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

Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances.

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Multi-scale residual network for energy disaggregation

International Journal of Sensor Networks ◽

10.1504/ijsnet.2019.100220 ◽

2019 ◽

Vol 30 (3) ◽

pp. 172

Author(s):

N.A. Wan' ◽

an Liu ◽

Liguo Weng ◽

Min Xia ◽

Yiqing Xu ◽

...

Keyword(s):

Residual Network ◽

Multi Scale ◽

Energy Disaggregation

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Learning conditional photometric stereo with high-resolution features

Computational Visual Media ◽

10.1007/s41095-021-0223-y ◽

2021 ◽

Vol 8 (1) ◽

pp. 105-118

Author(s):

Yakun Ju ◽

Yuxin Peng ◽

Muwei Jian ◽

Feng Gao ◽

Junyu Dong

Keyword(s):

Neural Networks ◽

High Resolution ◽

Feature Fusion ◽

Low Frequency ◽

Photometric Stereo ◽

Surface Orientation ◽

Smooth Functions ◽

Multi Scale ◽

Benchmark Datasets ◽

Deep Feature Extraction

AbstractPhotometric stereo aims to reconstruct 3D geometry by recovering the dense surface orientation of a 3D object from multiple images under differing illumination. Traditional methods normally adopt simplified reflectance models to make the surface orientation computable. However, the real reflectances of surfaces greatly limit applicability of such methods to real-world objects. While deep neural networks have been employed to handle non-Lambertian surfaces, these methods are subject to blurring and errors, especially in high-frequency regions (such as crinkles and edges), caused by spectral bias: neural networks favor low-frequency representations so exhibit a bias towards smooth functions. In this paper, therefore, we propose a self-learning conditional network with multi-scale features for photometric stereo, avoiding blurred reconstruction in such regions. Our explorations include: (i) a multi-scale feature fusion architecture, which keeps high-resolution representations and deep feature extraction, simultaneously, and (ii) an improved gradient-motivated conditionally parameterized convolution (GM-CondConv) in our photometric stereo network, with different combinations of convolution kernels for varying surfaces. Extensive experiments on public benchmark datasets show that our calibrated photometric stereo method outperforms the state-of-the-art.

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An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data

Sustainability ◽

10.3390/su11010251 ◽

2019 ◽

Vol 11 (1) ◽

pp. 251 ◽

Cited By ~ 4

Author(s):

Huijuan Wang ◽

Wenrong Yang ◽

Tingyu Chen ◽

Qingxin Yang

Keyword(s):

Power Consumption ◽

Optimization Model ◽

Smart Grids ◽

Large Scale ◽

Reactive Power ◽

Low Frequency ◽

Performance Comparison ◽

Consumption Pattern ◽

Frequency Data ◽

Load Disaggregation

In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to be installed, so it is not suitable for promotion to the smart grid environment. However, methods using low-frequency features are poorly-suited when several appliances are switched on at the same time, or devices with similar power values are used. In response to these problems, this paper proposes a load disaggregation method based on the power consumption patterns of appliances, combining an improved mathematical optimization model and optimized bird swarm algorithm (OBSA) for load disaggregation. Experiments show that the method can effectively identify the operating states of appliances, and deal with situations in which multiple instruments have similar power characteristics or are simultaneously switching. The performance comparison proves that the improved model is more efficient than the traditional active and reactive power (PQ) optimization model in load disaggregation performance and computation time, and also verifies the robustness of the proposed method and the convergence of OBSA. As an inexpensive method without extra measurement hardware installed, the process is suitable for large-scale applications in smart grids.

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Multi-scale residual network for energy disaggregation

International Journal of Sensor Networks ◽

10.1504/ijsnet.2019.10021955 ◽

2019 ◽

Vol 30 (3) ◽

pp. 172

Author(s):

Zhuhan Qiao ◽

Ke Wang ◽

Min Xia ◽

Yiqing Xu ◽

Wan'an Liu ◽

...

