Coefficient Of Determination R2 Values Research Articles

Dysfunctional Beliefs and Attitudes about Sleep-6 (DBAS-6): Data-driven shortened version from a machine learning approach

BackgroundThe Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16) is a widely used self-report instrument for identifying sleep-related cognition. However, its length can be cumbersome in clinical practice. This study aims to develop a data-driven shortened version of the DBAS-16 that efficiently predicts the DBAS-16 total score among the general population. MethodsWe collected 1000 responses to the DBAS-16 from the general population through three separate surveys, each focusing on different aspects of insomnia severity and related factors. Using Exploratory Factor Analysis (EFA) on the survey responses, we grouped DBAS-16 items based on response pattern similarities. The most representative item from each group, showing the highest regression performance with eXtreme Gradient Boosting (XGBoost) in predicting the DBAS-16 total score, was selected to create a shortened version of the DBAS-16. ResultsThrough EFA and XGBoost, we categorized the DBAS-16 items into six distinct groups. Selecting one item from each group, based on the highest coefficient of determination R2 values in predicting the DBAS-16 total score. After measuring the R2 values for all possible combinations of six items, items 4, 5, 7, 11, 13, and 15 were chosen, exhibiting the highest R2 value. Based on these six items, we developed the DBAS-6, a data-driven shortened version of the DBAS-16. The DBAS-6 exhibited outstanding predictive ability, achieving the highest R2 value of 0.90 for predicting the DBAS-16 total score, surpassing that of a previously developed shortened version. Notably, the DBAS-6 efficiently encapsulates the core aspects of the DBAS-16 and demonstrates robust predictive power over heterogeneous test data samples with distinct statistical characteristics from the training data. ConclusionWith its concise format and high predictive accuracy, the DBAS-6 offers a practical tool for assessing dysfunctional beliefs about sleep in clinical settings.

Statistical evaluation of satellite-based CHIRPS precipitation data averaged over the midland and highland regions of Kidangoor sub-catchment, Kerala

The steep topographical setting of Kerala, traversing from Western Ghats in the east to the sandy beaches on the west, demands the use of precipitation data at a very fine spatio-temporal resolution for a range of hydrological and hydrometeorological studies. The limitation of the existing rain gauge network data in representing the variability in the monsoon showers received, across the physiographic divisions of the state, could be overcome using satellite rainfall dataset offered at a finer resolution. In this paper, a statistical evaluation of the satellite derived CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) precipitation data for the Kidangoor sub-catchment was performed by comparing it with station rainfall data and IMD gridded data sets. The homogeneity test at 95 % confidence level classified the station data under ‘useful’ category. Additionally, the statistical performance matrices suggested that the CHIRPS data slightly underestimated the observed station rainfall data. However, the coefficient of determination R2 values (0.95-0.97) in the monthly series and (0.37 - 0.64) in the annual series demonstrated a strong to moderate positive correlation between the datasets. To summarize, the quantitative statistical performance matrices, evaluated for the first time in the study area, proposed that the CHIRPS rainfall estimates could very well reproduce the ground-based monthly rainfall datasets and could also serve as a good replacement for IMD gridded data.

“Climate Change Projections in Jammu and Kashmir under A1B, A2 & B2 Scenarios”

