Gated Recurrent Unit Research Articles

The Comparison of GRU and LSTM in Solar Power Generation Forecasting Application

Precise forecasting of solar power generation is essential for optimizing the incorporation of renewable energy into the electrical grid and maintaining the stability of energy systems. This paper offers a thorough comparative examination of Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks in forecasting solar power generation. Both models were assessed for predicted accuracy and computational efficiency, employing essential performance indicators including Mean Absolute Error (MAE) and R-squared (R²). The efficacy of LSTM and GRU in forecasting is assessed using a real-world dataset. Comprehensive experimental data demonstrate that the GRU model surpasses the LSTM model.

Hybrid Deep Neural Network Approaches for Power Quality Analysis in Electric Arc Furnaces

In this research, we investigate the power quality of the grid where an Electric Arc Furnace (EAF) with a very high load operates. An Electric Arc Furnace (EAF) is a highly nonlinear load that uses very high and variable currents, causing major power quality issues such as voltage sags, flickers, and harmonic distortions. These disturbances produce electrical grid instability, affect the operation of other equipment, and require strong mitigation measures to reduce their impact. To investigate these issues, data are collected from the Point of Common Coupling where the Electric Arc Furnace is fed. The following three main factors are identified for evaluating power quality: apparent power, active and reactive power, and distorted power. Along with these powers, Total Harmonic Distortion, an important indicator of power quality, is calculated. These data are collected during the full process of producing a complete steel batch. To create a Deep Neural Network that can model and forecast power quality parameters, a network is developed using LSTM layers, Convolutional Layers, and GRU Layers, all of which demonstrate good prediction performance. The results of the prediction models are examined, as well as the primary metrics characterizing the prediction, using the following: MAE, RMSE, R-squared, and sMAPE. Predicting active and reactive power and Total Harmonic Distortion (THD) proves useful for anticipating power quality problems in an Electric Arc Furnace (EAF). By reducing the EAF’s impact on the power system, accurate predictions will anticipate and minimize disturbances, optimize energy consumption, and improve grid stability. This research’s principal scientific contribution is the development of a hybrid deep neural network that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) layers. This deep neural network was designed to predict power quality metrics, including active power, reactive power, distortion power, and Total Harmonic Distortion (THD). The proposed methodology indicates an important step in improving the accuracy of power quality forecasting for Electric Arc Furnaces (EAFs). The hybrid model’s ability for analyzing both time-series data and complex nonlinear patterns improves its predictive accuracy compared to traditional methods.

A Performance Analysis of RNN Algorithms for Accurate Hypoglycemia Prediction in Type-1 Diabetes

Diabetes is a chronic disease originating from preeminent blood glucose levels where there is an impairment in the ability of the human body to produce or effectively use insulin, therefore resulting in possible complications to various organ systems. Hypoglycemia can be defined as a critical condition in diabetes that is characterized by very low levels of blood sugar and can emanate from any imbalance between insulin, glucose, and external factors such as medication or physical activity. The prediction of hypoglycemia is a very important task in diabetes management, which encompasses sophisticated technologies and deep-learning systems, such as RNNs, in performing analyses on patient-specific data. These must provide timely warnings to avoid hazardous blood sugar dips. This paper takes a close look at the performance of RNN in the case of accurate hypoglycemia prediction among patients with Type-1 Diabetes, considering a dataset from Shanghai T1DM. In this work, three different RNN architectures are considered for performance evaluation: Long-Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple RNN. The main goal was to compare their predictive performances in forecasting hypoglycemic events, which is an issue of utmost relevance when it comes to proactive diabetes management. The results show generally variable performances by the RNN models. Overall, GRU performed with striking accuracy in hypoglycemia predictions, while LSTM had high specificity. These findings underline that various metrics should be considered for the comprehensive evaluation of predictive models in the management of diabetes. It will give an idea about various RNN algorithms, strengths, and weaknesses to develop more effective and personalized strategies related to hypoglycemia prediction in Type-1 Diabetes.

