129 research outputs found
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
Examining package tourists’ experience on overall package tour satisfaction and behavioral ıntentions: First-time versus repeat package tourists
This paper examines the influence of package tour experience dimensions (i.e., educational, entertainment, escapism, and esthetic experience) on tour satisfaction and behavioral intentions by comparing first-time and repeat package tourists. For this purpose, a self-administered questionnaire was distributed to tourists visiting Istanbul with a package tour. A convenience sampling was adopted and a total of 375 usable questionnaires was included in the analysis. Partial Least Squares Structural Equation Modeling approach was used to examine the data. The study findings indicated that education and esthetic experience affects overall package tour satisfaction for first-time tourists; entertainment and esthetic experience affects overall package tour satisfaction for repeat tourists. Furthermore, the overall package tour satisfaction mediates between these variables and behavioral intentions for both groups. The findings have suggested theoretical and managerial implications, limitations, and suggestions for further studies
Terapötik Rekreasyon Kapsamında Mevleviliğin Değerlendirilmesi
Günümüz yaşam koşulları, insanları stres, depresyon, hayatın anlamı ve mana yokluğu gibi konularda psikolojik olarak
etkilemektedir. Bununla birlikte, toplum içerisindeki yaşlılar, engelliler ve hastalar da bedensel, zihinsel ve özellikle
ruhsal anlamda, zorlanmaktadır. İnsanlar, hayatlarının amacı ve anlamını bulabilmek için mistisizme yönelebilmektedir.
Yoga, Budizm gibi Doğu mistisizmin yanı sıra, İslami mistisizm olarak kabul edilen tasavvuf ta, yaşanan psikolojik
sorunlara bir çözüm olarak ön plana çıkmaktadır. Tasavvuf ekolü olarak Mevlevilik, insanlara, hayatın anlamı ve bireysel
amaç edinmede yol göstermekte; ayrıca, Mevlevi müziği, Mesnevi ve Sema da kişiler üzerinde terapötik etki
yaratabilmektedir. Mevleviliğin bu uygulamaları, terapötik rekreasyon kapsamında değerlendirilebilir. Terapötik
rekreasyon, kişilerin fiziksel, sosyolojik, psikolojik gelişimine katkı sağlamaktadır. Bu çalışmada, Mevlevilik usullerinin
psikolojik, sosyolojik ve felsefi etkilerinin terapötik yönleri kavramsal olarak incelemiştir
Diesel engine NOx emission modeling using a new experiment design and reduced set of regressors
n this paper, NOx emissions from a diesel engine are modeled with nonlinear autoregressive with exogenous input (NARX) model. Airpath and fuelpath channels are excited by chirp signals where the frequency profile of each channel is generated by increasing the number of sweeps. Past values of the output are employed only in linear prediction with all input regressors, and the most significant input regressors are selected for the nonlinear prediction by orthogonal least square (OLS) algorithm and error reduction ratio. Experimental results show that NOx emissions can be modeled with high validation performance and models obtained using a reduced set of regressors perform better in terms of stability and robustness
Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification
Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissions while keeping the power requirements. A reliable model of the diesel engine combustion process can be quite useful to search for the best engine operating conditions. In this study, nonlinear modeling of a heavy-duty diesel engine NOx emission formation is presented. As a new experiment design, air-path and fuel-path input channels were excited by chirp signals where the frequency profile of each channel is different in terms of the number and the direction of the sweeps. This method is proposed as an alternative to the steady-state experiment design based modeling approach to substantially reduce testing time and improve modeling accuracy in transient operating conditions. Sigmoid based nonlinear autoregressive with exogenous input (NARX) model is employed to predict NOx emissions with given input set under both steady-state and transient cycles. Models for different values of parameters are generated to analyze the sensitivity to parameter changes and a parameter selection method using an easy-to-interpret map is proposed to find the best modeling parameters. Experimental results show that the steady-state and the transient validation accuracies for the majority of the obtained models are higher than 80% and 70%, respectively
Estimating soot emission in diesel engines using gated recurrent unit networks
In this paper, a new data-driven modeling of a diesel engine soot emission formation using gated recurrent unit (GRU) networks is proposed. Different from the traditional time series prediction methods such as nonlinear autoregressive with exogenous input (NARX) approach, GRU structure does not require the determination of the pure time delay between the inputs and the output, and the number of regressors does not have to be chosen beforehand. Gates in a GRU network enable to capture such dependencies on the past input values without any prior knowledge. As a design of experiment, 30 different points in engine speed - injected fuel quantity plane are determined and the rest of the input channels, i.e., rail pressure, main start of injection, equivalence ratio, and intake oxygen concentration are excited with chirp signals in the intended regions of operation. Experimental results show that the prediction performances of GRU based soot models are quite satisfactory with 77% training and 57% validation fit accuracies and normalized root mean square error (NRMSE) values are less than 0.038 and 0.069, respectively. GRU soot models surpass the traditional NARX based soot models in both steady-state and transient cycles
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