Search Results (366)

High-efficiency electric motors represent a core enabling technology for sustainable industrial systems, providing substantial opportunities to reduce electricity consumption, operating costs, and associated greenhouse gas emissions across motor-driven processes. This paper presents a structured synthesis of recent progress in high-efficiency motor technologies within the IE3–IE5 efficiency classes, with emphasis on design innovations in electromagnetic optimization, advanced materials, and thermal management that collectively improve efficiency retention, reliability, and service lifetime under practical duty cycle conditions. Beyond component-level advances, the review analyses how high-efficiency motor–drive systems are being embedded within Industry 5.0 manufacturing environments, where human-centric automation and data-driven intelligence extend motor functionality toward adaptive, condition-aware operation. In this context, the integration of IoT-enabled sensing, AI-based analytics, and digital twin models supports predictive maintenance, real-time condition assessment, fault diagnostics, adaptive control, and duty cycle-responsive energy optimization, thereby improving both energy management and operational resilience. The paper also discusses implementation considerations that commonly constrain industrial adoption, including interoperability with legacy infrastructure, control architecture compatibility, data quality and model robustness, cybersecurity concerns, and lifecycle-oriented sustainability requirements such as material criticality and end-of-life pathways. Representative industrial case studies are synthesized to illustrate typical deployment architectures, observed implementation effects, and recurring technical challenges, together with practical mitigation strategies. This article advances the viewpoint that, under the Industry 5.0 paradigm, the value of high-efficiency motors is evolving from a component-level efficiency upgrade to a cyber-physical enabling asset that shapes lifecycle carbon performance and manufacturing resilience; realizing this shift requires integrated co-design spanning electromagnetics, thermodynamics, information science, and control. Full article

Common-mode voltage (CMV) is a critical concern in motor drive applications employing multilevel inverters, as it can lead to significant issues such as high-frequency noise, electromagnetic interference, and motor bearing degradation. These effects can compromise the reliability, reduce the operational lifespan of electric machines, and introduce safety hazards. In this study, an enhanced Direct Torque Control (DTC) strategy incorporating Space Vector Modulation (SVM) is proposed to specifically address CMV-related challenges in induction motors (IM) driven by a three-level Neutral-Point-Clamped (NPC) inverter. The proposed DTC scheme utilizes a specialized modulation technique that effectively mitigates CMV while also minimizing current harmonic content, and torque and flux ripples with a constant switching frequency. The developed SVM algorithm simplifies the three-level space vector representation into six equivalent two-level diagrams, enabling more efficient control. The zero-voltage vector is synthesized virtually by combining two active vectors within a two-level hexagonal structure. The effectiveness of the proposed DTC approach is validated through both simulation and Hardware-In-the-Loop (HIL) testing. Compared to the conventional DTC method, the proposed solution demonstrates superior performance in CMV minimization and leakage current reduction. Notably, it limits the CMV amplitude to V_dc/6, a significant improvement over the V_dc/2 typically observed with the standard DTC approach. Full article

This paper presents a systematic numerical investigation of a hybrid plasmonic–photonic Panda-ring antenna with an embedded gold grating, designed to enable efficient dual-mode radiation for optical and terahertz communication systems. The proposed structure integrates high-Q whispering-gallery mode (WGM) confinement in a multi-ring dielectric resonator with plasmonic out-coupling at the metal–dielectric interface, allowing controlled conversion of resonantly stored photonic energy into free-space radiation. The electromagnetic behavior is analyzed through a hierarchical structural evolution, progressing from a linear silicon waveguide to single-ring, add–drop, and Panda-ring resonator configurations. Gold is modeled using a dispersive Drude formulation with complex permittivity to accurately capture frequency-dependent plasmonic response at 1.55 µm. Power redistribution within the resonator system is described using coupled-mode theory, with coupling and loss parameters evaluated consistently from full-wave numerical simulations. Full-wave simulations using OptiFDTD and CST Studio Suite demonstrate that purely photonic resonators exhibit strong WGM confinement but negligible radiation, while plasmonic gratings alone suffer from low efficiency due to the absence of coherent photonic excitation. In contrast, the proposed hybrid Panda-ring antenna achieves stable and directive far-field radiation under WGM excitation, with a realized gain of approximately 8.05 dBi at 193.5 THz. The performance enhancement originates from synergistic hybrid SPP–WGM coupling, establishing a WGM-driven radiation mechanism suitable for Li-Fi and terahertz wireless applications. Full article

