Search Results (7,099)

As the core component of electrified railway power supply systems, the fatigue performance and reliability of catenary support structures are directly related to the operational safety of high-speed railways. To address the problem of structural fatigue damage caused by increasing train speed and high-frequency operation, this study develops a refined finite element model including a support structure, suspension system and support column, and the dynamic response characteristics and fatigue life evolution law under train operation conditions are systematically analyzed. The results show that under the conditions of 250 km/h speed and 100 times daily traffic, the fatigue lives of the limit locator and positioning support are 43.56 years and 34.48 years, respectively, whereas the transverse cantilever connection and inclined cantilever have infinite life characteristics. When the train speed increases to 400 km/h, the annual fatigue damage of the positioning bearing increases from 0.029 to 0.065, and the service life is shortened by 55.7% to 15.27 years, which proves that high-speed working conditions significantly aggravate the deterioration of fatigue in the structure. The reliability analysis based on Monte Carlo simulation reveals that when the speed is 400 km/h and the daily traffic is 130 times, the structural reliability shows an exponential declining trend with increasing service life. If the daily traffic frequency exceeds 130, the 15-year reliability decreases to 92.5%, the 20-year reliability suddenly decreases to 82.4%, and there is a significant inflection point of failure in the 15–20 years of service. Considering the coupling effect of environmental factors (wind load, temperature and freezing), the actual failure risk may be higher than the theoretical value. On the basis of these findings, engineering suggestions are proposed: for high-speed lines with a daily traffic frequency of more than 130 times, shortening the overhaul cycle of the catenary support structure to 7–10 years and strengthening the periodic inspection and maintenance of positioning support and limit locators are recommended. The research results provide a theoretical basis for the safety assessment and maintenance decision making of high-speed railway catenary systems. Full article

To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated with maximum torque per ampere (MTPA) operation. Traditional MPC methods often suffer from limited prediction horizons and high computational burden when handling strong coupling and time-varying loads, compromising real-time performance. To overcome these limitations, a Laguerre function approximation is employed to model the dynamic evolution of control increments using a set of orthogonal basis functions, effectively reducing the control dimensionality while accelerating convergence. Furthermore, to enhance energy efficiency, the MTPA strategy is embedded by reformulating the current allocation process using d- and q-axis current variables and deriving equivalent reference currents to simplify the optimization structure. A cost function is designed to simultaneously ensure current accuracy and achieve maximum torque per unit current. Simulation results under typical electric vehicle conditions demonstrate that the proposed Laguerre-MTPA MPC controller significantly improves steady-state performance, reduces energy consumption, and ensures faster response to load disturbances compared to traditional MTPA-based control schemes. This work provides a practical and scalable control framework for energy-saving applications in sustainable electric transportation systems. Full article

This article proposes an improved Visible Light Communication (VLC) solution that, besides the indoor lighting and data transfer, offers an energy-efficient alternative for modern workspaces. Unlike Light-Fidelity (LiFi), designed for high-speed data communication, VLC primarily targets applications where fast data rates are not essential. The developed prototype ensures reliable communication under variable lighting conditions, addressing low-speed requirements such as test bench monitoring, occupancy detection, remote commands, logging or access control. Although the tested data rate was limited to 100 kb/s with a Bit Error Rate (BER) below 10⁻⁷, the key innovation is the light dimming dynamic adaptation. Therefore, the system self-adjusts the LED duty cycle between 10% and 90%, based on natural or artificial ambient light, to maintain a minimum illuminance of 300 lx at the workspace level. Additionally, this work includes a scalability analysis through simulations conducted in an office scenario with up to six users. The results show that the system can adjust the lighting level and maintain the connectivity according to users’ presence, significantly reducing energy consumption without compromising visual comfort or communication performance. With this light intensity regulation algorithm, the proposed solution demonstrates real potential for implementation in smart indoor environments focused on sustainability and connectivity. Full article

