Particle swarm based arc detection on time series in pantograph-catenary system

Aydın, İlhan; Yaman, Orhan; Karaköse, Mehmet; Çelebi, Selahattin Barış

doi:10.1109/inista.2014.6873642

Cited by 27 publications

(19 citation statements)

References 16 publications

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“…The system is initialized as a set of random solutions called particles. Through the particle flight in the search house, it does not have the crossover and mutation operator of genetic algorithm, but the particle in the solution space to follow the optimal particle search [13].…”

Section: Improved Pso Algorithm a Particle Swarm Optimizationmentioning

confidence: 99%

Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

Guo

2020

IEEE Access

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With the development of computer network technology and the expansion of network system, sensitive data is facing the threat of hacker attack. Intrusion detection is an active network security defense measure, which is an attempt to invade, an ongoing intrusion or an intrusion that has occurred to identify the process. At present, the detection rate of intrusion detection method is low, the false alarm rate and false alarm rate is high, and the real-time performance is poor. It needs a large number of or complete data to achieve better detection performance. In this paper, the concept, characteristics, classification, research contents and difficulties of traditional intrusion detection for mass multimedia data transmission network are described. Then, the basic principle of neural network and particle swarm optimization (PSO) algorithm and the basic idea of particle swarm optimization algorithm with quantum (QPSO) behaviour are introduced. It is emphasized that QPSO has better convergence performance than PSO algorithm in global optimization problems. In this paper, the concept, characteristics and structure of neural network are described, and the algorithm and classification of wavelet neural network are introduced. Then taking wavelet neural network (WNN) as the object, using the QPSO algorithm as the training algorithm, the concrete operation process is given. The research work in this paper shows that the performance of neural network trained by QPSO algorithm and improved QPSO algorithm is better than that of other intelligent algorithms such as PSO algorithm and genetic algorithm, and the convergence speed is faster than that of PSO algorithm or GA algorithm. QPSO is a high performance neural network training algorithm, which can play a good role in neural network anomaly detection. INDEX TERMS Anomaly detection, particle swarm optimization, data transmission, wavelet neural network.

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Section: Improved Pso Algorithm a Particle Swarm Optimizationmentioning

confidence: 99%

Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

Guo

2020

IEEE Access

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“…In this study, a new computer vision based method is proposed for analyzing the rail that one of components of railway systems. There are image processing based several techniques in the literature [16]- [18]. The rails are determined by means of image processing techniques using the proposed method.…”

Section: Proposed Approachmentioning

confidence: 99%

A New Computer Vision Based Method for Rail Track Detection and Fault Diagnosis in Railways

Karaköse¹,

Yaman²,

Bayğın³

et al. 2017

IJMERR

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Computer vision-based condition monitoring methods, the methods are increasingly used on railway systems. Rail condition monitoring process can be performed using data obtained with the help of computers using these methods. In this study, a computer-based visual rail condition monitoring is proposed. By means of a camera placed on top of the train the rail that the train is on and the neighbor rail images are taken. On these images, the edge and feature extraction methods are applied to determine the rails. The resulting several faults between railways were studied to determine if there is a failure. The results obtained are given at the end of the study. Experimental results show that the proposed method is examined, it is observed that a healthy and effective results.  Index Terms-condition monitoring, railway systems, image processing, fault diagnosis interests are image processing, fault diagnosis, computer vision, railway inspection systems and fuzzy systems.

