Görüntü işleme ve bulanık mantık tabanlı pantograf geometrik modelin tespiti

Bu çalışmada elektrikli trenlerde kullanılan pantograf türünün belirlenmesi için model tabanlı bir yaklaşım önerilmektedir. Elektrikli trenlerin kullanım şartlarına göre pantograf katener sistemin yapısı değişmektedir. Pantograf katener sistemlerinden alınan görüntüler kullanılarak pantograf sisteminin geometrik modeli oluşturulmaktadır. Oluşturulan modelin hangi tür pantografa ait olduğu tespit edilmektedir. İlk olarak kenar çıkarımı ve Hough dönüşümü ile pantografta bulunan bütün doğrular tespit edilmektedir. Tespit edilen doğrulardan alınan bazı bilgiler bulanık mantık işleminde kullanılarak pantografın türü belirlenmektedir. Pantograf türünün belirlenmesi pantografın yüksekliğini tahmin etmek ve katener ile pantograf arasındaki temas noktasını analiz etmek için uygundur. Böylece ark oluşumu ve aşırı temas kuvveti gibi temas noktası problemleri tespit edilebilecektir.In this study, a model based approach is proposed for the recognition of the pantograph type used in electric trains. The shape of the pantograph-catenary changes according to usage conditions of electric trains. A geometric model of the pantograph is constructed by using images taken from the pantograph-catenary system. The pantograph type is determined by using the constructed model. First, all straight lines are extracted from the image by applying the edge detection and Hough transform to the image. Some knowledge obtained from straight lines are given to fuzzy logic and type of pantograph is determined. The determination of pantograph type is useful to estimate the pantograph height and to analyze of contact point between pantograph and catenary. Therefore, contact point problems such as arcing and excessive contact force can be detected

Reinforcement Learning Based Artificial Immune Classifier

One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method

An AIS-based hybrid algorithm for static job shop scheduling problem

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