Search Results (382)

Electromagnetic pneumatic microvalves, widely used in knitting machines, typically operate based on a spring-return mechanism. When the coil is energized, the electromagnetic force overcomes the spring force to attract the armature, opening the valve. Upon de-energization, the armature returns to its original position under the restoring force of the spring, closing the valve. However, most existing electromagnetic microvalves adopt a radially asymmetric magnetic yoke design, which generates additional radial forces during operation, leading to armature misalignment or even sticking. Additionally, the inductance effect of the coil causes a significant delay in the armature release response, making it difficult to meet the knitting machine’s requirements for rapid response and high reliability. To address these issues, this paper proposes an improved electromagnetic microvalve design. First, the magnetic yoke structure is modified to be radially symmetric, eliminating unnecessary radial forces and preventing armature sticking during operation. Second, a permanent magnet assist mechanism is introduced at the armature release end to enhance release speed and reduce delays caused by the inductance effect. The effectiveness of the proposed design is validated through electromagnetic numerical simulations, and a multi-objective genetic algorithm is further employed to optimize the geometric dimensions of the electromagnet. The optimization results indicate that, while maintaining the fundamental power supply principle of conventional designs, the new microvalve structure achieves a pull-in time comparable to traditional designs during engagement but significantly reduces the release response time by approximately 80.2%, effectively preventing armature sticking due to radial forces. The findings of this study provide a feasible and efficient technical solution for the design of electromagnetic microvalves in textile machinery applications. Full article

Noncoplanar spin textures give rise not only to unusual magnetic structures but also to emergent electromagnetic responses stemming from scalar spin chirality, such as the topological Hall effect. In this study, we theoretically investigate nonreciprocal transport phenomena induced by noncoplanar magnetic orderings through microscopic model analyses. By focusing on meron–antimeron spin textures that exhibit local scalar spin chirality while maintaining vanishing global chirality, we demonstrate that the electronic band structure becomes asymmetrically modulated, which leads to the emergence of nonreciprocal transport. The present mechanism arises purely from the noncoplanar magnetic texture itself and requires neither net magnetization nor relativistic spin–orbit coupling. We further discuss the potential relevance of our findings to the compound Gd₂PdSi₃, which has been suggested to host a meron–antimeron crystal phase at low temperatures. Full article

At present, the redundant structures are one of the most effective methods for solving magnetic levitation bearing coil failure. Coil failure causes residual effective magnetic poles to form different support structures and even asymmetrical structures. For the magnetic bearing with redundant structures, how to construct the electromagnetic force (EMF) that occurs under different support structures to achieve support reconstruction is the key to realizing fault tolerance control. To reveal the support reconstruction mechanism of magnetic bearing with a redundant structure, firstly, this paper takes a single-degree-of-freedom magnetic suspension body as an example to conduct a linearization theory analysis of the offset current, clarifying the concept of the current distribution matrix (CDM) and its function; then, the nonlinear EMF mode of magnetic bearing with an eight-pole is constructed, and it is linearized by using the theory of bias current linearization. Furthermore, the conditions of no coils fail, the 8th coil fails, and the 6–8th coils fail are considered, and, with the maximum principle function of EMF, the corresponding current matrices are obtained. Meanwhile, based on the CDM, the corresponding magnetic flux densities were calculated, proving that EMF reconstruction can be achieved under the three support structures. Finally, with the CDM and position control law, a fault-tolerant control system was constructed, and the simulation of the magnetic bearing with a redundant structure was carried out. The simulation results reveal the mechanism of support reconstruction with three aspects of rotor displacement, the value and direction of currents that occur in each coil. The simulation results show that, in the 8-pole magnetic bearing, this study can achieve support reconstruction in the case of faults in up to two coils. Under the three working conditions of wireless no coil failure, the 8th coil fails and the 6–8th coils fail, the current distribution strategy was adjusted through the CDM. The instantaneous displacement disturbance during the support reconstruction process was less than 0.28 μm, and the EMF after reconstruction was basically consistent with the expected value. Full article

