Recently, deep learning-based lossy image compression methods have been proposed. However, their efficiency in terms of storage and computational costs has not been addressed adequately. In this paper, we propose effi...
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ISBN:
(纸本)9781509066315
Recently, deep learning-based lossy image compression methods have been proposed. However, their efficiency in terms of storage and computational costs has not been addressed adequately. In this paper, we propose efficient lossy image compression methods based on asymmetric autoen-coder and decoder pruning. Experimental results demonstrate the effectiveness of our methods.
Remaining Useful Life (RUL) prediction is an essential aspect of Prognostics and Health Management (PHM), facilitating the assessment of mechanical components' health statuses and their times to failure. Currently...
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Remaining Useful Life (RUL) prediction is an essential aspect of Prognostics and Health Management (PHM), facilitating the assessment of mechanical components' health statuses and their times to failure. Currently, most deep learning-based RUL prediction methods can achieve accurate RUL point estimations. However, due to sample variability and degradation randomness, point estimations may contain uncertainties. To obtain both RUL prediction values and their corresponding uncertainty estimations, this paper proposes a novel hybrid-driven prediction method that effectively combines an asymmetric Dual-Channel autoencoder and the Nonlinear Wiener Process (ADCAE-NWP). To achieve comprehensive feature extraction, two feature extraction channels are parallelly combined in the encoder. Moreover, to reduce the space-time overhead of the model training process, an asymmetric form of the autoencoder is composed by using only the fully connected layer in the decoder. Subsequently, the ADCAE model is trained to construct health indicators in an unsupervised manner. Finally, the RUL Probability Density Functions (PDFs) are calculated using the NWP. RUL predictions containing uncertainty estimations are obtained by calculating expectations over confidence intervals. The proposed model is experimentally validated and compared on two datasets, and the results demonstrate that the proposed scheme achieves better prediction performance than competing approaches.
Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensin...
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ISBN:
(纸本)9798350328127
Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.
Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology f...
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Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time-frequency domain features from raw data using a priori knowledge and setting artificial thresholds;this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an asymmetric Residual Shrinkage Convolutional autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods.
Security in human-robot interaction is the focus of research in this field. Rapid detection of abnormal events that may cause danger in the interaction process can effectively reduce the probability of occurrence of d...
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ISBN:
(纸本)9781728165974
Security in human-robot interaction is the focus of research in this field. Rapid detection of abnormal events that may cause danger in the interaction process can effectively reduce the probability of occurrence of danger. In general anomaly detection methods, 2D or 3D convolutional autoencoders are widely used for anomaly detection. Among them, 2D convolutional autoencoders are with good real-time performance and lower detection accuracy, while 3D convolutional autoencoders are with higher detection accuracy and insufficient real-time performance. In order to ensure realtime performance and obtain higher accuracy, an end-to-end asymmetric convolutional autoencoder network (ACANet) using both 2D and 3D convolutions is designed. Specifically, 3D convolution is used to build the encoder to learn comprehensive information in continuous input frames, and 2D convolution is used to build the decoder to model the information fast, a dimensional alignment module is constructed to connect the encoder and the decoder while avoiding a large number of calculations in the latent space of the 3D features output by the encoder, and the skip connections module is used to obtain accurate predictions. Anomaly detection can then be completed by evaluating the differences between results predicted by the ACANet and real frames. The experimental results show that our method achieves competitive accuracy on mainstream datasets and at the same time obtains the fastest speed. Compared with mainstream methods, this method is more suitable for anomaly detection tasks in human-robot interaction.
In this paper, we propose a new asymmetric supervised deep autoencoder approach to retrieve 3D shapes based on depth images. The asymmetric supervised autoencoder is trained with real and synthetic depth images togeth...
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In this paper, we propose a new asymmetric supervised deep autoencoder approach to retrieve 3D shapes based on depth images. The asymmetric supervised autoencoder is trained with real and synthetic depth images together. The novelty of this research lies in the asymmetric structure of a supervised deep autoencoder. The proposed asymmetric deep supervised autoencoder deals with the incompleteness and ambiguity present in the depth images by balancing reconstruction and classification capabilities in a unified way with mixed depth images. We investigate the relationship between the encoder layers and decoder layers, and claim that an asymmetric structure of a supervised deep autoencoder reduces the chance of overfitting by 8% and is capable of extracting more robust features with respect to the variance of input than that of a symmetric structure. The experimental results on the NYUD2 and ModelNet10 datasets demonstrate that the proposed supervised method outperforms the recent approaches for cross modal 3D model retrieval.
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