Rainfall prediction using generative adversarial networks with convolution neural network

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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Very Short-Term Rainfall Prediction Using Ground Radar Observations and Conditional Generative Adversarial Networks

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2021.3108812 ◽

2021 ◽

pp. 1-8

Author(s):

Yerin Kim ◽

Sungwook Hong

Keyword(s):

Generative Adversarial Networks ◽

Short Term ◽

Radar Observations ◽

Rainfall Prediction ◽

Adversarial Networks

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ASM1D-GAN: An Intelligent Fault Diagnosis Method Based on Assembled 1D Convolutional Neural Network and Generative Adversarial Networks

Journal of Signal Processing Systems ◽

10.1007/s11265-019-01463-8 ◽

2019 ◽

Vol 91 (10) ◽

pp. 1237-1247 ◽

Cited By ~ 6

Author(s):

Shengyao Gao ◽

Xueren Wang ◽

Xuhong Miao ◽

Changwei Su ◽

Yibin Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Generative Adversarial Networks ◽

Intelligent Fault Diagnosis ◽

Adversarial Networks ◽

Diagnosis Method

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Multi-Attribute Transfer via Disentangled Representation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019195 ◽

2019 ◽

Vol 33 ◽

pp. 9195-9202 ◽

Cited By ~ 4

Author(s):

Jianfu Zhang ◽

Yuanyuan Huang ◽

Yaoyi Li ◽

Weijie Zhao ◽

Liqing Zhang

Keyword(s):

Neural Network ◽

Facial Expression ◽

Generative Adversarial Networks ◽

Significant Progress ◽

Target Domain ◽

Adversarial Networks ◽

Proposed Model ◽

Image Translation ◽

Realistic Images ◽

Novel Model

Recent studies show significant progress in image-to-image translation task, especially facilitated by Generative Adversarial Networks. They can synthesize highly realistic images and alter the attribute labels for the images. However, these works employ attribute vectors to specify the target domain which diminishes image-level attribute diversity. In this paper, we propose a novel model formulating disentangled representations by projecting images to latent units, grouped feature channels of Convolutional Neural Network, to disassemble the information between different attributes. Thanks to disentangled representation, we can transfer attributes according to the attribute labels and moreover retain the diversity beyond the labels, namely, the styles inside each image. This is achieved by specifying some attributes and swapping the corresponding latent units to “swap” the attributes appearance, or applying channel-wise interpolation to blend different attributes. To verify the motivation of our proposed model, we train and evaluate our model on face dataset CelebA. Furthermore, the evaluation of another facial expression dataset RaFD demonstrates the generalizability of our proposed model.

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CAGAN: Consistent Adversarial Training Enhanced GANs

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/359 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yao Ni ◽

Dandan Song ◽

Xi Zhang ◽

Hao Wu ◽

Lejian Liao

Keyword(s):

Neural Network ◽

Parameter Space ◽

Supervised Classification ◽

State Of The Art ◽

Generative Adversarial Networks ◽

Image Generation ◽

Real Samples ◽

Adversarial Networks ◽

Novel Approach ◽

Adversarial Training

Generative adversarial networks (GANs) have shown impressive results, however, the generator and the discriminator are optimized in finite parameter space which means their performance still need to be improved. In this paper, we propose a novel approach of adversarial training between one generator and an exponential number of critics which are sampled from the original discriminative neural network via dropout. As discrepancy between outputs of different sub-networks of a same sample can measure the consistency of these critics, we encourage the critics to be consistent to real samples and inconsistent to generated samples during training, while the generator is trained to generate consistent samples for different critics. Experimental results demonstrate that our method can obtain state-of-the-art Inception scores of 9.17 and 10.02 on supervised CIFAR-10 and unsupervised STL-10 image generation tasks, respectively, as well as achieve competitive semi-supervised classification results on several benchmarks. Importantly, we demonstrate that our method can maintain stability in training and alleviate mode collapse.

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Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks

Big Data and Cognitive Computing ◽

10.3390/bdcc4020011 ◽

2020 ◽

Vol 4 (2) ◽

pp. 11

Author(s):

Shayan Taheri ◽

Aminollah Khormali ◽

Milad Salem ◽

Jiann-Shiun Yuan

Keyword(s):

Neural Network ◽

Generative Adversarial Networks ◽

Defense System ◽

Adversarial Networks ◽

Adversarial Examples ◽

Automated Pipeline ◽

Simulation Results ◽

Attack And Defense ◽

Unique Power ◽

Do So

In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using combination of pre-trained convolutional neural network and an external GAN, that is, Pix2Pix conditional GAN, to determine the transformations between adversarial examples and clean data, and to automatically synthesize new adversarial examples. These adversarial examples are employed to strengthen the model, attack, and defense in an iterative pipeline. Our simulation results demonstrate the success of the proposed method.

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