Bidirectional Gated Recurrent Unit Research Articles

Adipose Tissue Segmentation after Lung Slice Localization in Chest CT Images Based on ConvBiGRU and Multi-Module UNet

The distribution of adipose tissue in the lungs is intricately linked to a variety of lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Accurate detection and quantitative analysis of subcutaneous and visceral adipose tissue surrounding the lungs are essential for effectively diagnosing and managing these diseases. However, there remains a noticeable scarcity of studies focusing on adipose tissue within the lungs on a global scale. Thus, this paper introduces a ConvBiGRU model for localizing lung slices and a multi-module UNet-based model for segmenting subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), contributing to the analysis of lung adipose tissue and the auxiliary diagnosis of lung diseases. In this study, we propose a bidirectional gated recurrent unit (BiGRU) structure for precise lung slice localization and a modified multi-module UNet model for accurate SAT and VAT segmentations, incorporating an additive weight penalty term for model refinement. For segmentation, we integrate attention, competition, and multi-resolution mechanisms within the UNet architecture to optimize performance and conduct a comparative analysis of its impact on SAT and VAT. The proposed model achieves satisfactory results across multiple performance metrics, including the Dice Score (92.0% for SAT and 82.7% for VAT), F1 Score (82.2% for SAT and 78.8% for VAT), Precision (96.7% for SAT and 78.9% for VAT), and Recall (75.8% for SAT and 79.1% for VAT). Overall, the proposed localization and segmentation framework exhibits high accuracy and reliability, validating its potential application in computer-aided diagnosis (CAD) for medical tasks in this domain.

Prediction of Drug-Target Affinity Using Attention Neural Network

Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.

Precision forecasting of grinding wheel Wear: A TransBiGRU model for advanced industrial predictive maintenance

In intelligent manufacturing, accurate prediction of grinding wheel wear is essential to reduce maintenance costs and improve production efficiency. To achieve accurate forecasts, this paper introduces a grinding wheel wear prediction model, TransBiGRU, incorporating a Transformer encoder, Bidirectional Gated Recurrent Unit (BiGRU), positional encoding layer, and position-wise feedforward layer. The model extracts features by analyzing current signal characteristics in basic statistical, time, and frequency domains during the machining process. Training is conducted through K-fold cross-validation to ensure model stability. The experimental results indicate that the model achieved good performance with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) evaluation metrics, obtaining values of 2.0898, 3.262, and 0.9338, respectively. Systematically reducing modules validates the importance of each module in enhancing predictive capabilities. This research achieves the practical application of low-cost current sensors in optimizing maintenance plans and reducing downtime in predicting grinding wheel wear.

An IIoT Temporal Data Anomaly Detection Method Combining Transformer and Adversarial Training

The existing Industrial Internet of Things (IIoT) temporal data analysis methods often suffer from issues such as information loss, difficulty balancing spatial and temporal features, and being affected by training data noise, which can lead to varying degrees of reduced model accuracy. Therefore, a new anomaly detection method was proposed, which integrated Transformer and adversarial training. Firstly, a bidirectional spatiotemporal feature extraction module was constructed by combining Graph Attention Networks (GAT) and Bidirectional Gated Recurrent Unit (BiGRU), which can simultaneously extract spatial and temporal features. Then, by combining multi-scale convolution with Long Short-Term Memory (LSTM), multi-scale contextual information was captured. Finally, an improved Transformer was used to fuse multi-dimensional features, combined with an adversarial-trained variational autoencoder to calculate the anomalies of the input data. This method outperforms other comparison models by conducting experiments on four publicly available datasets.

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-conditional generative adversarial network (CGAN), which relies on a CGAN that combines one-dimensional convolutional neural network (CNN) with a bidirectional gate recurrent unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers early fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier

Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhibit cancer cell growth and survival. Traditional classification approaches for ACPs are expensive and time-consuming. This paper proposes a pre-trained classifier model, ESM2-GRU, for ACP prediction to make it easier to predict ACPs, gain a better understanding of the structural and functional differences of anti-cancer peptides, and optimize the design for the development of more effective anti-cancer treatment strategies. The model is made up of the ESM2 pre-trained model, a bidirectional GRU recurrent neural network, and a fully connected layer. ACP sequences are first fed into the ESM2 model, which then expands the dimensions before feeding the findings back into the bidirectional GRU recurrent neural network. Finally, the fully connected layer generates the ultimate output. Experimental validation demonstrates that the ESM2-GRU model greatly improves classification performance on the benchmark dataset ACP606, with AUC, ACC, and MCC values of 0.975, 0.852, and 0.738, respectively. This exceptional prediction potential helps to identify specific types of anti-cancer peptides, improving their targeting and selectivity and, therefore, furthering the development of tailored medicine and treatments.

