Search Results (52)

Calibration of power equipment has become an essential task in modern power systems. This paper proposes a distributed remote calibration prototype based on a cloud–edge–end architecture by integrating intelligent sensing, Internet of Things (IoT) communication, and edge computing technologies. The prototype employs a high-precision frequency-to-voltage conversion module leveraging satellite signals to address traceability and value transmission challenges in remote calibration, thereby ensuring reliability and stability throughout the process. Additionally, an environmental monitoring module tracks parameters such as temperature, humidity, and electromagnetic interference. Combined with video surveillance and optical character recognition (OCR), this enables intelligent, end-to-end recording and automated data extraction during calibration. Furthermore, a cloud-edge task scheduling algorithm is implemented to offload computational tasks to edge nodes, maximizing resource utilization within the cloud–edge collaborative system and enhancing service quality. The proposed prototype extends existing cloud–edge collaboration frameworks by incorporating calibration instruments and sensing devices into the network, thereby improving the intelligence and accuracy of remote calibration across multiple layers. Furthermore, this approach facilitates synchronized communication and calibration operations across symmetrically deployed remote facilities and reference devices, providing solid technical support to ensure that measurement equipment meets the required precision and performance criteria. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

This paper presents a comprehensive analytical framework for investigating loss mechanisms and thermal behavior in high-speed magnetic field-modulated motors for flywheel energy storage systems. Through systematic classification of electromagnetic, mechanical, and additional losses, we reveal that modulator components constitute approximately 45% of total system losses at rated speed. Finite element analysis demonstrates significant spatial non-uniformity in loss distribution, with peak loss densities of 5.5 × 10⁵ W/m³ occurring in the modulator region, while end-region losses exceed central-region values by 42% due to three-dimensional field effects. Our optimized design, implementing composite rotor structures, dual-material permanent magnets, and integrated thermal management solutions, achieves a 43.2% reduction in total electromagnetic losses, with permanent magnet eddy current losses decreasing by 68.7%. The maximum temperature hotspots decrease from 143 °C to 98 °C under identical operating conditions, with temperature gradients reduced by 58%. Peak efficiency increases from 92.3% to 95.8%, with the η > 90% region expanding by 42% in the speed–torque plane. Experimental validation confirms model accuracy with mean absolute percentage errors below 4.2%. The optimized design demonstrates 24.8% faster response times during charging transients while maintaining 41.7% smaller speed oscillations during sudden load changes. These quantitative improvements address critical limitations in existing systems, providing a viable pathway toward high-reliability, grid-scale energy storage solutions with extended operational lifetimes and improved round-trip efficiency. Full article

The increasing density and complexity of electromagnetic signals have brought new challenges to multi-component radar signal recognition. To address the problem of low recognition accuracy under low signal-to-noise ratios (SNR) in adapting the common recognition framework of combining time–frequency transformations (TFTs) with convolutional neural networks (CNNs), this paper proposes a new dual-component radar signal recognition framework (TFGM-RMNet) that combines a deep time–frequency generation module with a Transformer-based residual network. First, the received noisy signal is preprocessed. Then, the deep time–frequency generation module is used to learn the complete basis function to obtain various TF features of the time signal, and the corresponding time–frequency representation (TFR) is output under the supervision of high-quality images. Next, a ResNet combined with cascaded multi-head attention (MHSA) is applied to extract local and global features from the TFR. Finally, modulation format prediction is achieved through multi-label classification. The proposed framework does not require explicit TFT during testing, and the TFT process is built into TFGM to replace the traditional TFT. The classification results and ideal TFR are obtained during testing, realizing an end-to-end deep learning (DL) framework. The simulation results show that, when SNR > −8 dB, this method can achieve an average recognition accuracy close to 100%. It achieves 97% accuracy even at an SNR of −10 dB. At the same time, under low SNR, the recognition performance is better than the existing algorithms including DCNN-RAMIML, DCNN-MLL, and DCNN-MIML. Full article

