Search Results (4,071)

Wind turbines face significant operational challenges due to their complex electromechanical systems, exposure to harsh environmental conditions, and high maintenance costs. Reliable structural health monitoring and condition monitoring are therefore essential for early fault detection, minimizing downtime, and optimizing maintenance strategies. Traditional approaches typically rely on either Trend Monitoring (TM) or Change Point Detection (CPD). TM methods track the long-term behaviour of process parameters, using statistical analysis or machine learning (ML) to identify abnormal patterns that may indicate emerging faults. In contrast, CPD techniques focus on detecting abrupt changes in time-series data, identifying shifts in mean, variance, or distribution, and providing accurate fault onset detection. While each approach has strengths, they also face limitations: TM effectively identifies fault type but lacks precision in timing, while CPD excels at locating fault occurrence but lacks detailed fault classification. This review critically examines the integration of TM and CPD methods for wind turbine diagnostics, highlighting their complementary strengths and weaknesses through an analysis of widely used TM techniques (e.g., Fast Fourier Transform, Wavelet Transform, Hilbert–Huang Transform, Empirical Mode Decomposition) and CPD methods (e.g., Bayesian Online Change Point Detection, Kullback–Leibler Divergence, Cumulative Sum). By combining both approaches, diagnostic accuracy can be enhanced, leveraging TM’s detailed fault characterization with CPD’s precise fault timing. The effectiveness of this synthesis is demonstrated in a case study on wind turbine blade fault diagnosis. Results shows that TM–CPD integration enhances early detection through coupling vibration and frequency trend analysis with robust statistical validation of fault onset. Full article

Multimedia Forensics (MMF) investigates techniques to automatically assess the integrity of multimedia content, e.g., images, videos, or audio clips. Data-driven methodologies like Neural Networks (NNs) represent the state of the art in the field. Despite their efficacy, NNs are often considered “black boxes” due to their lack of transparency, which limits their usage in critical applications. In this work, we assess the interpretability properties of Deep High-Frequency Residuals (DHFRs), i.e., noise residuals extracted from images by NNs for forensic purposes, that nowadays represent a powerful tool for image splicing localization. Our research demonstrates that DHFRs not only serve as a visual aid in identifying manipulated regions in the image but also reveal the nature of the editing techniques applied to tamper with the sample under analysis. Through extensive experimentation on spliced amplitude Synthetic Aperture Radar (SAR) images, we establish a correlation between the appearance of the DHFRs in the tampered-with zones and their high-frequency energy content. Our findings suggest that, despite the deep learning nature of DHFRs, they possess significant interpretability properties, encouraging further exploration in other forensic applications. Full article

The virtual synchronous generator (VSG) technique plays a crucial role in power systems with high penetration of power electronics, as it can provide virtual inertia and damping performance by emulating the swing characteristics of a synchronous generator (SG). However, the VSG faces challenges due to its inherent limitations, such as vulnerability to disturbances and instability in strong grid conditions. To address these issues, this article proposes an exchanged VSG (eVSG) control strategy. In this approach, the phase information (θ) is derived from reactive power (Q), while the voltage information (E) is derived from active power (P). Furthermore, a Magnitude-Phase Motion Equation (MPME) is introduced to analyze the eVSG system from a physical perspective. Additionally, this article is the first to illustrate the oscillation propagation effect between P and frequency (f) in both VSG and eVSG systems. Finally, the advantages of the eVSG strategy are comprehensively demonstrated through three aspects: (1) comparing the motion trajectory of f using the MPME model, (2) evaluating the oscillation propagation effect between VSG and eVSG systems, and (3) conducting simulations and experiments. Full article

Background: Perianal Crohn’s disease (pCD) is one of the most disabling complications of inflammatory bowel disease, characterized by fistulas and abscesses that demand precise imaging for diagnosis, treatment planning, and follow-up. Magnetic resonance imaging (MRI) is considered the reference standard, but endoanal ultrasound (EAUS) with high-frequency 360° probes provide a readily available, cost-effective, and repeatable alternative. Methods: We performed a narrative review of the literature, evaluating studies on the EAUS technique, diagnostic applications, distinguishing features of Crohn’s-related fistulas, and comparative analyses with MRI. Consensus documents and structured reporting initiatives were also included. Results: EAUS provides high-resolution visualization of the anal sphincter complex and intersphincteric space, enabling the reliable detection of fistulas and abscesses. Characteristic features such as tract width > 4 mm, bifurcation, hyperechoic debris, the Crohn’s Ultrasound Fistula Sign (CUFS), and the rosary sign assist in differentiating Crohn’s from cryptoglandular fistulas. EAUS is well-suited for serial monitoring, perioperative seton guidance, and therapeutic decision-making. Emerging tools such as Doppler and shear wave elastography provide additional information on activity and fibrosis. MRI remains indispensable for supralevator disease, deep pelvic sepsis, and standardized activity indices. Comparative studies indicate similar sensitivity for simple fistulas, with MRI superior in complex cases; combining both modalities maximizes accuracy. Conclusions: EAUS is a practical and repeatable imaging tool that complements MRI in the multidisciplinary management of perianal Crohn’s disease. Advances such as 3D imaging, contrast enhancement, and elastography may enable validated activity scoring—for example, a future PEACE (Perianal Endosonographic Activity in Chron’s Evaluation) Index—further strengthening its role in longitudinal care. Full article