Short-term office temperature forecasting through a data-driven approach integrated with bidirectional gated recurrent neural network

Accurate indoor temperature forecasting can facilitate the energy savings, thus improving the building energy efficiency. Based on the discontinuous sensor data collection in this research, a novel gated recurrent neural network model, bidirectional Gated Recurrent Unit (Bi-GRU) is developed, achieving superior performances and optimizing the forecasting accuracy, compared to other state-of-the-art models. The developed data-driven approach based on integrating data pre-processing, feature engineering, Kalman time-series smoothing, and neural network models is applied for indoor temperature forecasting, calibrated on two test scenarios. The prediction results are examined by error performance metrics, and the relative prediction residuals are visually compared in time-series plots and quantile–quantile (Q-Q) plots. Kalman smoothing has effectively improved the forecasting accuracy of regression models by reducing their prediction errors and upgrading the prediction accuracies with approximately 4 ∼ 9 % and 6 ∼ 19 % in single-step- and multi-step-ahead forecasting tasks, respectively. Bi-GRU with Kalman smoothing is verified to be the most efficacious forecasting solution by yielding the accuracy of 94.28 % and 91.58 % in Test scenario I, and 91.58 % and 83.82 % in Test scenario II for single-step- and multi-step-ahead predictions, respectively.

Revealing the degradation patterns of lithium-ion batteries from impedance spectroscopy using variational auto-encoders

The aging life estimation of lithium-ion batteries (LIBs) is of great significance to the use, maintenance and economic analysis of energy storage systems. The estimation method of aging life based on electrochemical impedance spectroscopy (EIS) has received more attention due to its high accuracy. Due to the high dimensionality of impedance data, it cannot directly reflect the decline trend, this paper proposes a method of combining variational auto-encoders (VAE) and bidirectional gated recurrent unit (BiGRU) for automatic feature extraction from EIS data and battery aging life estimation. Reducing the dimensionality of impedance data through VAE can greatly reduce computational complexity, and the high-quality features extracted are mapped to battery capacity using BiGRU, which can improve estimation accuracy. The validation results show that the method can adapt to various working conditions, and has good estimation accuracy and robustness. MAE and RMSE are less than 1.27 mAh and 1.43 mAh, respectively. We demonstrate that the latent variables extracted from EIS data using unsupervised learning method reliably represent the degradation patterns of LIBs.

Railway Accident Causation Prediction with Improved Transformer Model Based on Lexical Information and Contextual Relationships

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents.

Design of urban road fault detection system based on artificial neural network and deep learning.

In urban traffic management, the timely detection of road faults plays a crucial role in improving traffic efficiency and safety. However, conventional methods often fail to fully leverage the information from road topology and traffic data. To address this issue, we propose an innovative detection system that combines Artificial Neural Networks (ANNs), specifically Graph Convolutional Networks (GCN), Bidirectional Gated Recurrent Units (BiGRU), and self-attention mechanisms. Our approach begins by representing the road topology as a graph and utilizing GCN to model it. This allows us to learn the relationships between roads and capture their structural dependencies. By doing so, we can effectively incorporate the spatial information provided by the road network. Next, we employ BiGRU to model the historical traffic data, enabling us to capture the temporal dynamics and patterns in the traffic flow. The BiGRU architecture allows for bidirectional processing, which aids in understanding the traffic conditions based on both past and future information. This temporal modeling enhances our system's ability to handle time-varying traffic patterns. To further enhance the feature representations, we leverage self-attention mechanisms. By combining the hidden states of the BiGRU with self-attention, we can assign importance weights to different temporal features, focusing on the most relevant information. This attention mechanism helps to extract salient features from the traffic data. Subsequently, we merge the features learned by GCN from the road topology and BiGRU from the traffic data. This fusion of spatial and temporal information provides a comprehensive representation of the road status. By employing a Multilayer Perceptron (MLP) as a classifier, we can effectively determine whether a road is experiencing a fault. The MLP model is trained using labeled road fault data through supervised learning, optimizing its performance for fault detection. Experimental evaluations of our system demonstrate excellent performance in road fault detection. Compared to traditional methods, our system achieves more accurate fault detection, thereby improving the efficiency of urban traffic management. This is of significant importance for city administrators, as they can promptly identify road faults and take appropriate measures for repair and traffic diversion.

