In-process vision monitoring methods for aircraft coating laser cleaning based on deep learning

BackgroundThe World Health Organization predicts that by 2030, chronic obstructive pulmonary disease (COPD) will be the third leading cause of death and the seventh leading cause of morbidity worldwide. Pulmonary function tests (PFT) are the gold standard for COPD diagnosis. Since COPD is an incurable disease that takes a considerable amount of time to diagnose, even by an experienced specialist, it becomes important to provide an analysis of abnormalities in a simple manner. Although many deep learning (DL) methods based on computed tomography (CT) have been developed to identify COPD, the pathological changes of COPD based on CT are multi-dimensional and highly spatially heterogeneous, and their predictive performance still needs to be improved.ObjectiveThe purpose of this study was to develop a DL-based multimodal feature fusion model to accurately estimate PFT parameters from chest CT images and verify its performance.Materials and methodsIn this retrospective study, participants underwent chest CT examination and PFT at the Fourth Clinical Medical College of Xinjiang Medical University between January 2018 and July 2024. In this study, the 1-s forced expiratory volume (FEV1), forced vital capacity (FVC), 1-s forced expiratory volume ratio forced vital capacity (FEV1/FVC), 1-s forced expiratory volume to predicted value (FEV1%), and forced vital capacity to predicted value (FVC%) of PFT parameters were used as predictors and the corresponding chest CT of 3108 participants. The data were randomly assigned to the training group and the validation group at a ratio of 9:1, and the model was cross-validated using 10-fold cross-validation. Each parameter was trained and evaluated separately on the DL network. The mean absolute error (MAE), mean squared error (MSE), and Pearson correlation coefficient (r) were used as evaluation indices, and the consistency between the predicted and actual values was analyzed using the Bland-Altman plot. The interpretability of the model’s prediction process was analyzed using the Grad-CAM visualization technique.ResultsA total of 2408 subjects were included (average age 66 ± 12 years; 1479 males). Among these, 822 cases were used for encoder training to extract image features, and 1,586 cases were used for the development and validation of a multimodal feature fusion model based on a multilayer perceptron (MLP). The MAE, MSE, and r predicted between PFT and model estimates for FEV1 were 0.34, 0.20, and 0.84, respectively. For FVC, the MAE, MSE, and r were 0.42, 0.31, and 0.81, respectively. For FEV1/FVC, the MAE, MSE, and r were 6.64, 0.73, and 0.77, respectively. For FEV1%, the MAE, MSE, and r were 13.42, 3.01, and 0.73, respectively. For FVC%, the MAE, MSE, and r were 13.33, 2.97, and 0.61, respectively. It was observed that there was a strong correlation between the measured and predicted indices of FEV1, FVC, FEV1/FVC, and FEV1%. The Bland-Altman plot analysis showed good consistency between the estimated values and the measured values of all PFT parameters.ConclusionsThe preliminary research results indicate that the MLP-based multimodal feature fusion model has the potential to predict PFT parameters in COPD patients in real time. However, it is worth noting that the study used indicators before the use of bronchodilators, which may affect the interpretation of the results. Future studies should use measurements taken after bronchodilator administration to better align with clinical standards.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12890-025-03957-7.

Effective traffic congestion prediction is need of the hour in a modern smart city to save time and improve the quality of life for citizens. In this study, AB_AO (ARIMA Bi-LSTM using Aquila optimizer), a hybrid predictive model, is proposed using the most effective time-series data prediction statistical model ARIMA (Autoregressive Integrated Moving Average) and sequential predictive Deep Learning (DL) technique LSTM (Long Short-Term Memory) which helps in traffic congestion prediction with a minimum error rate. Also, the Aquila optimizer (AO) is used to elevate the adequacy of the AB_AO model. Three road traffic datasets of different cities from the “CityPulse EU FP7 project” are used to implement the proposed hybrid model. In a time-series dataset, two components need to be handled with care, i.e., linear and nonlinear. In this study, the ARIMA model has been used to manage linear components and Bi-LSTM is used to handle nonlinear components of the time-series dataset. The Aquila Optimizer (AO) is used for hyperparametric tuning to enhance the performance of Bi-LSTM. Error measurement parameters like the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are used to validate the results. A detailed mathematical and empirical analysis is given to justify the performance of the AB_AO model using an ablation study and comparative analysis. The AB_AO model acquires more stable and precise results with MSE as 18.78, MAE as 3.18, and MAPE as 0.21 than other models. It may further help to predict the vehicle count on the road, which may be of great help in reducing wastage of time in traffic congestion.

