Search Results (4,816)

Short-term building cooling load prediction is crucial for optimizing building energy management and promoting sustainability. While data-driven models excel in this task, their performance heavily depends on the input feature set. Feature selection must balance predictive accuracy (relevance) and model simplicity (minimal redundancy), a challenge that existing methods often address incompletely. This study proposes a novel feature optimization framework that integrates the Maximum Information Coefficient (MIC) to measure non-linear relevance and the Maximum Relevance Minimum Redundancy (MRMR) principle to control redundancy. The proposed MRMR-MIC method was evaluated against four benchmark feature selection methods using three predictive models in a simulated office building case study. The results demonstrate that MRMR-MIC significantly outperforms other methods: it reduces the feature dimensionality from over 170 to merely 40 variables while maintaining a prediction error below 5%. This represents a substantial reduction in model complexity without sacrificing accuracy. Furthermore, the selected features cover a more comprehensive and physically meaningful set of attributes compared to other redundancy-control methods. The study concludes that the MRMR-MIC framework provides a robust, systematic methodology for identifying essential feature variables, which can not only enhance the performance of prediction models, but also offer practical guidance for designing cost-effective data acquisition systems in real-building applications. Full article

Autonomous beach-cleaning robots require reliable, low-cost navigation on sand. We study Sim-to-Real transfer of deep reinforcement learning (DRL) policies using a minimal sensor suite—wheel-encoder odometry and a single 2-D LiDAR—on a 30 kg differential-drive platform (Raspberry Pi 4). Two policies, Proximal Policy Optimization (PPO) and a masked-action variant (PPO-Mask), were trained in Gazebo + Gymnasium and deployed on the physical robot without hyperparameter retuning. Field trials on firm sand and on a natural loose-sand beach show that PPO-Mask reduces tracking error versus PPO on firm ground (16.6% ISE reduction; 5.2% IAE reduction) and executes multi-waypoint paths faster (square path: 112.48 s vs. 103.46 s). On beach sand, all waypoints were reached within a 1 m tolerance, with mission times of 115.72 s (square) and 81.77 s (triangle). These results indicate that DRL-based navigation with minimal sensing and low-cost compute is feasible in beach settings. Full article

This study compared two physiological modeling approaches, the Peronnet-Thibault (P-T) model and the Minimal Power (MP) model, to identify key parameters representing individual physiological characteristics and to explore their applications in running training. Model parameters were estimated using nonlinear least squares fitting, and predictive performance was evaluated by the mean absolute error (MAE). Results from the World Running Records (WRR) indicated that the MP model generally outperformed the P-T model in linking running performance with physiological variables, demonstrating greater capability in extracting physiological parameters. Further validation using the British Runner Records (BRR) showed that the MP model achieved MAE values of 3.02% for males and 3.47% for females, reflecting strong generalization to real running performance. Furthermore, descriptive analyses of the relationships between MP model parameters and running performance further support its potential value in personalized training and performance prediction. Full article

In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of $1\times {10}^{-3}$ by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article

This study aimed to test the reliability of seven functional performance tests in amateur trail runners, including ankle mobility, balance, hopping, and countermovement jump (CMJ) tests. The sample consisted of 35 runners who were evaluated in two sessions separated by 7 to 14 days, which varied due to participants’ scheduling constraints. Relative reliability was assessed using the Intraclass Correlation Coefficient (ICC, which indicates consistency between repeated measures), the Standard Error of Measurement (SEM, which reflects measurement precision), and the Minimal Detectable Change (MDC, which represents the smallest real change beyond measurement error). The results show high reliability in almost all tests. The Lunge Test obtained an ICC of 0.990 and 0.983 for distance, and 0.941 and 0.958 for angular measurements in both legs. The Hop Tests showed moderate reliability with ICC above 0.7 In contrast, the Y Balance Test demonstrated lower reliability, with ICC values ranging from 0.554 to 0.732. The CMJ test showed good reliability, with an ICC ranging from 0.753 to 0.894, an SEM between 5.79% and 11.3%, and an MDC ranging from 15.54% to 31.44%, making it useful for assessing lower limb explosive strength. Both tests presented comparatively higher error values, which should be considered when interpreting individual changes. These findings support the use of these tests as valid and reliable tools for evaluating ankle dorsiflexion, balance, functional symmetry, and lower limb explosive strength in amateur trail runners, prior to training programs or injury prevention strategies, provided that standardized protocols and validated measuring instruments are used. Full article