Spectral detection technology offers non-destructive, in situ, and high-speed capabilities, making it widely applicable for detecting biological and chemical samples and quantifying their concentrations. Water resources, essential to life on Earth, are widely distributed across the planet. The application of spectral technology to underwater environments is useful for wide-area water resource monitoring. Although spectral detection technology is well-established, its underwater application presents challenges, including waterproof housing design, power supply, and data transmission, which limit widespread application of underwater spectral detection. Furthermore, underwater spectral detection necessitates the development of compatible computational methods for sample classification or regression analysis. Focusing on underwater spectral detection, this work involved the construction of a suitable hardware system. A compact spectrometer and LEDs (400 nm–800 nm) were employed as the detection and light source modules, respectively, resulting in a compact system architecture. Extensive tests confirmed that the miniaturized design-maintained system performance. Further, this study addressed the estimation of total phosphorus (TP) concentration in water using spectral data. Samples with varying TP concentrations were prepared and calibrated against standard detection instruments. Subsequently, classification algorithms applied to the acquired spectral data enabled the in situ underwater determination of TP concentration in these samples. This work demonstrates the feasibility of underwater spectral detection for future in situ, high-speed monitoring of aquatic biochemical indicators. In the future, after adding UV LED light source, more water quality parameter information can be obtained. Full article

Fingerprint authentication systems encounter growing threats from presentation attacks, making strong liveness detection crucial. This work presents a deep learning-based framework integrating EfficientNetB0 with a Squeeze-and-Excitation (SE) attention approach, using transfer learning to enhance feature extraction. The LivDet 2015 dataset, composed of both real and fake fingerprints taken using four optical sensors and spoofs made using PlayDoh, Ecoflex, and Gelatine, is used to train and test the model architecture. Stratified splitting is performed once the images being input have been scaled and normalized to conform to EfficientNetB0’s format. The SE module adaptively improves appropriate features to competently differentiate live from fake inputs. The classification head comprises fully connected layers, dropout, batch normalization, and a sigmoid output. Empirical results exhibit accuracy between 98.50% and 99.50%, with an AUC varying from 0.978 to 0.9995, providing high precision and recall for genuine users, and robust generalization across unseen spoof types. Compared to existing methods like Slim-ResCNN and HyiPAD, the novelty of our model lies in the Squeeze-and-Excitation mechanism, which enhances feature discrimination by adaptively recalibrating the channels of the feature maps, thereby improving the model’s ability to differentiate between live and spoofed fingerprints. This model has practical implications for deployment in real-time biometric systems, including mobile authentication and secure access control, presenting an efficient solution for protecting against sophisticated spoofing methods. Future research will focus on sensor-invariant learning and adaptive thresholds to further enhance resilience against varying spoofing attacks. Full article

Background/Objectives: Breast cancer continues to be one of the primary causes of death among women worldwide, emphasizing the necessity for accurate and efficient diagnostic approaches. This work focuses on developing an automated diagnostic framework based on convolutional neural networks (CNNs) capable of handling multiple imaging modalities. Methods: The proposed CNN model are trained and evaluated on several benchmark datasets, including mammography (DDSM, MIAS, INbreast), ultrasound, magnetic resonance imaging (MRI), and histopathology (BreaKHis). Standardized preprocessing procedures were applied across all datasets, and the outcomes were compared with leading state-of-the-art techniques. Results: The model attained strong classification performance with accuracy scores of 99.2% (DDSM), 98.97% (MIAS), 99.43% (INbreast), 98.00% (Ultrasound), 98.43% (MRI), and 86.42% (BreaKHis). These findings indicate superior results compared to many existing approaches, confirming the robustness of the method. Conclusions: This study introduces a reliable and scalable diagnostic system that can support radiologists in early breast cancer detection. Its high accuracy, efficiency, and adaptability across different imaging modalities make it a promising tool for integration into clinical practice. Full article

This paper presents a novel mechanically beam-steered antenna system for 10 GHz applications, enabled by multi-material 3D-printing technology. The proposed design eliminates the need for complex electronic circuitry by integrating a mechanically rotatable, 3D-printed hemispherical lens with a conventional rectangular patch antenna. The system comprises three main components: a 10-GHz patch antenna, a precision-fabricated hemispherical dielectric lens produced via stereolithography (SLA), and a structurally robust rotation assembly fabricated using fused deposition modeling (FDM). The mechanical rotation of the lens enables discrete beam-steering from −45° to +45° in 5° steps. Experimental results demonstrate a gain improvement from 6.21 dBi (standalone patch) to 10.47 dBi with the integrated lens, with minimal degradation across steering angles (down to 9.59 dBi). Simulations and measurements show strong agreement, with the complete system achieving 94% accuracy in beam direction. This work confirms the feasibility of integrating additive manufacturing with passive beam-steering structures to deliver a low-cost, scalable, and high-performance alternative to electronically scanned arrays. Moreover, the design is readily adaptable for motorized actuation and closed-loop control via embedded systems, enabling future development of real-time, programmable beam-steering platforms. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

