Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network

Energy disaggregation technology is a key technology to realise real-time non-intrusive load monitoring (NILM). Current energy disaggregation methods use the same scale to extract features from the sequence, which makes part of the local features lost, resulting in low recognition accuracy of electrical appliances. Aiming at the low accuracy of non-intrusive energy disaggregation with low-frequency sampling, a sequential multi-scale residual network is proposed. Multi-scale residual network extracts multi-scale feature information through multi-scale convolution, and uses residual learning to deepen the network structure to further improve the performance of the algorithm. Different scales features are captured by multi-scale convolution to improve the recognition accuracy of electrical appliances with low using frequency. Sequence-to-sequence energy disaggregation mechanism can improve the disaggregation efficiency of the algorithm. The experimental comparison results show that the model can get better disaggregation effect, and can effectively identify the start-stop state of electrical appliances.

Non-intrusive load disaggregation is designed to estimate the power consumption of each appliance based on the total power of the appliance in the household. Conventional machine learning algorithms cannot accurately extract semantic information from time series data, which motivates us to implement nonintrusive load disaggregation using residual gated recurrent neural networks model (Res-GRU). First, the networks model use multi-scale convolution kernels networks model extract time series data features, and will get multiple map fusions. Secondly, the networks model use residual learning to deepen the network to extract deep load features. Finally, the networks model use the gated recurrent unit to reset and update high level features. In this way, we can get the output power value of the target appliance. The experimental results show that the proposed network model has a good disaggregation effect.

Side-channel attacks based on attention mechanism and multi-scale convolutional neural network

Data interpolation is a very important step in seismic data processing. At present, there are many methods based on deep learning to solve the problem of seismic data interpolation, but few studies adopt the idea of multi-scale residuals combined with attention mechanism. In this paper, a seismic data interpolation algorithm based on multi-scale residual attention network is proposed. Multi-scale convolution is applied to residual structure for the first time, and the information of seismic data is extracted adaptively by cascade of multi-scale residual blocks. Then, the output of each residual block is used as hierarchical feature for global feature fusion. In the stage of residual mapping, the residual channel attention block is introduced to promote the flow of information features and make the channel attention mechanism focus on the high frequency and edge information of seismic data, thus enhancing the ability of network representation. A lot of numerical experiments show that the algorithm have short computation time and can achieve good reconstruction results.

Deep attention and generative neural networks for nonintrusive load monitoring

With the diversification of network traffic and the popularization of encrypted communication, traditional traffic classification methods have been difficult to cope with the challenges of new encryption protocols and hidden traffic. Deep learning, as a powerful feature extraction and classification tool, has been widely used in network traffic classification. In this paper, a deep network traffic classification model based on multi-scale feature fusion and attention mechanism (MSF-AttNet) is proposed. Features of different time scales are extracted through multi-scale convolution, and the adaptive weighting of channel attention and temporal self-attention mechanisms is combined to significantly improve the classification accuracy and interpretability of the model. Experiments show that MSF-AttNet outperforms traditional methods and other deep learning models on the ISCX VPN-nonVPN dataset.

Current image super-resolution (SR) reconstruction methods based on deep learning focus more on using the learning capabilities of deeper networks to obtain better reconstruction results, but the increase in depth will increase the complexity of the network structure and the difficulty of training. To address this problem, considering the advantages of residual learning that can overcome the problem of low learning efficiency, and the ability of multiscale networks that can extract global and local features at different scales, this paper proposes a multi-scale residual block (MSRB) for feature learning. Based on the constructed MSRB, a cascaded multi-scale residual network (CMSRN) is developed for image SR reconstruction. In the network, to reconstruct richer image texture details, multiple multi-scale residual blocks are cascaded to construct the residual feature learning part. Experimental results on four datasets show that the proposed network can obtain better reconstruction results, and is superior to state-of-the-art SR reconstruction methods in terms of subjective observation and objective quantitative evaluation.

Deep learning methods have outclassed traditional methods in hyperspectral image classification (HIC). However, due to the limited size of input 3D cube, most HIC networks are shallow in depth, resulting in effectiveness constrained. Multi-scale convolution operation has arisen to alleviate this issue by expanding width of neural networks. Though multi-scale CNN is able to enhance the classification capacity in some extent, there are still some inherent disadvantages, such as heavy computation burden and inadequate multi-scale features extraction. To address these, this paper proposes a multi-scale dense network based on multilevel wavelet decomposition for HIC. The proposed approach characterizes multi-scale features by establishing multiple branches with 2D discrete wavelet transform rather than multi-scale convolution and pooling. Various features are joined with others from adjacent branches by level-wise short cut for supporting classification decision. Otherwise, the dense block is modified to fuse multi-frequency features and fetch underlying scale information with dense connections. The experimental results on Pavia University and University of Houston datasets demonstrate that proposed approach acquires competitive performance with the state-of-the-arts and only requires less computation and time consumptions.KeywordsHyperspectral image classificationMulti-scale CNNMultilevel wavelet decompositionDense connection

