Search Results (125)

Micro-motors are widely used in industrial applications, which require effective fault diagnosis to maintain safe equipment operation. However, fault signals from micro-motors often exhibit weak signal strength and ambiguous features. To address these challenges, this study proposes a novel fault diagnosis method. Initially, the Jump plus AM-FM Mode Decomposition (JMD) technique was utilized to decompose the measured signals into amplitude-modulated–frequency-modulated (AM-FM) oscillation components and discontinuous (jump) components. The proposed process extracts valuable fault features and integrates them into a new time-domain signal, while also suppressing modal aliasing. Subsequently, a novel Global Relationship Matrix (GRM) is employed to transform one-dimensional signals into two-dimensional images, thereby enhancing the representation of fault features. These images are then input into an Optimized Convolutional Neural Network (OCNN) with an AdamW optimizer, which effectively reduces overfitting during training. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy rate of 99.0476% for multiple fault types, outperforming four comparative methods. This approach offers a reliable solution for quality inspection of micro-motors in a manufacturing environment. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

A window-embedded broadband vehicle-mounted antenna for frequency modulation (FM) broadcast application is proposed. Antenna miniaturization at sub-gigahertz frequencies remains challenging due to the inherently long wavelengths, which impose strict constraints on compactness, bandwidth, and structural weight. A promising strategy to alleviate this problem is to use the vehicle itself as an effective radiator to enhance the bandwidth and maintain good radiation performance. In this work, the potentialities of the radiation patterns offered by the vehicle are analyzed by using the characteristic mode theory (CMT). A compact T-shape coupling element, with dimensions of 0.2λ₀ × 0.08λ₀ × 0.01λ₀, is employed to simultaneously excite multiple significant characteristic modes, thereby broadening the operating band. Both simulated and measured results validate that the proposed antenna can cover the FM broadcast operating band from 87 MHz to 108 MHz, with the 1:10 scaled prototype achieving a maximum measured gain of 7.4 dBi at 950 MHz. The proposed antenna and design strategy have advantages in radio broadcasting, radio navigation, and military and law enforcement communication systems for its low-cost, compact, and easy conformal structure. Full article

With the continuous development of the robotics industry, using a single wireless system to charge different types of robots has become a critical issue that urgently needs to be addressed. To solve this problem, in the present work, we propose a single-switch inverter module wireless charging system based on parallel module number frequency modulation to achieve the expected variable voltage output by adjusting the operating frequency and the number of parallel modules, thereby enhancing the interoperability between devices. To meet the charging requirements of lithium batteries, which require constant current (CC) first and constant voltage (CV) thereafter, we first discuss how to implement CC and CV charging modes, then demonstrate that the proposed system can provide the required CC and CV output under various load conditions. Subsequently, a simplified equivalent circuit model to achieve this wireless charging system is proposed and an exact expression for its equivalent input voltage source is provided. Subsequently, based on the analysis of the amplitude–frequency characteristics of voltage gain under the CV mode, we propose the relevant method and program to realize this variable output system, and specifically build a prototype system based on a three-module parallel configuration. Experimental results show that the present prototype system can indeed provide the constant current (CC) and constant voltage (CV) outputs required for lithium battery charging, and the expected variable voltage output achieved by frequency modulation (FM) is verified. Its maximum efficiency can approach 91.3%. Compared with other wireless charging systems with single-switch inverters, this prototype experimental system possesses significant advantages in completing the full charging process of lithium batteries, maintaining stable voltage output during the constant voltage phase, and enabling flexible multi-voltage output. Full article

Accurate segmentation of lesion regions is essential for skin cancer diagnosis. As dermoscopic images of skin lesions demonstrate different sizes, diverse shapes, fuzzy boundaries, and so on, accurate segmentation still faces great challenges. To address these issues, we propose a new dermatologic image segmentation network, FFM-Net. In FFM-Net, we design a new FM block encoder based on state space models (SSMs), which integrates a low-frequency information extraction module (LEM) and an edge detail extraction module (EEM) to extract broader overall structural information and more accurate edge detail information, respectively. At the same time, we dynamically adjust the input channel ratios of the two module branches at different stages of our network, so that the model can learn the correlation relationship between the overall structure and edge detail features more effectively. Furthermore, we designed the cross-channel spatial attention (CCSA) module to improve the model’s sensitivity to channel and spatial dimensions. We deploy a multi-level feature fusion module (MFFM) at the bottleneck layer to aggregate rich multi-scale contextual representations. Finally, we conducted extensive experiments on three publicly available skin lesion segmentation datasets, ISIC2017, ISIC2018, and PH2, and the experimental results show that the FFM-Net model outperforms most existing skin lesion segmentation methods. Full article

The high proportion of wind power access makes the system frequency regulation face serious challenges, and the time delay of wind turbine FM response exacerbates the frequency security problem. For this reason, this paper proposes a joint dispatch model for power grid and wind farms considering frequency modulation delay. First, the wind turbine response characteristics and frequency safety constraints are derived by equivalently modeling the wind turbine FM delay. Second, power grid-wind farm joint dispatch model is constructed on this basis, where the system level optimizes the operation cost under the premise of satisfying the frequency safety constraints, and the wind farm level tracks the wind power output target issued by the system to meet the FM demand. Finally, by the case study, Scenario 1 reduces average frequency nadir deviation from 0.205 Hz to 0.098 Hz and RoCoF from 0.216 Hz/s to 0.168 Hz/s in the IEEE-39 system. The stability of the system is enhanced, which verifies the effectiveness of the proposed method. Full article

