Search Results (11,325)

Conventional electro-permanent magnet (EPM) lifting/holding systems, typically based on NdFeB magnets, face efficiency limitations because continuous current is required either for standby condition to avoid accidentally attracting the objects around or for gently approaching and separating from sensitive iron-based target objects during gripping and releasing processes. Low Coercive Force (LCF) magnets offer an alternative, as their magnetization can be tuned with short current pulses and maintained without continuous current. However, this approach demands fast and precise flux control to eliminate the issues mentioned above. This paper introduces a novel flux control method based on the Hill Climb Search (HCS) algorithm. Once the required flux is identified, the system rapidly adjusts the magnetization of LCF magnet by applying optimized pulse trains within a short time. Experimental evaluation confirms that the proposed method effectively establishes and sustains the target magnetization level without additional current input. This approach has significant potential to advance and expand the use of Low Coercivity EPM systems as an alternative to classical systems. Full article

Power outages during extreme heat events threaten occupant safety by exposing buildings to rapid indoor overheating. However, current building thermal resilience assessments rely mainly on physics-based simulations or IoT sensor data, which are computationally expensive and slow to scale. This study develops an Artificial Intelligence (AI)-driven workflow that integrates Building Information Modeling (BIM)-based residential models, automated EnergyPlus simulations, and supervised Machine Learning (ML) algorithms to predict indoor thermal trajectories and calculate thermal resilience against power failure events in hot seasons. Four representative U.S. residential building typologies were simulated across fourteen ASHRAE climate zones to generate 16,856 scenarios over 45.8 h of runtime. The resulting dataset spans diverse climates and envelopes and enables systematic AI training for energy performance and resilience assessment. It included both time-series of indoor thermal conditions and static thermal resilience metrics such as Passive Survivability Index (PSI) and Weighted Unmet Thermal Performance (WUMTP). Trained on this dataset, ensemble boosting models, notably XGBoost, achieved near-perfect accuracy with an average R² of 0.9994 and nMAE of 1.10% across time-series (indoor temperature, humidity, and cooling energy) recorded every 3 min for a 5-day simulation period with 72 h of outage. It also showed strong performance for predicting static resilience metrics, including WUMTP (R² = 0.9521) and PSI (R² = 0.9375), and required only 1148 s for training. Feature importance analysis revealed that windows contribute 74.3% of the envelope-related influence on passive thermal response. This study demonstrates that the novelty lies not in the algorithm itself, but in applying the model to resilience context of power outages, to reduce computations from days to seconds. The proposed workflow serves as a scalable and accurate tool not only to support resilience planning, but also to guide retrofit prioritization and inform building codes. Full article

Spaceborne Synthetic Aperture Radar (SAR) can be applied for monitoring tropical cyclones (TCs), but co-polarized C-band SAR suffers from signal saturation such that it is improper for high wind-speed conditions. In contrast, cross-polarized SAR data does not suffer from this issue, but the retrieval algorithm needs more deliberation. Previously, many geophysical model functions (GMFs) have been developed using cross-polarized data, which obtain wind speeds using the complex relationships described by radar backscatter, incidence angle, wind direction, and radar look direction. In this regard, the rapid development of artificial intelligence technology has provided versatile machine learning methods for such a nonlinear inversion problem. In this study, we comprehensively compare the wind-speed retrieval performance of several models including Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN), which were developed based on spatio-temporal matching and correlation analysis of stepped frequency microwave radiometer (SFMR) and dual-polarized Sentinel-1 SAR data after noise removal. A data set with ~2800 samples is generated during TCs for training and validating the inversion model. The generalization ability of different models is tested by the reserved independent data. When using similar parameters with GMFs, RF inversion has the highest accuracy with a Root Mean Square Error (RMSE) of 3.40 m/s and correlation coefficient of 0.94. Furthermore, considering that the sea surface temperature is a crucial factor for generating TCs and influencing ocean backscattering, its effects on the proposed RF model are also explored, the results of which show improved wind-speed retrieval performances. Full article