Recognition of Real-Time Video Activities Using Stacked Bi-GRU with Fusion-based Deep Architecture

Recognizing and understanding human activities in real-time videos is a challenging task due to the complex nature of video data and the need for efficient and accurate analysis. This research pioneers a breakthrough in video activity recognition by introducing a robust framework leveraging the power of a stacked Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) architecture, harmonized within a fusion-based deep model. The stacked Bi-LSTM-GRU model capitalizes on its dual recurrent architecture, capturing nuanced temporal dependencies within video sequences. The fusion-based deep architecture synergizes spatial and temporal features, enabling the model to discern intricate patterns in human activities. To further enhance the discriminative power of the model, we introduce a fusion module in the proposed deep architecture. The fusion module integrates multi-modal features extracted from different levels of the network hierarchy, allowing for a more comprehensive representation of video activities. We demonstrate the efficacy of our approach through rigorous experimentation on UCF50, UCF101, and HMDB51 datasets. In experiments on the UCF50 dataset, our model achieves an accuracy of 97.01% and 95.86% on training and validation sets respectively, showcasing its proficiency in discerning activities across a diverse range of scenarios. The evaluation extends to the UCF101 dataset, where the proposed approach achieves a competitive accuracy of 97.62% and 96.93% on training and validation sets, surpassing previous benchmarks by a margin of approx 1%. Further-more, on the challenging HMDB51 dataset, the model demonstrates a robust accuracy of 89.71%and 88.88% on training and validation sets, solidifying its efficacy in intricate action recognition tasks.

Semantic ghost imaging based on semantic coding

To address the challenge of reconstructing high-quality images under low sampling during the Ghost Imaging (GI) procedure, we propose a semantic GI method based on semantic encoding. Through training, the obtained continuous weights of the convolution kernels are served as the speckle patterns. A processing module and a Recurrent Neural Network (RNN) are then employed to decode and reconstruct the images. Both the speckle patterns and their corresponding bucket values are optimized, so that the semantic information of the target object can be better extracted. With different gated recurrent units (GRU) layers for the target objects in different datasets, the feasibility of the proposed GI method is validated by the numerical simulations and experiments on the simple target objects (the handwriting digits “0” to “9”) and more complex target objects (“NUPT”). The results show that the target objects (the handwriting digits) can be reconstructed with higher quality even at a lower sampling rate of 1.28%. Additionally, the proposed method has applicability for more complex objects (such as “NUPT”) in real applications. In comparison with those results by using traditional ghost imaging (TGI), deep convolution auto-encoder network (DCAN), and RNN-based GI (GI-RNN), the proposed GI method shows a better performance in terms of the quality of reconstructed images and the training time, that is, the proposed method can have both good reconstructed image quality and less training time. By introducing the concept of semantic communication into GI, the proposed method provides a new idea for the model-based GI.

DVDS: A deep visual dynamic slam system

Simultaneous localization and mapping (SLAM) utilizing visual sensors represent an extensively investigated research area, holding significant potential for advancements in robotics and autonomous vehicular systems. Recently, dense SLAM systems underpinned by learning-based methodologies have showcased superior accuracy and robustness compared to conventional techniques. Nevertheless, contemporary learning-based SLAM systems exhibit notable discrepancies in pose estimation, particularly within dynamic environments. In addition, the constrained receptive field of convolutional features in these methods impedes their efficacy when confronted with homogeneous, texture-less images, rendering them vulnerable to noise perturbations. We develop a novel deep visual dynamic slam (DVDS) system that exploits solely static pixels within images to retrieve the camera poses. Specifically, we formulate a dynamic object exclusion mechanism that excises dynamic constituents within the scene before the optical flow computation, thus optimizing the precision of the estimation. In addition, we unveil an efficient dispersive transformer (DisFormer) that facilitates per-pixel features in assimilating long-range information from surrounding features, culminating in constructing more precise 4D correlation volumes. Building on the DisFormer, we suggest a Disformer-based gated recurrent unit (GRU) to generate a refined flow field coupled with a confidence map, which is subsequently employed by the dense bundle adjustment layer to iteratively rectify the residuals of inverse depths and associated camera poses. The global receptive field provided by the DisFormer promotes information integration from a wider contextual window, thus improving the robustness of our SLAM system. Comprehensive experiments underscore that our proposed DVDS system manifests superior efficacy compared with state-of-the-art works across both static and dynamic scenes.

