Search Results (11,525)

With the continuous maturation of prefabricated buildings, the errors and efficiency issues in the design of prefabricated buildings have gradually drawn the attention of architectural designers. The characteristics of standardized design for prefabricated buildings also provide a foundation for the application of computer-learning methods in the field of architectural design, thereby improving design quality and efficiency. This study combined BIM technology to construct the information data on prefabricated buildings, applied the transfer-learning method to build the training model, and utilized the traditional architectural design collision concept to construct a prediction model applicable to the collision detection of prefabricated building design. The training set and test set were constructed in a 9:1 ratio, and the loss function and accuracy function were calculated. The error rate of the model was verified to be within 10% through trial calculations based on engineering cases. The results show that, in the selected engineering cases, the collision detection accuracy of the model reached 90.3%, with an average absolute error (MAE) of 0.199 and a root mean square error (RMSE) of 0.245. The prediction error rate was controlled within 10%, representing an approximately 65% improvement in efficiency compared to traditional manual inspections. This method significantly improves the efficiency and accuracy of collision detection, providing reliable technical support for the optimization of prefabricated building design. Full article

Supersonic cabins are characterized by high heat flux and high occupant density, which can adversely affect passenger comfort, health, and energy efficiency. This study proposed a multi-objective optimization framework for determining supply air parameters in a supersonic aircraft cabin, evaluating the performances of different optimization methods. The optimization focused on three design objectives: thermal comfort (PMV), air freshness (air age), and the temperature differential between the supply and exhaust air. Two fast calculation methods—Proper Orthogonal Decomposition (POD) and Artificial Neural Networks (ANN)—were compared alongside two optimization algorithms: Multi-Objective Genetic Algorithm (MOGA) and Pareto search. The results indicate that the POD method has a smaller relative root mean square error compared to the ANN method. The relative root mean square error of the ANN method in predicting PMV is 2.7 times higher than the POD method and 3.9 times higher in air age prediction. The Pareto search algorithm outperformed MOGA in computational efficiency, generating 3.3 times more Pareto-optimal solutions in less time. The entropy weight method was used to assign weight for both optimization algorithms, revealing that neither algorithm achieved universally optimal performance across all objectives. Therefore, selecting the best solution requires aligning optimization outcomes with specific design priorities. Full article

Accurate and non-destructive assessment of fruit firmness is critical for evaluating quality and ripeness, particularly in postharvest handling and supply chain management. This study presents the development of an image-based vibration analysis system for evaluating the firmness of kiwifruit using computer vision and machine learning. In the proposed setup, 120 kiwifruits were subjected to controlled excitation in the frequency range of 200–300 Hz using a vibration motor. A digital camera captured surface displacement over time (for 20 s), enabling the extraction of key dynamic features, namely, the damping coefficient (damping is a measure of a material’s ability to dissipate energy) and natural frequency (the first peak in the frequency spectrum), through image processing techniques. Results showed that firmer fruits exhibited higher natural frequencies and lower damping, while softer, more ripened fruits showed the opposite trend. These vibration-based features were then used as inputs to a feed-forward backpropagation neural network to predict fruit firmness. The neural network consisted of an input layer with two neurons (damping coefficient and natural frequency), a hidden layer with ten neurons, and an output layer representing firmness. The model demonstrated strong predictive performance, with a correlation coefficient (R²) of 0.9951 and a root mean square error (RMSE) of 0.0185, confirming its high accuracy. This study confirms the feasibility of using vibration-induced image data combined with machine learning for non-destructive firmness evaluation. The proposed method provides a reliable and efficient alternative to traditional firmness testing techniques and offers potential for real-time implementation in automated grading and quality control systems for kiwi and other fruit types. Full article

This study focuses on predicting β-carotene content using hyperspectral images captured in the near-infrared (NIR) region during the drying process. Several machine learning models are compared, including Partial Least Squares Regression (PLSR), Stacked Autoencoders (SAEs) combined with Random Forest (RF), and Convolutional Neural Networks (CNNs) in three configurations: 1D-CNN, 2D-CNN, and 3D-CNN. The models are evaluated using R², Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The PLSR model showed excellent results with R² = 0.97 for both training and testing, indicating minimal overfitting. The SAE-RF model also performed well, with R² values of 0.82 and 0.83 for training and testing, respectively, showing strong consistency. The CNN models displayed varying results: 1D-CNN achieved moderate performance, while 2D-CNN and 3D-CNN exhibited signs of overfitting, especially on testing data. Overall, the findings suggest that although CNNs are capable of capturing complex patterns, the PLSR and SAE-RF models deliver more reliable and robust predictions for β-carotene content in hyperspectral imaging. Full article

