One-dimensional Convolutional Neural Network Research Articles

A novel sensor preference method for proton exchange membrane fuel cell flooding fault diagnosis based on multi-algorithm

ABSTRACT During periods of high-power operation, proton exchange membrane fuel cell (PEMFC) produce water, which gradually accumulates at the cathode. It causes flooding faults and significantly impacts on PEMFC safety. The data collected by the sensors is a fundamental prerequisite for the flooding faults diagnosis in PEMFC. Consequently, it is critical to select sensors. However, the conventional methods for selecting sensors consider sensitivity and noise immunity, the number of sensors selected and the flood diagnostic performance are not optimized. To solve the above problems, this paper proposes a sensor preference method that combines the Mantel test and Spearman coefficient (MS method). This method first uses the Mantel test to analyze the correlation between two flooding evaluation indicators and the sensors, it identifies suitable sensors for diagnosing flooding faults. Subsequently, the Spearman coefficient eliminates redundant sensors. Additionally, a one-dimensional convolutional neural network is employed to construct a flooding diagnosis model. The MS method is contrasted with two traditional sensor selection methods. The results show that the MS method selected sensors yield superior model training and enhanced accuracy in diagnosing flooding, thereby ensuring PEMFC safety.

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest

Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.

Skip-AttSeqNet: Leveraging skip connection and attention-driven Seq2seq model to enhance eye movement event detection in Parkinson’s disease

To address the limitations of traditional algorithms in detecting eye movement events, particularly in Parkinson’s disease (PD) patients, this study introduces Skip-AttSeqNet. It presents an innovative approach combining skip-connected, one-dimensional convolutional neural networks with an attention-enhanced, bidirectional long short-term memory network. This hybrid architecture significantly advances smooth pursuit (SP) event detection, as evidenced by its performance on both the GazeCom dataset and a unique dataset of PD patient eye movements. Key innovations in this work include the utilization of skip connections and attention mechanisms, along with optimized training–validation set division, collectively enhancing the model’s accuracy while mitigating overfitting. Skip-AttSeqNet outperforms existing algorithms, achieving a 3.2% higher sample-level F1 score and a notable 6.2% increase in event-level F1 scores for SP detection. Furthermore, we established a smooth-pursuit experimental paradigm and identified significant differences in saccade and SP features between PD patients and healthy older adults through statistical analysis using the Mann–Whitney test. These findings underscore the potential of eye movement metrics as biomarkers for PD, thereby not only strengthening PD diagnosis but also enriching the intersection of computer vision and biomedical research domains.

A fault diagnosis method based on an improved diffusion model under limited sample conditions.

As a critical component in mechanical systems, the operational status of rolling bearings plays a pivotal role in ensuring the stability and safety of the entire system. However, in practical applications, the fault diagnosis of rolling bearings often encounters limitations due to the constraint of sample size, leading to suboptimal diagnostic accuracy. This article proposes a rolling bearing fault diagnosis method based on an improved denoising diffusion probability model (DDPM) to address this issue. The practical value of this research lies in its ability to address the limitation of small sample sizes in rolling bearing fault diagnosis. By leveraging DDPM to generate one-dimensional vibration data, the proposed method significantly enriches the datasets and consequently enhances the generalization capability of the diagnostic model. During the model training process, we innovatively introduce the feature differences between the original vibration data and the predicted vibration data generated based on prediction noise into the loss function, making the generated data more directional and targeted. In addition, this article adopts a one-dimensional convolutional neural network (1D-CNN) to construct a fault diagnosis model to more accurately extract and focus on key feature information related to faults. The experimental results show that this method can effectively improve the accuracy and reliability of rolling bearing fault diagnosis, providing new ideas and methods for fault detection and prevention in industrial applications. This advancement in diagnostic technology has the potential to significantly reduce the risk of system failures, enhance operational efficiency, and lower maintenance costs, thus contributing significantly to the safety and efficiency of mechanical systems.