Pelabelan Sentimen Berbasis Semi-Supervised Learning menggunakan Algoritma LSTM dan GRU

In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling (machine annotation). This research uses machine annotators in the form of Deep Learning algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The word weighting method used in this research is Word2Vec Continuous Bag of Word (CBoW). The results showed that the GRU algorithm tends to have a better accuracy rate than the LSTM algorithm. The average accuracy of the training results of the LSTM and GRU algorithm models is 0.904 and 0.913. In contrast, the average accuracy of labeling by LSTM and GRU is 0.569 and 0.592, respectively.

Application of Deep Learning for Heart Attack Prediction with Explainable Artificial Intelligence

Heart disease remains a leading cause of mortality worldwide, and the timely and accurate prediction of heart attack is crucial yet challenging due to the complexity of the condition and the limitations of traditional diagnostic methods. These challenges include the need for resource-intensive diagnostics and the difficulty in interpreting complex predictive models in clinical settings. In this study, we apply and compare the performance of five well-known Deep Learning (DL) models, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a Hybrid model, to a heart attack prediction dataset. Each model was properly tuned and evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC) as performance metrics. Additionally, by integrating an Explainable Artificial intelligence (XAI) technique, specifically Shapley Additive Explanations (SHAP), we enhance the interpretability of the predictions, making them actionable for healthcare professionals and thereby enhancing clinical applicability. The experimental results revealed that the Hybrid model prevailed, achieving the highest performance across all metrics. Specifically, the Hybrid model attained an accuracy of 91%, precision of 89%, recall of 90%, F1-score of 89%, and an AUC of 0.95. These results highlighted the Hybrid model’s superior ability to predict heart attacks, attributed to its efficient handling of sequential data and long-term dependencies.

Forecasting glucose values for patients with type 1 diabetes using heart rate data

Background:Type 1 Diabetes Mellitus (T1DM) is a chronic metabolic disease affecting millions of people worldwide. T1DM requires patients to continuously monitor their blood glucose levels. Due to pancreatic dysfunctions, patients use insulin injections to correct glucose values by synthetic insulin. Continuous Glucose Monitoring (CGM) is a system which includes an algorithm allowing to measure (and in some cases to predict) glucose levels at a frequent sampling time. This enable implementing advanced devices, including automated insulin pump delivery. Nevertheless, CGM still presents some limitations, including (i) the delay (time lag) in detecting change in glucose levels compared to the traditional blood glucose measurement, and (ii) the lack of a sufficient and acceptable time to accurately predict glucose values. Methods:We propose a framework based on a Gated Recurrent Unit (GRU) model to forecast both short- and long-term glucose values using heart rate (HR) and interstitial glucose (IG) values. The framework acquires HR and IG data and predicts glucose values with higher precision compared to state-of-the-art models. For training and testing the proposed framework, we used the OhioT1DM Dataset, which includes physiological data such as HR and IG values collected over an 8-week observation period. Additionally, we validated our framework using two other glucose datasets to ensure its generalizability across different HR and IG sampling frequencies. The proposed framework can be used to optimize the CGM system by incorporating patient HR measurements, thereby improving the prediction of short- and long-term glucose levels and reducing risks associated with conditions like hypoglycemia. Results:Experimental tests were conducted using HR and IG data from the OhioT1DM Dataset, as well as from two additional T1DM patient datasets. We analyzed 6 patients from Ohio dataset while we validated the algorithm on 23 patients coming from two different university hospitals (6 from the University of Catanzaro medical hospital and 17 gathered from a validated study at IRCCS San Matteo Hospital in Pavia) for a total number of 29 patients. Our framework demonstrates an improvement in forecasting IG values in terms of RMSE and MAE for different choice of prediction horizons (PH). In the case of a PH of 5, 10, 20, 30, and 60 min, we reach an RMSE of 5.0, 9.38, 15.27, 20.48, and 34.16 respectively. The framework is freely available as an open-source, with an example dataset on a GitHub repository (see https://github.com/rafgia/attention_to_glycemia). Conclusion:Our framework offers a promising solution for improving glucose level prediction and management in T1DM patients. By leveraging a GRU model and incorporating HR and IG values, we achieve more precise glucose level forecasting compared to state-of-the-art models. This approach not only enhances the accuracy of glucose predictions but also mitigates the risks associated with hypoglycemia.

