Real-Time Eye State Detection System for Driver Drowsiness Using Convolutional Neural Network

2020 ◽  
Author(s):  
Wissarut Kongcharoen ◽  
Siranee Nuchitprasitchai ◽  
Yuenyong Nilsiam ◽  
Joshua M. Pearce
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection System ◽  
Driver Drowsiness ◽  
State Detection

scholarly journals Klasifikasi jenis kejadian menggunakan kombinasi NeuroNER dan Recurrent Convolutional Neural Network pada data Twitter

2018 ◽  
Vol 4 (2) ◽  
pp. 81
Author(s):  
Fatra Nonggala Putra ◽  
Chastine Fatichah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection System ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Experimental Result ◽  
Incident Detection ◽  
Named Entity ◽  
F Measure

Sistem deteksi kejadian dari data Twitter bertujuan untuk mendapatkan data secara real-time sebagai alternatif sistem deteksi kejadian yang murah. Penelitian tentang sistem deteksi kejadian telah dilakukan sebelumnya. Salah satu modul utama dari sistem deteksi kejadian adalah modul klasifikasi jenis kejadian. Informasi dapat diklasifikasikan sebagai kejadian penting jika memiliki entitas yang merepresentasikan di mana lokasi kejadian terjadi. Beberapa penelitian sebelumnya masih memanfaatkan fitur ‘buatan tangan’, maupun fitur model berbasis pipeline seperti n-gram sebagai penentuan fitur kunci klasifikasi yang tidak efektif dengan performa kurang optimal. Oleh karena itu, diusulkan penggabungan metode Neuro Named Entity Recognition (NeuroNER) dan klasifier Recurrent Convolutional Neural Network (RCNN) yang diharapkan dapat melakukan deteksi kejadian secara efektif dan optimal. Pertama, sistem melakukan pengenalan entitas bernama pada data tweet untuk mengenali entitas lokasi yang terdapat dalam teks tweet, karena informasi kejadian haruslah memiliki minimal satu entitas lokasi. Kedua, jika tweet terdeteksi memiliki entitas lokasi maka akan dilakukan proses klasifikasi kejadian menggunakan klasifier RCNN. Berdasarkan hasil uji coba, disimpulkan bahwa sistem deteksi kejadian menggunakan penggabungan NeuroNER dan RCNN bekerja dengan sangat baik dengan nilai rata-rata precision, recall, dan f-measure masing-masing 94,87%, 92,73%, dan 93,73%.    The incident detection system from Twitter data aims to obtain real-time information as an alternative low-cost incident detection system. One of the main modules in the incident detection system is the classification module. Information is classified as important incident if it has an entity that represents where the incident occurred. Some previous studies still use 'handmade' features as well as feature-based pipeline models such as n-grams as the key features for classification which are deemed as ineffective. Therefore, this research propose a combination of Neuro Named Entity Recognition (NeuroNER) and Recurrent Convolutional Neural Network (RCNN) as an effective classification method for incident detection. First, the system perform named entity recognition to identify the location contained in the tweet text because the event information should have at least one location entity. Then, if the location is successfully identified, the incident will be classified using RCNN. Experimental result shows that the incident detection system using combination  of NeuroNER and RCNN works very well with the average value of precision, recall, and f-measure 92.44%, 94.76%, and 93.53% respectively.

scholarly journals Identification of line status changes using phasor measurements in transient states through deep learning networks

2020 ◽  
Vol 216 ◽  
pp. 01035
Author(s):  
Natalja Gotman ◽  
Galina Shumilova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Electrical Network ◽  
Learning Networks ◽  
Transient States ◽  
Phasor Measurements ◽  
State Detection

The problem of detecting changes in a topology of an electrical network in real time is solved. This paper proposes a line state detection method based on a convolutional neural network (CNN) classifier using phasor measurements of bus voltages and currents in transient states.

scholarly journals Iris Image Acquisition and Real-Time Detection System Using Convolutional Neural Network

2021 ◽  
Author(s):  
Yuan Hu ◽  
Xiaoyong Si
Keyword(s):  
Neural Network ◽  
Data Acquisition ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Error Rate ◽  
Detection System ◽  
Image Acquisition ◽  
Iris Image ◽  
Bilateral Filters ◽  
Iris Data

Abstract The aim is to further improve the efficiency of iris detection and ensure real-time iris data acquisition. Here, the light field refocusing algorithm can collect the data in real-time based on the existing iris data acquisition and detection system, and the DL (Deep Learning) CNN (Convolutional Neural Network) is introduced. Consequently, an iris image acquisition and real-time detection system based on CNN is proposed, and the system for image acquisition, processing, and displaying is constructed based on FPGA (Field Programmable Gate Array). The spatial filtering algorithm can compare the performance of the proposed bilateral filters with common filters. The results indicate that the proposed bilateral filters can pick out qualified iris images in real-time, greatly improving the accuracy of the iris image recognition system. The average time for real-time quality assessment of each frame image is less than 0.05 seconds. The classification accuracy of the iris image quality assessment algorithm based on DL is 96.38%, higher than the other two algorithms, and the average classification error rate is 3.69%, lower than the average error rate of other algorithms. The results can provide a reference for real-time iris image detection and data acquisition.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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