With the widespread use of high-density polyethylene (HDPE) pipes in various industrial and municipal applications, ensuring the structural integrity of their joints is crucial. This paper presents a novel defect detection method based on a microwave resonant probe, designed to perform efficient and non-destructive evaluation of butt fusion joints in HDPE pipes. The experimental setup integrates a microwave antenna and resonant cavity to record real-time variations in resonance frequency and S₂₁ magnitude while scanning the pipe surface. This method effectively detects common defects, including cracks, holes, and inclusions, within the butt fusion joints. The results show that the microwave resonant probe exhibits high sensitivity in detecting HDPE pipe defects. It can identify different sizes of cracks and holes, and can distinguish between talc powder and sand particles. This technique offers a promising solution for pipeline health monitoring, particularly for evaluating the quality of welded joints in non-metallic materials. Full article

This work details the development of a 10-stage solid-stage linear transformer driver (SSLTD) capable of producing 24 kV, 1 kA pulses with a rise-time of ∼10 ns utilizing SiC MOSFET switches. Throughout the development process, various design parameters were investigated for their influence on the LTD’s performance. Among these considerations was an evaluation of the behavior of several nanocrystalline magnetic core materials subject to high-voltage pulsed conditions, with an emphasis on minimizing energy losses. Another design parameter of interest lies in the physical layout of the LTD structure, particularly the diameter of the central stalk and the dielectric material, which together define the characteristics of the coaxial transmission line, as well as the overall height of each stage. The influence of each of these parameters was weighed to optimize the final design for fastest output pulse rise-time, highest efficiency, and cleanest output pulse waveform profile across varying load resistance. This work also introduces a pulsed reset technique, where repetition-rated burst testing was used to find the maximum operational frequency of the LTD without driving the magnetic cores into saturation. Full article

Current ringing in dual-active bridge (DAB) converters significantly degrades efficiency and reliability, particularly due to resonant interactions in the magnetic tank impedance network. We propose a novel impedance resonant channel shaping technique to suppress the ringing by systematically modifying the converter’s equivalent impedance model. The method begins with establishing a high-fidelity network representation of the magnetic tank, incorporating transformer parasitics, external inductors, and distributed capacitances, where secondary-side components are referred to the primary via the turns ratio squared. Critical damping is achieved through a rank-one modification of the coupling denominator, which is analytically normalized to a second-order form with explicit expressions for resonant frequency and damping ratio. The optimal series–RC damping network parameters are derived as functions of leakage inductance and winding capacitance, enabling precise control over the effective damping factor while accounting for core loss effects. Furthermore, the integrated network with the damping network dynamically shapes the impedance response, thereby attenuating ringing currents without compromising converter dynamics. Experimental validation confirms that the proposed approach reduces peak ringing amplitude by over 60% compared to the conventional snubber-based methods, while maintaining full soft-switching capability. Full article

The pharmacological quality of Astragalus membranaceus var. mongholicus (AMM) is determined by its bioactive compounds, and developing a rapid prediction method is essential for quality assessment. This study proposes a predictive model for AMM bioactive compounds using hyperspectral imaging (HSI) and wavelet domain multivariate features. The model employs techniques such as the first-order derivative (FD) algorithm and the continuum removal (CR) algorithm for initial feature extraction. Unlike existing models that primarily focus on a single-feature extraction algorithm, the proposed tree-structured feature extraction module based on discrete wavelet transform and one-dimensional convolutional neural network (1D-CNN) integrates FD and CR, enabling robust multivariate feature extraction. Subsequently, the multivariate feature cross-fusion module is introduced to implement multivariate feature interaction, facilitating mutual enhancement between high- and low-frequency features through hierarchical recombination. Additionally, a multi-objective prediction mechanism is proposed to simultaneously predict the contents of flavonoids, saponins, and polysaccharides in AMM, effectively leveraging the enhanced, recombined spectral features. During testing, the model achieved excellent predictive performance with R² values of 0.981 for flavonoids, 0.992 for saponins, and 0.992 for polysaccharides. The corresponding RMSE values were 0.37, 0.04, and 0.86; RPD values reached 7.30, 10.97, and 11.16; while MAE values were 0.14, 0.02, and 0.38, respectively. These results demonstrate that integrating multivariate features extracted through diverse methods with 1D-CNN enables efficient prediction of AMM bioactive compounds using HSI. Full article

