Search Results (18,500)

Aiming at the problem of a lack of positioning satellites and no available beacons in underground space, an injected ground electrode current field signal localization method is proposed. An extremely low-frequency current field signal is applied to two pairs of electrodes inserted into the earth to form a ground current field underground, and the ground electrode current field signal detected at the detection end is used for localization, which can effectively provide reference localization for the underground space when the satellite positioning fails. On this basis, considering that the ground electrode current field signal is susceptible to the influence of the geological structure, electromagnetic interference, and the complexity of the propagation path during underground transmission, which results in the signal showing strong non-stationary characteristics, it is difficult for the traditional time–frequency analysis method to accurately extract stable and reliable positioning characteristics. In order to improve the signal-processing accuracy and robustness, this paper introduces fractional Fourier transform (FRFT) to process the detected signals, and focuses the signal energy more effectively under the optimal order. In order to verify the effectiveness of the localization method, several experiments on the localization of ground electrode current field signals are carried out in the underground space. The experimental results show that, in the positioning environment of more than 10,000 square meters, the average positioning error is 6.896 m. The application of this method will provide a solid technical support for life rescue in underground space, provide the ‘last protection’ for rescue, and complete the life chain of emergency first aid, which has an important application prospect and practical value. Full article

In order to determine thermally safe driving parameters of ignition coils for hydrogen internal combustion engines (ICE), a reliable estimation of internal power losses is essential. These losses include resistive winding losses, magnetic core losses due to hysteresis and eddy currents, dielectric losses in the insulation, and electronic switching losses. Direct experimental assessment is difficult because the components are inaccessible, while conventional computer-aided engineering (CAE) approaches face challenges such as the need for accurate input data, the need for detailed 3D models, long computation times, and uncertainties in loss prediction for complex structures. To address these limitations, we propose an artificial intelligence (AI)-based framework for estimating internal losses from external temperature measurements. The method relies on an artificial neural network (ANN), trained to capture the relationship between external coil temperatures and internal power losses. The trained model is then employed within an optimization process to identify losses corresponding to experimental temperature values. Validation is performed by introducing the identified power losses into a CAE thermal model to compare predicted and experimental temperatures. The results show excellent agreement, with errors below 3% across the −30°C to 125°C range. This demonstrates that the proposed hybrid ANN–CAE approach achieves high accuracy while reducing experimental effort and computational demand. Furthermore, the methodology allows for a straightforward determination of the coil safe operating area (SOA). Starting from estimates derived from fitted linear trends, the SOA limits can be efficiently refined through iterative verification with the CAE model. Overall, the ANN–CAE framework provides a robust and practical tool to accelerate thermal analysis and support coil development for hydrogen ICE applications. Full article

Orthogonal Frequency Division Multiplexing (OFDM) modulation currently dominates the physical layer design in modern transmission systems. Its primary advantage is the simple reconstruction of channel frequency response (CFR). However, the Least Squares (LS) algorithm commonly used here is prone to significant estimation errors due to noise interference. A promising and relatively simple alternative is a DFT-based strategy that uses a pre-computed refinement/correction matrix to improve estimation performance. This paper investigates two implementation approaches for CFR reconstruction with pre-computed matrices. Focusing on multiplication operations, a threshold number of active subcarriers was identified at which these two implementations exhibit comparable numerical complexity. A numerical performance factor was defined and a detailed performance analysis was carried out for different guard interval (GI) lengths and the number of active subcarriers in the OFDM signal. Additionally, to maintain channel estimation quality irrespective of GI length, a channel impulse response (CIR) energy detection procedure was introduced. This procedure refines the results of the symbol synchronization process and, by using the circular shift property, preserves constant values of the precomputed matrix coefficients without system performance loss, as measured by Bit Error Rate (BER) and Mean Square Error (MSE) metrics. Full article