The rapid development of Internet information technology has made e-commerce enterprises face complex and changing financial problems. Combining artificial intelligence algorithms and dynamic monitoring of financial risks has been a current research hotspot. Based on this, this paper conducts an empirical study with a sample of listed Chinese e-commerce enterprises from 2012 to 2022. Firstly, using factor analysis (FA) to obtain the common factors between the original financial and non-financial indicators has the effect of reducing the overfitting risk of the model. Secondly, the mean square error (MSE) of the output and predicted values of the Long Short-Term Memory neural network (LSTM) is used as the fitness function of the intelligent swarm optimization algorithm, and then the Particle Swarm Optimization (PSO) algorithm is used to optimize the learning rate (LR) and the number of hidden layer neurons in the Long Short-Term Memory (LSTM) neural network. Finally, a financial risk prediction model based on FA-PSO-LSTM deep learning is constructed, and multiple benchmark models are introduced for comparative analysis on each evaluation index. The study shows that for nonlinear multivariate data with complex structure, the fused deep learning model proposed in this paper achieves the lowest values in mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). This indicates that the model has the best prediction effect, which is helpful to help managers make relevant decisions efficiently and scientifically and make the enterprise sustainable.

Efficient removal of SiO2 contaminated particles from sol-gel SiO2 filmed fused silica surface was achieved by single shot dry laser cleaning and airflow displacement system assisted laser cleaning using a Q switched Nd:YAG pulse laser at 355 nm in wavelength. The experiment result shows that for single-shot laser dry cleaning of sol-gel film fused silica samples under the condition of satisfying the theoretical cleaning threshold, the optimal laser energy density was 2.28 J/cm2, which is different from the process parameters of uncoated fused silica. In the optimal process parameters, single shot laser cleaning had obvious cleaning effect on SiO2 particles with particle diameter larger than 1 μm, and the removal ratio was 64.81%. When the density of particle was too high, the cleaning effect was weakened and the damage to the substrate was caused. The re-attachment of crushed particles also leaded to making the cleaning effect worse. The airflow displacement system assisted laser cleaning method further enhanced the effect of removing the optical surface contaminated particles and the decontamination efficiency was improved to 80.53% at 1.71 J/cm2. Our results demonstrated that airflow displacement system will be useful for single shot dry laser cleaning of SiO2 particles on an optical surface of fused silica.

Mapping nutrient pollution in inland water bodies using multi-platform hyperspectral imagery and deep regression network.

A CNN encoder decoder LSTM model for sustainable wind power predictive analytics

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO2, O3, and PM10; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R2, a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health.

With the large-scale application of composite materials in military aircraft, various composite material detection technologies with infrared nondestructive and ultrasonic nondestructive testing as the core have played an important role in detecting composite material component damage in military aircraft. At present, the damage of composite materials is mostly recognized manually, which is time-consuming, laborious, and inefficient. It can effectively improve detection efficiency and accuracy by using intelligent detection methods to detect and recognize damage. Moreover, the effect of infrared detection is significantly reduced with increasing detection depth, while ultrasonic detection has shallow-blind areas. A cascade fusion R-CNN network is proposed in order to comprehensively identify composite material damage. This network realizes the intelligent fusion recognition of infrared and ultrasonic damage images of composite materials. The network is based on a cascade R-CNN network, using fusion modules and BiFPN for improvement. For the infrared image and ultrasonic C-scan image data set established in this paper, the algorithm can identify the type and location of damage detected by infrared and ultrasonic testing. Its recognition accuracy is 99.3% and mean average precision (mAP) is 90.4%. In the detection process, the characteristics of infrared and ultrasonic images are used to realize the recognition of damage depth. Compared to SSD, YOLOv4, faster R-CNN and cascade R-CNN, the network proposed in this paper is better and more effective.

In order to solve the problem of identifying and locating the stains of steel plates before laser cleaning, a real-time monitoring system for laser cleaning of steel plate based on deep learning is proposed. First of all, this paper uses K-means to cluster the points on the image, then binarizes the image, segments the steel plate image from the background. Secondly, it uses YOLOv4 for steel plate stain detection, uses CSPDreknet53 to perform feature extraction on steel plates, performs stain detection through three different feature layers, and finally outputs stain category and coordinate information. 5000 datasets were obtained through the laser cleaning platform for model training, the division ratio of training set, validation set and test set was 8:1:1. The mAP of this method for stain detection on steel plate images is 96.37%. Compared with SSD, Faster-RCNN, and YOLOv3, the accuracy rate is better, which can meet the real-time monitoring of laser cleaning.