A hybrid ensemble optimized BiGRU method for short-term photovoltaic generation forecasting

In the context of prominent energy crisis, photovoltaic power (PV) generation has received increasing attention, then accurate PV generation forecasting is crucial for ensuring the smooth operation of power stations. However, existing research is insufficient in comprehensively analyzing the impact of PV generation volatility. To fill the gaps and enhance the prediction accuracy, this paper proposes a new hybrid forecasting method. We first introduce the Locally Weighted Scatterplot Smoothing (LOWESS) method to process the data and enhance the data stability, and use Pearson correlation coefficient (PCC) and Random Forests (RF) for feature selection to improve the quality of input data. Then we use Attention mechanism and Convolutional Neural Network (CNN) layer to optimize Bi-directional Gate Recurrent Unit (BiGRU) model and form a new hybrid model. Finally, based on the Bagging algorithm, we use ensemble learning to further optimize the hybrid BiGRU model to enhance the depth and performance. The proposed method is validated through case analysis results from two different locations, Xuhui District in Shanghai, China and the DKASC area in Alice Springs, Australia. The results demonstrate that, compared with other models, the developed method exhibits exceptional prediction performance and effectively enhances the accuracy of PV generation forecasting. Keywords: photovoltaic power generation; Locally Weighted Scatterplot Smoothing; feature selection; ensemble learning; Bi-directional Gate Recurrent Unit.

Hyperspectral crop image classification via ensemble of classification model with optimal training

Agriculture is a significant source of income, and categorizing the crop has turned into vital factor that aids more in the crop production sector. Traditionally, crop development stage determination is done manually by eye inspection. However, producing high-quality crop type maps using modern approaches remains difficult. In this paper, the hyperspectral crop image classification model is proposed that includes four stages, they are (a) preprocessing, (b) segmentation, (c) feature extraction and (d) classification. In the preprocessing step, the hyperspectral image is provided as input, where the filtering process will carried out using median filtering. The filtered image is then used as the segmentation’s input. The image is segmented in the segmentation step using the enhanced entropy-based fuzzy c-means technique. Subsequently, spectral spatial features and vegetation index-based features are derived from segmented images. The final step is the classification, where the ensemble of classification model will be used that includes models like Convolutional Neural Networks (CNN), Deep Maxout (DMO), Recurrent Neural Networks (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU), respectively. The proposed Self Improved Tasmanian devil Optimization (SI-TDO) approach has optimally adjusted the Bi-GRU model’s training weights to enhance ensemble classification performance. Finally, the effectiveness of the proposed SI-TDO method compared to the traditional algorithm is examined for several metrics. The SI-TDO obtained the greatest accuracy of 94.68% in training rate 80, while other existing models have the lowest ratings.

Improved deep learning intrusion detection model based on GAN data enhancement

With the development of information technology, the number and methods of cyber attacks continue to increase, making network security issues increasingly important. Intrusion detection has become a vital means of dealing with cyber threats. Current intrusion detection methods predominantly rely on machine learning. However, machine learning suffers from limitations in detection capability and the requirement for extensive feature engineering. Additionally, current intrusion detection datasets face the challenge of data imbalance. To address these challenges, this paper proposes a novel solution leveraging Generative Adversarial Networks (GANs) to balance the dataset and introduces an attention mechanism into the generator to efficiently extract key feature information, the mechanism can effectively sort the key information of the data and quickly capture important features. Subsequently, a combination of 1D Convolutional Neural Networks (1DCNN) and Bidirectional Gated Recurrent Units (BiGRU) is employed to construct a classification model capable of extracting both spatial and temporal features. Furthermore, Particle Swarm Optimization (PSO) is utilized to optimize the input weights and hidden biases of the model, so as to further improve the accuracy and robustness of the model. Finally, the model is trained and implemented for network intrusion detection. To demonstrate the applicability of the model, experiments were conducted using the NSL-KDD dataset and the UNSW-NB15 dataset. The final results showed that the proposed model outperformed other models, achieving accuracies of 99.15% and 97.33% on the respective datasets. This indicates that the model improves the efficiency of network intrusion detection and better ensures the effectiveness of network security.

