scholarly journals ReS2 Charge Trapping Synaptic Device for Face Recognition Application

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Ze-Hui Fan ◽  
Min Zhang ◽  
Lu-Rong Gan ◽  
Lin Chen ◽  
Hao Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Charge Trapping ◽  
Long Term Potentiation ◽  
Efficient System ◽  
Artificial Neural ◽  
Conductance States

AbstractSynaptic devices are necessary to meet the growing demand for the smarter and more efficient system. In this work, the anisotropic rhenium disulfide (ReS2) is used as a channel material to construct a synaptic device and successfully emulate the long-term potentiation/depression behavior. To demonstrate that our device can be used in a large-scale neural network system, 165 pictures from Yale Face database are selected for evaluation, of which 120 pictures are used for artificial neural network (ANN) training, and the remaining 45 pictures are used for ANN testing. A three-layer ANN containing more than 105 weights is proposed for the face recognition task. Also 120 continuous modulated conductance states are selected to replace weights in our well-trained ANN. The results show that an excellent recognition rate of 100% is achieved with only 120 conductance states, which proves a high potential of our device in the artificial neural network field.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

Pengecaman Insan Berasaskan Kaedah Profil Sentroid dan Pengelas Rangkaian Neural Buatan

2012 ◽  
Author(s):  
Nooritawati Md Tahir ◽  
Aini Hussain ◽  
Salina Abdul Samad ◽  
Hafizah Husain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Task ◽  
Classification Rate ◽  
Human Recognition ◽  
Gait Features ◽  
Static Images ◽  
The Subject ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Dalam kajian ini, teknik profil sentroid yang berdasarkan pendekatan berasaskan model digunakan bagi tugas pengecaman insan. Kaedah ini dilaksanakan secara mengekstrak ciri–ciri unik perwakilan isyarat gaya lenggang insan serta bukan insan secara automatik dan pasif berasaskan imej pegun. Untuk menilai kekuatan algoritma sarian teknik profil sentroid yang dihasilkan, Rangkaian Neural Buatan (RNB) digunakan sebagai pengelas. Keputusan yang diperolehi membuktikan ciri sarian profil sentroid sesuai digunakan sebagai perwakilan vektor ciri bagi pengelasan insan dengan kadar pengelasan RNB yang dicapai melebihi 98%. Kata kunci: Pengecaman insan; rangkaian neural tiruan; profil sentroid In this study, centroidal profile which is a model based approach is employed for human recognition task. This is done by extracting unique representation of gait features of the subject automatically and passively from static images of human or non human. To evaluate the effectiveness of the generated centroidal profile, Artificial Neural Network (RNB) is used as classifier. Results attained proven that the centroidal profile is appropriate as feature extraction to be used as feature vectors for human shape classification based on classification rate of RNB achieved specifically above 98%. Key words: Human recognition; artificial neural network (ANN); centroidal profile

scholarly journals Partial discharge detection for stator winding insulation of motors using artificial neural network

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401878612 ◽  
Author(s):  
Yu-Tung Chen ◽  
Jui-Chien Lai ◽  
Yu-Ming Jheng ◽  
Cheng-Chien Kuo ◽  
Hong-Chan Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Voltage ◽  
Partial Discharge ◽  
Recognition Rate ◽  
Gradient Algorithm ◽  
Normal Operation ◽  
Test Models ◽  
Artificial Neural ◽  
Stator Fault

In this article, the insulation fault detection of high-voltage motors by the artificial neural network algorithm is used. The proposed method can evaluate the status of operating motor without interrupting the normal operation. According to the measurement of partial discharge information, this research establishes the relationship of stator failures and pattern features. This study uses common high-voltage motor stator fault types to experimentally produce four types of stator test models with insulation defects; these models are compared with a healthy motor model. Through the learning of the artificial neural network, the experimental results show that the artificial neural network–based stator fault diagnosis system proposed in this article has a recognition rate as high as 90% when the conjugate gradient algorithm is used, and there are 20 neurons in the hidden layer.

Face Recognition Method Based on the Fuzzy Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 134-137
Author(s):  
Xiang Pan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Fuzzy Neural Network ◽  
Experimental Results ◽  
Fuzzy Rules ◽  
Good Effect ◽  
Recognition Method ◽  
Fuzzy Neural ◽  
Artificial Neural

This paper discusses a face recognition method based on the fuzzy neural network (FNN). The fuzzy neural network has more advantages than artificial neural network alone. The paper firstly introduces the structure of the FNN. Than proposed the fuzzy rules and the study algorithm. Thirdly it researches on the process of face recognition. The experimental results prove that this method can achieve good location performance and good effect of extraction.

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