For the processing of echo signals in defect detection, wavelet transform and principal component analysis are mostly used to extract features. However, the feature values obtained by these methods often lead to redun...
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ISBN:
(纸本)9798350350319;9798350350302
For the processing of echo signals in defect detection, wavelet transform and principal component analysis are mostly used to extract features. However, the feature values obtained by these methods often lead to redundancy, resulting in the waste of a lot of resources for defect identification. This paper, in the context of defects in thick-walled steel plates with rough surfaces, proposes a defect category recognition classification method based on an autoencoder-BP neural network. It uses signals from electromagnetic ultrasonic and pulsed eddy current composite detection as the neural network learning signals. Impedance analysis is used to more comprehensively reflect the characteristics of defects, thereby improving the accuracy of defect identification. autoencoder is selected to extract the geometric features of the composite detection signals, which can effectively extract useful features from the dataset. The feature dataset is then divided into training and testing sets. Simulation experiments show that the trained neural network model has achieved a classification accuracy of 90.8% in the testing.
Community detection intends to cluster graph nodes with relevant information, and community detection for attributed graphs is of great practical importance. However, the existing work is still insufficient in terms o...
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ISBN:
(纸本)9798350359329;9798350359312
Community detection intends to cluster graph nodes with relevant information, and community detection for attributed graphs is of great practical importance. However, the existing work is still insufficient in terms of the representation of network topology and attribute information. In addition, the impact of noise and missing attributes on attributed graph representation learning has been rarely considered. To address the above issues, this paper proposes an innovative masked dual graph autoencoder (MDGAE) method for attributed graph community detection. The method consists of a graph attention autoencoder module, a masked graph autoencoder module, a graph representation fusion module, and a self-optimizing clustering module. Firstly, MDGAE fully explores graph topological and attribute information through the graph attention autoencoder and the masked graph autoencoder, respectively, and employs a masking strategy to solve the problem of noise and missing attributes efficiently;then, the low-dimensional representations obtained from the two autoencoders are weighted and fused for clustering;finally, the clustering results are iteratively optimized by joining with the self-optimizing clustering module. The experimental results on three benchmark datasets demonstrate the advantages of the MDGAE method over the existing algorithms.
Training autoencoders is non-trivial. Convergence to the identity function or over fitting are common pitfalls. Population based algorithms like coevolutionary algorithms can provide diversity. To more robustly train ...
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ISBN:
(纸本)9798400704949
Training autoencoders is non-trivial. Convergence to the identity function or over fitting are common pitfalls. Population based algorithms like coevolutionary algorithms can provide diversity. To more robustly train autoencoders, we introduce a novel cooperative coevolutionary algorithm that exploits a spatial topology. We investigate the impact of algorithm parameters and design choices on the performance. On a simple tunable benchmark problem we observe that the performance can be improved over that of an conventionally trained autoencoder. However, the training convergence can be slow, despite the final model performance being competitive with a conventional autoencoder.
These days, one of the major downsides of Generalized Frequency Division Multiplexing (GFDM) systems is a high peak-to-average power ratio (PAPR). In this research, we present a novel deep learning autoencoder-based m...
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These days, one of the major downsides of Generalized Frequency Division Multiplexing (GFDM) systems is a high peak-to-average power ratio (PAPR). In this research, we present a novel deep learning autoencoder-based method to lower the PAPR of GFDM. The PAPR-reducing network (PRNet), also known as the PAPR-reducing method, is based on the encoder-decoder neural network (autoencoder). In the PAPR-reducing network (PRNet), the bit error rate (BER) and the PAPR of the GFDM system are jointly minimised by adaptively determining the constellation mapping and damping of symbols on each subcarrier and sub-symbol.
WiFi human sensing is highly regarded for its low-cost and privacy advantages in recognizing human activities. However, its effectiveness is largely confined to controlled, single-user, line-of-sight settings, limited...
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ISBN:
(纸本)9798350344868;9798350344851
WiFi human sensing is highly regarded for its low-cost and privacy advantages in recognizing human activities. However, its effectiveness is largely confined to controlled, single-user, line-of-sight settings, limited by data collection complexities and the scarcity of labeled datasets. Traditional cross-modal methods, aimed at mitigating these limitations by enabling self-supervised learning without labeled data, struggle to extract meaningful features from amplitude-phase combinations. In response, we introduce AutoSen, an innovative automatic WiFi sensing solution that departs from conventional approaches. AutoSen establishes a direct link between amplitude and phase through automated cross-modal autoencoder learning. This autoencoder efficiently extracts valuable features from unlabeled CSI data, encompassing amplitude and phase information while eliminating their respective unique noises. These features are then leveraged for specific tasks using few-shot learning techniques. AutoSen's performance is rigorously evaluated on a publicly accessible benchmark dataset, demonstrating its exceptional capabilities in automatic WiFi sensing through the extraction of comprehensive cross-modal features.
A novel approach aimed at elevating the performance of offline signature verification systems by harnessing the combined power of autoencoders and Convolutional Neural Networks (CNNs). Our evaluation encompassed Convo...