In this study, a material-driven strategy is presented to realize tunable triboelectric–electromagnetic hybrid generators while overcoming the form-factor limitations of conventional magnet-assisted systems. A magneto-dielectric hybrid generator (MDHG) was constructed using a soft magnetized dielectric composite, where NdFeB microparticles were embedded in an Ecoflex matrix and activated by pulse magnetization, allowing a single compliant layer to operate simultaneously as a triboelectric contact medium and a magnetic flux source coupled to a coil. The magnetic filler loading was systematically optimized to elucidate the trade-off between enhanced electromagnetic induction and a non-monotonic triboelectric response governed by dielectric polarization, surface potential, and interfacial energetics. To selectively strengthen the triboelectric branch without sacrificing electromagnetic output, nanoscale BaTiO₃ was introduced as an interstitial dielectric phase to promote polarization-active pathways and suppress screening-driven charge-utilization loss. Under contact–separation operation, the optimized MDHG produced triboelectric outputs up to a V_OC of 400.40 V and I_SC of 56.95 μA, while the electromagnetic branch delivered up to a V_OC of 260.04 mV and I_SC of 0.89 mA, corresponding to 2.87- and 2.62-fold increases in triboelectric V_OC and I_SC over pristine Ecoflex. Finally, the hybrid signatures enabled a wearable smart-skin interface capable of decoupling touch occurrence, intensity, and counter-material identity. Full article

This paper introduces a vanadium dioxide-integrated broadband metamaterial absorber designed for the terahertz frequency range. The simulation results for the proposed structure demonstrate a wide 90% absorption bandwidth of 8.23 THz, corresponding to a fractional bandwidth of 89.5%. By leveraging the phase-transition properties of VO₂, the absorber demonstrated dynamic adjustability by modulating the absorption from 3% to 98.74%. The absorption mechanism was analyzed through the impedance matching theory and electromagnetic field distributions, confirming the role of magnetic resonance and interference. Furthermore, machine learning algorithms, specifically Linear Regression, Support Vector Regression, and Random Forest (RF), were applied to accelerate the design process and optimize the structural parameters. Among these, the RF model demonstrated superior prediction accuracy. The machine learning-assisted optimization successfully extended the effective absorption bandwidth to 9 THz, representing an improvement by 9.4% compared to the traditional optimization methods. These results validate the efficacy of combining electromagnetic simulation with data-driven techniques for advanced metamaterial design. Full article

We investigate the multi-layer atmospheric impacts of Typhoon Hagibis (2019), which formed on 6 October, tracked across 12–35° N and 135–155° E, and made landfall on 12 October over the Izu Peninsula, central Honshu, Japan. We present a multi-layer study that involves the troposphere, stratosphere and upper ionosphere to examine the thermodynamic and electromagnetic coupling between these layers due to such extreme weather conditions. Using ERA5 reanalysis, we identify pronounced stratospheric temperature perturbations, elevated atmospheric gravity wave (AGW) potential energy, substantial spatiotemporal variability in the zonal (U) and meridional (V) wind components, relative humidity, and specific rainwater content throughout the cyclone’s evolution. Quantitatively, AGW potential energy increased from background levels of <5 J kg⁻¹ to >40 J kg⁻¹ near the cyclone core, while tropospheric wind anomalies reached $\pm 30$–40 m s⁻¹, accompanied by relative humidity values exceeding 90% and specific rainwater content up to $1.5\times {10}^{-3}$ kg kg⁻¹, indicative of vigorous moist convection and strong vertical energy transport. The ionospheric response, derived from GPS-based Total Electron Content (TEC) at 10 Japanese IGS stations, reveals vertical TEC (VTEC) perturbations whose amplitudes and temporal evolution vary systematically with GPS-station-to-typhoon-eye distance, including clear enhancements and reductions around the closest-approach day. These signatures indicate a measurable ionospheric response to cyclone-driven atmospheric forcing under geomagnetically quiet conditions, confirming that Hagibis produced vertically coupled disturbances linking stratospheric AGW activity with ionospheric electron density variability. Full article