Control point detection is a critical initial step in camera calibration. For checkerboard corner points, detection is based on inferences about local gradients in the image. Infrared (IR) imaging, however, poses challenges due to its low resolution and low signal-to-noise ratio, hindering the identification of clear local features. This study proposes a physics-informed neural network (PINN) based on the YOLO target detection model to detect checkerboard corner points in infrared images, aiming to enhance the calibration accuracy of infrared thermal cameras. This method first optimizes the YOLO model used for corner detection based on the idea of enhancing image gradient information extraction and then incorporates camera physical information into the training process so that the model can learn the intrinsic constraints between corner coordinates. Camera physical information is applied to the loss calculation process during training, avoiding the impact of label errors on the model and further improving detection accuracy. Compared with the baselines, the proposed method reduces the root mean square error (RMSE) by at least 30% on average across five test sets, indicating that the PINN-based corner detection method can effectively handle low-quality infrared images and achieve more accurate camera calibration. Full article

This study combines three optimization algorithms, Tunicate Swarm Algorithm (TSA), Whale Optimization Algorithm (WOA), and Jellyfish Search Optimizer (JSO), with random forest (RF) to predict the shear bond strength of cable bolts under different types and grouting conditions. Based on the original dataset, a database of 860 samples was generated by introducing random noise around each data point. After establishing three hybrid models (RF-WOA, RF-JSO, RF-TSA) and training them, the obtained models were evaluated using six metrics: coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), variance account for (VAF), and A-20 index. The results indicate that the RF-JSO model exhibits superior performance compared to the other models. The RF-JSO model achieved an excellent performance on the testing set (R² = 0.981, RMSE = 11.063, MAE = 6.457, MAPE = 9, VAF = 98.168, A-20 = 0.891). In addition, Shapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Local Interpretable Model-agnostic Explanations (LIME) were used to analyze the interpretability of the model, and it was found that confining pressure (Stress), elastic modulus (E), and a standard cable type (cable type_standard) contributed the most to the prediction of shear bond strength. In summary, the hybrid model proposed in this study can effectively predict the shear bond strength of cable bolts. Full article

This paper focuses on enhancing master cylinder pressure control in pressure-sensorless Electro-Hydraulic Brake (EHB) systems. A novel control strategy is developed, integrating a Piecewise Sliding Mode Controller (Piecewise-SMC) with an Extended Sliding Mode Observer (ESMO) based on a newly derived pressure–position–velocity model that accounts for rack position and velocity effects. To handle external disturbances and parameter uncertainties, the ESMO provides accurate pressure estimation. The nonlinear EHB model is approximated piecewise linearly to facilitate controller design. The proposed Piecewise-SMC regulates motor torque to achieve precise pressure tracking. Experimental validation under step-change braking conditions demonstrates that the Piecewise-SMC reduces response time by 31.8%, overshoot by 35.8%, and tracking root mean square error by 9.6% compared to traditional SMC, confirming its effectiveness and robustness for pressure-sensorless EHB applications. Full article

Blood glucose monitoring is crucial for the daily management of diabetic patients. In this study, we developed a differential absorbance and photoplethysmography (PPG)-based non-invasive blood glucose measurement system (NIBGMS) using visible–near-infrared (Vis-NIR) light. Three light-emitting diodes (LEDs) (625 nm, 850 nm, and 940 nm) and three photodetectors (PDs) with different source–detector separation distances were used to detect the differential absorbance of tissues at different depths and PPG signals of the index finger. A spatiotemporal multimodal fused long short-term memory (STMF-LSTM) model was developed to improve the prediction accuracy of blood glucose levels by multimodal fusion of optical spatial information (differential absorbance and PPG signals) and glucose temporal information. The validity of the NIBGMS was preliminarily verified using multilayer perceptron (MLP), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XG Boost) models on datasets collected from 15 non-diabetic subjects and 3 type-2 diabetic subjects, with a total of 805 samples. Additionally, a continuous dataset consisting 272 samples from four non-diabetic subjects was used to validate the developed STMF-LSTM model. The results demonstrate that the STMF-LSTM model indicated improved prediction performance with a root mean square error (RMSE) of 0.811 mmol/L and a percentage of 100% for Parkes error grid analysis (EGA) Zone A and B in 8-fold cross validation. Therefore, the developed NIBGMS and STMF-LSTM model show potential in practical non-invasive blood glucose monitoring. Full article