Development of low-cost portable spectrometer equipped with 18-band spectral sensors using deep learning model for evaluating moisture content of rubber sheets

While the choice of spectrometer can vary depending on its intended use, the increased cost of high-performance spectrometers may not be justified in certain applications. Therefore, this research developed an affordable and portable device using 18-band spectral sensors incorporating a deep learning model for accurately determining the moisture content in rubber sheets. A set of 286 rubber sheets was randomly separated into two categories: 200 for model calibration and 86 for model validation. In the calibration process, the spectral data were calibrated using a one-dimensional convolutional neural network (1D-CNN) and then were compared with a recognized linear model using partial least squares regression (PLSR). The experiments revealed the exceptional performance of the 1D-CNN model in predicting the moisture content of rubber sheets, outperforming the PLSR model. The 1D-CNN model had a better prediction accuracy, with a coefficient of determination (R2) of 0.962, a root mean squared error of prediction (RMSEP) of 0.410 %, a prediction-to-deviation ratio (RPD) of 5.2, and an error range ratio (RER) of 18.0. A portable device was constructed by incorporating the 1D-CNN model into a 32-bit microcontroller, which was embedded within the measurement device. During testing of the instrument, the results indicated that its predictive performance did not differ significantly from that of the primary calibration model. Therefore, it could be concluded that the designed instrument was capable of accurately measuring the moisture content of rubber sheets and is suitable for field use due to its portability and cost-effectiveness.

Recognizing Digital Ink Chinese Characters Written by International Students Using a Residual Network with 1-Dimensional Dilated Convolution

Due to the complex nature of Chinese characters, junior international students often encounter writing problems related to strokes, components, and their combinations when writing Chinese characters. Digital ink Chinese characters (DICCs) are obtained by sampling the writing trajectory of Chinese characters with a pen input device. DICCs contain rich information, such as the time and space of strokes and sampling points. Recognizing DICCs is crucial for evaluating and correcting writing errors and enhancing the quality of Chinese character teaching for international students. Here, the paper first employs a one-dimensional dilated convolution to digital ink Chinese character recognition (DICCR) and proposes a novel residual network with one-dimensional dilated convolution (1-D ResNetDC). The 1-D ResNetDC not only utilizes multi-scale convolution kernels, but also employs different dilation rates on a single-scale convolution kernel to obtain information from various ranges. Additionally, residual connections facilitate the training of deep one-dimensional convolutional neural networks. Moreover, the paper proposes a more expressive ten-dimensional feature representation that includes spatial, temporal, and writing direction information for each sampling point, thereby improving classification accuracy. Because the DICC dataset of international students is small and unbalanced, the 1-D ResNetDC is pre-trained on the published available dataset. The experiments demonstrate that our approach is effective and superior. This model features a compact architecture, a reduced number of parameters, and excellent scalability.

Detection of ultra-low concentration NH3, SO2, and NO using UV-DOAS combined with multidimensional spectral fusion

Since nitric oxide (NO), sulfur dioxide (SO2), and ammonia (NH3) in flue gas cause severe air pollution, accurate measurements of their concentrations are crucial for optimizing the efficiency of flue gas denitrification in coal-fired power plants and protecting the atmosphere. Due to their large absorption cross sections in the ultraviolet (UV) band, NH3, SO2, and NO in flue gas can be detected by UV Differential Optical Absorption Spectroscopy (UV-DOAS) techniques. However, spectral overlap and noise caused by short optical path limitations in the measurement are the main obstacles to achieve concentration inversion. Here, we propose a multidimensional spectral fusion method one-dimensional convolutional neural network combined with two-dimensional convolutional neural network (1D-2D-CNN) with the ability to invert concentration of NH3, SO2 and NO at ppb level with 0.5 m optical-length. To separate the absorption features, one-dimensional spectra are converted into two-dimensional time-frequency plots by continuous wavelet transform (CWT). After denoising the spectra by wavelet thresholding, one-dimensional and two-dimensional spectral features are extracted by 1D-2D-CNN model and fused for inversion. The experimental results show that the proposed system can effectively eliminate interferences from NH3, SO2, and NO, significantly improving the concentration inversion accuracy. The detection limits for NH3, SO2 and NO in the mixture are 0.95 ppb∙m, 6.31 ppb∙m, and 2.53 ppb∙m, respectively, demonstrating that our approach outperforms other reported methods for flue gas analysis. The proposed system is expected to be applied to ammonia slip monitoring of Selective Catalytic Reduction (SCR) in power plants.

