A Climate Adaptation Device-Free Sensing Approach for Target Recognition in Foliage Environments

Zhong, Yi; Bi, Tianqi; Wang, Ju; Zeng, Jie; Huang, Yan; Jiang, Ting; Wu, Siliang

doi:10.1109/tgrs.2022.3225267

Cited by 75 publications

(5 citation statements)

References 44 publications

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“…Recently, ML‐based technologies have been applied to numerous one‐dimensional signal‐processing domains, for example, wireless signals processing 8,9 . Especially in the field of photovoltaic power generation forecasting, ML‐based technologies for PV power prediction get better results than existing physical methods 10 .…”

Section: Introductionmentioning

confidence: 99%

“…Experiment results show that DL based approaches have stronger nonlinear representation and generalization capabilities. However, changing environment has a negative impact on DL models 8 …”

Section: Introductionmentioning

confidence: 99%

“…Recently, ML-based technologies have been applied to numerous one-dimensional signal-processing domains, for example, wireless signals processing. 8,9 Especially in the field of photovoltaic power generation forecasting, ML-based technologies for PV power prediction get better results than existing physical methods. 10 For example, Support Vector Machine (SVM) [11][12][13][14] is applied on PV power prediction with good nonlinear mapping ability, which can accurately fit future PV power generation prediction.…”

Section: Introductionmentioning

confidence: 99%

“…However, changing environment has a negative impact on DL models. 8 Most of above-mentioned methods require accurate future meteorological information to achieve their performance. However, it is very difficult to get accurate meteorological information when environmental factors fluctuate randomly due to clouds or rain.…”

Section: Introductionmentioning

confidence: 99%

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Short‐term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence

Lu,

Chen,

et al. 2023

Computational Intelligence

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Photovoltaic (PV) power generation is widely utilized to satisfy the increasing energy demand due to its cleanness and inexhaustibility. Accurate PV power forecasting can improve the penetration of PV power in the grid. However, it is pretty challenging to predict PV power in short‐term under precious future meteorological information absence conditions. To address this problem, this study proposes the hybrid Contrastive Learning and Temporal Convolutional Network (CL‐TCN), and this forecasting approach consists of two parts, including model training and adaptive processes of forecasting models. In the model training stage, this forecasting method firstly trains 18 TCN models for 18 time points from 9:00 a.m. to 17:30 p.m. These TCN models are trained by only using historical PV power data samples, and each model is used to predict the next half‐hour power output. The adaptive process of models means that, in a practical forecasting stage, PV power samples from historical data are firstly evaluated and scored by a CL based data scoring mechanism to search for the most similar data samples to current measured samples. Then these similar samples are further applied to training a single above‐mentioned well‐trained TCN model to improve its performance in forecasting the next half‐hour PV power. The experimental results tested at the time resolution of 30 min demonstrate that the proposed approach has superior performance in forecasting accuracy not only in smooth PV power samples but also in fluctuating PV power samples. Moreover, the proposed CL based data scoring mechanism can filter useless data samples effectively accelerating the forecasting process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short‐term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence

Lu,

Chen,

et al. 2023

Computational Intelligence

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“…Perception methods based on wireless radio frequency signals can effectively overcome these shortcomings and have gradually gained attention [ 9 , 10 ]. Millimeter-wave [ 11 , 12 ] and ultra-wideband signals [ 13 , 14 ] have strong penetration and detection capabilities and have been widely used in the military, autonomous driving, safety rescue, and other fields. However, specialized signal transceiver devices are costly and challenging to popularize in daily life.…”

Section: Introductionmentioning

confidence: 99%

A Novel Lightweight Human Activity Recognition Method Via L-CTCN

Ding,

Li,

et al. 2023

Sensors

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Wi-Fi-based human activity recognition has attracted significant attention. Deep learning methods are widely used to achieve feature representation and activity sensing. While more learnable parameters in the neural networks model lead to richer feature extraction, it results in significant resource consumption, rendering the model unsuitable for lightweight Internet of Things (IoT) devices. Furthermore, the sensing performance heavily relies on the quality and quantity of data, which is a time-consuming and labor-intensive task. Therefore, there is a need to explore methods that reduce the dependence on the quality and quantity of the dataset while ensuring recognition performance and decreasing model complexity to adapt to ubiquitous lightweight IoT devices. In this paper, we propose a novel Lightweight-Complex Temporal Convolution Network (L-CTCN) for human activity recognition. Specifically, this approach effectively combines complex convolution with a Temporal Convolution Network (TCN). Complex convolution can extract richer information from limited raw complex data, reducing the reliance on the quality and quantity of training samples. Based on the designed TCN framework with 1D convolution and residual blocks, the proposed model can achieve lightweight human activity recognition. Extensive experiments verify the effectiveness of the proposed method. We can achieve an average recognition accuracy of 96.6% with only 0.17 M parameter size. This method performs well under conditions of low sampling rates and a low number of subcarriers and samples.

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An Unsupervised Domain Adaptation-Based Device-Free Sensing Approach for Cross-Weather Target Recognition in Foliage Environments

Zhong,

Bi,

Wang

et al. 2024

Lecture Notes in Electrical Engineering

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A Climate Adaptation Device-Free Sensing Approach for Target Recognition in Foliage Environments

Cited by 75 publications

References 44 publications

Short‐term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence

Short‐term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence

A Novel Lightweight Human Activity Recognition Method Via L-CTCN

An Unsupervised Domain Adaptation-Based Device-Free Sensing Approach for Cross-Weather Target Recognition in Foliage Environments

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