One-Dimensional Convolutional Neural Networks for Real-Time Damage Detection of Rotating Machinery

Domain generation algorithms (DGAs) represent a class of malware used to generate large numbers of new domain names to achieve command-and-control (C2) communication between the malware program and its C2 server to avoid detection by cybersecurity measures. Deep learning has proven successful in serving as a mechanism to implement real-time DGA detection, specifically through the use of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This paper compares several state-of-the-art deep-learning implementations of DGA detection found in the literature with two novel models: a deeper CNN model and a one-dimensional (1D) Capsule Networks (CapsNet) model. The comparison shows that the 1D CapsNet model performs as well as the best-performing model from the literature.

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A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks

Sensors ◽

10.3390/s20143837 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3837 ◽

Cited By ~ 2

Author(s):

Jingli Yang ◽

Shuangyan Yin ◽

Yongqi Chang ◽

Tianyu Gao

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Convolutional Neural Networks ◽

Activation Function ◽

Rotating Machinery ◽

Identification Accuracy ◽

Stability And Convergence ◽

Fault Identification ◽

One Dimensional ◽

The Stability

Aiming at the fault diagnosis issue of rotating machinery, a novel method based on the deep learning theory is presented in this paper. By combining one-dimensional convolutional neural networks (1D-CNN) with self-normalizing neural networks (SNN), the proposed method can achieve high fault identification accuracy in a simple and compact architecture configuration. By taking advantage of the self-normalizing properties of the activation function SeLU, the stability and convergence of the fault diagnosis model are maintained. By introducing α -dropout mechanism twice to regularize the training process, the overfitting problem is resolved and the generalization capability of the model is further improved. The experimental results on the benchmark dataset show that the proposed method possesses high fault identification accuracy and excellent cross-load fault diagnosis capability.

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Structural Damage Detection in Real Time: Implementation of 1D Convolutional Neural Networks for SHM Applications

Structural Health Monitoring & Damage Detection, Volume 7 - Conference Proceedings of the Society for Experimental Mechanics Series ◽

10.1007/978-3-319-54109-9_6 ◽

2017 ◽

pp. 49-54 ◽

Cited By ~ 9

Author(s):

Onur Avci ◽

Osama Abdeljaber ◽

Serkan Kiranyaz ◽

Daniel Inman

Keyword(s):

Neural Networks ◽

Damage Detection ◽

Real Time ◽

Convolutional Neural Networks ◽

Structural Damage ◽

Structural Damage Detection

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

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190114151255 ◽

2020 ◽

Vol 16 (5) ◽

pp. 584-591 ◽

Cited By ~ 1

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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