Time series forecasting using transformer neural network

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is proposed.This model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes.

Vessel Trajectory Prediction Based on Automatic Identification System Data: Multi-Gated Attention Encoder Decoder Network

Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in maritime traffic studies. Within this domain, the precise forecasting of ship trajectories stands as a central challenge. In this study, we propose the multi-gated attention encoder decoder (MGAED) network, a model based on an encoder–decoder structure specialized for predicting ship trajectories in canals. The model employs a long short-term memory network (LSTM) as an encoder, combined with multiple Gated Recurrent Units (GRUs) and an attention mechanism for the decoder. Long-term dependencies in time-series data are captured through GRUs, while the attention mechanism is used to strengthen the model’s ability to capture key information, and a soft threshold residual structure is introduced to handle sparse features, thus enhancing the model’s generalization ability and robustness. The efficacy of our model is substantiated by an extensive evaluation against current deep learning benchmarks. Through comprehensive comparison experiments with existing deep learning methods, our model shows significant improvements in prediction accuracy, with an at least 9.63% reduction in the mean error (MAE) and an at least 20.0% reduction in the mean square error (MSE), providing a new solution to improve the accuracy and efficiency of ship trajectory prediction.

Comparative analysis of novel data‐driven techniques for remaining useful life estimation of wind turbine high‐speed shaft bearings

AbstractAs the global momentum for wind power generation accelerates, the industry faces substantial challenges due to premature failures in wind turbine components. These failures, particularly in critical elements like the high‐speed shaft bearing, lead to significant operational losses, including unplanned downtime and elevated maintenance costs. To mitigate these issues, it's crucial to have precise predictions of the remaining useful life (RUL) of these components, enabling timely interventions and more efficient maintenance schedules. This article proposes advanced, data‐driven approaches for estimating the RUL of wind turbine high‐speed shaft bearings, utilizing cutting‐edge techniques such as long short‐term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent units (GRU), and random forest (RF) algorithms. Our analysis leverages vibration data from a 2 MW wind turbine equipped with a 20‐tooth pinion gear, providing a thorough validation and comparison of these methodologies against traditional models. Our results reveal that the LSTM and BiLSTM models excel in both accuracy and computational efficiency for predicting RUL and enhancing system prognosis, surpassing the performance of conventional RF and GRU methods. This research underscores the potential of our innovative data‐driven strategies to develop effective RUL estimation algorithms, significantly advancing wind turbine proactive operation and maintenance operations.

SSRL-UAVs: A Self-Supervised Deep Representation Learning Approach for GPS Spoofing Attack Detection in Small Unmanned Aerial Vehicles