Keyword(s):

Residual Network ◽

Multi Scale ◽

Energy Disaggregation

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Imperfect Wheat Grain Recognition Combined with an Attention Mechanism and Residual Network

Applied Sciences ◽

10.3390/app11115139 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5139

Author(s):

Weiwei Zhang ◽

Huimin Ma ◽

Xiaohong Li ◽

Xiaoli Liu ◽

Jun Jiao ◽

...

Keyword(s):

Machine Vision ◽

Network Model ◽

Recognition Accuracy ◽

Recognition Rate ◽

Attention Mechanism ◽

Residual Network ◽

Wheat Grains ◽

Key Factor ◽

Different Depths ◽

Deep Learning Network

Intelligent detection of imperfect wheat grains based on machine vision is of great significance to correctly and rapidly evaluate wheat quality. There is little difference between the partial characteristics of imperfect and perfect wheat grains, which is a key factor limiting the classification and recognition accuracy of imperfect wheat based on a deep learning network model. In this paper, we propose a method for imperfect wheat grains recognition combined with an attention mechanism and residual network (ResNet), and verify its recognition accuracy by adding an attention mechanism module into different depths of residual network. Five residual networks with different depths (18, 34, 50, 101, and 152) were selected for the experiment, it was found that the recognition accuracy of each network model was improved with the attention mechanism, and the average recognition rate of ResNet-50 with the addition of the attention mechanism reached 96.5%. For ResNet-50 with the attention mechanism, the optimal learning rate was further screened as 0.0003. The average recognition accuracy reached 97.5%, among which the recognition rates of scab wheat grains, insect-damaged wheat grains, sprouted wheat grains, mildew wheat grains, broken wheat grains, and perfect wheat grains reached 97%, 99%, 99%, 95%, 96%, and 99% respectively. This work can provide guidance for the detection and recognition of imperfect wheat grains using machine vision.

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Image Compressed Sensing and Reconstruction of Multi-Scale Residual Network Combined with Channel Attention Mechanism

Journal of Physics Conference Series ◽

10.1088/1742-6596/2010/1/012134 ◽

2021 ◽

Vol 2010 (1) ◽

pp. 012134

Author(s):

Chunyan Zeng ◽

Zhenghui Wang ◽

Zhifeng Wang ◽

Kang Yan ◽

Yan Yu

Keyword(s):

Compressed Sensing ◽

Attention Mechanism ◽

Residual Network ◽

Multi Scale

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Super-Resolution Network with Information Distillation and Multi-Scale Attention for Medical CT Image

Sensors ◽

10.3390/s21206870 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6870

Author(s):

Tianliu Zhao ◽

Lei Hu ◽

Yongmei Zhang ◽

Jianying Fang

Keyword(s):

Super Resolution ◽

Objective Evaluation ◽

Learning Ability ◽

Ct Image ◽

Residual Network ◽

Multi Scale ◽

Series Of Experiments ◽

Feature Information ◽

Image Super Resolution ◽

Feature Utilization

The CT image is an important reference for clinical diagnosis. However, due to the external influence and equipment limitation in the imaging, the CT image often has problems such as blurring, a lack of detail and unclear edges, which affect the subsequent diagnosis. In order to obtain high-quality medical CT images, we propose an information distillation and multi-scale attention network (IDMAN) for medical CT image super-resolution reconstruction. In a deep residual network, instead of only adding the convolution layer repeatedly, we introduce information distillation to make full use of the feature information. In addition, in order to better capture information and focus on more important features, we use a multi-scale attention block with multiple branches, which can automatically generate weights to adjust the network. Through these improvements, our model effectively solves the problems of insufficient feature utilization and single attention source, improves the learning ability and expression ability, and thus can reconstruct the higher quality medical CT image. We conduct a series of experiments; the results show that our method outperforms the previous algorithms and has a better performance of medical CT image reconstruction in the objective evaluation and visual effect.

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