Wire sawing is an important process in the cutting of NdFeB magnets and the sawed surface topography and surface roughness (SR) are important indicators for assessing surface quality. This paper analyzed the effects of process parameters on the sawed NdFeB surface topography and SR based on orthogonal experiments and then presented an SR prediction model called ISSA-BP, which was based on a BP neural network using an improved sparrow search algorithm (ISSA). For the problem of insufficient optimization capability of the traditional sparrow search algorithm (SSA), Cubic chaotic mapping, Latin hypercube sampling, the sine–cosine algorithm, Levy flight, and Cauchy mutation were introduced to improve the traditional sparrow search algorithm (SSA) to obtain ISSA, improving algorithm convergence speed and global optimization. The ISSA was then used to optimize the initial weights and thresholds of the BP neural network for predicting Ra. Research shows that the sawed surface topography reflects a combination of brittle and ductile material removal. As the workpiece feed speed and size decrease and the wire speed increases, there is a reduction in SR. Compared with the SSA-BP and traditional BP models, the ISSA-BP prediction model has reduced various error indicators such as mean absolute error (MAE) and mean square error (MSE). The mean absolute error (MAE) of the prediction model optimized by the ISSA is 0.064475, the mean square error (MSE) is 0.0072297, the root mean square error (RMSE) is 0.085028, and the mean absolute percentage error (MAPE) is 3.7171%. The research results provide an experimental basis and technical support for predicting the SR and optimizing the process parameters in diamond wire-sawing NdFeB.

Background/Objectives: Smartphone addiction (SA) and gaming addiction (GA) have become risk factors for individuals of all ages in recent years. Especially during adolescence, it has become very difficult for parents to control this situation. Physical activity and the effective use of free time are the most important factors in eliminating such addictions. This study aimed to test a new machine learning method by combining routine regression analysis with the gradient-boosting machine (GBM) and random forest (RF) methods to analyze the relationship between SA and GA with leisure time management (LTM) and the enjoyment of physical activity (EPA) among adolescents. Methods: This study presents the results obtained using our developed GBM + RF hybrid model, which incorporates LTM and EPA scores as inputs for predicting SA and GA, following the preprocessing of data collected from 1107 high school students aged 15-19 years. The results were compared with those obtained using routine regression results and the lasso, ElasticNet, RF, GBM, AdaBoost, bagging, support vector regression (SVR), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and light gradient-boosting machine (LightGBM) models. In the GBM + RF model, probability scores obtained from GBM were used as input to RF to produce final predictions. The performance of the models was evaluated using the R2, mean absolute error (MAE), and mean squared error (MSE) metrics. Results: Classical regression analyses revealed a significant negative relationship between SA scores and both LTM and EPA scores. Specifically, as LTM and EPA scores increased, SA scores decreased significantly. In contrast, GA scores showed a significant negative relationship only with LTM scores, whereas EPA was not a significant determinant of GA. In contrast to the relatively low explanatory power of classical regression models, ML algorithms have demonstrated significantly higher prediction accuracy. The best performance for SA prediction was achieved using the Hybrid GBM + RF model (MAE = 0.095, MSE = 0.010, R2 = 0.9299), whereas the SVR model showed the weakest performance (MAE = 0.310, MSE = 0.096, R2 = 0.8615). Similarly, the Hybrid GBM + RF model also showed the highest performance for GA prediction (MAE = 0.090, MSE = 0.014, R2 = 0.9699). Conclusions: These findings demonstrate that classical regression analyses have limited explanatory power in capturing complex relationships between variables, whereas ML algorithms, particularly our GBM + RF hybrid model, offer more robust and accurate modeling capabilities for multifactorial cognitive and performance-related predictions.

Recent advancements in machine learning and, more specifically, deep learning methodologies showed promising results in Computed tomography (CT) imaging, either by enhancing existing reconstruction methodologies by cleaning the signal from the collected data or used for the complete reconstruction of the images directly from raw data. The loss function used for the training of these deep learning methodologies can significantly impact the training process. This paper compares different deep learning loss functions in the framework of denoising and recon-structing sinograms based on Single-photon emission computed tomography (SPECT) imaging. The loss functions evaluated in the proposed work are Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Logarithmic Error (MSLE), Huber, Peak Signal-to-Noise ratio (PSRN) and Structural Similarity Index (SSIM). These loss functions were used to train an identical proposed convolutional-based deep learning methodology to denoise sinograms constructed using software phantoms and compared the results. Furthermore, a study is constructed to measure the reconstructed accuracy of the denoised sinograms and compare the results using the ordered subset expectation maximisation (OSEM) method. Based on our analysis, some guidelines are derived for selecting a loss function in denoising data used for inverse problems such as sinogram reconstruction for medical image applications.