Vaccinium uliginosum (bog bilberry) is widely consumed in North America and Asia but has been historically avoided in many parts of Europe due to its alleged poisonous effects. We aimed to address this discrepancy in a systematic way with a combined phytochemical and ethnopharmacological approach, using UHPLC and UHPSFC for the chemical analysis, model organisms Caenorhabditis elegans and human liver cells GFP-Huh-7 for the bioactivity and toxicity testing, as well as fermentation experiments. Phytochemical analysis revealed minimal differences in the metabolite pattern between European and North American samples, with no evidence of toxic alkaloids or harmful secondary metabolites. Extracts exhibited no strongly toxic effects in the tested concentrations, neither in vitro (cell viability) nor in vivo (C. elegans). Berries infected by Monilinia megalospora showed altered flavonoid and anthocyanin contents but no increased toxicity. Notably, bog bilberries demonstrated a fermentation potential superior to Vaccinium myrtillus, resulting in an alcohol content of 4.8–5.8% ABV in unsweetened juices, thus potentially explaining historical accounts of inebriation. In conclusion, direct toxicity derived from these fruits is unlikely, but the alcohol content due to fruit fermentation is a plausible explanation for the folklore names (“drunk, inebriating berry”). However, additional factors such as human error, individual intolerance, or endophytic activity need to be considered. Full article

This study explores the effectiveness of 3D Digital Image Correlation (DIC) for measuring displacement and strain of a propeller undergoing angular motion. Traditional methods, such as strain gauges, face limitations including physical interference, technical difficulties in sensor connections, and restricted measurement points, leading to inaccuracies in capturing true conditions. To overcome these challenges, this research utilizes non-contact 3D DIC technology, enabling measurement of surface displacements and deformations without interfering with the tested component. Experiments were conducted using the model aircraft propellers mounted on a custom-built test stand for partial angular motion. The 1 Mpx high-speed cameras captured strain and displacement data across the propeller blades during motion. The DIC strain measurements were then compared to strain gauge data to evaluate their accuracy and reliability. The results demonstrate that 3D DIC enables precise displacement measurements, while strain measurements are subject to certain limitations. Displacement measurements were achieved with a noise level of ±10 μm, while strain measurement noise ranged from 26 to 174 µm/m depending on direction. Strain gauge measurements were also performed for verification of the DIC measurements and calibration of the filtering procedure. Two types of non-metallic materials were used in the study: Nylon LGF60 PA6 for the propeller and 3D-printed PC ABS for the cantilever beam used in strain measurement validation. This study underscores the potential of DIC for monitoring rotating components, with a particular focus on measuring strains that are often overlooked in publications addressing similar topics. Additionally, it focuses on comparing DIC strain measurements with strain gauge data on rotating components, addressing a critical gap in existing literature, as strain measurement in rotating structures remains underexplored in current research. 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

The article presents the results of research on improving construction safety using the eye tracking method. The analysis was carried out during training in the field of construction safety. Eye tracker allows for analysis of the way in which training participants process visual information and elements that attract their attention and the effectiveness of learning the principles of work safety. Eye tracking studies, in the aspect of construction safety, determine the effectiveness of training in this area. Moreover, the main advantage of such studies lies in the possibility of identifying elements of the construction site that are omitted or misunderstood by training participants, and which are important from the point of view of safe implementation of construction works. The study found that employees achieved the highest level of error detection (70%), with a shorter fixation time (240 ms), suggesting the role of experience and cognitive automation. Post-trained students demonstrated the longest fixation time (350 ms) and moderate error detection (35%), suggesting greater cognitive engagement but lower efficiency than experts. Students without training achieved the lowest results (30% detection, 200 ms FT), which is related to a lack of knowledge and experience. ANOVA confirmed statistically significant differences between groups in fixation time (F(3,36) = 244.83; p < 0.0001), with a high confidence level (>99.99%). Tukey’s post hoc test indicated significant differences between untrained and post-trained students and between post-trained students and employees (p < 0.001), underscoring the importance of both training and professional practice. Full article

Fatigue damage failure is a process where the mechanical properties of different materials continuously degrade under the action of cyclic loads. The cumulative analysis of fatigue damage has a significant impact on the service structure of major equipment. This paper starts from the mechanism of fatigue damage evolution, comprehensively considers the influence of the order of high-low cycle load mixed cyclic loading on the fatigue life performance, and based on the Manson-Halford nonlinear fatigue damage accumulation theory and the mechanism of relative cumulative damage, a new nonlinear damage accumulation fatigue life model is established, and a fatigue damage accumulation influencing factor D_cr is introduced to improve the prediction accuracy of the model. The new model proposed in this paper is verified through multi-level fatigue load data. By comparing the prediction results with other models under the same experimental conditions, the fatigue life prediction error precision of the new model is the best in similar cases, generally with an error precision between 10% and 20%, which proves the effectiveness and accuracy of the nonlinear damage accumulation model proposed in this paper. At the same time, the improved method in this paper has better stability while ensuring prediction accuracy. Full article

