rapid development of information technologies and the widespread use of the internet, the volume and diversity of data has also increased. Meaningful information and important results can be obtained by processing this data, which is expressed with the concept of big data. In this study, a machine learning platform that can automatically learn from data sets with different data types and dimensions has been developed. When the dataset of any field is given as an input to the developed automatic machine learning platform, the most appropriate machine learning model is determined. With this platform, which has a Web interface that can be easily used by people who are experts in their field but do not have sufficient knowledge in the field of machine learning and data science, the most suitable machine learning model for the data set is suggested to the users and training and test results for different models can be obtained and compared. The experimental studies have shown that the developed platform is successful in knitting a machine learning model suitable for the dataset.
2018 26th Signal Processing and Communications Applications Conference (SIU)
Number of mobile devices that are an important part of everyday life and the users who are intere... more Number of mobile devices that are an important part of everyday life and the users who are interested in this technology are increasing. Increasing mobile applications and malwares are leading the privacy of personal data. Existing security approaches are not enough because malwares are quickly modified and malware detection becomes difficult. In this work, a new malware detection system based on multilayer perceptron for detection of Android malware has been developed. In the developed system, a dataset consisting of 7210 applications including malicious applications in Drebin dataset and normal applications obtained through the Google Play Store were used. The analysis results show that the developed system performs malware detection with 95.658% success rate.
2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)
Sentiment analysis is one of the most popular research topics in last years. There are lots of da... more Sentiment analysis is one of the most popular research topics in last years. There are lots of data on web which require analysis in order for them to become useful. Many researchers have focused on making sense of these data. Therefore, sentiment analysis concept is proposed. Sentiment analysis methods try to emerge any opinions, feelings, and subjectivity behind the text. Machine learning algorithms and vocabulary based methods are used to perform sentiment analysis. In this research, (i) recently studied researches on machine learning based sentiment analysis are investigated to give background; (ii) they are classified according to their tasks on extracting information; (iii) the encountered and potential challenges on this research topic are revisited and discussed.
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Papers by M. Akcayol