Wyniki wyszukiwania - Biblioteka Nauki

1

An analysis of the effect between the heat index and Long Short-Term Memory model to electricity load forecasting

100%

Anh N. T. N. , Hoang V. V. , Chau D. T. T. , Linh B. D. , Huy N. D.

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tom Vol. 27

1--6

EN

Accurate electricity load forecasting is essential for operating electrical systems. Most of the studies on electricity load forecasting are based on electricity load data or weather data, which is air temperature, but there are not consider the heat index. This paper proposes a short-term electricity load forecasting model using Long-Short Term Memory (LSTM) based on electricity load history and heat index data. In addition, the proposed model is applied to the data of IEVN NLDC (National Load Dispatching Center) in forecasting electricity load before 48 hours. This model is used to predict the electricity load of the Vietnamese nation and the power corporations of Vietnam. For a fair comparison, the LSTM network has fixed parameters, then compared the results when using temperature and the heat index. According to experimental results based on the Mean absolute percentage errors (MAPE) assessment, the proposed model has better accuracy than the model based on electricity load history and temperature.

2

Hydrological Forecasts Modeling Using Artificial Intelligence and Conceptual Models of KébirRhumel Watershed, Algeria

86%

Ramzi K. , Nadir M. , Tewfik B. M. , Hakim D. K.

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2024

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tom Vol. 25, iss. 9

68--80

EN

This study models the rainfall-runoff relationship in the Kebir-Rhumel River watershed in the Constantine Highlands, Algeria, using data from three concomitant rainfall and hydrometric stations. Statistical tests confirmed the absence of breaks in the series. We applied four conceptual models (GR4J, IHAC6, MORDOR, TOPMO8) and neural network models (RNN, NARX, LSTM) over three- and ten-year periods. Among the conceptual models, GR4J provided the best fit, highlighting the non-stationary nature of the relationship. The PMC neural network model performed well over three years but was less effective over ten years due to low flow influence. Notably, the NARX-RNN and RNN-LSTM models showed excellent predictive accuracy, with NARX-RNN perfectlycapturing flow dynamics and RNN-LSTM achieving minimal RMSE and high correlation coefficients. This study lies the comparative analysis of conceptual and neural network models, specifically the NARX-RNN and RNN-LSTM models, which have not been extensively applied in this context. This research fills the gap in understanding the effectiveness of neural network models in modelling non-stationary rainfall-runoff relationships in the region.

3

A segment-wise reconstruction method based on bidirectional long short term memory for Power Line Interference suppression

72%

Qiu Y. , Huang K. , Xiao F. , Shen H.

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tom Vol. 38, no. 2

217--227

EN

The overlap between the signal components of Power Line Interference (PLI) and biomedical signals in the frequency domain makes the filtered results prone to severe distortion. Electrocardiogram (ECG) is a type of biomedical electronic signal used for cardiac diagnosis. The objective of this work is to suppress the PLI components from biomedical signals with minimal distortion, and the object of study is mainly the ECG signals. In this study, we propose a novel segment-wise reconstruction method to suppress the PLI in biomedical signals based on the Bidirectional Recurrent Neural Networks with Long Short Term Memory (Bi-LSTM). Experiments are conducted on both synthetic and real signals, and quantitative comparisons are made with a traditional IIR notch filter and two state-of-the-art methods in the literature. The results show that by our method, the output Signal-to-Noise Ratio (SNR) is improved by more than 7 dB and the settling time for step response is reduced to 0.09 s on average. The results also demonstrate that our method has enough generalization ability for unforeseen signals without retraining.

4

Sequence-to-sequence deep neural network with spatio-spectro and temporal features for motor imagery classification

72%

Rammy S. A. , Abbas W. , Mahmood S. S. , Riaz H. , Rehman H. U. , Abideen R. Z. U. , Aqeel M. , Zhang W.

Biocybernetics and Biomedical Engineering

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2021

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tom Vol. 41, no. 1

97--110

EN

Electroencephalography (EEG) is a method of the brain–computer interface (BCI) that measures brain activities. EEG is a method of (non-)invasive recording ofthe electrical activity ofthe brain. This can be used to build BCIs. From the last decade, EEG has grasped researchers' attention to distinguish human activities. However, temporal information has rarely been retained to incorporate temporal information for multi-class (more than two classes) motor imagery classification. This research proposes a long-short-term-memory-based deep learning model to learn the hidden sequential patterns. Two types of features are used to feed the proposed model, including Fourier Transform Energy Maps (FTEMs) and Common Spatial Patterns (CSPs) filters. Multiple experiments have been conducted on a publicly available dataset. Extraction of spatial and spectro-temporal features using CSP filters and FTEM allow the sequence-tosequence based proposed model to learn the hidden sequential features. The proposed method is trained, evaluated, and optimized for a publicly available benchmark data set and resulted in 0.81 mean kappa value. Obtained results depict the model robustness for the artifacts and suitable for real-life applications with comparable classification accuracy. The code and findings will be available at https://github.com/waseemabbaas/Motor-Imagery-Classification.git.

5

Blood glucose prediction with deep neural networks using weighted decision level fusion

58%

Dudukcu H. V. , Taskiran M. , Yildirim T.

Biocybernetics and Biomedical Engineering

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2021

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tom Vol. 41, no. 3

1208--1223

EN

Background and Objective: Diabetes mellitus is a chronic disease that requires regular monitoring of blood glucose in the circulatory system. If the amount of glucose in the blood is not regulated constantly, this may have vital consequences for the individual. For this reason, there are many studies in the literature that perform blood glucose (BG) prediction. Methods: Blood glucose prediction is generally performed by using many parameters. In this paper, it was attempted to predict the future blood glucose values of the patient by using only the blood glucose values of diabetes patients’ history. For this purpose, Long short term memory (LSTM), WaveNet and Gated Recurrent Units (GRU) and decision-level combinations of these architectures were used to predict blood glucose. First of all, hyper-parameters were selected for the most efficient operation of these network architectures and experimental studies were conducted using the extended OhioT1DM data set which has blood glucose history of 12 diabetes patients. Results: Experimental studies using 30, 45 and 60 min prediction horizon (PH), the average lowest RMSE value were obtained by the fusion of three networks as 21.90 mg/dl, 29.12 mg/dl, 35.10 mg/dl respectively. Conclusions: When the obtained RMSE value compared to state-of-art studies in the literature, the results show that the proposed method is quite successful for short-term blood glucose prediction. In addition, the proposed fusion method gives a new perspective for future studies in the literature for BG prediction.

6

Capsule network assisted electrocardiogram classification model for smart healthcare

58%

Jiao Y. , Qi H. , Wu J.

Biocybernetics and Biomedical Engineering

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2022

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tom Vol. 42, no. 2

543--555

EN

Improving the classification accuracy of electrocardiogram (ECG) signals is of great significance for diagnosing heart abnormalities and arrhythmias and preventing cardiovascular diseases (CVDs). The traditional classification method depends on medical experience to select and extract features artificially, lacks the generalization ability to deal with big medical data. The vital feature extraction ability of neural networks has become a hot topic to solve this problem. Based on this, the capsule network is applied to ECG signal classification in this paper. Based on the original network architecture, long short-term memory (LSTM) network and 1D convolutional neural network (CNN) are added as a parallel feature extraction layer to extract the spatial and temporal features of the ECG signal. In addition, the enhanced routing algorithm is proposed, which uses the prior probability of subcapsules as a weighting factor for routing algorithm classification to weaken the influence of noise capsules. The proposed model is superior to the existing state-of-the-art techniques when tested on the MIT-BIH arrhythmia database.