This paper presents an investigation into the mechanical performance of a subway station prefabricated assembly air duct (PAAD), constructed by assembling the prefabricated reinforced concrete segments. The study is implemented through numerical analysis, focusing on the impact from the grouting defects in the sleeve grouting connection and assembly error defects along the assembly direction. The results demonstrate that the assembly error defect has almost no impact on the mechanical performance of the PAAD, satisfying the safety requirements for use. However, the grouting defects in the sleeve grouting connection can influence the mechanical performance of the PAAD, in which the maximum tensile stress of concrete in the sleeve grouting connection with a 20 mm-long bottom grouting defect is greater than the tensile strength of that concrete, and strengthening treatment is thus required for ensuring the structure’s safety and reliability. This study provides the basis for applying a PAAD in subway station construction. Full article

Industrial environments frequently rely on analog control instruments due to their reliability and robustness; however, automating the interpretation of these controls remains challenging due to variability in design, lighting conditions, and scale precision requirements. This research investigates the effectiveness of Vision–Language Models (VLMs) for automated interpretation of industrial controls through analysis of three distinct approaches: general-purpose VLMs, fine-tuned specialized models, and lightweight models optimized for edge computing. Each approach was evaluated using two prompting strategies, Holistic-Thought Protocol (HTP) and sequential Chain-of-Thought (CoT), across a representative dataset of continuous and discrete industrial controls. The results demonstrate that the fine-tuned Generative Pre-trained Transformer 4 omni (GPT-4o) significantly outperformed other approaches, achieving low Mean Absolute Error (MAE) for continuous controls and the highest accuracy and Matthews Correlation Coefficient (MCC) for discrete controls. Fine-tuned models demonstrated less sensitivity to prompt variations, enhancing their reliability. In contrast, although general-purpose VLMs showed acceptable zero-shot performance, edge-optimized models exhibited severe limitations. This work highlights the capability of fine-tuned VLMs for practical deployment in industrial scenarios, balancing precision, computational efficiency, and data annotation requirements. Full article

Efficient and explainable vehicle damage inspection is essential due to the increasing complexity and volume of vehicular incidents. Traditional manual inspection approaches are not time-effective, prone to human error, and lead to inefficiencies in insurance claims and repair workflows. Existing deep learning methods, such as CNNs, often struggle with generalization, require large annotated datasets, and lack interpretability. This study presents a robust and interpretable deep learning framework for vehicle damage classification, integrating Vision Transformers (ViTs) and ensemble detection strategies. The proposed architecture employs a RetinaNet backbone with a ViT-enhanced detection head, implemented in PyTorch using the Detectron2 object detection technique. It is pretrained on COCO weights and fine-tuned through focal loss and aggressive augmentation techniques to improve generalization under real-world damage variability. The proposed system applies the Weighted Box Fusion (WBF) ensemble strategy to refine detection outputs from multiple models, offering improved spatial precision. To ensure interpretability and transparency, we adopt numerous explainability techniques—Grad-CAM, Grad-CAM++, and SHAP—offering semantic and visual insights into model decisions. A custom vehicle damage dataset with 4500 images has been built, consisting of approximately 60% curated images collected through targeted web scraping and crawling covering various damage types (such as bumper dents, panel scratches, and frontal impacts), along with 40% COCO dataset images to support model generalization. Comparative evaluations show that Hybrid ViT-RetinaNet achieves superior performance with an F1-score of 84.6%, mAP of 87.2%, and 22 FPS inference speed. In an ablation analysis, WBF, augmentation, transfer learning, and focal loss significantly improve performance, with focal loss increasing F1 by 6.3% for underrepresented classes and COCO pretraining boosting mAP by 8.7%. Additional architectural comparisons demonstrate that our full hybrid configuration not only maintains competitive accuracy but also achieves up to 150 FPS, making it well suited for real-time use cases. Robustness tests under challenging conditions, including real-world visual disturbances (smoke, fire, motion blur, varying lighting, and occlusions) and artificial noise (Gaussian; salt-and-pepper), confirm the model’s generalization ability. This work contributes a scalable, explainable, and high-performance solution for real-world vehicle damage diagnostics. Full article