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ISBN:
(纸本)9798350360806;9798350360790
A novel approach aimed at elevating the performance of offline signature verification systems by harnessing the combined power of autoencoders and Convolutional Neural Networks (CNNs). Our evaluation encompassed Convolutional Neural Networks (CNN), K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Gaussian Temporal Rule, Probabilistic Neural Networks, Multi-layered Perception, Long Short-Term Memory (LSTM), and various combinations thereof. Notably, our proposed autoencoder with Convolutional Neural Networks (AE with CNN) outshone all other approaches, achieving an impressive accuracy rate of 98.48%. While CNN displayed commendable performance at 89%, KNN and SVM fusion attained 78.50% accuracy, suggesting room for improvement in distinguishing genuine and forged signatures. The Gaussian Temporal Rule proved robust, with an accuracy of 91.20%, and Probabilistic Neural Networks and Multi-layered Perception with SVM reached accuracies of 92.06% and 91.67%, respectively. The introduction of LSTM in conjunction with SVM and KNN significantly enhanced accuracy to 95.40%, 95.20%, and 92.70%, respectively. Collectively, these findings provide valuable insights into the potential of AE with CNN as a leading solution for achieving highly accurate signature verification, particularly in contexts where the distinction between authentic and counterfeit signatures is critical.
Time series anomaly detection is one of the critical tasks in time series analysis. There are many existing works using autoencoders for time series anomaly detection;however, autoencoders can not only reconstruct nor...
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ISBN:
(纸本)9798350349184;9798350349191
Time series anomaly detection is one of the critical tasks in time series analysis. There are many existing works using autoencoders for time series anomaly detection;however, autoencoders can not only reconstruct normal sequences but also reconstruct anomalous sequences sometimes, which leads to the poor effect of anomaly detection based on reconstruction error. At the same time, the real-world time series have the characteristic of temporal coupling, and the existing methods often cannot take into account the multiple modes of the time series, which leads to the low accuracy of time series anomaly detection. Aiming at the above problems, we propose a Time Series Anomaly Detection method incorporating Wavelet Decomposition and Temporal Decoupled autoencoder (WDAE). Specifically, WDAE first reshapes the original 1D time series into 2D data harboring different temporal patterns based on frequency components and independently extracts the different temporal features of the time series data. Then, the original data are reconstructed in the time domain by the autoencoder to obtain the reconstructed time-domain sequence, and in the frequency domain, the 2D data are subjected to the discrete wavelet transform and the inverse wavelet transform after weight adjustment to obtain the reconstructed frequency domain sequence, and the captured normal sequence pattern in the frequency domain restricts the latent feature space in the time domain so that the autoencoder can rebuild the normal sequence to avoid reconstructing the anomalous sequence well. Experiments on five publicly available data show that WDAE significantly improves the F1 value over related state-of-the-art baseline methods.
This paper proposes a hardware accelerator for machine learning-based anomaly detection to enhance IoT security. Our model integrates Multilayer Perceptron (MLP) with Long Short-Term Memory (LSTM), utilizing an MLP-ba...
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ISBN:
(纸本)9798350330991;9798350331004
This paper proposes a hardware accelerator for machine learning-based anomaly detection to enhance IoT security. Our model integrates Multilayer Perceptron (MLP) with Long Short-Term Memory (LSTM), utilizing an MLP-based autoencoder and Isolation Forest algorithm for data dimensionality reduction and computational complexity reduction. Prototyped on a Zynq UltraScale+ XCZU9EG FPGA, our AE-LSTM model surpasses baseline MLP-only and LSTM-only models in resource utilization efficiency and detection accuracy. Compared to these baselines, it reduces parameters by 79.4% and 98% and LUT usage by 61.4% and 90.8%, respectively, while minimizing other resource utilization. Furthermore, power consumption is lowered to about 40% of the MLP-based model's consumption rate and 36% of the LSTM-based model's rate, with latency reduced to less than one-third from both baselines.
Analyzing colors in images is an approach to understanding semantic meaning. However, existing research often faces challenges due to limited dataset sizes or the need to create custom datasets. Insufficient data can ...
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ISBN:
(纸本)9798350385113;9798350385106
Analyzing colors in images is an approach to understanding semantic meaning. However, existing research often faces challenges due to limited dataset sizes or the need to create custom datasets. Insufficient data can lead to overfitting during model training and hinder generalization. To address this, we propose a two-stage machine learning method that leverages Kobayashi's Color Image Scale (CIS), a publicly available color image dataset, to enhance the predictive accuracy of color image classifier. In our method, we not only extract colors and image categories as features but also capture the xy-coordinates of color schemes from the CIS. These coordinates play a significant role in improving the accuracy of color image classification. Our two-stage learning method provides a straightforward and effective solution for enhancing predictive accuracy. Through our method, we achieve an impressive 97.36% accuracy in color image classification on the test dataset.
The polarimetric synthetic aperture radar (PolSAR) images contain fine characteristics and abstract spatial features, which attention to them can improve the classification accuracy. In this work, the residual convolu...
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ISBN:
(纸本)9798350350494;9798350350500
The polarimetric synthetic aperture radar (PolSAR) images contain fine characteristics and abstract spatial features, which attention to them can improve the classification accuracy. In this work, the residual convolutional neural network with autoencoder based attention (RCNN-AA) is proposed for PolSAR image classification. The scaled difference of the convolutional autoencoder with the original input patch is used as the weight, which contains information about the fine spatial features. Multiplication of this normalized difference in the input patch provides the attention feature maps that can be concatenated with the original input and used as input of the RCNN. An ablation study is done, and also, the proposed RCNN-AA model is compared to some deep learning based models. The results show preference of the RCNN-AA with respect to the competitors.
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