The flexible transparent conductive films (TCFs) of a ZnS/Cu/Ag/TiO₂ multilayered structure were deposited on a flexible PET substrate with high surface roughness using magnetic sputtering, and the effects of structural characteristics on the performance of the films were analyzed. The TCFs with TiO₂/Cu/Ag/TiO₂ and ZnS/Cu/Ag/ZnS symmetric structures were also prepared for comparison. The TCF samples were deposited using ZnS, TiO₂, Cu and Ag targets, and they were analyzed using scanning electronic microscopy, atomic force microscopy, grazing incidence X-ray diffraction, spectrophotometry and a four-probe tester. The TCFs exhibit generally uniform surface morphology, excellent light transmittance and electrical conductivity with optimized structure. The optimal values are 84.40%, 5.52 Ω/sq and 33.19 × 10⁻³ Ω⁻¹ for the transmittance, sheet resistance and figure of merit, respectively, in the visible spectrum. The satisfactory properties of the asymmetric multilayered TCF deposited on a rough-surface substrate should be mainly attributed to the optimized structure parameters and reasonable interfacial compatibilities. Full article

Structural magnetic resonance imaging (sMRI) is a vital tool for diagnosing neurological brain diseases. However, sMRI scans often show significant structural changes only in limited brain regions due to localised atrophy, making the identification of discriminative features a key challenge. Importantly, the human brain exhibits inherent bilateral symmetry, and deviations from this symmetry—such as asymmetric atrophy—are strong indicators of early Alzheimer’s disease (AD). Patch-based methods help capture local brain changes for early AD diagnosis, but they often struggle with fixed-size limitations, potentially missing subtle asymmetries or broader contextual cues. To address these limitations, we propose a novel augmented reality (AR)-enhanced patch-level explainable deep learning (ARE-PaLED) system. It includes an adaptive multi-scale patch extraction network (AMPEN) to adjust patch sizes based on anatomical characteristics and spatial context, as well as an informative patch selection algorithm (IPSA) to identify discriminative patches, including those reflecting asymmetry patterns associated with AD; additionally, an AR module is proposed for future immersive explainability, complementing the patch-level interpretation framework. Evaluated on 1862 subjects from the ADNI and AIBL datasets, the framework achieved an accuracy of 92.5% (AD vs. NC) and 85.9% (AD vs. MCI). The proposed ARE-PaLED demonstrates potential as an interpretable and immersive diagnostic aid for sMRI-based AD diagnosis, supporting the interpretation of model predictions for AD diagnosis. Full article

With the rise of the Internet of Things (IoT), smart sensors are increasingly being deployed as compact edge processing units, necessitating continuously writable memory with low power consumption and fast access times. Magnetic random-access memory (MRAM) has emerged as a promising non-volatile alternative to conventional DRAM and SDRAM, offering advantages such as faster access speeds, reduced power consumption, and enhanced endurance. However, MRAM is subject to challenges including process variations and thermal fluctuations, which can induce random bit errors and result in imbalanced probabilities of 0 and 1 bits. To address these issues, we propose a novel sparse coding scheme characterized by a minimum Hamming distance of three. During the encoding process, three check bits are appended to the user data and processed using a generator matrix. If the resulting codeword fails to satisfy the sparsity constraint, it is inverted to comply with the coding requirement. This method is based on the error characteristics inherent in MRAM to facilitate effective error correction. Furthermore, we introduce a dynamic threshold detection technique that updates bit probability estimates in real time during data transmission. Simulation results demonstrate substantial improvements in both error resilience and decoding accuracy, particularly as MRAM density increases. Full article

In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article

Recently, as carbon emission regulations enforced by the International Maritime Organization (IMO) have become stricter and pressure from the World Trade Organization (WTO) to abolish tax-free fuel subsidies has increased, the demand for electric propulsion systems in the marine sector has grown. Most small domestic fishing vessels rely on tax-free fuel and have limited cruising ranges and constant-speed operation, which makes them well-suited for electric propulsion. This paper proposes replacing the internal combustion engine system of such vessels with an electric propulsion system. Based on real operating conditions, an Interior Permanent Magnet Synchronous Motor (IPMSM) was designed and optimized. The Savitsky method was used to calculate total resistance at a typical cruising speed, from which the required torque and output were determined. To reduce torque ripple, an asymmetric dummy slot structure was proposed, with two dummy slots of different widths and depths placed in each stator slot. These dimensions, along with the magnet angle, were set as optimization parameters, and a metamodel-based optimal design was carried out. As a result, while meeting the design constraints, torque ripple decreased by 2.91% and the total harmonic distortion (THD) of the back-EMF was lowered by 1.32%. Full article