Advancing predictive accuracy of shallow landslide using strategic data augmentation

Rainfall-induced shallow landslides pose one of significant geological hazards, necessitating precise monitoring and prediction for effective disaster mitigation. Most studies on landslide prediction have focused on optimizing machine learning (ML) algorithms, very limited attention has been paid to enhancing data quality for improved predictive performance. This study employs strategic data augmentation (DA) techniques to enhance the accuracy of shallow landslide prediction. Using five DA methods including singular spectrum analysis (SSA), moving averages (MA), wavelet denoising (WD), variational mode decomposition (VMD), and linear interpolation (LI), we utilize strategies such as smoothing, denoising, trend decomposition, and synthetic data generation to improve the training dataset. Four machine learning algorithms, i.e. artificial neural network (ANN), recurrent neural network (RNN), one-dimensional convolutional neural network (CNN1D), and long short-term memory (LSTM), are used to forecast landslide displacement. The case study of a landslide in southwest China shows the effectiveness of our approach in predicting landslide displacements, despite the inherent limitations of the monitoring dataset. VMD proves the most effective for smoothing and denoising, improving R2, RMSE, and MAPE by 172.16%, 71.82%, and 98.9%, respectively. SSA addresses missing data, while LI is effective with limited data samples, improving metrics by 21.6%, 52.59%, and 47.87%, respectively. This study demonstrates the potential of DA techniques to mitigate the impact of data defects on landslide prediction accuracy, with implications for similar cases.

Indirect prediction of the 3D printability of polysaccharide gels using multiple machine learning (ML) models

In this paper, the capabilities of NIR spectroscopy and LF-NMR data were compared for rapidly predicting the rheological properties of polysaccharide gels and assessing their printability. Seven machine learning (ML) models were established for rheological property prediction based on partial least squares regression (PLSR), support vector regression (SVR), back propagation artificial neural network (BPANN), one-dimensional convolutional neural network (1D CNN), recurrent neural network (RNN), long short-term memory (LSTM), and Transformer. The results showed that among the seven models, the SVR, BPANN, and 1D CNN models based on NIR spectroscopy effectively predicted the rheological parameters of polysaccharide gels, with the highest R2 in the prediction set reaching 0.9796 and the highest RPD reaching 7.0708. For most polysaccharide gels, using the LF-NMR relaxation time distribution curves provided better predictions of rheological properties than using transverse relaxation time and peak area. Among the seven models, the PLSR, SVR, 1D CNN, and Transformer models effectively predicted the rheological characteristics based on LF-NMR parameters, with the highest R2 in the prediction set reaching 0.9869 and the highest RPD reaching 8.7220. This study successfully established a prediction system for the rheological behaviors and 3D printing performance of polysaccharide gels using NIR spectroscopy and LF-NMR data combined with ML methods, achieving an intelligent assessment of the 3D printing behavior of polysaccharide gels.