Abstract: The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climate impact studies. Downscaling is required to extract the sub-grid and local scale information. The present study was undertaken to study the effect of climate change on weather parameters like maximum temperature, minimum temperature and precipitation for A1B, A2 and B2 scenarios. The study uses the Hadley centre coupled model (HadCM3) of the Intergovernmental Panel for Climate Change (IPCC) Forth Assessment Report. Thirty year weather data (1985-2015) obtained form Indian Metrological Department (IMD) Srinagar was used for the study. Statistical Downscaling Model (SDSM), R software and Artificial Neural Network (ANN) models were tested for the Srinagar area. This research investigates which among the three is better model for downscaling climate data for Srinagar area. The modelling results showed a first rate agreement between the experimental data and predicted values for temperature series with high coefficient of determination R2 values varying from (0.93-0.95) for different models. In case of precipitation R2 values varied from (0.08-0.249) for different models. The low values of coefficient of determination in precipitation time series are due to lot of uncertainty. occurrence of precipitation which could not be defined by the selected models. Based on Mean squared error (MSE), Root mean squared error (RMSE), Absolute average deviation (AAD), correlation coefficient and coefficient of determination, the R software performed better than the SDSM and ANN for maximum temperature, minimum temperature & precipitation. Thus R software was used for climate scenario generation. According to our simulated model, precipitation showed a decreasing trend whereas maximum and minimum temperatures showed an increasing trend. An overall increasing pattern of (9.81%) for A1B scenario, (15.24%) for A2 scenario & (10.32%) for B2 scenario for maximum temperature, (2.18%) for A1B scenario, (29.15%) for A2 scenario & (19.90%) for B2 scenario for minimum temperature and an overall decreasing pattern (22.60%) for A1B scenario, (17.23%) for A2 scenario & (7.11%) for B2 scenario for precipitation was noted

A vibration signal processing method based on SE-PSO-VMD for ultrasonic machining

Ultrasonic Machining (USM) has broad applicability in numerous industrial fields. Accurate capture of ultrasonic vibration signals is pivotal to the functioning of USM, making them areas of significant research interest. However, the nonlinear and non-stationary nature of the USM vibration signal makes it unsuitable for analysis with conventional methods such as Fast Fourier Transform. Despite current methodologies like Discrete Wavelet Transformation (DWT) yielding valuable insights, they involve manual parameter selection and could lead to sub-optimal results. This paper presents a novel method, using Variational Mode Decomposition (VMD) to automatically decompose USM vibration signals into intrinsic mode functions (IMFs). This method is complemented by Particle Swarm Optimization (PSO) algorithm to optimize the number of IMFs and penalty factor, with Sample Entropy (SE) serving as a fitness function. The innovative SE-PSO-VMD method is validated in ultrasonic metal welding and demonstrates a notable ability in predicting the pull force of welding materials with a high coefficient of determination R2 value of 0.78.

A method for calculating and simulating phenotype of soybean based on 3D reconstruction

In plant phenotypic research, accurate organ segmentation and crop simulation are crucial for optimizing crop planting and increasing yield. In this study, an efficient method for soybean organ segmentation and phenotypic growth simulation was explored based on 3D reconstruction technology. Taking Dongnong252 soybean as the research object, the soybean phenotype acquisition system based on Kinect sensor was used to achieve high-precision and non-destructive acquisition of soybean canopy images during the whole growth period. First, conditional filtering and statistical filtering were used to remove noise. Then, combined with the Intrinsic Shape Signatures-Coherent Point Drift (ISS-CPD) and the Iterative Closest Point (ICP) algorithms, the multi-view three-dimensional (3D) canopy structure of soybean was reconstructed, which provided a reliable basis for plant stem and leaf segmentation. On this basis, the average accuracy of plant stem and leaf segmentation was 79.99% by using the Distance-field-based segmentation pipeline (DFSP) algorithm. Furthermore, the accurate information was provided for extracting and calculating 3D phenotypic parameters such as plant height, crown width, stem thickness, leaf length and leaf width from three scales of whole plant, stem and leaf. The calculated values were highly consistent with the measured values, with an average coefficient of determination of 0.9654 and an average percentage error of 3.4862%. Meanwhile, by analyzing the quantitative relationship between phenotypic parameters and physiological development time, the data-driven Richards growth simulation model was introduced to accurately predict the growth process of soybean plants. The coefficient of determination R2 values of each phenotypic simulation model reached above 0.9357, which improved the goodness of fit of the model by 0.03 compared with the Logistic model, and its root mean square error (RMSE) ranged from 0.0020 to 0.1112. The research results indicated that this method had high accuracy and reliability in 3D reconstruction of soybean canopy, phenotype calculation, and growth simulation. It could provide quantitative indicators for soybean variety selection, planting, and management, and provide technical support and reference for high-throughput phenotype acquisition and analysis of field crops.