Polyimide composites represent a class of advanced materials with remarkable mechanical robustness and thermal stability, making them highly suitable for applications in extreme environments. Their unique ability to maintain performance under high temperatures and corrosive conditions, combined with a favorable strength-to-weight ratio, positions them as critical components in aerospace, electronics, and automotive systems. Several leading aerospace and electronics corporations have made significant investments in incorporating polyimide composites into their products, indicating the material’s transformative potential. This review paper provides an overview of mechanical and thermal behaviors of polyimide composites, summarizing recent developments and research trends. It examines the influence of various reinforcements, processing techniques, and composite architectures on material performance under mechanical loading and thermal stress. The paper synthesizes findings from experimental studies and modeling efforts to highlight the critical factors affecting strength, durability, and thermal stability. Discussion and recommendations regarding applications in aerospace, electronics, and other high-temperature environments are provided, emphasizing the challenges and opportunities presented by these advanced materials. This review adopts a broad scope to reflect the interdisciplinary nature of polyimide research. Due to gaps in literature, this work aims to provide a foundational overview that supports future, more specialized investigations. Full article

Plasma has become an up-and-coming advanced oxidation technology for wastewater treatment. However, its efficiency is often limited due to the lack of high-performance catalytic materials. In this study, one-dimensional carbon nanofiber precursors were first fabricated via electrospinning, followed by the in situ growth of the Zn/Fe-MOF on their surfaces. After pyrolysis at different temperatures, a series of carbon-based catalysts (FeNFC) were obtained. This new type of catalyst possesses advantages such as high porosity, a large specific surface area, and mechanical stability. Using tetracycline (TTCH) as the target pollutant, the performance of the catalyst was evaluated in the dielectric barrier discharge (DBD) system. The study showed that the addition of FeNFC significantly increased the degradation rate of TTCH in the system. Comparing different pyrolysis temperatures, at 900 °C, the comprehensive performance of the catalyst (FeNFC-900) was the best (the kinetic constant was k_obs = 0.126 min⁻¹, and the removal rate of TTCH was 91.8% within 30 min). The catalytic performance was influenced by factors such as the dosage of the catalyst, the concentration of TTCH, the power of DBD, and the initial pH. The catalytic effect of the material increased within a certain range with the increase in the catalyst dosage. The increase in TTCH concentration led to a decrease in the catalytic performance. The higher the power of the DBD, the higher the removal rate of TTCH. Moreover, when the initial pH was strongly alkaline, the catalytic effect of the catalyst was the best (k_obs = 0.275 min⁻¹, and the removal rate of TTCH was 98.7% within 30 min). Ionic interference tests demonstrated the strong resistance of FeNFC to common water matrix components, while radical quenching experiments revealed that multiple reactive species contributed to TTCH degradation. This work has broad application prospects for enhancing the efficiency of DBD systems in the removal of TTCH. Full article

Diamond-like carbon (DLC) films are valued for their high hardness and wear resistance, but their application in harsh environments is limited by high internal stress and poor corrosion resistance. Co-doping with transition metals offers a promising route to overcome these drawbacks by tailoring microstructure and enhancing multifunctional performance. However, the synergistic effects of Ni and Cr co-doping in DLC remain underexplored. In this study, Ni and Cr co-doped DLC (NiCr-DLC) films were fabricated using filtered cathodic vacuum arc deposition (FCVAD). By varying the C₂H₂ flow rate, the carbon content and microstructure evolved from columnar to fine-grained and compact structures. The optimized film (F55) achieved an ultralow surface roughness (Sa = 0.26 nm), even smoother than the Si substrate. The Ni–Cr co-doping promoted a nanocomposite structure, yielding a maximum hardness of 15.56 GPa and excellent wear resistance (wear rate: 4.45 × 10⁻⁷ mm³/N·m). Electrochemical tests revealed significantly improved corrosion resistance compared to AISI 304L stainless steel, with F55 exhibiting the highest corrosion potential, the lowest current density, and the largest impedance modulus. This work demonstrates that Ni-Cr co-doping effectively enhances the mechanical and corrosion properties of DLC films while improving surface quality, providing a viable strategy for developing robust, multifunctional protective coatings for demanding applications in aerospace, automotive, and biomedical systems. Full article