Self-Supervised Representation Learning (SSRL) has become a potent strategy for addressing the growing threat of Global Positioning System (GPS) spoofing to small Unmanned Aerial Vehicles (UAVs) by capturing more abstract and high-level contributing features. This study focuses on enhancing attack detection capabilities by incorporating SSRL techniques. An innovative hybrid architecture integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to detect attacks on small UAVs alongside two additional architectures, LSTM-Recurrent Neural Network (RNN) and Deep Neural Network (DNN), for detecting GPS spoofing attacks. The proposed model leverages SSRL, autonomously extracting meaningful features without the need for many labelled instances. Key configurations include LSTM-GRU, with 64 neurons in the input and concatenate layers and 32 neurons in the second layer. Ablation analysis explores various parameter settings, with the model achieving an impressive 99.9% accuracy after 10 epoch iterations, effectively countering GPS spoofing attacks. To further enhance this approach, transfer learning techniques are also incorporated, which help to improve the adaptability and generalisation of the SSRL model. By saving and applying pre-trained weights to a new dataset, we leverage prior knowledge to improve performance. This integration of SSRL and transfer learning yields a validation accuracy of 79.0%, demonstrating enhanced generalisation to new data and reduced training time. The combined approach underscores the robustness and efficiency of GPS spoofing detection in UAVs.

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.

Multiagent trajectory decision‐making for complex systems based on transformer large model structure

AbstractThe article explores the application of transformer‐based large models for trajectory decision‐making in complex decision systems, focusing on multiagent nodes in a topological structure. By leveraging the capabilities of large models, the proposed approach enhances decision‐making in model‐free control environments characterized by numerous agents, diverse internal tasks, and unknown conditions. The proposed framework integrates advanced transformer models with a sophisticated decision‐making framework to enhance the efficiency, safety, and reliability of multiagent operations in dynamic environments. The results demonstrate the superior performance of the transformer model, achieving a trajectory decision‐making accuracy of 97.8%, significantly outperforming other models such as long short‐term memory (LSTM), gate recurrent unit (GRU), and traditional approaches. The visualizations highlight the close alignment between the true and predicted outcomes of complex decision‐making systems, showcasing the model's ability to capture complex dependencies and interactions within the multiagent environment. High task completion rates and low collision rates further underscore the effectiveness of the decision‐making framework in optimizing agent coordination and ensuring safe operations.

CGAOA-AttBiGRU: A Novel Deep Learning Framework for Forecasting CO2 Emissions

Accurately predicting carbon dioxide (CO2) emissions is crucial for environmental protection. Currently, there are two main issues with predicting CO2 emissions: (1) existing CO2 emission prediction models mainly rely on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) models, which can only model unidirectional temporal features, resulting in insufficient accuracy: (2) existing research on CO2 emissions mainly focuses on designing predictive models, without paying attention to model optimization, resulting in models being unable to achieve their optimal performance. To address these issues, this paper proposes a framework for predicting CO2 emissions, called CGAOA-AttBiGRU. In this framework, Attentional-Bidirectional Gate Recurrent Unit (AttBiGRU) is a prediction model that uses BiGRU units to extract bidirectional temporal features from the data, and adopts an attention mechanism to adaptively weight the bidirectional temporal features, thereby improving prediction accuracy. CGAOA is an improved Arithmetic Optimization Algorithm (AOA) used to optimize the five key hyperparameters of the AttBiGRU. We first validated the optimization performance of the improved CGAOA algorithm on 24 benchmark functions. Then, CGAOA was used to optimize AttBiGRU and compared with 12 optimization algorithms. The results indicate that the AttBiGRU optimized by CGAOA has the best predictive performance.

How to build vibrant communities by utilizing functional zones? A community-detection-based approach for revealing the association between land use and community vibrancy

Investigating the association between land use and urban vibrancy is crucial for sustainable urban development. To that end, according to the four conditions proposed by Jacobs (1962), suitable units should be identified before investigating this association. In this study, we consider spatial-temporal human activity patterns and propose a novel community-detection approach integrating graph neural network (GNN) and gated recurrent unit (GRU) to identify suitable geographic units. Then, the impact of land-use-relevance variables on community vibrancy is measured by the geographically and temporally weighted regression (GTWR) model. Using the cell phone signaling data from Beijing, we initially identify 66 communities, primarily comprising intra-travel (the origin and destination are in the same community) and a small proportion of inter-community travel, showing distinct vibrancy patterns and different land use characteristics. Then, we develop a GTWR model to reveal the associations between land use and community vibrancy, revealing that land-use-based variables, such as mixed land use and densities of points of interest (POIs), have spatial-temporal-heterogeneous impacts on community vibrancy, implying the heterogeneity among the development focus of communities, such as population density and functional zone structure. Consequently, we propose heterogeneous development strategies for communities, which could offer valuable suggestions for enhancing urban vibrancy.

