The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowerin...
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The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowering latency, and protecting data privacy. Motivated by these benefits, this research aims to design a lightweight autoencoder deep model that has a shallow architecture with a small number of input features and a few hidden neurons. To achieve this objective, an efficient two-layer optimizer is used to evolve a lightweight deep autoencoder model by performing simultaneous selection for the input features, the training instances, and the number of hidden neurons. The optimized deep model is constructed guided by both the accuracy of a K-nearest neighbor (KNN) classifier and the complexity of the autoencoder model. To evaluate the performance of the proposed optimized model, it has been applied for the N-baiot intrusion detection dataset. Reported results showed that the proposed model achieved anomaly detection accuracy of 99% with a lightweight autoencoder model with on average input features around 30 and output hidden neurons of 2 only. In addition, the proposed two-layers optimizer was able to outperform several optimizers such as Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimization (PSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).
Hyperspectral classification is a fundamental problem for applying hyperspectral technology, and unsupervised learning is a promising direction to address the issue. Unsupervised feature learning algorithm extracts re...
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Hyperspectral classification is a fundamental problem for applying hyperspectral technology, and unsupervised learning is a promising direction to address the issue. Unsupervised feature learning algorithm extracts representative features, which help efficiently classify the hyperspectral pixels. This paper combines the popular contrastive learning method (prototypical contrastive learning) and the classic representation learning method (autoencoder) to design an unsupervised feature learning network for hyperspectral classification. First, two different encoders are used to extract different features as augmentation function in a contrastive network (ContrastNet). Second, the prototypical contrastive learning method is adopted to train a contrastive network. Third, features extracted by the contrastive network are used for classification. Experiments have proved that our two proposed autoencoder networks show good feature learning capabilities by themselves and the designed contrastive learning network can further learn better representative features from the two modules. The three modules compose an efficient framework for unsupervised feature learning. Our method also performs reasonably fast in the testing phase, which implies that it is applicable in practice under specific conditions. (C) 2021 Elsevier B.V. All rights reserved.
Under variable working conditions, a problem arises, which is that it is difficult to obtain enough labeled data;to address this problem, an adaptive transfer autoencoder (ATAE) is established to diagnose faults in ro...
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Under variable working conditions, a problem arises, which is that it is difficult to obtain enough labeled data;to address this problem, an adaptive transfer autoencoder (ATAE) is established to diagnose faults in rotating machinery. First, a data adaptation module, which calculates the maximum mean discrepancy for the network hidden-layer data in reproducing kernel Hilbert space, is introduced to the autoencoder network, thus making the classification model operate under variable working conditions. Variation particle-swarm optimization is then invoked to optimize the data adaptation parameters. Finally, the k-nearest neighbors algorithm, as the classification layer of the network, identifies the state of health of the rotating machinery. The capabilities of the intelligent fault-diagnosis network are verified using vibration signals from a bearing test rig and a gearbox test rig. The experimental results suggest that, compared with state-of-the-art diagnosis methods, the proposed ATAE network can significantly boost diagnostic performance in the absence of target vibration signal labels.
autoencoder (AE) has been widely used in multivariate statistical process monitoring (MSPM) and various AE-based methods have been applied in fault detection. Process data is often contaminated by outliers and measure...
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autoencoder (AE) has been widely used in multivariate statistical process monitoring (MSPM) and various AE-based methods have been applied in fault detection. Process data is often contaminated by outliers and measurement noise which may lead to the overfitting problem for AE-based methods. In this paper, a novel feature extraction method called low-rank reconstruction-based autoencoder (LRAE) is proposed for robust fault detection. LRAE decomposes the input into a combination of a low-rank data matrix and a noise matrix. By penalizing the rank of the data matrix, LRAE separates the low-rank clean data from the contaminated process data. Instead of directly reconstructing the loss between the input data and the output data, we design a low-rank reconstruction strategy, i.e. reconstruct the loss between the low-rank clean data and the output of the AE. The proposed LRAE can be trained end-to-end by jointly optimizing an AE and a low-rank approximation. LRAE is a nonlinear method which can tackle the complicated process data better than the linear methods such as principal component analysis (PCA). Moreover, the optimization of the low-rank approximation provides the robustness of LRAE to reconstruct the clean data in the output layer when the input process data is contaminated. After training, the features of the hidden layer can be computed for further fault detection. Extensive experiments demonstrate that LRAE outperforms traditional fault detection methods, including PCA, robust principal component analysis (RPCA), kernel principal component analysis (KPCA), AE, and denoising autoencoder (DAE). Especially, LRAE provides more robust results when the process data suffer from outliers and measurement noise.
This paper proposes a robust autoencoder withWasserstein distance metric to extract the linear separability features from the input data. To minimize the difference between the reconstructed feature space and the orig...