Autonomous driving has emerged as a rapidly advancing field in both industry and academia over the past decade. Among the enabling technologies, computer vision (CV) has demonstrated high accuracy across various domains, making it a critical component of autonomous vehicle systems. However, CV tasks are computationally intensive and often require hardware accelerators to achieve real-time performance. Field Programmable Gate Arrays (FPGAs) have gained popularity in this context due to their reconfigurability and high energy efficiency. Numerous researchers have explored FPGA-accelerated CV solutions for autonomous driving, addressing key tasks such as lane detection, pedestrian recognition, traffic sign and signal classification, vehicle detection, object detection, environmental variability sensing, and fault analysis. Despite this growing body of work, the field remains fragmented, with significant variability in implementation approaches, evaluation metrics, and hardware platforms. Crucial performance factors, including latency, throughput, power consumption, energy efficiency, detection accuracy, datasets, and FPGA architectures, are often assessed inconsistently. To address this gap, this paper presents a comprehensive literature review of FPGA-accelerated, vision-based autonomous driving systems. It systematically examines existing solutions across sub-domains, categorizes key performance factors and synthesizes the current state of research. This study aims to provide a consolidated reference for researchers, supporting the development of more efficient and reliable next generation autonomous driving systems by highlighting trends, challenges, and opportunities in the field. Full article

The utilization of carbon dioxide (CO₂) for synthesizing value-added chemicals represents a promising environmentally sustainable strategy. Herein, we synthesized a series of Mn₂O₃ catalysts derived from metal-organic frameworks (MOFs) incorporating three different ligands—homophthalic tricarboxylic acid (H₃BTC), 1,4-benzenedicarboxylic acid (H₂BDC), and a combination of polyvinyl pyrrolidone (PVP) with H₃BTC—via a hydrothermal method for ethylene urea (EU) production from CO₂ and ethylenediamine (EDA). The Mn₂O₃ catalyst derived from the H₃BTC+PVP ligand system (designated MnTP) demonstrated superior catalytic performance, achieving 97% EDA conversion and 97% EU selectivity under mild conditions (100 °C, 1 min), which surpassed all previously reported catalysts under comparable conditions. The enhanced activity originated from structural improvements induced by the H₃BTC+PVP precursors, particularly the promotion of oxygen vacancies and Mn³⁺ species, thereby facilitating efficient CO₂ activation and binding. This work establishes a novel strategy for the sustainable conversion of CO₂ into high-value cyclic ureas through rational catalyst design. Full article

The use of machine learning methods for the development of robust and flexible visual inspection systems has shown promising results. However, their performance is highly dependent on the large amount and diversity of training data, which is difficult to obtain in practice. Recent developments in synthetic dataset generation have seen increasing success in overcoming these problems. However, the prevailing work revolves around the usage of generative models, which suffer from data shortages, hallucinations, and provide limited support for unobserved edge-cases. In this work, we present the first synthetic data generation pipeline that is capable of generating large datasets of physically realistic textures exhibiting sophisticated structured patterns. Our framework is based on procedural texture modelling with interpretable parameters, uniquely allowing us to guarantee precise control over the texture parameters as we generate a high variety of observed and unobserved texture instances. We publish the dual dataset used in this paper, presenting models of sandblasting, parallel, and spiral milling textures, which are commonly present on manufactured metal products. To evaluate the dataset quality, we go beyond final model performance comparison by measuring different image similarities between the real and synthetic domains. This uncovered a trend, indicating these metrics could be used to predict downstream detection performance, which can strongly impact future developments of synthetic data. Full article

Power converters are increasingly pushing toward higher switching frequencies, with current designs typically operating between tens of kilohertz and a few megahertz. The commercialization of gallium nitride (GaN) power transistors has opened new possibilities, offering performance far beyond the limitations of conventional silicon devices. Despite this promise, the potential of GaN technology remains underutilized. This paper explores the feasibility of achieving sub-gigahertz switching frequencies using GaN-based switch-mode power converters, a regime currently inaccessible to silicon-based counterparts. To reach such operating speeds, it is essential to understand and quantify the intrinsic frequency limitations imposed by GaN device physics and associated parasitics. Existing power conversion topologies and control techniques are unsuitable at these frequencies due to excessive switching losses and inadequate drive capability. This work presents a detailed, systematic study of GaN transistor behavior at high frequencies, aiming to identify both fundamental and practical switching limits. A compact analytical model is developed to estimate the maximum soft-switching frequency, considering only intrinsic device parameters. Under idealized converter conditions, this upper bound is derived as a function of internal losses and the system’s target efficiency. From this, a soft-switching figure of merit is proposed to guide the design and layout of GaN field-effect transistors for highly integrated power systems. The key contribution of this study lies in its analytical insight into the performance boundaries of GaN transistors, highlighting the roles of parasitic elements and loss mechanisms. These findings provide a foundation for developing next-generation, high-frequency, chip-scale power converters. Full article