The use of oil–water rings has become an emerging, effective, and energy-saving method of transporting heavy oil. Maintaining the shape of the oil–water ring and preventing rupture during the transport of heavy oil are of great scientific significance in oil–water annular flow transportation. To ensure the oil–water ring passes smoothly through the elbow without rupture, this article proposes an asymmetrical magnetohydrodynamic (MHD) propulsion method to utilize the significant difference between the conductivity of heavy oil and electrolyte solution to achieve an accelerating effect on the outer water ring. The magnetohydrodynamic device designed by this method can generate a magnetic field and provide Lorentzian magnetic force to achieve the asymmetric acceleration of the oil and water rings, to homogenize the water ring velocity on the inner and outer elbows, to push the deviated oil core back to the center of the pipeline, and to repair the rupture of the water film. The flow state of the oil–water ring in the bend pipe under the joint action of the electric field and magnetic field is simulated by a differential MHD thick oil simulation flow model, which confirms that the device can realize the repair of the oil–water ring flow at the bend pipe and ensure that the oil–water ring flow passes through the bend pipe stably. Meanwhile, the effects of coil current, electrode plate voltage, and the conductivity of electrolyte solution on the morphology and velocity of the oil–water ring in the elbow are investigated. In addition, the role of the device in maintaining the morphology under different gravitational conditions is investigated. These results provide a reference design for related devices and offer a new approach to heavy oil transportation. Full article

Multiphase permanent-magnet motors are very attractive solutions for a large variety of applications, and specifically for electric vehicle applications. However, with a higher number of stator phases, multiphase permanent-magnet motors are more subjected to stator failures. Thus, diagnosing the stator status is necessary to guarantee the required efficiency of the motor. This paper deals with two techniques suitable for detecting and localizing open-phase faults in closed-loop controlled six-phase AC permanent-magnet motors. More specifically, this paper is aimed at assessing the diagnosis of open-phase faults based on current and voltage signature analysis. It is shown that the presence of specific harmonics can significantly affect the diagnosis process. Here, two diagnostic space vectors elaborated in the fifth α-β plane, based on the current and voltage signals, are proposed to cope with this limitation. The main contributions of the proposed approach are its implementation simplicity, and the effective immunity of the current-based analysis and voltage-based analysis against harmonic disturbances. The effectiveness of the proposed diagnostic space vector has been analyzed by numerical simulations, then experimentally validated. Full article

As critical strategic mineral resources underpinning high-tech industries and national defense security, rare earth elements have become a central focus of international competition, with their global industrial chain configuration deeply intertwined with geopolitical dynamics. Leveraging a novel multilateral database encompassing 140 countries’ geopolitical relationships and rare earth trade flows (2001–2023), this study employs social network analysis and temporal exponential random graph models (TERGMs) to decode structural interdependencies across upstream mineral concentrates, midstream smelting, and downstream permanent magnet sectors. Empirical results show that topological density trajectories reveal intensified network coupling, with upstream/downstream sectors demonstrating strong clustering. Geopolitical cooperation and conflict exert differential impacts along the value chain: downstream trade exhibits heightened sensitivity to cooperative effects, whereas midstream trade suffers the most pronounced obstruction from conflicts. Cooperation fosters long-term trade relationships, whereas conflicts primarily impose short-term suppression. In addition, centrality metrics reveal asymmetric mechanisms. Each unit increase in cooperation degree centrality amplifies the mid/downstream trade by 3.29 times, whereas conflict centrality depresses the midstream trade by 4.76%. The eigenvector centrality of cooperation hub nations enhances the midstream trade probability by 5.37-fold per unit gain, in contrast with the 25.09% midstream trade erosion from conflict-prone nations’ centrality increments. These insights provide implications for mitigating geopolitical risks and achieving sustainable governance in key mineral resource supply chains. Full article

This paper presents a novel control strategy employing a deep reinforcement learning (DRL) scheme for online selection of optimal weighting factors in cost functions of the finite control set model predictive current controller of a permanent magnet synchronous motor (PMSM). Indeed, when designing predictive controllers for PMSMs’ phase currents, competing objectives appear, such as managing current convergence and switching transitions. These objectives result in an asymmetric cost function where they have to be balanced through weighting factors in order to enhance the inverter and motor performance. Leveraging the twin delayed deep deterministic policy gradient algorithm, the optimal weighting factor selection policy is obtained for online balancing of the choice between current deviation in the $dq$ frame and inverter commutations. For comparison, a metaheuristic-based artificial neural network is trained on static data obtained through a multi-objective genetic algorithm to predict the weights. The key performance markers, such as torque ripple, total harmonic distortion, switching frequency, steady-state, and dynamic performance, are provided through numerical simulations to verify the effectiveness of the proposed tuning scheme. The results of these simulations confirm that the proposed dynamic control scheme effectively resolves the challenges of weighting factor choice, meeting inverter performance requirements, and delivering better dynamic and steady-state performance. Full article