The fifth generation (5G) of mobile communications promotes human exposure to electromagnetic fields exploiting the 3.5 GHz frequency band. We analyzed behaviors, cognitive functions, and gene expression in mice submitted to asymmetrical head exposure to a 5G-modulated 3.5 GHz signal. The exposures were applied for 1 h daily, 5 days per week over a six-week period, at a specific absorption rate (SAR) averaging 0.19 W/kg over the brain. Locomotor activities in an open field, object location, and object recognition memories were assessed repeatedly after four weeks of exposure and did not reveal any significant effect on the locomotion/exploration, anxiety level, or memory processes. mRNA profiling was performed at the end of the exposure period in two symmetrical areas of the right and left cerebral cortex, in which the SAR values were 0.43 and 0.14 W/kg, respectively. We found significant changes in the expression of less than 1% of the expressed genes, with over-representations of genes related to glutamatergic synapses. The right cortical area differed from the left one by an over-representation of responsive genes encoded by the mitochondrial genome. Our data show that repeated head exposures to a 5G-3.5 GHz signal can trigger mild transcriptome alterations without changes in memory capacities or emotional state. Full article

This study aims to conduct an assessment of the dynamic characteristics of a proposed 6.6 kW bidirectional bridgeless three-leg interleaved totem-pole power factor correction (PFC) boost converter developed for the front-end stage of electric vehicle onboard charger applications during load cycles. This proposed PFC boost converter integrates the self-developed silicon carbide (SiC) power MOSFET modules for achieving high efficiency and high power density. To assess the switching transient behavior, power loss, and efficiency of the SiC MOSFET power modules, a fully integrated electromagnetic-circuit coupled simulation (ECCS) model that incorporates an electromagnetic model, an equivalent circuit model, and an SiC MOSFET characterization model are used. In this simulation model, the impact of parasitic effects on the system’s performance is considered. The accuracy of the ECCS model is confirmed through comparing the calculated results with the experimental data obtained through the double pulse test and the closed-loop converter operation. Furthermore, a comparative study between the interleaved and non-interleaved topologies is also performed in terms of power loss and efficiency. Additionally, the performance of the SiC MOSFET-based PFC boost converter is further compared with that of the silicon (Si) insulated gate bipolar transistor (IGBT)-based one. Finally, a parametric analysis is carried out to explore the impact of several operating conditions on the power loss of the proposed totem-pole PFC boost converter. Full article

Adhesive joining has the severe limitation that damages/defects developed in the bondline are difficult to assess. Conventional non-destructive examination (NDE) techniques are adequate to reveal disbonding defects in fabrication and delamination near the end of service life but are not helpful in detecting and monitoring in-service degradation of the joint. Several techniques suitable for long-term joint integrity monitoring are proposed. Fiber Bragg grating (FBG) sensors embedded in the joint are one of the promising candidates. It has the advantages of being close to the damage and immune to environmental attack and electromagnetic interference. Damage and disbonding inside an adhesive joint will give rise to a non-uniform strain field that may bring about peak splitting and chirping of the FBG spectrum. It is shown that the evolution of the full spectral responses can closely reveal the development of damages inside the adhesive joints during tensile and fatigue failures. However, recording and comparing the successive full spectra in the course of damage is tedious and can be subjective. An energy modulation interrogation technique is proposed using a pair of tunable optical filters. Changes in the full FBG spectral responses are modulated by the filters and converted into a conveniently measurable voltage output by photodiodes. Monitoring damage development can then be easily automated, and the technique is well-suited for practical applications. Filter spectrum width of 5 nm and initial overlap with the FBG spectrum to give 40% of the maximum output voltage is found to be optimal for measurement. The technique is tested on embedded FBGs from different adhesive lap-joint specimens and successfully reflected the severity of changes in the full spectral shapes during the course of tensile failure. Moreover, the trends in these PD outputs corroborate with the V value previously proposed to describe the qualitative change in FBG spectral shape. Full article