Nighttime sea fog exhibits high frequency and prolonged duration, posing significant risks to maritime navigation safety. Current detection methods primarily rely on the dual-infrared channel brightness temperature difference technique, which faces challenges such as threshold selection difficulties and a tendency toward overestimation. In contrast, the VIIRS Day/Night Band (DNB) offers exceptional nighttime visible-like cloud imaging capabilities, offering a new solution to alleviate the overestimation issues inherent in infrared detection algorithms. Recent advances in artificial intelligence have further addressed the threshold selection problem in traditional detection methods. Leveraging these developments, this study proposes a novel generative adversarial network model incorporating attention mechanisms (SEGAN) to achieve accurate nighttime sea fog detection using DNB data. Experimental results demonstrate that SEGAN achieves satisfactory performance, with probability of detection, false alarm rate, and critical success index reaching 0.8708, 0.0266, and 0.7395, respectively. Compared with the operational infrared detection algorithm, these metrics show improvements of 0.0632, 0.0287, and 0.1587. Notably, SEGAN excels at detecting sea fog obscured by thin cloud cover, a scenario where conventional infrared detection algorithms typically fail. SEGAN emphasizes semantic consistency in its output, endowing it with enhanced robustness across varying sea fog concentrations. Full article

A bias-free antenna tuning technique that eliminates conventional DC biasing networks is presented. The tuning mechanism is based on a Light-Dependent Resistor (LDR) embedded within the antenna structure. Optical illumination is used to modulate the LDR’s resistance, thereby altering the antenna’s effective electrical length and enabling tuning of its resonant frequency and operating bands. By removing the need for bias lines, RF chokes, blocking capacitors, and control circuitry, the proposed approach minimizes parasitic effects, losses, biasing energy, and routing complexity. This makes it particularly suitable for compact and energy-constrained platforms, such as Internet of Things (IoT) devices. As proof of concept, an LDR is integrated into a ring monopole antenna, achieving tri-band operation in both high and low resistance states. In the high-resistance (OFF) state, the fabricated prototype operates across 2.1–3.1 GHz, 3.5–4 GHz, and 5–7 GHz. In the low-resistance (ON) state, the LDR bridges the two arcs of the monopole, extending the current path and shifting the lowest band to 1.36–2.35 GHz, with only minor changes to the mid and upper bands. The antenna maintains linear polarization across all bands and switching states, with measured gains reaching up to 5.3 dBi. Owing to its compact, bias-free, and low-cost architecture, the proposed design is well-suited for integration into portable wireless devices, low-power IoT nodes, and rapidly deployable communications systems where electrical biasing is impractical. Full article

Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but this collaborative approach also introduces vulnerabilities to security threats—most notably, Spectrum Sensing Data Falsification (SSDF) attacks. In such attacks, malicious nodes deliberately report false sensing information, undermining the reliability and performance of the network. This paper investigates the application of machine learning techniques to detect and mitigate SSDF attacks in MCRNs, particularly considering the additional challenges introduced by node mobility. We propose a hybrid detection framework that integrates a reputation-based weighting mechanism with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers to improve detection accuracy and reduce the influence of falsified data. Experimental results on software defined radio (SDR) demonstrate that the proposed method significantly enhances the system’s ability to identify malicious behavior, achieving high detection accuracy, reduces the rate of data falsification by approximately 5–20%, increases the probability of attack detection, and supports the dynamic creation of a blacklist to isolate malicious nodes. These results underscore the potential of combining machine learning with trust-based mechanisms to strengthen the security and reliability of mobile cognitive radio networks. Full article

