Reconstruction Of Binary Electrical Conductivity Distributions Using Genetic Algorithms

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2010Elektriksel empedans görüntüleme, son yıllarda artan bir oranla kullanım alanı bulan girişimsel olmayan bir görüntüleme tekniğidir. Bu tekniğinin geniş uygulama alanlarında kabul görmesinin başlıca nedenleri yöntemin güvenliği, kendine özgü taşınabilirliği ve yeterince ucuz veri toplama donanımına bağımlı olmasıdır. Ancak, görüntü oluşturma problemi; ölçülen veri ve bilinmeyen iletkenlik parametreleri arasındaki doğrusal olmayan ilişki nedeniyle son derece kötü koşullu bir problemdir. Bu tezde, elektriksel empedans görüntüleme yöntemi kullanılarak iki boyutlu ve ikili iletkenlik dağılımlarının yeniden oluşturulmasını sağlayan iyileştirilmiş bir genetik algoritma geliştirilmiştir. Kullanılan elektriksel empedans görüntüleme yöntemi; ölçülen ve hesaplanan elektrot gerilim değerlerinin farklılıklarının en küçük kareler yaklaşımıyla minimizasyonuna dayanmaktadır. Hesaplanan elektrot gerilimleri iki boyutlu sonlu elemanlar modeli kullanılarak elde edilmiştir. İletkenlik dağılımının merkez bölgesindeki hassaslık sorununun çözümü olarak yeni bir ağırlık fonksiyonu geliştirildi. Görüntü oluşturma probleminin çözümü için geliştirilen genetik algoritma, her bir aşaması farklı hedeflere ve farklı genetik operatörlere sahip olmak üzere iki aşamadan oluşmaktadır. Tez çalışması kapsamında dört yeni mutasyon operatörü ve iyileştirilmiş sıra orantılı seçilim operatörü geliştirilmiştir. Genetik algoritmanın önemli parametreleri, popülasyonun çeşitliliğini verimli bir düzeyde korunmak için uyarlamalı olarak kontrol edildi. Genetik algoritmanın değişik şartlardaki başarımının gözlemlenmesi için denemeler gerçekleştirildi. Bu denemelerin büyük çoğunluğunda genetik algoritma tam iletkenlik dağılımına ulaşarak oldukça iyi bir performans gösterdi.Electrical impedance imaging is a noninvasive technique that has been increasingly used in recent years. The wide acceptance of this imaging technique is mainly due to its safety, unique portability, and its dependence on sufficiently inexpensive data acquisition hardware. However, the problem of image reconstruction is extremely ill conditioned due to the nonlinear relationship between the measured data and the unknown conductivity parameters. In this thesis, an improved genetic algorithm is developed for the reconstruction of two-dimensional and binary conductivity distributions in electrical impedance imaging method. The electrical impedance imaging method used in this thesis is based on the minimization of the discrepancies between measured and computed electrode voltages in a least-square sense. The computed electrode voltages are obtained from the model developed using the finite element method. To overcome the sensitivity problem near the center of conductivity distribution, a special weight function is introduced. The genetic algorithm for the image reconstruction problem consists of two stages, each with different objectives and different genetic operators. Four new mutation operators and an improved ranked proportionate selection operator are introduced in this thesis. Important parameters of the genetic algorithm are controlled adaptively to maintain the diversity of the population at an efficient level. A series of tests is conducted to observe the genetic algorithms performance on various conditions. The genetic algorithm performed well by reaching the exact conductivity distribution in most of the tests.Yüksek LisansM.Sc

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