Enhanced choroidal thickness, total choroidal area and choroidal vascularity index from deep‐learning generated OCT images

Aims/Purpose: Our goal is to enhance the visualization of the choroid in clinical standard spectral‐domain OCT (SDOCT) scans using a deep‐learning (DL) approach that uses matching swept‐source OCT (SSOCT). We aim to improve the measurement of the sub‐foveal choroidal thickness (SFCT), total choroidal area (TCA), and choroidal vascularity index (CVI) in SDOCT images.Methods: A total of 105 350 SDOCT and SSOCT B‐scan images (Zeiss Cirrus 5000 and Zeiss PlexElite) were used to develop the DL model. The data set included B‐scans from healthy (238 eye pairs from 155 patients) and glaucoma (192 eye pairs from 122 patients) subjects. The DL model learned deep anatomical features from SSOCT scans and generated enhanced SDOCT images. The peak signal‐to‐noise ratio (PSNR) and structural similarity index metric (SSIM) were used to assess the quality of generated images. In addition, we compared the anatomical parameters derived from the generated and ground‐truth SSOCT images, including SVCT, TCA and CVI values.Results: We tested our method using 20 580 B‐scan image pairs from 84 eyes. The DL‐generated images effectively preserved the anatomical structure of the retina layers and improved the visualization of the choroid. The enhanced SDOCT images had higher image quality than the original SDOCT images, as demonstrated by the higher PSNR (31 dB vs. 18 dB) and SSIM values (3.4 times higher SSIM than the original SDOCT images). SVCT, TCA, and CVI measurements from the DL‐generated SDOCT images showed strong correlations with corresponding measurements using SSOCT with high Pearson's coefficients (0.93, 0.99, and 0.93), high intra‐class correlation indices (0.96, 0.99 and 0.96), and high coefficient of determination r2 values (0.85, 0.98 and 0.86).Conclusions: Our DL model enhances the visibility of the choroid in images, surpassing the capabilities of standard clinical SDOCT images. As a result, accurate measurement of SVCT, TCA and CVI, which were previously limited due to imaging depth constraints of SDOCT, becomes possible. This technology opens up new possibilities for utilizing affordable SDOCT devices to study the choroid in healthy and pathological conditions.

Correlations between p-wave velocity and SPT-N for saturated and unsaturated alluvial clays using dimensional analysis

The aim of this research is to establish practical and robust correlations between dilatational wave velocities (p-wave velocities, Vp) and the Standard Penetration Test blow count (SPT-N). Very low levels of correlation are recorded with the conventional single variable approaches, in consequence, a multivariable study is needed for higher precision. A dimensional analysis was conducted including other soil parameters that condition this physical phenomenon, such as the void ratio (e), the degree of saturation (Sr), the effective overburden pressure (σ′v), and the particle density (γs), for greater precision with Vp and SPT-N correlations. Over 100 seismic and SPT tests were performed around the coastal city of Juchitán de Zaragoza (Oaxaca), Mexico, to gather information for this research. As a result, two dimensionless indexes -DSIu (Undrained Dilatational Stiffness Index) and MDSIu (Modified Undrained Dilatational Stiffness Index)- yielded two alternative expressions between both indexes and Vp. In this way, a strong relationship between Vp and SPT-N was obtained with an adjusted coefficient of determination R2 value of 0.93.

Physics-informed neural networks for spherical indentation problems

A scientific deep learning (SciDL) approach was developed by integrating a regression-based spherical indentation method with an artificial neural network (ANN) to extract elastic–plastic properties from indentation load-depth curves. Different combinations of material parameters are constructed through Latin Hypercube Sampling (LHS) process to create a database of indentation parameters. An attempt is made to reversely obtain load-depth (P-h) data using a regression function for a given set of material parameters; this method is further verified by performing finite element (FE) simulations. SciDL models i.e., physics-informed artificial neural network (PI-ANN) with autoencoder (AE) are built based on PyTorch library, and the models are trained using the generated database. Transfer learning (TL) techniques are employed to achieve better training performance with the PI-ANN model. Compared with data-driven models, SciDL models produce consistent predictions with higher accuracy; the coefficient of determination R2 values are observed greater than 0.960. TL techniques allows the SciDL model to learn much faster (≈ 42 epochs) than traditional method (≈ 2400 epochs). Finally, we perform spherical indentation experiments on STS304 and SM45C, and validate the performance of the trained SciDL models; AE integrated PI-ANN model with tanh activation function predicts the material properties close to reference values of SS400 and SM45C. The proposed SciDL approach can be extended for characterizing engineering materials and structures by incorporating any priorly developed mechanical testing method with ANN.