A rapid method for detecting l-Theanine during kombucha fermentation using SERS combined with machine/deep learning

The rapid detection of key indicators during the fermentation is crucial for improving product quality. In light of this, a strategy was developed for the rapid detection of l-Theanine during kombucha fermentation using surface-enhanced Raman Scattering (SERS) coupled with machine/deep learning methodologies. After analyzing the attribution of l-Theanine SERS characteristic peaks, effective variable features, converted features, and depth features were extracted, and calibration models were constructed employing Partial Least Squares (PLS), Support Vector Machine (SVM), and a one-dimensional Convolutional Neural Network (1DCNN). Based on the first 10 principal components derived via principal component analysis (PCA), the established model, PCA10-1DCNN, displayed superior performance with a determination coefficient (Rp2) of 0.9750. PCA10-1DCNN achieved commendable verification efficacy, with a maximum error of 4.4460 µg/mL, indicating the efficacy of this strategy for the l-Theanine rapid detection. This study offers a viable strategy for the l-Theanine rapid detection in the production of tea-based beverages.

Deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy employed for rapid and accurate identification of bismuth brass

BackgroundOwing to its excellent machinability and less toxicity, bismuth brass has been widely used in manufacturing various industrial products. Thus, it is of significance to perform rapid and accurate identification of bismuth brass to reveal the alloying properties. However, the analytical lines of various elements in bismuth brass alloy products based on conventional laser-induced breakdown spectroscopy (LIBS) are usually weak. Moreover, the analytical lines of various elements are often overlaped, seriously interfering with the identification of bismuth brass alloys. To address these challenges, developing an advanced strategy enabling to achieve ultra-high accuracy identification of bismuth brass alloys is highly desirable. ResultsThis work proposed a novel method for rapidly and accurately identifying bismuth brass samples using deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS). With the help of fs-LA-SIBS, a spectral database containing high quality LIBS spectra on element components were constructed. Then, one-dimensional convolutional neural network (CNN) was introduced to distinguish five species of bismuth brass alloy. Amazingly, the optimal CNN model can provide an identification accuracy of 100 % for specie identification. To figure out the spectral features, we proposed a novel approach named “segmented fs-LA-SIBS wavelength”. The identification contribution from various wavelength intervals were extracted by optimal CNN model. It clearly showed that, the differences of spectra feature in the wavelength interval from 336.05 to 364.66 nm can produce the largest identification contribution for an identification accuracy of 100 %. More importantly, the feature differences in the four elements such as Ni, Cu, Sn, and Zn, were verified to mostly contribute to identification accuracy of 100 %. SignificanceTo the best of our knowledge, it is the first study on one-dimensional CNN configuration assisted with fs-LA-SIBS successfully employed for performing identification of bismuth brass. Compared with conventional machine learning methods, CNN has shown significant more superiority. To reveal the tiny spectra differences, the classification contribution from spectra features were accurately defined by our proposed “segmented fs-LA-SIBS wavelength” method. It can be expected that, CNN assisted with fs-LA-SIBS has great promising for identifying the differences from various element components in metallurgical field.

Research on variety identification of common bean seeds based on hyperspectral and deep learning

Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common bean seeds non-destructively. In this study, the average spectrum of 3078 hyperspectral images from 500 varieties was obtained after image segmentation and sensitive region extraction, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to achieve the equilibrium of the samples of various varieties. A one-dimensional convolutional neural network model (IResCNN) incorporating Inception module and residual structure was proposed to identify seed varieties, and Support Vector Machine (SVM), K-Nearest Neighbor (KNN), VGG19, AlexNet, ResNet50 were established to compare the identification effect. After analyzing the effects of multiple spectral preprocessing methods on the model, the study selected Savitzky-Golay smoothing correction (SG) for spectral preprocessing and extracted 66 characteristic wavelengths using Successive Projections Algorithm (SPA) as inputs to the discriminative model. Ultimately, the IResCNN model achieved the highest accuracy of 93.06% on the test set, indicating that hyperspectral technology can accurately identify bean varieties, and the study provides a correct method of thinking for the non-destructive classification of multi-species small-sample bean varieties.

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.

Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy

Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in agriculture. However, directly extracting constituent information from the NIR spectra is challenging due to many issues such as broad absorption band, overlapping and non-specific nature. To solve these problems and extract implicit features from the raw data of NIR spectra to improve performance of quantitative models, a one-dimensional shallow convolutional neural network (CNN) model based on an eXtreme Gradient Boosting (XGBoost) feature extraction method was proposed in this paper. The leaf node feature information in the XGBoost was encoded and reconstructed to obtain the implicit features of raw data in the NIR spectra. A two-parametric Swish (TSwish or TS) activation function was proposed to improve the performance of CNN, and the elastic net (EN) was also applied to avoid the overfitting problem of the CNN model. Performance of the developed XGBoost-CNN-TS-EN model was evaluated using two public NIR spectroscopy datasets of corn and soil, and the obtained determination coefficients (R2) for moisture, oil, protein, and starch of the corn on test set were 0.993, 0.991, 0.998, and 0.992, respectively, with that of the soil organic matter being 0.992. The XGBoost-CNN-TS-EN model exhibits superior stability, good prediction accuracy, and generalization ability, demonstrating its great potentials for quantitative analysis of multi-constituents in spectroscopic applications.

Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD

A high-pressure hydraulically adjusted servomotor is an electromechanical–hydraulic integrated system centered on a servo valve that plays a crucial role in ensuring the safe and stable operation of steam turbines. To address the issues of difficult fault diagnoses and the low maintenance efficiency of adjusted hydraulic servomotors, this study proposes a model for detecting abnormalities of hydraulically adjusted servomotors. This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. Then, this model was used as a feature extractor to create a feature set of normal data. Finally, an abnormal detection model for the hydraulically adjusted servomotor was constructed by optimizing the support vector data domain based on this feature set using a genetic algorithm. The proposed method was experimentally validated on a hydraulically adjusted servomotor dataset. The results showed that, compared with the traditional single-scale one-dimensional residual neural network, the multi-scale feature vectors fused by the multi-scale one-dimensional convolutional neural network contained richer state-sensitive information, effectively improving the performance of detecting abnormalities in the hydraulically adjusted servomotor.

Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver region of interest (ROI) is outlined manually. In this study, we proposed an approach for liver fibrosis assessment using deep learning models on ultrasound RF signals. The proposed method consisted of two-dimensional (2D) convolutional neural networks (CNNs) for automatic liver ROI segmentation from reconstructed B-mode ultrasound images and one-dimensional (1D) CNNs for liver fibrosis stage classification based on the frequency spectra (amplitude, phase, and power) of the segmented ROI signals. The Fourier transform was used to obtain the three kinds of frequency spectra. Two classical 2D CNNs were employed for liver ROI segmentation: U-Net and Attention U-Net. ROI spectrum signals were normalized and augmented using a sliding window technique. Ultrasound RF signals collected (with a 3-MHz transducer) from 613 participants (Group A) were included for liver ROI segmentation and those from 237 participants (Group B) for liver fibrosis stage classification, with a liver biopsy as the reference standard (Fibrosis stage: F0 = 27, F1 = 49, F2 = 51, F3 = 49, F4 = 61). In the test set of Group A, U-Net and Attention U-Net yielded Dice similarity coefficients of 95.05% and 94.68%, respectively. In the test set of Group B, the 1D CNN performed the best when using ROI phase spectrum signals to evaluate liver fibrosis stages ≥F1 (area under the receive operating characteristic curve, AUC: 0.957; accuracy: 89.19%; sensitivity: 85.17%; specificity: 93.75%), ≥F2 (AUC: 0.808; accuracy: 83.34%; sensitivity: 87.50%; specificity: 78.57%), and ≥F4 (AUC: 0.876; accuracy: 85.71%; sensitivity: 77.78%; specificity: 94.12%), and when using the power spectrum signals to evaluate ≥F3 (AUC: 0.729; accuracy: 77.14%; sensitivity: 77.27%; specificity: 76.92%). The experimental results demonstrated the feasibility of both the 2D and 1D CNNs in liver parenchyma detection and liver fibrosis characterization. The proposed methods have provided a new strategy for liver fibrosis assessment based on ultrasound RF signals, especially for early fibrosis detection. The findings of this study shed light on deep learning analysis of ultrasound RF signals in the frequency domain with automatic ROI segmentation.

