Gaining followers on the Twitter platform has become a rapid way to increase one's credibility on this social network, that in the last few years has become a launch pad for new trends and to influence people opin...
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ISBN:
(纸本)9781450349512
Gaining followers on the Twitter platform has become a rapid way to increase one's credibility on this social network, that in the last few years has become a launch pad for new trends and to influence people opinions. So, many people have begun to buy fake followers on underground markets appositely created to sold them. Therefore, identifying fake followers profiles is useful to maintain the balance between real influential people on the network and people who simply exploited this mechanism. This work presents a model based on artificial neural networks able to detect fake Twitter profiles. In particular, a denoising autoencoder has been implemented as anomaly detector trained with a semi-supervised learning approach. The model has been tested on a benchmark already used in literature and results are presented.
In this paper, we discuss a method to detect defects in industrial products by using denoising autoencoder Generative Adversarial Networks. In previous methods, a defective area is detected by restoring a defective pr...
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ISBN:
(纸本)9781538626153
In this paper, we discuss a method to detect defects in industrial products by using denoising autoencoder Generative Adversarial Networks. In previous methods, a defective area is detected by restoring a defective product image which added an artificial defect to a non-defective product image by denoising autoencoder (DAE). Therefore, a defective area is detected by subtracted image of them. We discuss whether further accuracy improvement is possible by introducing a framework of adversarial learning to DAE in order to restore a defective image to a non-defective image clearer.
Since it has been recognized that the disordered breathing during sleep is related to cardiovascular diseases, it is possible to predict cardiovascular diseases from sleep breathing data, which however is usually inev...
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ISBN:
(纸本)9783030316532;9783030316549
Since it has been recognized that the disordered breathing during sleep is related to cardiovascular diseases, it is possible to predict cardiovascular diseases from sleep breathing data, which however is usually inevitable to have missing data, resulted probability from the loss to follow-up, failure to attend medical appointments, lack of measurements, failure to send or retrieve questionnaires, and inaccurate data transfer. In this paper, we propose a denoising autoencoder-based imputation (DAEimp) algorithm to impute the missing values in the sleep heart health study (SHHS) dataset for the predication of cardiovascular diseases. This algorithm consists of three major steps: (1) based on the missing completely at random assumption, the random uniform noise is added to the positions of missing values to convert missing data imputation into a denoising problem, (2) feed the noisy data and a missing position indicator matrix into an autoencoder model and use the reconstruction error, divided into observation positions reconstruction error and missing positions error, for denoising, and (3) the logistic regression is applied to the generated complete dataset for the identification of cardiovascular diseases. Our results on the SHHS dataset indicate that the proposed DAEimp algorithm achieves state-of-the-art performance in missing data imputation and sleep breathing data-based identification of cardiovascular diseases.
In view of the complexity and variability of bathymetric data, the paper introduces a new algorithm named DAE-WGAN to construct sea bottom trend surface. This new model is an alternative to traditional GAN training me...
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ISBN:
(数字)9781510623002
ISBN:
(纸本)9781510623002
In view of the complexity and variability of bathymetric data, the paper introduces a new algorithm named DAE-WGAN to construct sea bottom trend surface. This new model is an alternative to traditional GAN training method, combined with the advantages of denoising autoencoder (DAE) and Wasserstein Generative Adversarial Network (WGAN). Firstly, the network structure is introduced in detail, in which the critic (or 'discriminator') estimates the Wasserstein-1 distance between the generated-sample distributions and the real-sample distributions, and optimizes the generator to approximate the minimum Wasserstein-1 distance, which effectively improves the stability of the adversarial training. Moreover, the generalized denoising autoencoder algorithm is added to train the back-propagation process, having a better ability of dimensionality reduction, which improves the robustness of the whole algorithm. Then, using two different types of bathymetric data (seabed tiny-terrain data and Electronic Nautical Chart data), we had long-time experiments to train the DAE-WGAN till optimality, and got the better sea bottom trend surface. Finally, by comparison with other GAN models (such as InFoGAN, LSGAN), the results show that the proposed method has an obvious improvement in accuracy, stability and robustness, and further illustrate the feasibility of this method in bathymetric precise data processing area.
Degradation indicator construction is essential for the lifetime estimation process, since it provides useful indicator for lifetime estimation effectively. However, the degradation indicator is hard to constructed be...