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“…Ayrıca pantograf katener sistemlerde görüntü üzerinde eşikleme yapılarak özellik çıkarımı da yapılmaktadır [20].…”

Section: Gradient Hesabı Ve Istenmeyen Ayrıntıların Yok Edilmesiunclassified

“…[15] [18,19]. Canny kenar çıkarım algoritması çok aşamalı olarak gerçekleşen bir kenar çıkarım algoritmasıdır ve diğer kenar çıkarım algoritmalarına göre daha iyi sonuç vermektedir [20]. Canny kenar çıkarım algoritmasının genel akış şeması Şekil 7'de verilmektedir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Pantograf-Katener Sistemler için Bulanık Mantık Tabanlı Belirlenen Pantograf Modeli Kullanılarak Ark Tespiti Yaklaşımı

Yaman¹,

Karaköse²,

Aydın³

et al. 2017

SAÜ Fen Bilimleri Enstitüsü Dergisi

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ÖZElektrikli trenler, günümüzde yaygın olarak kullanılan, önemli bir ulaşım türüdür. Diğer ulaşım türleri ile karşılaştırıldığında elektrikli trenlerin güç tüketimi ve çevre kirliliğine olan etkisi daha azdır. Ayrıca konfor ve güvenlik gibi birçok avantajı vardır. Elektrikli trenlerde kullanılan en önemli bileşenlerden biri pantograf katener sistemidir. Pantograf katener sistemi, demiryolu hattı boyunca bulunan katener sistemi ve lokomotif üzerinde bulunan pantograf sisteminden oluşmaktadır. Lokomotifin ihtiyaç duyduğu elektrik enerjisi pantograf üst bölgesi ile katener telinin teması sonucunda sağlanmaktadır. Temas sırasında birçok nedenden dolayı arklar oluşmaktadır. Oluşan arklar elektrik enerjisinin sağlıklı aktarılmasını engellemektedir. Lokomotifin sağlıklı bir şekilde çalışabilmesi için elektrik enerjisinin sürekli alınabilmesi gerekmektedir. Bu nedenle pantograf katener sistemi elektrikli trenler için oldukça kritik bileşenlerdir. Bu çalışmada, pantograf katener sistemi için model ve ark tespiti için yeni bir yöntem önerilmektedir. Görüntü işleme ve bulanık mantık tabanlı bu yöntemde, birçok farklı türde pantograf sisteminin görüntüsü kullanılmıştır. Kullanılan görüntüler üzerinde Canny kenar çıkarımı yapıldıktan sonra Hough dönüşümü ile doğrular elde edilmektedir. Elde edilen doğruların bazı özellikleri bulanık mantığın giriş verileri olarak kullanılmaktadır. Bulanık mantık sonucunda pantograf sisteminin modeli tespit edilmektedir. Tespit edilen pantograf modeli kullanılarak pantograf temas bölgesinde oluşan arklar tespit edilmektedir.Anahtar Kelimeler: Pantograf-Katener sistemi, Pantograf model tespiti, Pantograf ark tespiti, görüntü işleme, bulanık mantık.Arc detection approach using fuzzy logic based pantograph model for pantographcatenary systems ABSTRACT Electric trains are now widely used, is an important transport modes. Compared with other types of transportation, the effect of an electric train power consumption and environmental pollution are less. There are also many advantages such as comfort and safety. One of the most important tools used in the electric train is pantograph catenary system. Pantograph catenary system consists of two parts. a catenary system along the railway line and the pantograph system consists placed on the locomotive. The locomotive's electrical energy needs are provided as a result of contact with the upper region of the pantograph and catenary wire. Arcs can occur for many reasons during the contact. The arcing in the electrical energy in a healthy way hinders the transfer. Locomotive must be received continuous electrical energy to function in a healthy way. Therefore, the pantograph catenary system are quite critical components for electric railway. In this study, a new method to model and arc detection for pantograph catenary system is proposed. In this image processing and fuzzy logic based method, images of many different types of pantograph system are used. After Canny edge extraction on the images, the lines are obtained by the Hough transform. Some properties...

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Particle swarm based arc detection on time series in pantograph-catenary system

Cited by 27 publications

References 16 publications

Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

A New Computer Vision Based Method for Rail Track Detection and Fault Diagnosis in Railways

Pantograf-Katener Sistemler için Bulanık Mantık Tabanlı Belirlenen Pantograf Modeli Kullanılarak Ark Tespiti Yaklaşımı

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