Detection of drug residues in bean sprouts by hyperspectral imaging combined with 1DCNN with channel attention mechanism

As a crucial vegetable food in Asia, bean sprouts are widely cultivated. However, the common practice of using plant growth hormones and other drugs to boost yield and prolong storage poses health risks if not handled properly, so we introduce a novel approach for the non-destructive detection of drug residues in bean sprout cotyledons. In this paper, four experimental groups treated with different drugs and one drug-free control group are tested using hyperspectral imaging (400–1000 nm). A binarization mask is created using the maximum interclass variance approach for the region of interest (ROI) extraction. Preprocessing involves Savitzky-Golay (SG) smoothing and standard normalized variate (SNV). Additionally, three feature extraction methods are used to extract spectral features and reduce the redundancy of the data. The processed data are fed into several traditional machine learning models and latest models, as well as one-dimensional convolutional neural network (1DCNN). The overall accuracy of each model shows the two traditional machine learning models, PSO-SVM and ELM, achieve only 92.3 % and 92.6 % respectively. The 1DCNN model, on the other hand, exhibits an accuracy of 95.5 %, which increased to 96.3 % after applying the channel attention mechanism module. Moreover, in comparison with two state-of-the-art models with accuracies of 95.4 % and 96.5 %, respectively, the proposed model demonstrates competitive performance. The results demonstrate that the 1DCNN model significantly outperforms the two reference machine learning models in this hyperspectral classification task, and the integration of a channel attention mechanism further improves the accuracy, highlighting its potential for application in this field.

Semantic dependency and local convolution for enhancing naturalness and tone in text-to-speech synthesis

Self-attention-based networks have become increasingly popular due to their exceptional performance in parallel training and global context modeling. However, it may fall short of capturing local dependencies, particularly in datasets with strong local correlations. To address this challenge, we propose a novel method that utilizes semantic dependency to extract linguistic information from the original text. The semantic relationship between nodes serves as prior knowledge to refine the self-attention distribution. Additionally, to better fuse local contextual information, we introduce a one-dimensional convolution neural network to generate the query and value matrices in the self-attention mechanism, taking advantage of the strong correlation between input characters. We apply this variant of the self-attention network to text-to-speech tasks and propose a non-autoregressive neural text-to-speech model. To enhance pronunciation accuracy, we separate tones from phonemes as independent features in model training. Experimental results show that our model yields good performance in speech synthesis. Specifically, the proposed method significantly improves the processing of pause, stress, and intonation in speech.

Enhancing acceleration data cleaning through time–frequency feature integration using deep learning

In recent years, the safety issues of bridge has garnered significant attention from various sectors of society. Bridge structural health monitoring (SHM) systems have emerged as essential tools for real-time monitoring and assessing the operational safety of bridges. Despite the substantial implementation and operation of monitoring systems, challenges have arisen from abnormal data caused by equipment malfunctions, power outages, and environmental interference. These anomalies are challenging to manually remove within the vast dataset. To address the challenge of automated detection and classification of anomalies, this paper focuses on acceleration data and proposes a data cleaning method that integrates time–frequency features and one-dimensional convolutional neural network (1D CNN). The method utilizes extreme value envelopes and wavelet scattering transforms to extract time–frequency features from acceleration data, thereby achieving data dimensionality reduction and feature extraction goals. Subsequently, these features are employed to train and test a 1D CNN. The proposed method’s validation is conducted using acceleration monitoring data obtained from a large-span arch bridge. Through extensive testing, the model demonstrates a classification accuracy exceeding 98% on both the test set and unseen data, showcasing high classification precision and generalization capabilities. This approach provides an effective solution for automated data cleaning and anomaly identification within the context of bridge health monitoring systems.

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.