The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking space visual detection and image processing method based on deep learning. Firstly, a 360-degree panoramic system was designed to photograph the vehicle environment. The image has been processed to obtain a panoramic aerial view, which was input as the original image of the parking space detection system. Secondly, the Faster R-CNN (Region-Convolutional Neural Network) parking detection model was established based on deep learning. It was aimed to detect and extract the parking space from the input image. Thirdly, the problems of uneven illumination and complex background were solved effectively by removing the background light from the image. Finally, a parking space extraction method based on the connected region has been designed, which further simplified the parking space extraction and image processing. The experiment results show that the mAP (mean Average Precision) value of the Faster R-CNN model using 101-Floor ResNet as the feature extraction network is 89.30%, which is 2.28% higher than that of the Faster R-CNN model using 50-Floor ResNet as the feature extraction network. The model built in this paper can detect most parking spaces well. The position of the output target box is accurate. In some test scenarios, the confidence of parking space recognition can even reach 100%. In summary, the proposed method can realize the effective identification and accurate positioning of parking spaces.

Study question Can deep learning (DL) algorithms trained on time-lapse videos be used to detect and track the size and gender of pronuclei in developing human zygotes? Summary answer Our DL algorithm not only outperforms state-of-the-art models in detecting the pronuclei but can also accurately identify and track its gender and size over time. What is known already Recent researches have explored the use of DL to extract key morphological features of human embryos. Existing studies, however, focus either on blastocysts’ morphological measurements (Au et al. 2020) or on embryos’ general developmental stages classification (Gingold et al. 2018, Liu et al. 2019, Lau et al. 2019). So far, only one paper attempted to evaluate zygotes’ morphological components but stopped short of identifying the existence and location of their pronuclei (Leahy et al. 2020). We address this research gap by training a DL model that can detect, classify the gender, and quantify the size of zygotes’ pronuclei over time. Study design, size, duration A retrospective analysis using 91 fertilized oocytes from infertile patients undergoing IVF or ICSI treatment at Hanabusa Women’s Clinic between January 2011 and August 2019 was conducted. Each embryo was time-lapse monitored using Vitrolife which records an image every 15 minutes at 7 focal planes. For our study, we used videos of the first 1–2 days of the embryo from its 3 central focal planes, corresponding to 70–150 images per focal plane. Participants/materials, setting, methods All 273 timelapse videos were split into 30,387 grayscale still images at a 15-minute interval. Each image was checked and annotated by experienced embryologists where every pixel of the image was classified into 3 categories: male pronuclei, female pronuclei, and others. Images were converted into grayscale, resized into 500x500 pixels, and then fed into a neural network with the Mask R-CNN architecture and a ResNet101 backbone to produce a pronuclei instance segmentation model. Main results and the role of chance The 91 embryos were split into training (∼70% or 63 embryos) and validation (∼30% or 28 embryos). Our pronuclei model takes as input a single image and outputs a bounding box, mask, category, confidence score, and size measured in terms of pixel for each detected candidate. For prediction, we run the model on the 3 middle focal planes and merge candidates by using the one with the highest confidence score. We used the mean-average precision (mAP) score to evaluate our model’s ability to detect pronuclei and used the mean absolute percentage error (MAPE) between the actual size (as annotated by the embryologist) and the predicted one to check the model’s performance in tracking the pronuclei’s size. The mAP for detecting pronuclei, regardless of its gender, achieved by our model was 0.698, higher than the 0.680 value reported in the Leahy et al. paper (2020). Breakdown by gender, our model’s mAP for male and female pronuclei are 0.734 and 0.661 respectively. The overall MAPE for tracking pronuclei’s size is 21.8%. Breakdown by gender, our model’s MAPE for male and female pronuclei are 19.4% and 24.3% respectively. Limitations, reasons for caution Samples were collected from one clinic with videos recorded from one time-lapse system which can limit our results’ reproducibility. The accuracy of our DL model is also limited by the small number of embryos that we used. Wider implications of the findings: Even with a limited training dataset, our results indicate that we can accurately detect and track the gender and the size of zygotes’ pronuclei using time-lapse videos. In future models, we will increase our training dataset as well as include other time-lapse systems to improve our models’ accuracy and reproducibility. Trial registration number Not applicable