Deep predictions and transfer learning for simulation-driven structural health monitoring based on guided waves

Simulation-driven structural health monitoring (SHM) is crucial for enhancing the safety and reliability of structures. Its primary aim is to utilize numerical simulations to design efficient SHM systems, reducing costs while maintaining effectiveness. However, challenges arise due to disparities between simulations and physical measurements, stemming from uncertainties, noise, and simplifications. These challenges also extend to SHM methods using machine learning, particularly deep neural networks (DNNs).This paper bridges the gap between DNNs trained on simulated data and real-world experiments by investigating three transfer learning (TL) techniques: feature augmentation, direct fine-tuning, and boosting-based fine-tuning. The focus is on guided wave damage interaction coefficients (WDICs) as damage-sensitive features, modeled by DNNs for damage identification in thin plates, commonly found in aerospace structures like aircraft fuselages. This classification task compares measured and predicted WDICs in a common guided wave SHM configuration with one actuator and three sensors. Training and extensive testing datasets are generated using advanced finite element simulations and laser Doppler vibrometer experiments.The study demonstrates that TL techniques significantly enhance DNN prediction accuracy and improve damage identification performance for untrained scenarios compared to purely simulation-based DNNs. Among the TL approaches explored, feature augmentation with appropriate activation functions and boosting-based fine-tuning yield the most promising results. Using four out of twelve TL datasets, feature augmentation reduces the simulation-based mean absolute percentage error from 46.80% to 35.98% and increases coefficient of determination R2 values from 0.02 to 0.72. With six TL datasets and a boosting-based fine tuning, the mean absolute percentage error decreases from 47.42% to 35.20%, maintaining similar R2 values. While TL substantially enhances prediction accuracy, its impact on damage identification is slightly less pronounced, with improvements in relative classification accuracy by approximately 5% compared to simulation-based DNNs.These findings highlight the efficacy of TL techniques for damage identification in thin plates using WDICs and DNNs, emphasizing the influence of TL training and DNN model variations. Furthermore, they showcase the potential of TL methods to enhance DNN performance in future SHM applications, particularly emphasizing the promise of feature augmentation and boosting.

Yamula barajının hidroelektrik rezervuar işletiminin makine öğrenimi ile simülasyonu

Large-scale reservoirs provide operational flexibility to water managers by storing water during times with higher surface water availability and releasing water when it is most needed. Most large-scale reservoirs serve for multipurpose demands, such as water supply for agricultural, urban and environmental users, hydropower, recreation, fisheries and transportation. Due to its low operating cost, hydropower generation is often maximized in energy systems with mixed hydro and thermal sources. Hydropower generation is also used to meet peak demand by advantage of operating in short notice. This study aims to simulate reservoir operations of the Yamula Dam and hydropower plant using machine learning. Located on the Kızılırmak River, the Yamula Dam is a large-scale multipurpose reservoir with its 3476 million cubic meters storage capacity. Turbine release decisions are learned with Random Forests algorithm. The developed model successfully predict reservoir releases between 2006 and 2015, with a coefficient of determination r2 value of 0.87. Model prediction results are provided, and then hydropower load, generation and revenue are calculated and results are presented. The Yamula Dam generates about 362.3 GWh of energy per year, with an annual average revenue of 14.1 million Dollars.

Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning

Deep learning techniques play an important role in plant phenotype research, due to their powerful data processing and modeling capabilities. Multi-task learning has been researched for plant phenotype analysis, which can combine different plant traits and allow for a consideration of correlations between multiple phenotypic features for more comprehensive analysis. In this paper, an intelligent and optimized multi-task learning method for the phenotypic analysis of Arabidopsis thaliana is proposed and studied. Based on the VGG16 network, hard parameter sharing and task-dependent uncertainty are used to weight the loss function of each task, allowing parameters associated with genotype classification, leaf number counting, and leaf area prediction tasks to be learned jointly. The experiments were conducted on the Arabidopsis thaliana dataset, and the proposed model achieved weighted classification accuracy, precision, and Fw scores of 96.88%, 97.50%, and 96.74%, respectively. Furthermore, the coefficient of determination R2 values in the leaf number and leaf area regression tasks reached 0.7944 and 0.9787, respectively.