Complex associations among production cost factors, multi-source cost information silos, and opaque transmission mechanisms of hidden costs in open-pit coal mining were addressed. The production process—including drilling, blasting, excavation, transportation, and dumping—was taken as the application context. A corpus of 103 open-pit coal mining standards and related research documents was constructed. Eleven entity types and twelve relationship types were defined. Dynamic word vectors were obtained through transformer (BERT) pre-training. The optimal entity tag sequence was labeled using a bidirectional long short-term memory–conditional random field (BiLSTM–CRF) 9 model. A total of 3995 entities and 6035 relationships were identified, forming a symmetry-aware knowledge graph for open-pit coal mining costs based on the BERT–BiLSTM–CRF model. The results showed that, among nine entity types, including Parameters, the F1-scores all exceeded 60%, indicating more accurate entity recognition compared to conventional methods. Knowledge embedding was performed using the TransH inference algorithm, which outperformed traditional models in all reasoning metrics, with a Hits@10 of 0.636. This verifies its strong capability in capturing complex causal paths among cost factors, making it suitable for practical cost optimization. On this basis, a symmetry-aware BERT–BiLSTM–CRF knowledge graph of open-pit coal mining costs was constructed. Knowledge embedding was then performed with the TransH inference algorithm, and latent relationships among cost factors were mined. Finally, a knowledge-graph-based cost factor identification system was developed. The system lists, for each cost item, the influencing factors and their importance ranking, analyzes variations in relevant factors, and provides decision support. Full article

Reliable and efficient detection of persimmons provides the foundation for precise maturity evaluation. Persimmon ripeness detection remains challenging due to small target sizes, frequent occlusion by foliage, and motion- or focus-induced blur that degrades edge information. This study proposes YOLOv11-4ConvNeXtV2, an enhanced detection framework that integrates a ConvNeXtV2 backbone with Fully Convolutional Masked Auto-Encoder (FCMAE) pretraining, Global Response Normalization (GRN), and Single-Head Self-Attention (SHSA) mechanisms. We present a comprehensive persimmon dataset featuring sub-block segmentation that preserves local structural integrity while expanding dataset diversity. The model was trained on 4921 annotated images (original 703 + 6 × 703 augmented) collected under diverse orchard conditions and optimized for 300 epochs using the Adam optimizer with early stopping. Comprehensive experiments demonstrate that YOLOv11-4ConvNeXtV2 achieves 95.9% precision and 83.7% recall, with mAP@0.5 of 88.4% and mAP@0.5:0.95 of 74.8%, outperforming state-of-the-art YOLO variants (YOLOv5n, YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, YOLOv12n) by 3.8–6.3 percentage points in mAP@0.5:0.95. The model demonstrates superior robustness to blur, occlusion, and varying illumination conditions, making it suitable for deployment in challenging maturity detection environments. Full article

Temperature excursions during refrigerated transport strongly affect the quality and shelf life of perishable food, yet reproducing realistic, time-varying cold-chain temperature histories in the laboratory remains challenging. In this study, we present a compact, portable climate chamber driven by Peltier modules and an identification-guided control architecture designed to reproduce real refrigerated-truck temperature histories with high fidelity. Control is implemented as a cascaded regulator: an outer two-degree-of-freedom PID for air-temperature tracking and faster inner PID loops for module-face regulation, enhanced with derivative filtering, anti-windup back-calculation, a Smith predictor, and hysteresis-based bumpless switching to manage dead time and polarity reversals. The system integrates distributed temperature and humidity sensors to provide real-time feedback for precise thermal control, enabling accurate reproduction of cold-chain conditions. Validation comprised two independent 36-day reproductions of field traces and a focused 24-h comparison against traditional control baselines. Over the long trials, the chamber achieved very low long-run errors ($\mathrm{MAE}\cong 0.19 °\mathrm{C}$, $\mathrm{MedAE}\cong 0.10 °\mathrm{C}$, $\mathrm{RMSE}\cong 0.33 °\mathrm{C}$, $R^2=0.9985$). The 24-h test demonstrated that our optimized controller tracked the reference, improving both transient and steady-state behaviour. The system tolerated realistic humidity transients without loss of closed-loop performance. This portable platform functions as a reproducible physical twin for cold-chain experiments and a reliable data source for training predictive shelf-life and digital-twin models to reduce food waste. Full article

Objectives: The present study analyzes the effects of a high-intensity interval training (HIIT) program based on the Tabata method on physiological and psychological variables in contemporary dancers (n = 10) and sedentary individuals (n = 8), who performed a 10-week protocol, with sessions of self-loading exercises structured in intervals of 20 s of effort and 10 s of rest three times a week. Methods: Parameters of body composition, muscle strength, aerobic and anaerobic capacity, heart rate variability, as well as perceptions of health, anxiety, stress, sleep quality, and levels of physical activity and sedentary lifestyle were evaluated. Results: The results showed that no significant changes occurred in most body composition variables, except for visceral fat, where group differences were observed (F = 5.66, p = 0.030, η²ₚ = 0.261). In the indicators of strength and power, the dancers improved the height and relative power of the jump (F = 5.996, p = 0.026, η²ₚ = 0.273), while the sedentary ones increased the strength of the handgrip (p = 0.023). In terms of functional performance, both groups significantly increased anaerobic endurance (F = 10.374, p = 0.005, η²ₚ = 0.393), although no changes were recorded in maximal oxygen consumption or heart rate variability (p > 0.05). On a psychological level, improvements in healthy lifestyle habits and a decrease in the trait anxiety variable were evidenced in dancers (p = 0.023), while in sedentary participants no relevant effects were found. Conclusions: In conclusion, the Tabata protocol may represent an efficient and complementary strategy to enhance strength, anaerobic power, and psychological well-being, particularly among dancers. The observed improvements suggest potential benefits related to movement quality, injury prevention, and general physical conditioning. Full article