Wheeled mobile robots (WMRs) are being increasingly integrated into various sectors such as logistics and transportation. However, their accurate trajectory tracking remains a challenge. To address this control issue, this study proposes a trajectory correction technique for a wheeled mobile robot (WMR). This proposal uses a functional-link neural network (FLNN) that adjusts the trajectory error with the aim of minimizing it. This error is propagated backward by adjusting the different parameters of the controller. The controller was designed using a combination of linearization feedback, sliding mode control, and FLNN, where the latter provides adaptability to the controller. Using the Lyapunov stability theory, the stability of the proposal was demonstrated. Experiments and simulation analyses were also carried out to demonstrate the practical feasibility of the proposal. Full article

Sustainable aviation fuel (SAF) is a drop-in alternative to conventional jet fuels, designed to reduce greenhouse gas (GHG) emissions while requiring minimal infrastructure changes and certification under the American Society for Testing and Materials (ASTM) D7566 standard. This study assesses recently identified high-energy-density (HED) strained polycycloalkanes as SAF candidates. Strain energy (E_se) was calculated using density functional theory (DFT), while operational properties such as boiling point (BP) and flash point (FP) were predicted using support vector regression (SVR) models. The models demonstrated strong predictive performance (R² > 0.96) with mean absolute errors of 6.92 K for BP and 9.58 K for FP, with robustness sensitivity analysis. It is found that approximately 65% of these studied polycycloalkanes fall within the Jet A fuel property boundaries. The polycycloalkanes (C₉–C₁₅) with strain energies below approximately 60 kcal/mol achieve an balance between energy density and ignition safety, aligning with the specifications of Jet A. The majority of structures were dominated by five-membered rings, with a few three- or four-membered rings enhancing favorable trade-offs among BP, FP, and HED. This early pre-screening indicates that moderately strained polycycloalkanes are safe, energy-dense candidates for next-generation sustainable jet fuels and provide a framework for designing high-performance SAFs. Full article

This study compares enhanced turbulence models in a natural river channel 3D simulation under extreme hydrometeorological conditions. Using ANSYS Fluent 2024 R1 and the Volume of Fluid scheme, five RANS closures were evaluated: realizable k–ε, Renormalization-Group k–ε, Shear Stress Transport k–ω, Generalized k–ω, and Baseline-Explicit Algebraic Reynolds Stress model. A segment of the Santa Catarina River in Monterrey, Mexico, defined the computational domain, which produced high-energy, non-repeatable real-world flow conditions where hydrometric data were not yet available. Empirical validation was conducted using surface velocity estimations obtained through high-resolution video analysis. Systematic bias was minimized through mesh-independent validation (<1% error) and a benchmarked reference closure, ensuring a fair basis for inter-model comparison. All models were realized on a validated polyhedral mesh with consistent boundary conditions, evaluating performance in terms of mean velocity, turbulent viscosity, strain rate, and vorticity. Mean velocity predictions matched the empirical value of 4.43 [m/s]. The Baseline model offered the highest overall fidelity in turbulent viscosity structure (up to 43 [kg/m·s]) and anisotropy representation. Simulation runtimes ranged from 10 to 16 h, reflecting a computational cost that increases with model complexity but justified by improved flow anisotropy representation. Results show that all models yielded similar mean flow predictions within a narrow error margin. However, they differed notably in resolving low-velocity zones, turbulence intensity, and anisotropy within a purely hydrodynamic framework that does not include sediment transport. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