Research on CNN-LSTM Brake Pad Wear Condition Monitoring Based on GTO Multi-Objective Optimization

As the core component of the automobile braking system, brake pads have a complex structure and high failure rate. Their accurate and effective state monitoring can help to evaluate the safety performance of brake pads and avoid accidents caused by brake failure. The wear process of automobile brake pads is a gradual, nonlinear, and non-stationary time-varying system, and it is difficult to extract its features. Therefore, this paper proposes a CNN-LSTM brake pad wear state monitoring method. This method uses a Convolutional Neural Network (CNN) to complete the deep mining of brake pad wear characteristics and realize data dimensionality reduction, and a Long Short-Term Memory (LSTM) network to capture the time dependence of the brake pad wear sequence, so as to construct the nonlinear mapping relationship between brake pad wear characteristics and brake pad wear values. At the same time, the artificial Gorilla Troops Optimization (GTO) algorithm is used to perform multi-objective optimization of the network structure parameters in the CNN-LSTM model, and its powerful global search ability improves the monitoring effect of the brake pad wear status. The results show that the CNN-LSTM model based on GTO multi-objective optimization can effectively monitor the wear state of brake pads, and its coefficient of determination R2 value is 0.9944, the root mean square error RMSE value is 0.0023, and the mean absolute error MAE value is 0.0017. Compared with the BP model, CNN model, LSTM model, and CNN-LSTM model, the value of the coefficient of determination R2 is the closest to 1, which is increased by 8.29%, 5.52%, 4.47%, 3.30%, respectively, which can more effectively realize the monitoring and intelligent early warning of the brake pad wear state.

Machine Learning-Assisted Tensile Modulus Prediction for Flax Fiber/Shape Memory Epoxy Hygromorph Composites

Flax fiber/shape memory epoxy hygromorph composites are a promising area of research in the field of biocomposites. This paper focuses on the tensile modulus of these composites and investigates how it is affected by factors such as fiber orientation (0° and 90°), temperature (20 °C, 40 °C, 60 °C, 80 °C, and 100 °C), and humidity (50% and fully immersed) conditions. Machine learning algorithms were utilized to predict the tensile modulus based on non-linearly dependent initial variables. Both decision tree (DT) and random forest (RF) algorithms were employed to analyze the data, and the results showed high coefficient of determination R2 values of 0.94 and 0.95, respectively. These findings demonstrate the effectiveness of machine learning in analyzing large datasets of mechanical properties in biocomposites. Moreover, the study revealed that the orientation of the flax fibers had the greatest impact on the tensile modulus value (with feature importance of 0.598 and 0.605 for the DT and RF models, respectively), indicating that it is a crucial factor to consider when designing these materials.

Electrophilicity and nucleophilicity scales at different DFT computational levels

AbstractThe reference electrophilicity and nucleophilicity scales established at the B3LYP/6‐31G(d) level are herein extended by least squares regressions to the most common DFT computational methods used in theoretical organic chemistry studies, including B3LYP, MPWB1K, M062‐X, and ωB97x‐D, in combination with basis sets of the type 6‐31G and 6‐311G with different degrees of polarization and diffuse functions. Excellent linear correlations with coefficient of determination R2 values between 0.97 and 1.00 are obtained. In this work, the new lower and upper threshold values of the scales are established at 48 DFT computational levels, which enables the direct classification and quantification of the electrophilic and nucleophilic character of molecules without necessarily relying upon the B3LYP/6‐31G(d) scales. The present study shows that although the strong, moderate, or marginal thresholds of the electrophilicity and nucleophilicity scales can be modified up to a 24% (ωB97x‐D/6‐31G(d)) with the computational level, the relative position of the reagents on these scales does not change, allowing them to be used in the study of relative chemical reactivity.