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This paper proposes a robust autoencoder withWasserstein distance metric to extract the linear separability features from the input data. To minimize the difference between the reconstructed feature space and the original feature space, using Wasserstein distance realizes a homeomorphic transformation of the original feature space, i.e., the so-called the reconstruction of feature space. The autoencoder is used for features extraction of linear separability in the reconstructed feature space. Experiment results on real datasets show that the proposed method reaches up 0.9777 and 0.7112 on the low-dimensional and high-dimensional datasets in extracted accuracies, respectively, and also outperforms competitors. Results also confirm that compared with feature metric-based methods and deep network architectures-based method, the linear separabilities of those features extracted by distance metric-based methods win over them. More importantly, the linear separabilities of those features obtained by evaluating distance similarity of the data are better than those obtained by evaluating feature importance of data. We also demonstrate that the data distribution in the feature space reconstructed by a homeomorphic transformation can be closer to the original data distribution.
Various applications are deployed on mobile smart devices in almost every situations of our life, while in some of these situations sensitive applications are strictly prohibited, such as cameras in cinemas and browse...
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Various applications are deployed on mobile smart devices in almost every situations of our life, while in some of these situations sensitive applications are strictly prohibited, such as cameras in cinemas and browsers in examination halls. Real-time recognition of applications running on mobile smart devices is of great significance in these cases. However, most of the existing technologies have the limitation that they require system permissions to obtain the running application list which is banned by mainstream mobile operating systems. Noting that the launch of a certain application will emit a unique pattern of magnetic field, we introduce magnetic field side channel analysis to recognize running applications. However, magnetic field side channel analysis is challenging since it is hard to extract features from magnetic field data without domain experts. Besides, real-time applications identification demands accurate detection of applications launching. To overcome these challenges, we extract robust depth features using autoencoder and implement online application recognition by introducing finite-state machine to identify the application launch window from raw data. The proposed method is evaluated by recognizing 1000 different applications in real environment. The experiment results show that the proposed method is feasible and effective in real-time application identification.
Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss....
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Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.
In order to speed up the process of optimizing design of metasurface absorbers, an improved design model for metasurface absorbers based on autoencoder (AE) and BiLSTM-Attention-FCN-Net (including bidirectional long-s...
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In order to speed up the process of optimizing design of metasurface absorbers, an improved design model for metasurface absorbers based on autoencoder (AE) and BiLSTM-Attention-FCN-Net (including bidirectional long-short-term memory network, attention mechanism, and fully-connection layer network) is proposed. The metasurface structural parameters can be input into the forward prediction network to predict the corresponding absorption spectra. Meantime, the metasurface structural parameters can be obtained by inputting the absorption spectra into the inverse prediction network. Specially, in the inverse prediction network, the bidirectional long-short-term memory (BiLSTM) network can effectively capture the context relationship between absorption spectral sequence data, and the attention mechanism can enhance the BiLSTM output sequence features, which highlight the critical feature information. After the training, the mean square error (MSE) value on the validation set of the reverse prediction network converges to 0.0046, R2 reaches 0.975, and our network can accurately predict the metasurface structure parameters within 1.5 s with a maximum error of 0.03 mm. Moreover, this model can achieve the optimal design of multi-band metasurface absorbers, including the single-band, dual-band, and three-band absorptions. The proposed method can also be extended to other types of metasurface optimization design.
Hyperspectral X ray analysis is used in many industrial pipelines, from quality control to detection of low-density contaminants in food. Unfortunately, the signal acquired by X-ray sensors is often affected by a grea...
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Hyperspectral X ray analysis is used in many industrial pipelines, from quality control to detection of low-density contaminants in food. Unfortunately, the signal acquired by X-ray sensors is often affected by a great amount of noise. This hinders the performance of most of the applications building on top of these acquisitions (e.g., detection of food contaminants). Therefore, a good denoising pipeline is necessary. This article proposes a comparison between three different autoencoder variants: the Variational autoencoder, the Augmented autoencoder, and a plain vanilla autoencoder. All the networks are trained in an unsupervised fashion to denoise a given noisy spectrum. Focusing on the specific application of recognizing possible food contaminants, we force the latent space of the networks to have just two parameters, as suggested by the physical law of Lambert- Beer. We validate our experiments on a synthetic dataset composed of roughly 15 million spectra. Results suggest that the Augmented autoencoder is the best network configuration for this task, showing excellent performance without suffering from the nondeterministic behavior of the Variational autoencoder.
This article proposes an autoencoder-based method to enhance the information interaction between in-phase/quadrature (I/Q) channels of the input data for automatic modulation recognition (AMR). The proposed method uti...
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This article proposes an autoencoder-based method to enhance the information interaction between in-phase/quadrature (I/Q) channels of the input data for automatic modulation recognition (AMR). The proposed method utilizes an autoencoder built by fully-connected layers to correlate the features of I/Q data and obtain the interaction feature from the intermediate layer, which is concatenated together with the original I/Q data as model inputs. To accommodate the new data dimensions, a modification scheme for the existing representative deep learning based AMR (DL-AMR) models is presented. Experimental results show that our method can improve the recognition accuracy of the state-of-the-art baseline models, and has a smaller time overhead compared with complex-valued neural networks.
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