SUACDNet: Attentional change detection network based on siamese U-shaped structure

Song, Lei; Xia, Min; Jin, Junlan; Ming, Qian; Zhang, Yonghong

doi:10.1016/j.jag.2021.102597

Cited by 66 publications

(35 citation statements)

References 38 publications

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“…The overall algorithmic flow of the network model is shown in Figure 5, including the selection of the load device, the division of data samples, the method of data pre-processing, the construction of the model, the training of the model, and the tuning of the model. To better train the multilayer neural network, the BP algorithm, i.e., the error back propagation algorithm, is used, the pre-processed data samples are passed through the neural network one by one to calculate the weighted sum of the neurons of each layer, and then the result of the activation calculation using the nonlinear activation function of each layer is used as the input of the neurons of the next layer, and this process is repeated at each network layer to obtain the forward propagation calculation result [30,31]. In this paper, we use the mean square loss function, i.e., MSE , calculate the difference between the computation result of the last layer of the network and the target load power value, and this obtained error value is passed in a backward propagation way to calculate the difference of the previous layers separately, using the adaptive moment estimation optimizer Adam to adjust the parameters of the neurons in each layer to make them optimal, which is a complete learning process [32].…”

Section: Proposed Methodsmentioning

confidence: 99%

Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network

et al. 2022

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Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, leading to low accuracy. The method based on deep learning can automatically extract load characteristics, which improves the accuracy of load decomposition. However, with the deepening of the model structure, the number of parameters becomes too large, the training speed is slow, and the computing cost is high, which leads to the reduction of redundant features and the learning ability in some shallow networks, and the traditional deep learning model has a difficult time obtaining effective features on the time scale. To address these problems, a feature reused long short-term memory multiple output network (M-LSTM) is proposed and used for non-invasive load decomposition tasks. The network proposes an improved multiscale fusion residual module to extract basic load features and proposes the use of LSTM cyclic units to extract time series information. Feature reuse is achieved by combining it with the reorganization of the input data into multiple branches. The proposed structure reduces the difficulty of network optimization, and multi-scale fusion can obtain features on multiple time scales, which improves the ability of model feature extraction. Compared with common network models that tend to train network models for a single target load, the structure can simultaneously decompose the target load power while ensuring the accuracy of load decomposition, thus reducing computational costs, avoiding repetitive model training, and improving training efficiency.

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Section: Proposed Methodsmentioning

confidence: 99%

Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network

et al. 2022

Self Cite

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“…A CNN can well process two-dimensional grid data such as images [16][17][18][19][20], but most of people's daily life involves non-Euclideanspace data, so more and more people are engaged in research on GCNs [21][22][23][24][25][26]. There are two main methods used for GCNs: the spatial-based method and the spectral-based method.…”

Section: Graph Convolutional Networkmentioning

confidence: 99%

Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features

Ding

Jin

et al. 2022

Applied Sciences

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In the task of human motion recognition, the overall action span is changeable, and there may be an inclusion relationship between action semantics. This paper proposes a novel multi-scale time sampling module and a deep spatiotemporal feature extraction module, which strengthens the receptive field of the feature map and strengthens the extraction of spatiotemporal-related feature information via the network. We study and compare the performance of three existing multi-channel fusion methods to improve the recognition accuracy of the network on the open skeleton recognition dataset. In this paper, several groups of comparative experiments are carried out on two public datasets. The experimental results show that compared with the classical 2s-AGCN algorithm, the accuracy of the algorithm proposed in this paper shows an improvement of 1% on the Kinetics dataset and 0.4% and 1% on the two evaluating indicators of the NTU-RGB+D dataset, respectively.

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“…Therefore, the attention module is used to multiply the feature graph, combined with different scales to weigh the weight. The attention mechanism [27,28] has been shown to be helpful in numerous deep-learning-related tasks, such as image classification [29], image change detection [30], image segmentation [31,32]. This is an attention model that simulates the human brain.…”

Section: Deep Feature Extraction Modulementioning

confidence: 99%

Multi-Scale Feature Aggregation Network for Water Area Segmentation

Xia

et al. 2022

Remote Sensing

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Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomplete mining and insufficient utilization of semantic information, and the edge information of segmentation is rough. To solve the above problems, we propose a multi-scale feature aggregation network. In order to improve the ability of the network to process boundary information, we design a deep feature extraction module using a multi-scale pyramid to extract features, combined with the designed attention mechanism and strip convolution, extraction of multi-scale deep semantic information and enhancement of spatial and location information. Then, the multi-branch aggregation module is used to interact with different scale features to enhance the positioning information of the pixels. Finally, the two high-performance branches designed in the Feature Fusion Upsample module are used to deeply extract the semantic information of the image, and the deep information is fused with the shallow information generated by the multi-branch module to improve the ability of the network. Global and local features are used to determine the location distribution of each image category. The experimental results show that the accuracy of the segmentation method in this paper is better than that in the previous detection methods, and has important practical significance for the actual water area segmentation.

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SUACDNet: Attentional change detection network based on siamese U-shaped structure

Cited by 66 publications

References 38 publications

Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network

Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network

Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features

Multi-Scale Feature Aggregation Network for Water Area Segmentation

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