Assessing influences of farmyard manure addition, elevated CO2, soil sterilization, soil types, and soil moisture on clothianidin dissipation kinetics

Aim: To investigate the effects of farmyard manure addition, elevated CO2, soil sterilization, dry conditions, field-capacity conditions, and submerged conditions on clothianidin dissipation from the soil, as well as the effects of different pH levels, namely, pH 4.0, 7.0, and 9.2, on its dissipation from the aqueous solutions. Methodology: Samples were processed by the QuEChERS method from the soil and the dipping and shaking method from the aqueous solution, and then analyzed by Shimadzu HPLC-PDA. Results: The results revealed that clothianidin dissipation from soil and aqueous solution best fit first-order kinetics, with the coefficient of determination r2 value < 0.90. In the soil, varying clothianidin dissipation was recorded with significant impact and their decreasing dissipation rate was as follows: submerged conditions (t1/2103.7–107.5 days) > elevated CO2 (t1/2115.7–143.3 days) > farmyard manure addition (t1/2130.8–150.5 days) > field-capacity conditions (t1/2158.4–215.0 days) > dry conditions (t1/2250.8–334.4 days) > soil sterilization (t1/2342.0–376.2 days). In the aqueous solution, clothianidin dissipation was slow at pH 4.0 (t1/2430 days) but fast at pH 9.2 (t1/2273.6 d days). Interpretation: The findings suggest that clothianidin dissipation could be positively affected by FYM addition, elevated CO2, soil sterilization, dry conditions, field-capacity conditions, submerged conditions, and aqueous pH level in all the treatments. This information would help gain a better understanding of clothianidin dissipation in the environment for good agricultural practices,proper risk assessment, and monitoring guidelines. Key words: Aerobic-anaerobic degradation, Microbial activity, Organic carbon content, Persistence half-life period

Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management

This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that have a considerable influence on electricity consumption, (ii) utilizing a Bayesian optimization algorithm (BOA) to enhance the model’s hyperparameters, (iii) hybridizing the BOA with the machine learning algorithms, viz., support vector regression (SVR) and nonlinear autoregressive networks with exogenous inputs (NARX), for modeling individually the long-term electricity consumption, (iv) comparing their performances with the widely used classical time-series algorithm autoregressive integrated moving average with exogenous inputs (ARIMAX) with regard to the accuracy, computational efficiency, and generalizability, and (v) forecasting future yearly electricity consumption and validation. The population, gross domestic product (GDP), imports, and refined oil products were observed to be significant with the total yearly electricity consumption in Saudi Arabia. The coefficient of determination R2 values for all the developed models are >0.98, indicating an excellent fit of the models with historical data. However, among all three proposed models, the BOA–NARX has the best performance, improving the forecasting accuracy (root mean square error (RMSE)) by 71% and 80% compared to the ARIMAX and BOA–SVR models, respectively. The overall results of this study confirm the higher accuracy and reliability of the proposed methods in total electricity consumption forecasting that can be used by power system operators to more accurately forecast electricity consumption to ensure the sustainability of electric energy. This study can also provide significant guidance and helpful insights for researchers to enhance their understanding of crucial research, emerging trends, and new developments in future energy studies.

ESTIMATING AIRBORNE PARTICULATE MATTER IN THE NATIONAL CAPITAL REGION, PHILIPPINES USING MULTIPLE LINEAR REGRESSION AND GRADIENT BOOSTING ALGORITHM ON MODIS MAIAC AEROSOL OPTICAL DEPTH

Abstract. The generation of air quality concentration data is imperative for the health and environment of highly urbanized regions. Through remote sensing, air pollutant concentrations can be obtained over large areas for a long time. In this study, particulate matter (PM2.5 and PM10) concentrations were estimated using satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Optical Depth (AOD) values observed in the National Capital Region (NCR), Philippines. Models were generated using multiple linear regression (MLR) and gradient boosting regression to determine the best models for the whole data from 2017 to 2020, dry season, and wet season with a 70–30 split for the train-test sets. Initial models resulted with the best coefficient of determination R2 values of 2.6% and 1.2% using MLR and 2.0% using gradient boosting regression. The results for PM2.5 and PM10 showed the lowest Root Mean Square Error (RMSE) values of 8.79 μg/m3 and 18.99 μg/m3 using MLR and 8.08 μg/m3 and 16.85 μg/m3 using gradient boosting, respectively. The preliminary results indicate the relatively poor performance of models in estimating particulate matter using satellite-derived AOD images. Improvements in the models will include the integration of more in-situ data from air quality monitoring stations and the addition of additional variables and features such as meteorological parameters and geographical layers.