Millimeter-wave (mm-Wave) phased array systems need to meet the transmitter (Tx) equivalent isotropic radiated power (EIRP) requirement, and that depends mainly on the design of two key sub-components: (1) the antenna array and (2) the Tx power amplifier (PA) in the front-end-modules (FEMs). Simulations using an electromagnetic (EM) solver carried out in Cadence AWR with AXIEM suggest that for two uniform square patch antenna arrays at 24 GHz, the 4 element array has ~6 dB lower antenna gain and twice the half power beam width (HPBW) compared to the 16 element array. We also present measurements and post-layout parasitic-extracted (PEX) EM simulation data taken on two broadband mm-Wave PAs designed in our lab that cover the key portions of the fifth-generation (5G) FR2-band (i.e., 24.25–52.6 GHz) that lies between the super-high-frequency (SHF, i.e., 3–30 GHz) band and the extremely-high-frequency (EHF, i.e., 30–300 GHz) band: one designed in a 22 nm fully depleted silicon on insulator (FD-SOI) CMOS process, and the other in an advanced 40 nm Gallium Nitride (GaN) high-electron-mobility transistor (HEMT) process. The FD-SOI PA achieves saturated output power (P_OUT,SAT) of ~14 dBm and peak power-added efficiency (PAE) of ~20% with ~14 dB of gain and 3 dB bandwidth (BW) from ~19.1 to 46.5 GHz in measurement, while the GaN PA achieves measured P_OUT,SAT of ~24 dBm and peak PAE of ~20% with ~20 dB gain and 3 dB BW from ~19.9 to 35.2 GHz. The PAs’ measured data are in good agreement with the PEX EM simulated data, and 3rd Watt-level GaN PA design data are also presented, but with simulated PEX EM data only. Assuming each antenna element will be driven by one FEM and each phased array targets the same 65 dBm EIRP, millimeter wave (mm-Wave) antenna arrays using the Watt-level GaN PAs and FEMs are expected to achieve roughly 2× wider HPBW with 4× reduction in the array size compared with the arrays using Si FEMs, which shall alleviate the thorny mm-Wave line-of-sight (LOS)-blocking problems significantly. Full article

The fuel economy of electric vehicles (EVs) is an important factor in determining the competitiveness of EVs. Since the fuel economy is affected by the efficiency of an e-powertrain composed of a motor and inverter, it is necessary to select a high-efficiency topology for the e-powertrain. In this paper, a novel topology of e-powertrains to improve the fuel economy of EVs is proposed. The proposed topology aims to improve the system efficiency by integrating open-end winding (OEW) and winding changeover (WC). The proposed OEW-PMSM with WC enables to drive a permanent magnet synchronous motor (PMSM) in four different modes. Each mode can increase inverter efficiency and motor efficiency by changing motor parameters and maximum modulation index. In this paper, the system efficiency of the proposed topology was evaluated using electromagnetic finite element analysis and a loss model of power semiconductors. In addition, the vehicle simulations were performed to evaluate the fuel economy of the proposed topology, thereby proving the superiority of the proposed topology compared with the conventional PMSM. Full article

This paper presents an integrated passive device (IPD) structure based on fan-out wafer-level packaging (FOWLP) for the front end of frequency-modulated continuous wave (FMCW) radar systems, focusing on enhancing the integration efficiency and performance of large passive components like antennas. Additionally, a new metric is introduced to assess this structure’s effect on the average noise figure in FMCW systems. Using this metric as a loss function, we apply the support vector machine (SVM) for electromagnetic simulation and the genetic algorithm (GA) for optimization. The sample fitting variance is 2.42 dB, reducing computation time from 12 min to under 1 millisecond, with the entire optimization completed in less than 100 s. The optimized IPD structure is 0.7 × 0.9 × 0.014 ${{\lambda }_0}^3$ in size and achieves over 35 dB isolation between the transmitter and receiver. Compared to the IPD model calculated by empirical formulas, the optimized device lowers the average noise figure by 15.2 dB and increases maximum gain by 4.19 dB. Full article