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ISBN:
(纸本)9781538662434
Degradation indicator construction is essential for the lifetime estimation process, since it provides useful indicator for lifetime estimation effectively. However, the degradation indicator is hard to constructed because of the complex relationships between various parameters. This paper proposes a degradation indicator construction method for aeroengine based on denoising autoencoder (DAE) algorithm. To make the degradation data can indicate the equipment operation state easily, the spearman correlation was adopted for the process of equipment operating state characterizing. Thus, the data smoothing method was used to smooth the degradation data, in order to make the prediction results of the equipment lifetime more accurate. Moreover, the degradation construction method was verified to predict the aeroengine lifetime by adopting the aeroengine degradation data provide by NASA datasets. The expected results of this construction method for degradation indicator based on parameter fusion has a lower mean-square error.
A codec based on the excited linear prediction (CELP) speech compression method adopting a denoising autoencoder with spectral compensation (DAE-SC) for quality and intelligibility enhancement is proposed in this pape...
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ISBN:
(纸本)9781538678848
A codec based on the excited linear prediction (CELP) speech compression method adopting a denoising autoencoder with spectral compensation (DAE-SC) for quality and intelligibility enhancement is proposed in this paper. The sizes of CELP parameters in the encoder are carefully pruned to achieve a higher compression rate. To recover the speech quality and intelligibility degradation due to the pruned CELP parameters, a DAE-SC network with three hidden layers is employed in the *** with the conventional CELP codec at a 9.6k bps transmission rate, the proposed speech codec achieves extra 21.9% bit rate reduction with comparable speech quality and intelligibility that are evaluated by four commonly used speech performance metrics.
The parametric Bayesian Feature Enhancement (BFE) and a datadriven denoising autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different...
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Target detection is one of the most important applications of hyperspectral technology. However, due to spectral variations caused by noise or environment, the within-class variation is enlarged which degrades the per...
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Target detection is one of the most important applications of hyperspectral technology. However, due to spectral variations caused by noise or environment, the within-class variation is enlarged which degrades the performance of detectors, especially when the target size is small. Therefore, improving the detection performance of small targets and noisy targets is a key task. Considering the great feature extraction and representation ability of deep learning models, denoising autoencoder (DAE) is introduced to reconstruct spectrums and exploit the invariant information for target detection. To fully extract the features from the original spectrums, a multiscale denoising autoencoder (MSDAE) model is designed to incorporate complementary informationin in this paper. The final spectrum is fused by reconstructed spectrums from different scales representations, which provides more complex information and more robust features for subsequent spectral identification. Results on simulated hyperspectral images (HSIs) and real-world HSIs demonstrate that the proposed MSDAE model can effectively remove noise interference and lead to great improvements of the target detection. In addition, the proposed method shows significant potential in preserving small targets.
Traditional monitoring methods are trained with normal data and map the process variables into latent variables directly. However, for these methods, the process variables would become intertwined in the latent variab...
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Traditional monitoring methods are trained with normal data and map the process variables into latent variables directly. However, for these methods, the process variables would become intertwined in the latent variables, which results in that the fluctuations of process variables would be submerged in noise or neutralized in latent variables space. In order to address the submergence and neutralization problems, a novel algorithm load weighted denoising autoencoder (LWDAE) is proposed. According to the direction and magnitude of online data, the loading matrix of LWDAE is weighted to highlight the useful information of both training data and online data in latent variables space. In addition, to reduce the effect of noise on weighting matrix, LWDAE modifies the loss function by adding two new regularizations and revises the calculation logic of weighting matrix to consider the successive samples. Case studies of continuous stirred tank reactor demonstrate the effectiveness of LWDAE.
In this work, the Cu:ZnO based memristors were fabricated and modelled and its biological synaptic characteristics were realized. Phenomenon similar to long-term potentiation and long-term depression were observed in ...
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In this work, the Cu:ZnO based memristors were fabricated and modelled and its biological synaptic characteristics were realized. Phenomenon similar to long-term potentiation and long-term depression were observed in the proposed devices and spike timing dependent plasticity learning rule was established by engineering appropriate input voltage spikes, making the devices suitable for use in artificial neural networks. In order to demonstrate learning mechanism, a denoising autoencoder network was developed by incorporating the synaptic characteristics of the device along with the concept of rank coding. To evaluate the feasibility and performance of the network, images from the MNIST database for handwritten digits were employed. The training of the proposed network was accomplished by incorporating noisy images, and it was validated with images corrupted with Gaussian, Salt & Pepper and Speckle noises. Surprisingly, the obtained results demonstrated that the shape of the digits was recovered to a great extent and almost all noise in the background were removed. The accuracy of the denoising was found to be more than 90% for most cases. The proposed network shows the efficacy of ferroelectric Cu:ZnO memristors as artificial synapses in spiking neural networks, which opens up a new path towards developing future generation biologically compatible neuromorphic systems.
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