LNBi-GRU model for coal price prediction and pattern recognition analysis

Accurately predicting coal prices and identifying related factors are of great significance for energy market. However, limited attention has been devoted to the precision of predicting coal price trends and the comprehensive analysis of influencing factors. In this paper, we propose LNBi-GRU (Layer Normalization and Bidirectional GRU), which integrates Layer Normalization (LN) and Bidirectional network (Bi) to form the LNBi layer, thereby advancing coal price forecasting. Meanwhile, this study also achieves coal price pattern recognition through a combination of prediction, evaluation, and explanation. The results show that LNBi-GRU outperforms the selected baseline models in terms of both prediction accuracy and stability, and can predict mutation points more accurately. Ablation experiments prove the effectiveness of the added modules including LN and Bi. Moreover, market price emerges as a critical factor affecting coal prices. From a cyclical perspective, the dominant factor in the up cycle shifts from cost push to demand pull, while the dominant factor in the down cycle shifts from demand pull to cost push.

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1-measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1-measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance.

ST-TGR: Spatio-Temporal Representation Learning for Skeleton-Based Teaching Gesture Recognition.

Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher's gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher's skeleton and then inputs the recognized sequence of the teacher's skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed.

Fault transfer diagnosis of rolling bearings across different devices via multi-domain information fusion and multi-kernel maximum mean discrepancy

The current deep learning-based intelligent diagnosis algorithms depend on large amounts of well-labeled data, but they may not perform well in engineering practice where the fault data available from a single device is limited. The significant difference in data distribution between different devices makes it more difficult to transfer diagnosis across devices. Therefore, aiming at the problem that the great difference in the data distribution of source and target domains in cross-device transfer diagnosis leads to the low diagnosis accuracy, this paper investigates a deep transfer network model based on multi-domain information fusion and multi-kernel maximum mean discrepancy (MK-MMD) for fault diagnosis of rolling bearings across different devices. Firstly, to address the problem that single-domain information is difficult to adequately characterize rolling bearing health status information of different devices, a multi-domain information feature extractor based on multi-attention mechanism is constructed to capture the important features of vibration signals in different transform domains. Secondly, the extracted multi-domain features are input into the bidirectional gated recurrent unit (BiGRU) network for feature fusion, and MK-MMD is used to narrow the distribution distance between the source domain and target domain to obtain domain invariant features, and then achieve the transfer diagnosis of rolling bearing faults between different devices. Finally, the method investigated is verified by rolling bearing fault transfer diagnosis tests between different devices, and the results show that the suggested method can adapt to the feature distribution between different domains, and improve the transfer diagnosis accuracy between different devices.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

A fault diagnosis method for flexible converter valve equipment based on DSC-BIGRU-MA

Introduction: Precise fault diagnosis is crucial for enhancing the reliability and lifespan of the flexible converter valve equipment. To address this issue, depthwise separable convolution, bidirectional gate recurrent unit, and multi-head attention module (DSC-BiGRU-MAM) based fault diagnosis approach is proposed.Methods: By DSC and BiGRU operation, the model can capture the correlation between local features and temporal information when processing sequence data, thereby enhancing the representation ability and predictive performance of the model for complex sequential data. In addition, by incorporating a multi-head attention module, the proposed method dynamically learns important information from different time intervals and channels. The proposed MAM continuously stimulates fault features in both time and channel dimensions during training, while suppressing fault independent expressions. As a result, it has made an important contribution to improving the performance of the fault diagnosis model.Results and Discussion: Experimental results demonstrate that the proposed method achieves higher accuracy compared to existing methods, with an average accuracy of 95.45%, average precision of 88.67%, and average recall of 89.03%. Additionally, the proposed method has a moderate number of model parameters (17,626) and training time (935 s). Results indicate that the proposed method accurately diagnoses faults in flexible converter valve equipment, especially in real-world situations with noise overlapping signals.

The marine diesel engine exhaust gas temperature baseline model based on particle swarm optimised generalised regression neural network

ABSTRACT This study addresses limitations in traditional condition monitoring for marine diesel engine (MDE) reliability and stability by proposing a hybrid machine learning and deep learning (DL) model called Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) for real-time exhaust gas temperature (EGT) monitoring. The model integrates Mahalanobis distance (MD) calibration, wavelet packet denoising, and Pearson correlation analysis for comprehensive data preprocessing. Validation is conducted using historical '6L34DF' diesel engine data. Particle Swarm Optimization (PSO) optimizes the spread value (σ) of the Generalized Regression Neural Network (GRNN) to establish an optimal baseline model for EGT prediction. Experimental results show that PSO-GRNN outperforms Backpropagation Neural Network (BPNN), GRNN, Bidirectional Gated Recurrent Unit (BiGRU), Genetic Algorithm- Generalized Regression Neural Network (GA-GRNN), and Deep Belief Network-Support Vector Regression (DBN-SVR) in terms of training time and accuracy, demonstrating its suitability for MDE baseline modeling.