In this paper, an advanced model of a switched reluctance generator (SRG) with mutual coupling, iron losses, and remanent magnetism is presented. The proposed equivalent circuit for each SRG phase is represented by the winding resistance, phase inductance and electromotive forces (EMFs) induced by mutual flux-linkage and remanent magnetism. In the advanced SRG model, the phase inductance and equivalent iron-loss resistance need not be known, as the components of the phase current flowing through them are determined directly from appropriate look-up tables, making the advanced SRG model simpler. Both the magnitude of the mutual flux-linkage and its time derivative are considered in the advanced model. The proposed model only requires knowledge of data that can be obtained using the DC excitation method and does not require knowledge of the SRG material properties. For the first time, the remanent magnetic flux of the SRG is modeled and the induced EMS caused by it is included in the advanced SRG model. Stray losses within the SRG are considered negligible. Connection to an asymmetric bridge converter is assumed. Magnetization angles of individual SRG phases are provided by the terminal voltage controller. The results obtained with the advanced SRG model are compared with experiments carried out in the steady-state of the 8/6 SRG with a rated power of 1.1 kW SRG over a wide range of load, terminal voltage, turn-on angle, and rotor speed in single-pulse mode suitable for high-speed applications. Full article

Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy, caused by either sarcomere protein or other gene mutations. It is a complex and highly heterogeneous disorder, with phenotypes ranging from asymptomatic to severe disease, characterized by asymmetric left ventricular (LV) hypertrophy unexplained by loading conditions, which is also associated with myocardial fiber disarray, and preserved or increased ejection fraction without LV dilation. Comprehensive personal and family history, physical examination, and ECG testing raise suspicion of HCM, and echocardiogram represents the first-line imaging modality for confirming a diagnosis. Moreover, contrast-enhanced cardiac magnetic resonance (CMR) imaging has increasingly emerged as a fundamental diagnostic and prognostic tool in HCM management. This article reviews the role of CMR in HCM identification and differentiation from phenotypic mimics, characterization of HCM phenotypes, monitoring of disease progression, evaluation of pre- and post-septal reduction treatments, and selection of candidates for implantable cardioverter-defibrillator. By providing information on cardiac morphology and function and tissue characterization, CMR is particularly helpful in the quantification of myocardial wall thickness, the detection of hypertrophy in areas blind to echocardiogram, subtle morphologic features in the absence of LV hypertrophy, myocardial fibrosis, and apical aneurysm, the evaluation of LV outflow tract obstruction, and the assessment of LV function in end-stage dilated HCM. Full article

Accurate current sensing in rectangular conductors is challenged by mechanical deformations, including eccentricity (X/Y-axis shifts) and inclination (Z-axis tilt), which distort magnetic field distributions and induce measurement errors. To address this, we propose a bio-inspired error compensation strategy integrating an elliptically configured Hall sensor array with a hybrid Grey Wolf Optimizer (GWO)-enhanced backpropagation neural network. The eccentric displacement and tilt angle of the conductor are quantified via a three-dimensional magnetic field reconstruction and current inversion modeling. A dual-stage optimization framework is implemented: first, establishing a BP neural network for real-time conductor state estimations, and second, leveraging the GWO’s swarm intelligence to refine network weights and thresholds, thereby avoiding local optima and enhancing the robustness against asymmetric field patterns. The experimental validation under extreme mechanical deformations (X/Y-eccentricity: ±8 mm; Z-tilt: ±15°) demonstrates the strategy’s efficacy, achieving a 65.07%, 45.74%, and 76.15% error suppression for X-, Y-, and Z-axis deviations. The elliptical configuration reduces the installation footprint by 72.4% compared with conventional circular sensor arrays while maintaining a robust suppression of eccentricity- and tilt-induced errors, proving critical for space-constrained applications, such as electric vehicle powertrains and miniaturized industrial inverters. This work bridges bio-inspired algorithms and adaptive sensing hardware, offering a systematic solution to mechanical deformation-induced errors in high-density power systems. Full article