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Start Over Topic optimization algorithms Language turkish
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1. Mesafe ölçümleriyle belirlenen uygunluk fonksiyonunu kullanan yeni bir IIR temelli FIR süzgeç tasarım tekniği.

Author

Latifoğlu, Fatma and Arıkan, Sümeyya

Subjects
*INFINITE impulse response filters, *FINITE impulse response filters, *PARTICLE swarm optimization, *IMPULSE response, *OPTIMIZATION algorithms
Abstract

This paper aims to develop a high-performance method for designing Finite Impulse Response (FIR) filters that effectively control the over-frequency response parameters. The proposed approach involves designing an optimal FIR filter based on an Infinite Impulse Response (IIR) filter, considering various parameters such as pass and stop band ripples, stop band attenuation, transition bandwidth, and linear phase response. The advantages of the IIR filter's better frequency response are utilized in this design. The parameters of Butterworth and Chebyshev filters are optimized using Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms, incorporating novel fitness functions based on different distance metrics. Visual examination of the filtered signals shows that the proposed algorithm reduces phase delay compared to traditional filters. Additionally, the performance of the designed filter is evaluated by decomposing a sinusoidal signal with four different frequencies into its sub-bands. The numerical results demonstrate that the proposed approach achieves more precise decomposition compared to commonly used traditional filters. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Gri Kurt Optimizasyon Algoritmasına Dayanan Çok Seviyeli İmge Eşik Seçimi.

Author

KOC, Ismail, BAYKAN, Omer Kaan, and BABAOGLU, Ismail

Abstract

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2018
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3. MODELLEME VE BENZETİM ORTAMINDA PARAMETRE OPTİMİZASYONU VE KULLANILAN TEKNİKLER.

Author

KORKMAZ TAN, Rabia and BORA, Şebnem

Abstract

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2017
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4. Multilevel image thresholding selection based on grey wolf optimizer

Author

Koc, Ismail, Baykan, Omer Kaan, Babaoglu, Ismail, Selçuk Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Koc, Ismail, Baykan, Omer Kaan, and Babaoglu, Ismail

Subjects
herd intelligence, Engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Çok seviyeli imge eşikleme,otsu metodu,sürü zekası,optimizasyon algoritmaları,gri kurt algoritması, Mühendislik, optimization algorithms, otsu method, gray wolf algorithm, Multilevel image thresholding,otsu method,herd intelligence,optimization algorithms,gray wolf algorithm, Multilevel image thresholding
Abstract

WOS: 000448383600010, Multilevel thresholding is an important image process technique for image processing and pattern recognition. Selecting an optimal threshold value is one of the most crucial phase in image thresholding. While bi-level segmentation contains separating the original image into subdivided sections with help of a threshold value, multilevel segmentation involves multi threshold values. Especially in multilevel image tresholding, the computational time of detailed search increases exponentially with the number of preferred thresholds. For compelling problems, swarm intelligence is known as one of the successful and influential optimization methods. In this paper, the grey wolf optimizer (GWO), a recently proposed swarm-based meta-heuristic which imitates the social leadership and hunting behavior of gray wolves in nature is employed for solving the multilevel image thresholding problem. The experimental results on standard benchmark images indicate that the grey wolf optimizer algorithm is comparable with other state of the art algorithms.

Published
2018

5. Multilevel image thresholding selection based on grey wolf optimizer

Author

Ismail Babaoglu, Ömer Kaan Baykan, Ismail Koc, Selçuk Üniversitesi, Teknoloji Fakültesi, Bilgisayar Mühendisliği Bölümü, Koç, İsmail, Baykan, Ömer Kaan, and Babaoğlu, İsmail

Subjects
optimizasyon algoritmaları, optimization algorithms, Çok seviyeli imge eşikleme, Multilevel image thresholding
Abstract

Çoklu eşikleme, görüntü işleme ve örüntü tanıma için önemli bir görüntü işleme tekniğidir. Optimal bir eşik değerinin seçimi görüntü eşiklemede en ciddi aşamalardan birisidir. İki seviye segmentasyon eşik değeri yardımıyla orijinal resmi iki alt bölüme ayırmayı içerirken, çoklu segmentasyon çok eşik değerlerini içermektedir. Özellikle çok seviyeli görüntü eşiklemede, detaylı araştırmaya ilişkin hesaplama zamanı tercih edilen eşik sayısıyla birlikte üstel olarak artmaktadır. Zor problemler için, sürü zekâsı başarılı ve etkili optimizasyon metotlarından biri olarak bilinmektedir. Bu çalışmada, doğadaki gri kurtların sosyal liderlik ve avcılık davranışlarını taklit eden son zamanlarda önerilmiş sürü tabanlı meta sezgisel olan gri kurt algoritması (GWO) çok seviyeli görüntü eşikleme probleminin çözümü için kullanılmaktadır. Standart test resimleri üzerinde yapılan deneysel sonuçlar GWO algoritmasının diğer modern algoritmalarla karşılaştırılabilir olduğunu göstermektedir., Multilevel thresholding is an important image process technique for image processing and pattern recognition. Selecting an optimalthreshold value is one of the most crucial phase in image thresholding. While bi-level segmentation contains separating the originalimage into subdivided sections with help of a threshold value, multilevel segmentation involves multi threshold values. Especiallyin multilevel image tresholding, the computational time of detailed search increases exponentially with the number of preferredthresholds. For compelling problems, swarm intelligence is known as one of the successful and influential optimization methods.In this paper, the grey wolf optimizer (GWO), a recently proposed swarm-based meta-heuristic which imitates the social leadershipand hunting behavior of gray wolves in nature is employed for solving the multilevel image thresholding problem. The experimentalresults on standard benchmark images indicate that the grey wolf optimizer algorithm is comparable with other state of the artalgorithms.

Published
2018