Prediction of material composition in lateritic soil using spectroradiometer

Laterite soil products are vital in coastal region construction. Conventional identification methods are slow and require significant labour. Remote sensing expedites large-scale identification, making it essential in the process. Soil samples were randomly collected from the study area for laboratory analysis to determine soil iron content. Additionally, spectral signatures were obtained from a spectroradiometer to assess iron content variability. The considerable variability in material composition underscores its significance as an indicator of soil characteristics. The iron content serves as a crucial indicator of soil development and can be utilized as an index for assessing material composition in soil. The iron content in soil samples is analysed using Analytical Spectral Device (ASD) Field Spec 4 Spectroradiometer, which operates within the spectral wavelength range of 350–2500 nm. The Vis-NIR reflectance spectroscopy is an innovative and efficient method for predicting soil iron content, requiring less effort compared to conventional techniques. The study uses Partial Least Squares Regression (PLSR), a statistical regression method employed to predict iron content in soil from spectral reflectance data. The application of PLSR resulted in moderate prediction accuracy, with coefficient of determination R2 value for calibration and validation are 0.74 and 0.73, respectively. The analysis revealed that the spectroradiometer in combination with PLSR can accurately predict the material composition in lateritic soil with an efficiency of 70%.

CNN-LSTM based on attention mechanism for brake pad remaining life prediction

In order to predict the remaining service life of brake pads accurately and efficiently, and to achieve intelligent warning, this paper proposes a CNN-LSTM brake pad remaining life prediction model based on an attention mechanism. The model constructs a non-linear relationship between brake pad features such as brake temperature, brake oil pressure and brake speed and brake pad wear data through convolutional neural network (CNN) and long and short term memory network (LSTM), as well as capturing the time dependence that exists in the brake pad wear sequence. The attention mechanism is also introduced to assign different weight values to the features output from multiple historical moments, highlighting the features with high saliency and avoiding the influence of invalid features, so as to improve the prediction effect of the remaining brake pad life. The results show that the proposed CNN-LSTM-Attention model can effectively predict the remaining life of brake pads, with the mean absolute error MAE value of 0.0048, root mean square error RMSE value of 0.0059 and coefficient of determination R2 value of 0.9636; and compared with the BP model, CNN model, LSTM model and CNN-LSTM model, the coefficient of determination R2 values are closest to 1, with an improvement of 8.26%, 5.25%, 3.99% and 1.85% respectively, enabling more effective monitoring and intelligent warning of the remaining brake pad life.

A CNN-LSTM-PSO tool wear prediction method based on multi-channel feature fusion

In order to achieve predictive maintenance of CNC machining tools and to be able to change tools intelligently before tool wear is at a critical threshold, a CNN-LSTM tool wear prediction model based on particle swarm algorithm (PSO) optimization with multi-channel feature fusion is proposed. Firstly, the raw signals of seven channels of the machining process are collected using sensor technology and processed for noise reduction; secondly, the time-domain, frequency-domain and time-frequency-domain features of each channel signal are extracted, and a sample data set of spatio-temporal correlation of traffic flow is constructed by dimensionality reduction processing and information fusion of the above features; finally, the data set is input to the CNN-LSTM-PSO model for training and testing. The results show that the CNN-LSTM-PSO model can effectively predict tool wear with an average absolute error MAE value of 0.5848, a root mean square error RMSE value of 0.7281, and a coefficient of determination R2 value of 0.9964; and compared with the BP model, CNN model, LSTM model and CNN-LSTM model, its tool wear prediction accuracy improved by 7.56%, 2.60%, 2.98%, and 1.63%, respectively.