Each charging/discharging cycle leads to a gradual decrease in the battery’s capacity. The degradation of capacity in lithium-ion batteries represents a non-monotonous process with random jumps. Earlier studies claimed that the instantaneous degradation value of a lithium-ion battery is influenced by the historical dataset with long-range dependence. The existing methods ignore large jumps and long-range dependences in degradation processes. In order to capture long-range-dependent behavior with random jumps, we refer to the fractional Poisson process. We also outline the relationship between the long-range correlation and the Hurst index. The connection between random jumps in capacitance and long-range dependence of the fractional Poisson process is proven. In order to construct the fractional Poisson predictive model, we included fractional Brownian motion as the diffusion term and the fractional Poisson process as the jump term. The proposed approach is implemented on NASA’s dataset for Li-ion battery degradation. We believe that the error analysis for the fractional Poisson process is advantageous compared with that of the fractional Brownian motion, the fractional Levy stable motion, the Wiener model, and the long short-term memory model. Full article

This study developed an inertial measurement unit (IMU)-enhanced bidirectional reflectance distribution function (BRDF) imaging system to address angular errors in multi-angle polarimetric measurements. The system integrates IMU-based closed-loop feedback, motorized motion, and image calibration, achieving zenith angle error reduction of up to 1.2° and angular control precision of approximately 0.05°. With a modular and lightweight structure, it supports rapid deployment in field scenarios, while the 2000 mm rail span enables detection of large-scale targets and three-dimensional reconstruction beyond the capability of conventional tabletop devices. Experimental evaluations on six representative materials show that compared with mark-based reference angles, IMU feedback consistently improves polarimetric accuracy. Specifically, the degree of linear polarization (DoLP) mean deviations are reduced by about 5–12%, while standard deviation fluctuations are suppressed by 20–40%, enhancing measurement repeatability. For the angle of polarization (AoP), IMU feedback decreases mean errors by 10–45% and lowers standard deviations by 10–37%, ensuring greater spatial phase continuity even under high-reflection conditions. These results confirm that the proposed system not only eliminates systematic angular errors but also achieves robust stability in global measurements, providing a reliable technical foundation for material characterization, machine vision, and volumetric reconstruction. Full article

Corporate failure prediction remains a major topic in the literature. Numerous methodologies have been established for its assessment, while data envelopment analysis (DEA) has received particular attention. This study contributes to the literature, establishing a new approach in the construction process of prediction models based on the combination of logistic LASSO and an advanced version of data envelopment analysis (DEA). We adopt the modified slacks-based super-efficiency measure (modified super-SBM-DEA), following the “Worst practice frontier” approach, and focus on the selection process of predictive variables, implementing the logistic LASSO regression. A balanced sample with one-to-one matching between forty-five firms that filed for reorganization under U.S. bankruptcy law during the period 2014–2020 and forty-five non-failed firms of a similar size from the U.S. energy economic sector has been used for the empirical analysis. The proposed methodology offers superior results in terms of corporate failure prediction accuracy. For the dynamic assessment of failure, Malmquist DEA has been implemented during the five fiscal years prior to the event of failure, offering insights into financial distress before the event of a default. The model outperforms alternatives by achieving higher overall prediction accuracy (85.6%), the better identification of failed firms (91.1%), and the improved classification of non-failed firms (80%). Compared to prior DEA-based models, it demonstrates superior predictive performance with lower Type I and Type II errors and higher sensitivity as well as specificity. These results highlight the model’s effectiveness as a reliable early warning tool for bankruptcy prediction. Full article

Machine vision technologies play a critical role in the advancement of modern human-centric manufacturing systems. This study investigates their practical applications in improving both safety and productivity within industrial environments. Particular attention is given to areas such as quality assurance, worker protection, and process optimization, illustrating how intelligent visual inspection systems and real-time data analysis contribute to increased operational efficiency and higher safety standards. The research methodology combines an in-depth analysis of industrial case studies, including one from the frozen dough industry, with a systematic review of the current literature on machine vision technologies in manufacturing. The findings highlight the potential of such systems to reduce human error, maintain consistent product quality, minimize material waste, and promote safer and more adaptable work environments. This study offers valuable insights into the integration of advanced visual technologies within human-centered production environments, while also addressing key challenges and future opportunities for innovation and technological evolution. Full article