Transformer-based 2D-to-3D lifting methods have demonstrated outstanding performance in 3D human pose estimation from 2D pose sequences. However, they still encounter challenges with the relatively poor quality of 2D joints and substantial computational costs. In this paper, we propose a CGFusionFormer to address these problems. We propose a compact spatial representation (CSR) to robustly generate local spatial multihypothesis features from part of the 2D pose sequence. Specifically, CSR models spatial constraints based on body parts and incorporates 2D Gaussian filters and nonparametric reduction to improve spatial features against low-quality 2D poses and reduce the computational cost of subsequent temporal encoding. We design a residual-based Hybrid Adaptive Fusion module that combines multihypothesis features with global frequency domain features to accurately estimate the 3D human pose with minimal computational cost. We realize CGFusionFormer with a PoseFormer-like transformer backbone. Extensive experiments on the challenging Human3.6M and MPI-INF-3DHP benchmarks show that our method outperforms prior transformer-based variants in short receptive fields and achieves a superior accuracy–efficiency trade-off. On Human3.6M (sequence length 27, 3 input frames), it achieves 47.6 mm Mean Per Joint Position Error (MPJPE) at only 71.3 MFLOPs, representing about a 40 percent reduction in computation compared with PoseFormerV2 while attaining better accuracy. On MPI-INF-3DHP (81-frame sequences), it reaches 97.9 Percentage of Correct Keypoints (PCK), 78.5 Area Under the Curve (AUC), and 27.2 mm MPJPE, matching the best PCK and achieving the lowest MPJPE among the compared methods under the same setting. Full article

The prediction of stock prices is challenging due to their volatility, irregular patterns, and complex time-series structure. Reliably forecasting stock market data plays a crucial role in minimizing financial risk and optimizing investment strategies. However, traditional models often struggle to capture temporal dependencies and extract relevant features from noisy inputs, which limits their predictive performance. To improve this, we developed an enhanced recursive feature elimination (RFE) method that blends the importance of impurity-based features from random forest and gradient boosting models with Kendall tau correlation analysis, and we applied SHapley Additive exPlanations (SHAP) analysis to externally validate the reliability of the selected features. This approach leads to more consistent and reliable feature selection for short-term stock prediction over 1-, 3-, and 7-day intervals. The proposed deep learning (DL) architecture integrates a temporal convolutional network (TCN) for long-term pattern recognition, a gated recurrent unit (GRU) for sequence capture, and multi-head attention (MHA) for focusing on critical information, thereby achieving superior predictive performance. We evaluate the proposed approach using daily stock price data from three leading companies—HDFC Bank, Tata Consultancy Services (TCS), and Tesla—and two major stock indices: Nifty 50 and S&P 500. The performance of our model is compared against five benchmark models: temporal convolutional network (TCN), long short-term memory (LSTM), GRU, Bidirectional GRU, and a hybrid TCN–GRU model. Our method consistently shows lower error rates and higher predictive accuracy across all datasets, as measured by four commonly used performance metrics. Full article

Soil electrical conductivity (EC) and water content are key indicators of soil health, influencing nutrient availability, salinity stress, and crop productivity. Monitoring these parameters is critical for precision agriculture. However, most existing measurement systems are costly, which restricts their use in practical field conditions. The aim of this study was to develop and validate a low-cost, portable system for simultaneous measurement of soil EC, water content, and temperature, while maintaining accuracy comparable to laboratory-grade instruments. The system was designed with four electrodes arranged in two pairs and employed an AC bipolar pulse method with a constant-current circuit, precision rectifier, and peak detector to minimize electrode polarization. Experiments were carried out in sandy loam soil at water contents of 13%, 18%, and 22% and KNO₃ concentrations of 0, 0.1, 0.2, and 0.4 M. Measurements from the developed system were benchmarked against a professional impedance analyzer (E4990A). The findings demonstrated that EC increased with both frequency and water content. At 100 Hz, the mean error compared with the analyzer was 8.95%, rising slightly to 9.98% at 10 kHz. A strong linear relationship was observed between EC and KNO₃ concentration at 100 Hz (R² = 0.9898), and for the same salt concentration (0.1 M KNO₃) at 100 Hz, EC increased from ~0.26 mS/cm at 13% water content to ~0.43 mS/cm at 22%. In conclusion, the developed system consistently achieved <10% error while maintaining a cost of ~€55, significantly lower than commercial devices. These results confirm its potential as an affordable and reliable tool for soil salinity and water content monitoring in precision agriculture. Full article