During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations. Full article

Indoor Optical Wireless Communication (OWC) provides a promising solution for high-capacity, low-latency, and electromagnetic interference-resistant wireless communication. Over the past decade, there has been extensive research addressing key challenges in indoor OWC. This article provides an overview of the current development status, key technologies, and challenges faced in the field of indoor OWC. Furthermore, at the end of this overview, an experimental demonstration of an indoor non-line-of-sight (NLOS) OWC system utilizing a spatial light modulator (SLM) for beam steering is demonstrated, which is expected to inspire research on related technologies. Full article

Background and Objectives: Irritable bowel syndrome (IBS) is an invasive and potentially disabling syndrome characterized by a multitude of symptoms capable of reducing the quality of life of patients. Among the most disabling symptoms of IBS is certainly physical pain, which manifests itself mainly at the abdominal level but can also appear in other areas of the body, particularly in the form of chronic low-back pain (CLBP). Among the non-invasive methods of treating organ-specific pathologies and organ-related musculoskeletal problems, the use of Bioresonance Therapy (BT)—based on the administration of self-modulating Extremely Low-Frequency Electromagnetic Fields, capable of determining a rebalance of bio-electrical and metabolic activity in the presence of various functional alterations—is currently gaining acceptance. Therefore, we decided to monitor results obtained from patients suffering from IBS and CLBP subjected to a cycle of treatments with BT. Materials and Methods: We monitored 20 patients (12 women and 8 men, average age of 51 years) suffering from CLBP and other visceral symptoms related to IBS. Patients were monitored through the use of the Bristol Stool Form Scale (BSFS), the Fecal Calprotectin test and the Short-Form Health Survey 36 (SF-36), collected before (T0) and after (T1) the execution of the cycle of treatments. They undertook a treatment protocol consisting of eight sessions of BT carried out over about a month. Results: At the end of the treatments with BT, it was possible to observe a general and significant improvement in all the parameters observed, as well as a close inversely proportional correlation between the Calprotectin values detected and the quality of life experienced by the patients in relation to their perceived IBS symptoms. Conclusions: Overall, our pilot study would seem to suggest a potential beneficial effect of BT in modulating organic and musculoskeletal symptoms derived from IBS. Full article

In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the Hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&C signals with signal-to-noise ratios (SNRs) of −15 dB and above (mAP@0.5 > 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods. Full article

In the complex battlefield electromagnetic environment, multiple jamming signals can enter the radar receiver simultaneously due to the development of jammers and modulation technology. The received compound jamming signals aggravate the difficulty of recognition and subsequent counter-countermeasure. In the face of strong overlapping signals and unseen jamming signal combinations, the performance of existing recognition methods usually seriously degrades. In this paper, an end-to-end multi-label classification framework combining a complex-valued convolutional neural network (CV-CNN) and jamming class representations is proposed to automatically recognize the jamming signal components of compound jamming signals. A basic multi-label CV-CNN (ML-CV-CNN) is first designed to directly process time–domain complex signals and fully retain jamming signal information. Then, the jamming class representations are generated using prototype clustering implemented by learning vector quantization, and they are fused with the ML-CV-CNN using class decoupling implemented by the attention mechanism to construct a multi-label class representation CV-CNN (ML-CR-CV-CNN), which can better learn the class-related features required for recognition. Finally, an adaptive threshold calibration is adopted to obtain optimal recognition results by multi-threshold discrimination. Simulation results verify that the proposed method has superior recognition performance, which is reflected in the strong robustness to the varying jamming-to-noise ratio (JNR) and power ratio, faster convergence speed with high JNRs, and better generalization for unseen jamming signal combinations. Full article