Image enhancement in complex scenes is challenging due to the frequent coexistence of multiple degradations caused by adverse weather, imaging hardware, and transmission environments. Existing datasets remain limited to single or weather-specific degradation types, failing to capture real-world complexity. To address this gap, we introduce the Robust Multi-Type Degradation (RMTD) dataset, which synthesizes a wide range of degradations from meteorological, capture, and transmission sources to support model training and evaluation under realistic conditions. Furthermore, the superposition of multiple degradations often results in feature maps dominated by noise, obscuring underlying clean content. To tackle this, we propose the Feature Filter Transformer (FFformer), which includes: (1) a Gaussian-Filtered Self-Attention (GFSA) module that suppresses degradation-related activations by integrating Gaussian filtering into self-attention; and (2) a Feature-Shrinkage Feed-forward Network (FSFN) that applies soft-thresholding to aggressively reduce noise. Additionally, a Feature Enhancement Block (FEB) embedded in skip connections further reinforces clean background features to ensure high-fidelity restoration. Extensive experiments on RMTD and public benchmarks confirm that the proposed dataset and FFformer together bring substantial improvements to the task of complex-scene image enhancement. Full article

Noise is one of the main pollutants in urban contexts, even if it is not perceived as severe as other pollutants. Transportation, specifically road traffic, accounts for most of the urban environmental noise, and its monitoring is very important and sometimes compelled by law. To do this, two different approaches are possible: a direct measurement campaign or a simulation approach. The so-called Road Traffic Noise Models (RTNMs) are used for this second scope. In recent years, noise assessment has also been experimented with through Machine Learning (ML) techniques: ML is very interesting mainly because it is usable in unusual road traffic conditions, like in the presence of roundabouts and/or stops and traffic lights, or more generally when the free flow aspect is not verified, and the classic RTNMs fail. In this contribution, a large and comprehensive study on four different ML regressors is presented. After careful hyperparameter tuning, regressors have been calibrated by using two different approaches: a classic train/test split on real road traffic data, and by using a computed dataset. Results show a quantitative and qualitative description of the outputs of the ML regressors functioning, and how their calibration by using computed data instead of real data can give good output simulations. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

The blade is a core component of the gas turbine, and blade fouling is characterized by highly concealed failure modes in the early stages and significant destructive potential in later stages. To address the lack of intelligence in early warning systems for compressor fouling, this study proposes a data-driven approach combining a digital-twin-based dynamic simulation model with the Weibull Proportional Hazards Model (WPHM) algorithm to enable reliable fault early warning. A modular design methodology was first adopted to construct a digital gas turbine model of the gas–gas combined power system on a dynamic simulation platform. High-fidelity fault simulation data were then generated to represent both healthy and faulty operating conditions. Through data governance and uncertainty quantification, key parameters influencing compressor fouling were identified. The Pearson correlation coefficient was applied to screen the most sensitive indicators, ensuring effective input selection for the prognostic model. Using historical health data from the simulation platform, the WPHM algorithm was trained to learn degradation patterns and establish a baseline failure risk model. This trained WPHM was then deployed to monitor real-time performance trends and provide early warnings for compressor blade fouling. Validation results from multi-unit simulations show that the proposed method achieves a fault warning rate of 95.0%, demonstrating its effectiveness and readiness to meet practical engineering requirements. Full article

