Non-intrusive load disaggregation based on composite deep long short-term memory network

Xia, Min; Liu, Wan’an; Wang, Ke; Song, Wenzhu; Chen, Chunling; Li, Yaping

doi:10.1016/j.eswa.2020.113669

Cited by 74 publications

(36 citation statements)

References 41 publications

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“…The reason for this is that as networks become deeper, the number of channels gradually doubles, but there is actually channel redundancy for most tasks. For the land cover classification task, we use ResNet-50 [33,34] as the backbone, but the number of channels in the last convolutional layer has reached 2048. For the semantic segmentation task with four classes, the number of channels is far larger than what is actually needed.…”

Section: Channel Feature Compressionmentioning

confidence: 99%

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

2021

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Detailed information regarding land utilization/cover is a valuable resource in various fields. In recent years, remote sensing images, especially aerial images, have become higher in resolution and larger span in time and space, and the phenomenon that the objects in an identical category may yield a different spectrum would lead to the fact that relying on spectral features only is often insufficient to accurately segment the target objects. In convolutional neural networks, down-sampling operations are usually used to extract abstract semantic features, which leads to loss of details and fuzzy edges. To solve these problems, the paper proposes a Multi-level Feature Aggregation Network (MFANet), which is improved in two aspects: deep feature extraction and up-sampling feature fusion. Firstly, the proposed Channel Feature Compression module extracts the deep features and filters the redundant channel information from the backbone to optimize the learned context. Secondly, the proposed Multi-level Feature Aggregation Upsample module nestedly uses the idea that high-level features provide guidance information for low-level features, which is of great significance for positioning the restoration of high-resolution remote sensing images. Finally, the proposed Channel Ladder Refinement module is used to refine the restored high-resolution feature maps. Experimental results show that the proposed method achieves state-of-the-art performance 86.45% mean IOU on LandCover dataset.

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Section: Channel Feature Compressionmentioning

confidence: 99%

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

2021

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“…Appliance recognition and thereby disaggregation of energy using high frequency spectrogram feature (Short Time Fourier Transform (STFT)) was conducted in [33]. The authors in [15] used a composite deep LSTM (CD-LSTM) to study the disaggregation using multi-target setting. This is the only work that used multi-target disaggregation.…”

Section: Related Workmentioning

confidence: 99%

“…A disaggregation model can be trained either as a singletarget [5], [14] or multi-target [15] regression problem, or as a single-label [2], [16]- [18], or multi-label [3], [7], [19]- [22] classification problem. Single-and multi-label classification and multi-class classification were explored in many works in the literature.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Ayub¹,

Elsayed²

2020

IJACSA

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Non-Intrusive Load Monitoring (NILM) has become popular for smart meters in recent years due to its low cost installation and maintenance. However, it requires efficient and robust machine learning models to disaggregate the respective electrical appliance energy from the mains. This study investigated NILM in the form of direct point-to-point multiple and single target regression models using convolutional neural networks. Two model architectures have been utilized and compared using five different metrics on two benchmarking datasets (ENERTALK and REDD). The experimental results showed that multi-target disaggregation setting is more complex than single-target disaggregation. For multi-target setting of ENERTALK dataset, the highest individual F1-score is 95.37% and the overall average F1-score is 75.00%. Better results were obtained for the multi-target setting of the other dataset with higher overall average F1-score of 83.32%. Additionally, the robustness and knowledge transfer capability of the models through cross-appliance and cross-domain disaggregation was demonstrated by training for a specific appliance on a specific data, and testing for a different appliance, house and dataset. The proposed models can also disaggregate simultaneous operating appliances with higher F1-scores.

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“…Marine resources are abundant, but the marine environment is complicated, and underwater exploration by humans is difficult. Therefore, underwater robots [1,2] are widely used in the exploration of marine resources and for underwater target recognition [3][4][5][6][7]. However, due to the absorption of light and the scattering and diffusion of floating objects, underwater images often have distortion, color deviation, low contrast and low definition.…”

Section: Introductionmentioning

confidence: 99%

An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization

Zhang

et al. 2020

JMSE

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The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.

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Non-intrusive load disaggregation based on composite deep long short-term memory network

Cited by 74 publications

References 41 publications

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

MFANet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover

Multi-Target Energy Disaggregation using Convolutional Neural Networks

An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization

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