Electric vehicle (EV) drivetrains operate at high rotational speeds, which makes the noise, vibration, and harshness (NVH) performance of gear transmissions a critical design factor. Without the masking effect of an internal combustion engine, gear whine can become a prominent source of passenger discomfort. This paper provides the first comprehensive review focused specifically on gear tooth surface waviness, a subtle manufacturing-induced deviation that can excite tonal noise. Periodic, micron-scale undulations caused by finishing processes such as grinding may generate non-meshing frequency “ghost orders,” leading to tonal complaints even in high-quality gears. The article compares finishing technologies including honing and superfinishing, showing their influence on waviness and acoustic behavior. It also summarizes modern waviness detection techniques, from single-flank rolling tests to optical scanning systems, and highlights data-driven predictive approaches using machine learning. Industrial case studies illustrate the practical challenges of managing waviness, while recent proposals such as controlled surface texturing are also discussed. The review identifies gaps in current research: (i) the lack of standardized waviness metrics for consistent comparison across studies; (ii) the limited validation of digital twin approaches against measured data; and (iii) the insufficient integration of machine learning with physics-based models. Addressing these gaps will be essential for linking surface finish specifications with NVH performance, reducing development costs, and improving passenger comfort in EV transmissions. Full article

With the booming development of the 5G market in recent years, super-high frequency (SHF) resonators will play an increasingly critical role in 5G and future communication systems. Facing the growing market demand for miniaturized, high-bandwidth, and low insertion loss filters, the design of SHF resonators and filters with a high effective electromechanical coupling coefficient (K²_eff) and quality factor, low insertion loss, high passband flatness, strong out-of-band rejection, and high power handling capacity has placed high demands on piezoelectric material preparation, process optimization, and resonator design. The polarity-inverted Al(Sc)N multilayer substrate has become one of the key solutions for SHF resonators. This review provides a comprehensive overview of the recent advances in SHF Al(Sc)N bulk acoustic wave (BAW) resonators. It systematically discusses the device design methodologies, structural configurations, and material synthesis techniques for high-quality Al(Sc)N thin films. Particular emphasis is placed on the underlying mechanisms and engineering strategies for polarity control in Al(Sc)N-based periodically poled multilayer structures. The progress in periodically poled piezoelectric film (P3F) BAW resonators is also examined, with special attention to their ability to significantly boost the operating frequency of BAW devices without reducing the thickness of the piezoelectric layer, while maintaining a high K²_eff. Finally, the review outlines current challenges and future directions for achieving a higher quality factor (Q), improved frequency scalability, and greater integration compatibility in SHF acoustic devices, paving the way for next-generation radio frequency (RF) front-end technologies in 5G/6G and beyond. Full article

This study presents the design and experimental evaluation of a 10 MHz voltage regulator module (VRM) that incorporates a solenoid inductor embedded within a printed circuit board (PCB). To verify the performance of the inductor, a test PCB was fabricated and characterized using a vector network analyzer (VNA), with measurement data processed through 2x-thru de-embedding technique. A 10 MHz VRM was then implemented to assess the impact of the embedded inductor on system efficiency. Comparative measurements were conducted between two VRMs—one employing a surface-mounted (SMT) inductor and the other a PCB-embedded inductor. The SMT-based system achieved a peak efficiency of 65.24% at a load current of 800 mA, whereas the PCB-embedded inductor version reached 70.43% at 900 mA, reflecting an improvement of 5.19%. The VRM with an embedded inductor experienced less efficiency degradation under heavy load conditions, demonstrating superior energy delivery stability. These findings confirm the practical benefits of integrating solenoid inductors within a PCB for high-frequency, high-efficiency power conversion. Full article

This paper challenges the conventional wisdom of using uniform random resource selection for collision resolution in distributed scheduling, particularly in wireless protocols. Bluetooth, being one such technology, is analyzed through its frequency hopping mechanism to explore for a better alternative in random access MAC (medium access control). Using diffusion theory, we characterize Bluetooth’s original frequency hopping as exhibiting maximum diffusivity, which correlates with unnecessarily high collision rates and a short mean first encounter time (MFET) between nodes. MFET, defined as the expected time until two independent hopping sequences first collide on the same channel, serves as an intuitive metric for evaluating collision likelihood. This insight leads to the proposal of a new collision avoidance mechanism with reduced diffusivity, effectively increasing MFET while maintaining efficient spectrum utilization. Our analysis and simulation results demonstrate that it can significantly lower packet collisions, outperforming existing techniques such as adaptive frequency hopping. The results are further corroborated by a real-life prototype implementation that closely replicates the predicted performance. The proposed diffusion-based MAC, by explicitly targeting longer MFETs, is expected to better handle dense Bluetooth environments, which are becoming increasingly common. Full article