Despite growing evidence on the adaptive movement patterns that horses adopt during water treadmill (WT) exercise, underlying adaptations in muscle activity remain uninvestigated. This feasibility study aimed to develop a method for the synchronous measurement of muscle activity and movement of horses during WT exercise. Combined surface electromyography (sEMG) (2000 Hz) from selected hindlimb (biceps femoris, gluteus medius, tensor fascia latae) and epaxial (longissimus dorsi) muscles, and three-dimensional kinematic (200 Hz) data from the back and pelvis of one (n = 1) horse were collected during overground (OG), dry treadmill (TM), and WT walking conditions. Statistical parametric mapping evaluated differences in time- and amplitude-normalised sEMG and thoracolumbar and pelvis kinematic waveforms between conditions. Distinct, significant (p < 0.05) adaptations in hindlimb and epaxial muscle activation patterns and axial and pelvic kinematics, were observed in this horse across exercise conditions. Adaptive muscle activity was most pronounced in this horse during WT, compared to OG walking. These findings demonstrate the feasibility of this method, which combines sEMG and motion capture technologies to synchronously quantify equine movement and muscle activation patterns during WT exercise. This justifies the replication of this work in a larger sample of horses to inform evidence-based training and rehabilitation programmes. Full article

Autonomous driving is a cornerstone of intelligent transportation systems, where visual elements such as traffic signs, lights, and pedestrians are critical for safety and decision-making. Yet, existing datasets often lack diversity, underrepresent rare scenarios, and suffer from class imbalance, which limits the robustness of object detection models. While earlier reviews have examined general image enhancement, a systematic analysis of dataset augmentation for traffic visual elements remains lacking. This paper presents a comprehensive investigation of enhancement techniques tailored for transportation datasets. It pursues three objectives: establishing a classification framework for autonomous driving scenarios, assessing performance gains from augmentation methods on tasks such as detection and classification, and providing practical insights to guide dataset improvement in both research and industry. Four principal approaches are analyzed, including image transformation, GAN-based generation, diffusion models, and composite methods, with discussion of their strengths, limitations, and emerging strategies. Nearly 40 traffic-related datasets and 10 evaluation metrics are reviewed to support benchmarking. Results show that augmentation improves robustness under challenging conditions, with hybrid methods often yielding the best outcomes. Nonetheless, key challenges remain, including computational costs, unstable GAN training, and limited rare scene data. Future work should prioritize lightweight models, richer semantic context, specialized datasets, and scalable, efficient strategies. Full article

Binocular vision is a core module in humanoid robots, and stereo matching is one of the key challenges in binocular vision, relying on template matching techniques and mathematical optimization methods to achieve precise image matching. However, occlusion significantly affects matching accuracy and robustness in practical applications. To address this issue, we propose a novel hybrid matching strategy. This method does not require network training and has high computational efficiency, effectively addressing occlusion issues. First, we propose the Inverse Template Matching Mathematical Method (ITM), which is based on optimization theory. This method generates multiple new templates from the image to be matched using mathematical segmentation techniques and then matches them with the original template through an inverse optimization process, thereby effectively improving matching accuracy under mild occlusion conditions. Second, we propose the Iterative Matching Mathematical Method (IMM), which repeatedly executes ITM combined with optimization strategies to continuously refine the size of matching templates, thereby further improving matching accuracy under complex occlusion conditions. Concurrently, we adopt a local region selection strategy to selectively target areas related to occlusion regions for inverse optimization matching, significantly enhancing matching efficiency. Experimental results show that under severe occlusion conditions, the proposed method achieves a 93% improvement in accuracy compared to traditional template matching methods and a 37% improvement compared to methods based on convolutional neural networks (CNNs), reaching the current state of the art in the field. Our method introduces a reverse optimization paradigm into the field of template matching and provides an innovative mathematical solution to address occlusion issues. Full article

Weeds continue to pose a serious challenge to agriculture, reducing both the productivity and quality of crops. In this paper, we explore how modern deep learning, specifically Vision Transformers (ViTs), can help address this issue through fast and accurate weed classification. We developed a transformer-based model trained on the DeepWeeds dataset, which contains images of nine different weed species collected under various environmental conditions, such as changes in lighting and weather. By leveraging the ViT architecture, the model is able to capture complex patterns and spatial details in high-resolution images, leading to improved prediction accuracy. We also examined the effects of model optimization techniques, including fine-tuning and the use of pre-trained weights, along with different strategies for handling class imbalance. While traditional oversampling actually hurt performance, dropping accuracy to 94%, using class weights alongside strong data augmentation boosted accuracy to 96.9%. Overall, our ViT model outperformed standard Convolutional Neural Networks, achieving 96.9% accuracy on the held-out test set. Attention-based saliency maps were inspected to confirm that predictions were driven by weed regions, and model consistency under location shift and capture perturbations was assessed using the diverse acquisition sites in DeepWeeds. These findings show that with the right combination of model architecture and training strategies, Vision Transformers can offer a powerful solution for smarter weed detection and more efficient farming practices. Full article