Implementation of generative adversarial networks in HPCC systems using GNN bundle

<span id="docs-internal-guid-8df40bfb-7fff-fcb8-b52f-54f39570d649"><span>HPCC systems, an open source cluster computing platform for big data analytics consists of generalized neural network bundle with a wide variety of features which can be used for various neural network applications. To enhance the functionality of the bundle, this paper proposes the design and development of generative adversarial networks (GANs) on HPCC systems platform using ECL, a declarative language on which HPCC systems works. GANs have been developed on the HPCC platform by defining the generator and discriminator models separately, and training them by batches in the same epoch. In order to make sure that they train as adversaries, a certain weights transfer methodology was implemented. MNIST dataset which has been used to test the proposed approach has provided satisfactory results. The results obtained were unique images very similar to the MNIST dataset, as it were expected.</span></span>

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Neural Network Applications in Control

10.1049/pbce053e ◽

1995 ◽

Cited By ~ 70

Keyword(s):

Neural Network ◽

Network Applications ◽

Neural Network Applications

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Neural Network Applications: A Literature Review

10.21236/ada258148 ◽

1992 ◽

Author(s):

Vince L. Wiggins ◽

Sheree K. Engquist ◽

Larry T. Looper

Keyword(s):

Neural Network ◽

Literature Review ◽

Network Applications ◽

Neural Network Applications

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Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽

10.3390/pr9060919 ◽

2021 ◽

Vol 9 (6) ◽

pp. 919

Author(s):

Wanlu Jiang ◽

Chenyang Wang ◽

Jiayun Zou ◽

Shuqing Zhang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Generative Adversarial Networks ◽

Extraction Ability ◽

One Dimensional ◽

Adversarial Networks ◽

Diagnosis Model ◽

The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

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Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽

10.3390/s21154953 ◽

2021 ◽

Vol 21 (15) ◽

pp. 4953

Author(s):

Sara Al-Emadi ◽

Abdulla Al-Ali ◽

Abdulaziz Al-Ali

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Learning Algorithms ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Networks ◽

Detection And Identification ◽

Learning Techniques ◽

The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

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