Signatures verification based on PNN classifier optimised by PSO algorithm

Porwik, Piotr; Doroz, Rafał; Orczyk, Tomasz

doi:10.1016/j.patcog.2016.06.032

Cited by 50 publications

(17 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The summation layer adds the cumulative probability of the class to obtain the estimated density function of the fault patterm. Each summation node receives the outputs from pattern nodes associated with a given class [35]:…”

Section: Probabilistic Neural Network (Pnn)mentioning

confidence: 99%

Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Cao

Han

et al. 2019

Energies

View full text Add to dashboard Cite

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.

show abstract

Section: Probabilistic Neural Network (Pnn)mentioning

confidence: 99%

Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Cao

Han

et al. 2019

Energies

View full text Add to dashboard Cite

show abstract

“…The use of Deep Learning (DL) methods, which are the multi-layered structure of ANNs along with the improvements of GPU technology, have accelerated these advances. Furthermore, DL approaches have significantly outperformed state-of-the-art approaches in many fields such as object recognition [1,3,7,9,25,26], image processing [11,[27][28][29][30][31][32], computer vision [33][34][35][36], speech recognition [37][38][39], natural language processing (NLP) [10,21,27,[40][41][42], character recognition [5,30,[43][44][45][46], signature verification [2,6,[47][48][49][50][51]. Although the foundations of DL were based on ANN proposed by McCulloch and Pitts in 1943 [52], the real popularity has increased in 2012.…”

Section: Introduction (Gi̇ri̇ş)mentioning

confidence: 99%

Untitled

2019

GMBD

View full text Add to dashboard Cite

Derin öğrenme (Deep Learning-DL), birçok alanda önemli başarılar elde etmiş güçlü bir makine öğrenmesi yöntemidir. Özellikle son on yılda, bilgisayarlı görü, nesne tanıma, konuşma tanıma, doğal dil işleme gibi birçok araştırma alanında başarılı sonuçlar elde ederek, yapay zekanın derin uykudan uyanmasına yol açmıştır. Günümüzde, çeşitli alanlardaki birçok araştırmacı, DL yöntemlerini kullanarak alanlarında en iyi sonucu almaya çalışmaktadır. Bu tarama çalışmasında, DL modelleri ve DL ile çalışılabilecek önemli araştırma konuları hakkında bilgiler vererek araştırmacılara rehberlik etmeyi hedefliyoruz. Çalışmada Özerk Araçlar (Autonomous Vehicles), Doğal Dil İşleme (Natural Language Processing), El Yazısı Karakter Tanıma (Handwritten Character Recognition), İmza Doğrulama (Signature Verification), Ses ve Video Tanıma (Voice and Video Recognition), Tıbbi Görüntü İşleme (Medical İmage Processing), Büyük Veri (Big Data) gibi dünyanın en popüler ve en zorlu alanlarında yapılan DL çalışmalarını inceliyoruz. Ayrıca, araştırmacılara yardımcı olmak için, incelediğimiz bu alanlardaki DL ile çalışılabilecek, henüz çalışılmamış veya yeterince iyi sonuçlar elde edilememiş problemlere dikkat çekerek olası araştırma konularını listeliyoruz. Günümüzde, Derin öğrenme yöntemleri nesne tanıma alanında insanlardan daha iyi sonuçlara ulaşmıştır. DL üzerine yapılan çalışmalar dikkate alındığında, bu başarının otonom araçlar, tıbbi görüntü işleme, büyük veri analizi ve karakter tanıma gibi alanlarda da gerçekleşeceği öngörülmektedir. https://dx.Deep learning (DL) is an important machine learning field that has achieved considerable success in many research areas. In the last decade, the-state-of-the-art studies on many research areas such as computer vision, object recognition, speech recognition, and natural language processing were especially led to the awakening of the artificial intelligence from deep sleep. Nowadays, many researchers try to find solutions to many problems in various fields under the light of DL methods. In this study, it is presented important knowledge to guide about DL models and challenging topics that can be used in DL for researchers. This study investigated DL studies which are made in the most popular and challenging fields such as autonomous vehicles, natural language processing, handwritten character recognition, signature verification, voice and video recognition, medical image processing, and big data. Furthermore, this study points out the remaining challenges of these research areas that can be solved by DL, and discusses future topics to help the researchers. In the present day, Deep learning methods have reached better results than humans in object recognition. According to the literature studies on DL, It is foreseen that this success will be achieved in areas such as autonomous vehicles, medical image processing, big data analysis, and character recognition. https://dx.

show abstract

“…Recognition systems, classifiers, as well as self-adaptive classifiers (Porwik et al, 2016;Krawczyk and Woźniak, 2016), are developed and applied in many domains, e.g., electronics, biometrics (Putz-Leszczyńska, 2015;Pujol et al, 2016), medicine (Porwik et al, 2009;Porwik and Doroz, 2014;Koprowski, 2016;Kowal and Filipczuk, 2014;Mazurek and Oszutowska-Mazurek, 2014). Of the many biometric techniques, fingerprint identification is most prevalent, be it as a tool in police work and the courts, or in a range of commercial applications: banking, security systems, etc.…”

Section: Introductionmentioning

confidence: 99%

An accurate fingerprint reference point determination method based on curvature estimation of separated ridges

Doroz

Wróbel

Porwik

2018

International Journal of Applied Mathematics and Computer Science

Self Cite

View full text Add to dashboard Cite

This paper presents an effective method for the detection of a fingerprint’s reference point by analyzing fingerprint ridges’ curvatures. The proposed approach is a multi-stage system. The first step extracts the fingerprint ridges from an image and transforms them into chains of discrete points. In the second step, the obtained chains of points are processed by a dedicated algorithm to detect corners and other points of highest curvature on their planar surface. In a series of experiments we demonstrate that the proposed method based on this algorithm allows effective determination of fingerprint reference points. Furthermore, the proposed method is relatively simple and achieves better results when compared with the approaches known from the literature. The reference point detection experiments were conducted using publicly available fingerprint databases FVC2000, FVC2002, FVC2004 and NIST

show abstract

Signatures verification based on PNN classifier optimised by PSO algorithm

Cited by 50 publications

References 49 publications

Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Untitled

An accurate fingerprint reference point determination method based on curvature estimation of separated ridges

Contact Info

Product

Resources

About