Traditional GrabCut algorithms are limited by their reliance on manual intervention, often resulting in segmentation errors and missed detections, particularly against complex backgrounds. This study addresses these limitations by introducing the Auto Trademark Cut (AT-Cut), an advanced automated trademark image-segmentation method built upon an enhanced GrabCut framework. The proposed approach achieves superior performance through three key innovations: Firstly, histogram equalization is applied to the entire input image to mitigate noise induced by illumination variations and other environmental factors. Secondly, state-of-the-art object detection techniques are utilized to precisely identify and extract the foreground target, with dynamic region definition based on detection outcomes to ensure heightened segmentation accuracy. Thirdly, morphological erosion and dilation operations are employed to refine the contours of the segmented target, leading to significantly improved edge segmentation quality. Experimental results indicate that AT-Cut enables efficient, fully automated trademark segmentation while minimizing the necessity for labor-intensive manual labeling. Evaluation on the public Real-world Logos dataset demonstrates that the proposed method surpasses conventional GrabCut algorithms in both boundary localization accuracy and overall segmentation quality, achieving a mean accuracy of 90.5%. Full article

In traditional scallion cultivation, the bare-root transplanting method—which involves direct seeding, seedling raising in the field, and lifting—is commonly adopted to minimize seedling production costs. However, during the mechanized transplanting of bare-root scallion seedlings, practical problems such as severe seedling damage and poor planting uprightness exist. In this paper, the Hertz–Mindlin with Bonding contact model was used to establish the scallion seedling model. Combined with the Plackett–Burman experiment, steepest ascent experiment, and Box–Behnken experiment, the bonding parameters of scallion seedlings were calibrated. Furthermore, the accuracy of the scallion seedling model parameters was verified through the stress–strain characteristics observed during the actual loading and compression process of the scallion seedlings. The results indicate that the scallion seedling normal/tangential contact stiffness, scallion seedling normal/tangential ultimate stress, and scallion Poisson’s ratio significantly influence the mechanical properties of scallion seedlings. Through optimization experiments, the optimal combination of the above parameters was determined to be 4.84 × 10⁹ N/m, 5.64 × 10⁷ Pa, and 0.38. In this paper, the flexible planting components of scallion seedlings were taken as the research object. Flexible protrusions were added to the planting disc to reduce the damage rate of scallion seedlings, and an EDEM-ADAMS coupling interaction model between the planting components and scallion seedlings was established. Based on this model, optimization and verification were carried out on the key components of the planting components. Orthogonal experiments were conducted with the contact area between scallion seedlings and the disc, rotational speed of the flexible disc, furrow depth, and clamping force on scallion seedlings as experimental factors, and with the uprightness and damage status of scallion seedlings as evaluation criteria. The experimental results showed that when the contact area between scallion seedlings and the disc was 255 mm², the angular velocity was 0.278 rad/s, and the furrow depth was 102.15 mm, the performance of the scallion planting mechanism was optimal. At this point, the uprightness of the scallion seedlings was 94.80% and the damage rate was 3%. Field experiments were carried out based on the above parameters. The results indicated that the average uprightness of transplanted scallion seedlings was 93.86% and the damage rate was 2.76%, with an error of less than 2% compared with the simulation prediction values. Therefore, the parameter model constructed in this paper is reliable and effective, and the designed and improved transplanting mechanism can realize the upright and low-damage planting of scallion seedlings, providing a reference for the low-damage and high-uprightness transplanting operation of scallions. Full article

In reconfigurable radio frequency (RF) microsystems, the interconnect structure critically affects high-frequency signal integrity, and the accuracy of electromagnetic (EM) modeling directly determines the overall system performance. Conventional neural network-based surrogate models mainly focus on minimizing numerical errors, while neglecting essential physical constraints, such as causality and passivity, thereby limiting their applicability in both time and frequency domains. This paper proposes a physics-constrained Neuro-Transfer surrogate model with a broadband output architecture to directly predict S-parameters over the 1–50 GHz range. Causality and passivity are enforced through dedicated regularization terms during training. Furthermore, a particle swarm optimization (PSO)-based multi-objective intelligent optimization framework is developed, incorporating fixed-weight normalization and a linearly decreasing inertia weight strategy to simultaneously optimize the S11, S21, and S22 performance of a quasi-coaxial TSV composite structure. Target values are set to −25 dB, −0.54 dB, and −24 dB, respectively. The optimized structural parameters yield prediction-to-simulation deviations below 1 dB, with an average prediction error of 2.11% on the test set. Full article