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects' attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

Aiming to address the suboptimal diagnostic efficacy of conventional approaches in rolling bearing fault diagnosis under complex operational scenarios—particularly when subjected to intense noise contamination—this study introduces a novel fault diagnosis framework leveraging the synergistic attention mechanisms of MSCNN and GRU.The workflow begins with conditioning vibration signals sourced from the CRUW bearing dataset, followed by robust extraction of multi-scale fault features through a multi-scale convolutional neural network (MSCNN). To enhance temporal modeling, a gated recurrent unit (GRU) is integrated to extract temporal dependencies from the signals. Further improvements are achieved by enhancing the base model with Transformer layers and a convolutional attention module (CBAM), respectively. Experimental validation is conducted to compare the diagnostic performance of three configurations MSCNN+GRU, MSCNN+GRU+Transformer, and MSCNN+GRU+CBAM—under varying noise conditions. Outcomes demonstrate that the MSCNN+GRU+CBAM model exhibits superior fault detection accuracy and optimal generalization capacity across different load and rotational speed settings, confirming its notable advantage in rolling bearing fault diagnosis.

Non-intrusive load disaggregation based on deep dilated residual network

Hyperspectral image (HSI) classification is one of the most crucial tasks in remote sensing processing. The attention mechanism is preferable to a convolutional neural network (CNN), due to its superior ability to express information during HSI processing. Recently, numerous methods combining CNNs and attention mechanisms have been applied in HSI classification. However, it remains a challenge to achieve high-accuracy classification by fully extracting effective features from HSIs under the conditions of limited labeled samples. In this paper, we design a novel HSI classification network based on multiscale hybrid networks and attention mechanisms. The network consists of three subnetworks: a spectral-spatial feature extraction network, a spatial inverted pyramid network, and a classification network, which are employed to extract spectral-spatial features, to extract spatial features, and to obtain classification results, respectively. The multiscale fusion network and attention mechanisms complement each other by capturing local and global features separately. In the spatial pyramid network, multiscale spaces are formed through down-sampling, which can reduce redundant information while retaining important information. The structure helps the network better capture spatial features at different scales, and to improve classification accuracy. Experimental results on various public HSI datasets demonstrate that the designed network is extremely competitive compared to current advanced approaches, under the condition of insufficient samples.

Motor impairment is a critical health issue that restricts disabled people from living their lives normally and with comfort. Detecting motor imagery (MI) in electroencephalography (EEG) signals can make their lives easier. There has been a lot of work on detecting two or four different MI movements, which include bilateral, contralateral, and unilateral upper limb movements. However, there is little research on the challenging problem of detecting more than four motor imagery tasks and unilateral lower limb movements. As a solution to this problem, a spectral-spatio-temporal multiscale network (SSTMNet) has been introduced to detect six imagery tasks. It first performs a spectral analysis of an EEG trial and attends to the salient brain waves (rhythms) using an attention mechanism. Then, the temporal dependency across the entire EEG trial is worked out using a temporal dependency block, resulting in spectral-spatio-temporal features, which are passed to a multiscale block to learn multiscale spectral-–spatio-temporal features. Finally, these features are deeply analyzed by a sequential block to extract high-level features, which are used to detect an MI task. In addition, to deal with the small dataset problem for each MI task, the researchers introduce a data augmentation technique based on Fourier transform, which generates new EEG trials from EEG signals belonging to the same class in the frequency domain, with the idea that the coefficients of the same frequencies must be fused, ensuring label-preserving trials. SSTMNet is thoroughly evaluated on a public-domain benchmark dataset; it achieves an accuracy of 77.52% and an F1-score of 56.19%. t-SNE plots, confusion matrices, and ROC curves are presented, which show the effectiveness of SSTMNet. Furthermore, when it is trained on augmented data generated by the proposed data augmentation method, it results in a better performance, which validates the effectiveness of the proposed technique. The results indicate that its performance is comparable with the state-of-the-art methods. An analysis of the features learned by the model reveals that the block architectural design aids the model in distinguishing between multi-imagery tasks.

A multiscale neural network model for the prediction on the equivalent permeability of discrete fracture network

MSRConvNet: Classification of railway track defects using multi-scale residual convolutional neural network