While Forgetting Factor Recursive Least Square (FFRLS) algorithms with evaluation mechanisms have been developed to address SOC-dependent parameter mapping shifts and their efficacy has been proven in Li-ion batteries, their applicability to lithium–sulfur (Li-S) batteries remains uncertain due to different electrochemical characteristics. This study critically evaluates the applicability of a Fisher information matrix-constrained FFRLS framework for online parameter identification in Li-S battery equivalent circuit network (ECN) models. Experimental validation using distinct drive cycles showed that the identification results of polarization-related parameters are significantly biased between different current excitations, and root mean square error (RMSE) variations diverge by 100%, with terminal voltage estimation errors more than 0.05 V. The parametric uncertainty under variable excitation profiles and voltage plateau estimation deficiencies confirms the inadequacy of such approaches, constraining model-based online identification viability for Li-S automotive applications. Future research should therefore prioritize hybrid estimation architectures integrating electrochemical knowledge with data-driven observers, alongside excitation capturing specifically optimized for Li-S online parameter observability requirements and cell nonuniformity and aging condition consideration. Full article

This study proposes a system identification (SI) technique based on the constrained recursive least squares (CRLS) method to model the dynamics of the P-SUROII. By simplifying the dynamic model in consideration of the inherent characteristics of underwater vehicles and minimizing the number of parameters to be estimated, the proposed approach aims to improve estimation accuracy. In addition, a simplified thruster input model was applied to quantify the actual thruster output and improve the reliability of the input data. To satisfy the persistent excitation (PE) condition during the estimation process, experiments incorporating various motion modes were designed, and free-running and S-shaped maneuvering tests were additionally conducted to validate the model’s generalization capability and prediction performance. The coefficients estimated using the CRLS method, which is robust to noise and bias, were evaluated using quantitative similarity metrics such as root mean squared error (RMSE) and mean absolute error (MAE), confirming their validity. The proposed method effectively captures the actual dynamics of the underwater vehicle and is expected to serve as a key enabling technology for the future development of high-performance control systems and autonomous operation systems. Full article

Long-term and accurate prediction of air pollutant concentrations can serve as a foundation for air pollution warning and prevention, which is crucial for social development and human health. In this study, we provide a model for predicting the concentration of air pollutants based on big data spatiotemporal correlation analysis and deep learning methods. Based on an improved temporal convolutional network (TCN) and a bi-directional gated recurrent unit (BiGRU) as the fundamental architecture, the model adds two attention mechanisms to improve performance: Squeeze and Excitation Networks (SENet) and Convolutional Block Attention Module (CBAM). The improved TCN moves the residual connection layer to the network’s front end as a preprocessing procedure, improving the model’s performance and operating efficiency, particularly for big data jobs like air pollution concentration prediction. The use of SENet improves the model’s comprehension and extraction of long-term dependent features from pollutants and meteorological data. The incorporation of CBAM enhances the model’s perception ability towards key local regions through an attention mechanism in the spatial dimension of the feature map. The TCN-SENet-BiGRU-CBAM model successfully realizes the prediction of air pollutant concentrations by extracting the spatiotemporal features of the data. Compared with previous advanced deep learning models, the model has higher prediction accuracy and generalization ability. The model is suitable for prediction tasks from 1 to 12 h in the future, with root mean square error (RMSE) and mean absolute error (MAE) ranging from 5.309~14.043 and 3.507~9.200, respectively. Full article

Improved irrigation guidelines are needed to maximize crop water use efficiency. Combining field data with simulation models can provide information for better irrigation management. The objective of the present study was to evaluate the effects of two flood irrigation treatments on fiber yield (FY) and quality during the 2023 and 2024 growing seasons in Maricopa, Arizona, USA. Two irrigation treatments, denoted as F100% and F80%, were arranged in a randomized complete block design with three replicates. Then, AquaCrop was used to simulate cotton yield (Y_Tot), water use (ET_obs), and total soil water content (WC_Tot) for the two irrigation treatments. Six statistical metrics, including the coefficient of determination (R²), the normalized root-mean-square error (NRMSE), the mean absolute error (MAE), simulation error (S_e), the index of agreement (D_index), and the Nash–Sutcliffe efficiency coefficient (NSE), were employed to assess model performance. The results of the field trial demonstrated that reducing the irrigation rate to 80% of ET_c negatively impacted cotton FY and ET water productivity (ETWP); the FY declined by 45.2% (ETWP = 0.097 kg·ha⁻¹) in 2023 and by 38.1% (ETWP = 0.133 kg·ha⁻¹) in 2024. Conversely, F100% produced a more uniform and stronger fiber than F80%, with the uniformity index (UI) and fiber strength (STR) measuring 81.7% and 29.5 g tex⁻¹ in 2023 and 82.2% and 30.0 g tex⁻¹ in 2024, indicating that UI and STR were well correlated with soil water during both growing seasons. AquaCrop showed an excellent performance in simulating cotton CC during the two growing seasons. The R², NRMSE, D_index, and NSE were between 0.97 and 0.99, 8.45% and 14.36%, 0.98 and 0.99, and 0.96 and 0.98, respectively. Moreover, the AquaCrop model accurately simulated Y_Tot during these seasons, with R², NRMSE, D_index, and NSE for pooled yield data of 0.93, 8.05%, 0.95, and 0.78, respectively. The model consistently overestimated Y_Tot, ET_obs, and WC_Tot, but within an acceptable S_e (S_e < 15%) during both growing seasons, except for WC_Tot under the 80% treatment in 2023 (S_e = 26.4%). Consequently, AquaCrop can be considered an effective tool for irrigation management and yield prediction in arid climates such as Arizona. Full article