Time-varying or temporal metamaterials and metasurfaces, in which electromagnetic parameters are deliberately modulated in time, have emerged as a powerful route to engineer wave–matter interaction beyond what is possible in static media. By enabling the controlled exchange of energy and momentum with the fields, they underpin magnet-free nonreciprocity, low-loss frequency conversion, temporal impedance matching beyond Bode-Fano limit, and unconventional parametric gain and noise control. This survey provides a coherent framework that unifies the main theoretical and experimental developments in the area, from early analyses of velocity-modulated dielectrics to recent demonstrations of temporal photonic crystals, non-Foster temporal boundaries, and spatiotemporally driven metasurfaces relevant to nanophotonic platforms. We systematically compare time-varying permittivity, joint $\epsilon$-$\mu$ modulation, time-varying conductivity, plasmas, and circuit-equivalent implementations, including stochastic and rapidly sign-switching regimes, and relate them to acoustic and quantum analogs using common figures of merit, such as conversion efficiency, isolation versus insertion loss, modulation depth and speed, dynamic range, and stability. Our work concludes by outlining key challenges, loss and pump efficiency, high-speed modulation at the nanoscale, dispersion engineering for broadband operation, and fair benchmarking, which must be addressed for robust, integrable temporal metasurfaces. Full article

An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes. Full article

Incremental electromagnetic signal classification is crucial in realistic wireless environments where new signal types continuously emerge and historical training data are often unavailable. This paper proposes a model-based incremental learning method driven by vector space separation to mitigate catastrophic forgetting without accessing old-task samples or requiring semantic information. We show that forgetting is largely caused by insufficient separation between old and new classes in the classifier weight space. To address this issue, we jointly introduce weight normalization, a cosine-similarity separation loss, and regularization, together with cross-entropy supervision for new classes. Based on these designs, we propose an incremental learning method based on vector space separation for electromagnetic signal classification, enabling the model to continually recognize modulation signals without requiring semantic information or access to raw data from previous tasks during incremental updates. Experiments on two simulated modulation datasets under multiple task sequences demonstrate that the proposed method consistently alleviates catastrophic forgetting and achieves stable incremental performance, outperforming baselines while avoiding data rehearsal. Full article

As the core thrust output component of electromagnetic drive systems, the Dual Three-Phase Linear Induction Motor (DT-LIM) places stringent requirements on the stability and reliability of its control system, and its sensorless control strategy has emerged as a research hotspot. However, as the motor operating frequency increases and the control carrier ratio decreases significantly, conventional algorithms lack sufficient capability to suppress process noise during model discretization, leading to a severe degradation of their observation performance. To address this issue, this paper proposes a Nonlinear Kalman Filter (NLKF) based on the Improved Euler (IE) discretization, which mitigates the model’s process noise at the source of discretization. Through stability and convergence analyses, the feasibility of the proposed algorithm and its advantages in terms of error convergence bounds are verified. The correctness of the theoretical derivations is confirmed through simulations. Furthermore, an experimental platform is established to compare the proposed algorithm with commonly used Kalman filters. A comprehensive analysis is conducted from the perspectives of online observation performance, closed-loop control performance, and computational complexity, thus verifying the proposed algorithm’s performance advantages. Full article

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article

Advances in sensing technologies are increasingly transforming cutting operations by enabling data-driven condition monitoring, predictive maintenance, and process optimization. This review surveys recent developments in sensing modalities for cutting systems, including vibration sensors, acoustic emission sensors, optical and vision-based systems, eddy-current sensors, force sensors, and emerging hybrid/multi-modal sensing frameworks. Each sensing approach offers unique advantages in capturing mechanical, acoustic, geometric, or electromagnetic signatures related to tool wear, process instability, and fault development, while also showing modality-specific limitations such as noise sensitivity, environmental robustness, and integration complexity. Recent trends show a growing shift toward hybrid and multi-modal sensor fusion, where data from multiple sensors are combined using advanced data analytics and machine learning to improve diagnostic accuracy and reliability under changing cutting conditions. The review also discusses how artificial intelligence, Internet of Things connectivity, and edge computing enable scalable, real-time monitoring solutions, along with the challenges related to data needs, computational costs, and system integration. Future directions highlight the importance of robust fusion architectures, physics-informed and explainable models, digital twin integration, and cost-effective sensor deployment to accelerate adoption across various manufacturing environments. Overall, these advancements position advanced sensing and hybrid monitoring strategies as key drivers of intelligent, Industry 4.0-oriented cutting processes. Full article

