Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

Branco, Nathielle Waldrigues; Cavalca, Mariana Santos Matos; Stefenon, Stéfano Frizzo; Leithardt, Valderi Reis Quietinho

doi:10.20944/preprints202210.0004.v1

Cited by 16 publications

(1 citation statement)

References 66 publications

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“…To Secoli [35] the use of two or more medications, is directly associated with increased risk of DIs, which can cause serious adverse drug reactions. Artificial intelligence-based models are an alternative for dealing with these complex tasks in prediction (emergency [36], faults [37], and power generation [38]), optimization [39], and classification using k-nearest neighbors [40], convolutional neural networks [41], and other structures based on deep learning [42]. There is room for application of these models in several fields, such as in the study of electrical machines [43], combining with optimization methods [44], and sustainability [45].…”

Section: Goals Main Functionalities Findingsmentioning

confidence: 99%

Complex graph neural networks for medication interaction verification

Westarb

Stefenon

Hoppe

et al. 2023

IFS

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This paper presents the development and application of graph neural networks to verify drug interactions, consisting of drug-protein networks. For this, the DrugBank databases were used, creating four complex networks of interactions: target proteins, transport proteins, carrier proteins, and enzymes. The Louvain and Girvan-Newman community detection algorithms were used to establish communities and validate the interactions between them. Positive results were obtained when checking the interactions of two sets of drugs for disease treatments: diabetes and anxiety; diabetes and antibiotics. There were found 371 interactions by the Girvan-Newman algorithm and 58 interactions via Louvain.

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Section: Goals Main Functionalities Findingsmentioning

confidence: 99%

Complex graph neural networks for medication interaction verification

Westarb

Stefenon

Hoppe

et al. 2023

IFS

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Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Narkhede

Hiwale²,

Tidke³

et al. 2023

Algorithms

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Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world’s urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting.

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Impact-Type Sunflower Yield Sensor Signal Denoising Method Based on CEEMD-WTD

et al. 2023

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During the crop harvesting process, it is important to obtain the crop yield quickly, accurately and in real time to accelerate the development of smart agriculture. This paper investigated a denoising method applicable to the impact-type sunflower yield sensor signal under the influence of complex noise background in the pneumatic seed delivery structure for a sunflower combine harvester. A signal processing method combining complementary ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising (WTD) based on an adaptive decomposition capability was proposed by analyzing the non-smoothness of the signal with the impact-type sunflower yield sensor signal in sunflower fields. CEEMD was used to decompose the sunflower seed impact analog signal and field impact-type sunflower yield sensor signal adaptively, and the high frequency components were processed by WTD. Finally the de-noised signal was obtained by reconstruction. An evaluation objective function of the denoising ability of the algorithm based on signal-noise ratio, root mean square error, smoothness and waveform similarity indexes with different weights was also constructed. The results showed that the evaluation objective functions of the simulated and measured signals after denoising by the CEEMD-WTD method are 1.9719 and 4.5318, respectively, which are better than the single denoising methods of EMD (1.5096 and 4.0012), EEMD (1.8248 and 4.0724), CEEMD (1.9516 and 4.3384), and WTD (1.8737 and 4.5294). This method provides a new idea for signal denoising of the impact-type sunflower yield sensor installed in the pneumatic seed delivery structure, and further provides theoretical support and technical references for the development of sunflower high-precision yield measurements in smart agriculture.

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Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

Cited by 16 publications

References 66 publications

Complex graph neural networks for medication interaction verification

Complex graph neural networks for medication interaction verification

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Impact-Type Sunflower Yield Sensor Signal Denoising Method Based on CEEMD-WTD

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