Marble is widely used in the field of construction and home decoration because of its high strength, high hardness and good wear resistance. Diamond wire sawing has been applied in marble cutting in industry due to its features such as low material loss, high cutting accuracy and low noise. The sawing surface quality directly affects the subsequent processing efficiency and economic benefit of marble products. The surface quality is affected by multiple parameters such as process parameters and workpiece sizes, making it difficult to accurately predict through traditional empirical equations or linear models. To improve prediction accuracy, this paper proposes a prediction model based on backpropagation (BP) neural network. Firstly, through the experiments of sawing marbles with the diamond wire saw, the datasets of surface roughness and waviness under different process parameters were obtained. Secondly, a BP neural network model was established, and the mixed-strategy-improved whale optimization algorithm (IWOA) was used to optimize the initial weight and threshold of the network, and established the IWOA-BP neural network model. Finally, the performance of the model was verified by comparison with the traditional models. The results showed that the IWOA-BP neural network model demonstrated the optimal prediction performance in both the surface roughness Ra and waviness Wa. The minimum predicted values of the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 0.0342%, 0.0284% and 1.5614%, respectively, which proved that the model had higher prediction accuracy. This study provides experimental basis and technical support for the prediction of the surface quality of marble material cut by diamond wire saw. Full article

The real estate market has a major impact on the economy and everyday life. Accurate real estate valuation is essential for buyers, sellers, investors, and government institutions. Traditionally, valuation has been conducted using various estimation models. However, recent advancements in information technology, particularly in artificial intelligence and machine learning, have enabled more precise predictions of real estate prices. Machine learning allows computers to recognize patterns in data and create models that can predict prices based on the characteristics of the property, such as location, square footage, number of rooms, age of the building, and similar features. The aim of this paper is to investigate how the application of machine learning can be used to predict real estate prices. A machine learning model was developed using four algorithms: Linear Regression, Random Forest Regression, XGBoost, and K-Nearest Neighbors. The dataset used in this study was collected from major online real estate listing portals in Bosnia and Herzegovina. The performance of each model was evaluated using the R² score, Root Mean Squared Error (RMSE), scatter plots, and error distributions. Based on this evaluation, the most accurate model was selected. Additionally, a simple web interface was created to allow for non-experts to easily obtain property price estimates. Full article

Abnormally warm winters in recent years have accelerated flowering in fruit trees, increasing their vulnerability to late frost damage. To address this challenge, this study aimed to evaluate and compare the performance of three phenology models—the development rate (DVR), modified DVR (mDVR), and Chill Days (CD) models—for predicting full bloom dates of ‘Niitaka’ pear, using image-derived phenological observations. The goal was to identify the most reliable and regionally transferable model for nationwide application in South Korea. A key strength of this study lies in the integration of real-time orchard imagery with automated weather station (AWS) data, enabling standardized and objective phenological monitoring across multiple regions. Using five years of temperature data from seven orchard sites, chill and heat unit accumulations were calculated and compared with observed full bloom dates obtained from orchard imagery and field records. Correlation analysis revealed a strong negative relationship between cumulative heat units and bloom timing, with correlation coefficients ranging from –0.88 (DVR) to –0.94 (mDVR). Among the models, the mDVR model demonstrated the highest stability in chill unit estimation (CV = 6.3%), the lowest root-mean-square error (RMSE = 2.9 days), and the highest model efficiency (EF = 0.74), indicating superior predictive performance across diverse climatic conditions. In contrast, the DVR model showed limited generalizability beyond its original calibration zone. These findings suggest that the mDVR model, when supported by image-based phenological data, provides a robust and scalable tool for forecasting full bloom dates of temperate fruit trees and enhancing grower preparedness against late frost risks under changing climate conditions. Full article