In this paper, an anisotropic object electromagnetic image reconstruction system based on a two-stage multi-frequency extended network is developed by deep learning techniques. We obtain the scattered field information by irradiating the TM different polarization waves to uniaxial objects located in free space. We input the measured single-frequency scattered field into the Deep Residual Convolutional Neural Network (DRCNN) for training and to be further extended to multi-frequency data by the trained model. In the second stage, we feed the multi-frequency data into the Deep Convolutional Encoder–Decoder (DCED) architecture to reconstruct an accurate distribution of the dielectric constants. We focus on EMIS applications using Transverse Magnetic (TM) and Transverse Electric (TE) waves in 2D scenes. Numerical findings confirm that our method can effectively reconstruct high-contrast uniaxial objects under limited information. In addition, the TM/TE scattering from uniaxial anisotropic objects is governed by polarization-dependent Lippmann–Schwinger integral equations, yielding a nonlinear and severely ill-posed inverse operator that couples the dielectric tensor components with multi-frequency field responses. Within this mathematical framework, the proposed two-stage DRCNN–DCED architecture serves as a data-driven approximation to the anisotropic inverse scattering operator, providing improved stability and representational fidelity under limited-aperture measurement constraints. Full article

Conventional quantum mechanics treats the electron as a point-like particle endowed with intrinsic properties—mass, charge, and spin—that are inserted as axioms rather than derived from first principles. Here, we propose a thermodynamic reformulation of the electron grounded in entropy field dynamics, based on S-Theory. In this framework, the electron is composed of three distinct entropic components: S_core (a collapsed entropy core from configurational mass), S_EM (a structured electromagnetic entropy field from charge), and S_thermal (a diffuse entropy component from ambient interactions). We show that spin emerges as a rotating S_EM shell around S_core, and that electron collapse—as in quantum measurement—can be modeled as a Recursive Amplification of Sfield (RAS) process driven by entropic feedback. Through mathematical formulation and high-resolution simulations, we demonstrate how the S-field components evolve under entropic excitation, culminating in a collapse threshold defined by local entropy density matching. This model not only explains the emergence of quantum properties but also offers a thermodynamic mechanism for electron–photon interaction, wavefunction collapse, and spin generation, revealing the inner structure and dynamics of one of nature’s most fundamental particles. Full article

Transition metal (TM)-doped zinc oxide (ZnO) and zirconium dioxide (ZrO₂) nanocrystals exhibit complex correlations between crystal structure, defect chemistry, vibrational properties, and magnetic behavior that are strongly governed by synthesis route and dopant incorporation mechanisms. This review critically summarizes recent progress on Fe-, Mn-, and Co-doped ZnO and ZrO₂ nanocrystals synthesized by wet chemical, hydrothermal, and microwave-assisted hydrothermal methods, with emphasis on synthesis-driven phase evolution and apparent solubility limits. ZnO and ZrO₂ are treated as complementary host lattices: ZnO is a semiconducting, piezoelectric oxide with narrow solubility limits for most 3d dopants, while ZrO₂ is a dielectric, polymorphic oxide in which transition metal doping may stabilize tetragonal or cubic phases. Structural and microstructural studies using X-ray diffraction, electron microscopy, Raman spectroscopy, and Mössbauer spectroscopy demonstrate that at low dopant concentrations, TM ions may be partially incorporated into the host lattice, giving rise to diluted or defect-mediated magnetic behavior. When solubility limits are exceeded, nanoscopic secondary oxide phases emerge, leading to superparamagnetic, ferrimagnetic, or spin-glass-like responses. Magnetic measurements, including DC magnetization and AC susceptibility, reveal a continuous evolution from paramagnetism in lightly doped samples to dynamic magnetic states characteristic of nanoscale magnetic entities. Vibrational spectroscopy highlights phonon confinement, surface optical phonons, and disorder-activated modes that sensitively reflect nanocrystal size, lattice strain, and defect populations, and often correlate with magnetic dynamics. Rather than classifying these materials as diluted magnetic semiconductors, this review adopts a synthesis-driven and correlation-based framework that links dopant incorporation, local structural disorder, vibrational fingerprints, and magnetic response. By emphasizing multi-technique characterization strategies required to distinguish intrinsic from extrinsic magnetic contributions, this review provides practical guidelines for interpreting magnetism in TM-doped oxide nanocrystals and outlines implications for applications in photocatalysis, sensing, biomedicine, and electromagnetic interference (EMI) shielding. Full article