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc. However, most existing research focuses on using all local data to jointly construct prediction models, facing data security and privacy issues. In response to these challenges, this paper presents a Personalized Federated Learning method based on Client Reputation for traffic flow prediction. This method avoids direct sharing of original data by performing model training and information aggregation locally, thereby better protecting user privacy. We initially constructed the foundational model by integrating Graph Neural Networks (GCNs) and Gated Recurrent Units (GRUs) for federated learning. GCNs are capable of capturing the spatial relationships between nodes in a road network, thereby extracting the spatial features of traffic flow. On the other hand, GRUs has advantages at handling sequential data, effectively capturing the dynamic temporal characteristics of traffic flow. Consequently, by combining GCNs and GRUs, we are able to build a model that captures both the spatial and temporal features of traffic flow. Subsequently, we employed a federated learning framework to train the model. In the training process, we propose two evaluation algorithms to evaluate the reputation of all clients participating in federated learning. Then, different weights are customized for each client through a personalized algorithm based on the client's reputation to improve its personalization. Finally, we validated the method on the License Plate Recognition (LPR) datasets collected in Changsha city, China. The experimental results indicate that the method can achieve good prediction performance and stability while protecting the privacy of all participants.

RNN-Based Monthly Inflow Prediction for Dez Dam in Iran Considering the Effect of Wavelet Pre-Processing and Uncertainty Analysis

In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using various types of RNNs, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and stacked long short-term memory (Stacked LSTM). It was observed that considering flow discharge, temperature, and precipitation as inputs to the models yields the best results. Additionally, wavelet transform was employed to enhance the accuracy of the RNNs. Among the RNNs, the GRU model exhibited the best performance in simulating monthly streamflow without using wavelet transform, with RMSE, MAE, NSE, and R2 values of 0.061 m3/s, 0.038 m3/s, 0.556, and 0.642, respectively. Moreover, in the case of using wavelet transform, the Bi-LSTM model with db5 mother wavelet and decomposition level 5 was able to simulate the monthly streamflow with high accuracy, yielding RMSE, MAE, NSE, and R2 values of 0.014 m3/s, 0.008 m3/s, 0.9983, and 0.9981, respectively. Uncertainty analysis was conducted for the two mentioned superior models. To quantify the uncertainty, the concept of the 95 percent prediction uncertainty (95PPU) and the p-factor and r-factor criteria were utilized. For the GRU, the p-factor and r-factor values were 82% and 1.28, respectively. For the Bi-LSTM model, the p-factor and r-factor values were 94% and 1.06, respectively. The obtained p-factor and r-factor values for both models are within the acceptable and reliable range.

Structural damage detection of floating offshore wind turbine blades based on Conv1d-GRU-MHA network

Due to the global commitment to mitigate climate warming by replacing fossil fuels, the global wind power installed capacity continues to increase. The reliability of wind turbines is a vital factor in ensuring their safety and profitability. Early fault detection can significantly reduce the maintenance costs of wind farms. This paper constructs a Conv1d-GRU-MHA model for detecting blade structural damage in floating offshore wind turbines (FOWTs) operating under complex conditions. This model combines a one-dimensional convolutional neural network (Conv1d), gated recurrent units (GRU), and multi-head attention mechanism (MHA) to provide accurate detection of blade structural damage. This amalgamation enables the model to comprehend diverse scales of sequence data, effectively capturing intricate patterns and abstract features. The NREL OpenFAST software was used to model a 5 MW wind turbine, where structural damage was introduced on the blades. Acceleration data was collected from blades and tower in different directions and under different wind turbulence intensities (WTI). The Conv1d-GRU-MHA network predicts blade health with an accuracy of up to 97.58 % under the 5 % turbulence level background, with both precision and recall being superior to the comparative models.