For the requirements of frequency modulation (FM) broadcasting communication in open-sea areas (resonant frequency 98 MHz), ground-based station signals are difficult to cover, and the large size of rigid buoy antennas limits the number that vessels can carry. To address this issue, this paper designs a flexible buoy antenna, which achieves a volume compression ratio of 88% in the folded state. The antenna is a vertical monopole type, with a center frequency of 98 MHz and dimensions of 879 mm × 120 mm. Simulation results show that S₁₁ remains below −10 dB across 90–105 MHz, reaching a minimum of −30 dB. Measurement results demonstrate that within the 88–107 MHz band, S₁₁ is below −10 dB, with the minimum value increasing in magnitude to −24 dB. The measured center frequency achieves S₁₁ = −21 dB, and the VSWR remains below 3 across the entire operating frequency band, meeting the impedance matching requirements of marine broadcasting systems. On this basis, we further conduct simulations under seawater loading. The results show that seawater induces a resonance down-shift and efficiency degradation; however, by adjusting the radiator length L, the resonance and matching can be restored to the target band while maintaining VSWR ≤ 3 and stable in-band radiation, thereby enabling a rapid, single-parameter, engineering-oriented retuning. Full article

Auxiliary frequency modulation (FM) for coal-fired units has been recognized as a promising approach through multiple batteries, which is due to their rapid charging and discharging characteristics. However, long-period engineering application needs continuous optimization of operational strategies to resist the decay characteristics of the battery, which greatly increases the difficulty of promotion. Hence, two replacement strategies of the battery were first proposed in this work, and they are characterized by simple operation. To test their feasibility, a lead–acid battery was selected as one study example, and the corresponding relationship between the duration day and the replacement scheme was emphatically analyzed, according to the AGC instruction and the self-adjustment capacity of coal-fired units. Results showed that the replacement capacity of the battery is nearly linear in the duration day, while the difference from the discharge depth is negligible in this study. In addition, the capacity ratio of 1.3 to 5 is considered to have the best application potential because of the same duration days between old and new batteries. The commutative replacement can immortally extend the duration day, and obviously, the replacement process of old and new batteries always maintains that two battery groups work. Conclusively, the case analysis for two replacement strategies showed that they deeply lowered the initial capacity of the battery, which can reduce the investment costs. In a word, two replacement strategies for the battery proposed in this study provide a reference for the economic evaluation and optimization of battery use for auxiliary FM. Full article

Background: Respiratory rate (RR) is a key vital sign and one of the most sensitive indicators of physiological conditions, playing a crucial role in the early identification of clinical deterioration. The monitoring of RR using electrocardiography (ECG) and photoplethysmography (PPG) aims to overcome limitations of traditional methods in clinical settings. Methods: The proposed approach extracts RR from ECG and PPG signals using different morphological and temporal features from publicly available datasets (iAMwell and Capnobase). The algorithm was used to develop and test with a selection of relevant ECG (e.g., R-peak, QRS area, and QRS slope) and PPG (amplitude and frequency modulation) characteristics. Results: The results show promising performance, with the ECG-derived signal using the R-peak–based method yielding the lowest error, with a mean absolute error of 0.99 breaths/min in the iAMwell dataset and 3.07 breaths/min in the Capnobase dataset. In comparison, the RR PPG-derived signal showed higher errors of 5.10 breaths/min in the iAMwell dataset and 10.66 breaths/min in the Capnobase dataset, for the FM and AM method, respectively. Bland–Altman analysis revealed a small negative bias, approximately −0.97 breaths/min for the iAMwell dataset (with limits of agreement from −2.62 to 0.95) and −1.16 breaths/min for the Capnobase dataset (limits of agreement from −3.37 to 1.10) in the intra-subject analysis. In the inter-subject analysis, the bias was −0.84 breaths/min (limits of agreement from −1.76 to 0.20) for iAMwell and −1.22 breaths/min (limits of agreement from −7.91 to 5.35) for Capnobase, indicating a slight underestimation. Conversely, the PPG-derived signal tended to overestimate RR, resulting in higher variability and reduced accuracy. These findings highlight the higher reliability of ECG-derived features for RR estimation in the analyzed datasets. Conclusion: This study suggests that the proposed approach could guide the design of cost-effective, non-invasive methods for continuous respiration monitoring, offering a reliable tool for detecting conditions like stress, anxiety, and sleep disorders. Full article

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

Infrared small target detection (IRSTD) aims to locate and separate targets from complex backgrounds. The challenges in IRSTD primarily come from extremely sparse target features and strong background clutter interference. However, existing methods typically perform discrimination directly on the features extracted by deep networks, neglecting the distinct characteristics of weak and small targets in the frequency domain, thereby limiting the improvement of detection capability. In this paper, we propose a frequency-aware masked-attention network (FM-Net) that leverages multi-scale frequency clues to assist in representing global context and suppressing noise interference. Specifically, we design the wavelet residual block (WRB) to extract multi-scale spatial and frequency features, which introduces a wavelet pyramid as the intermediate layer of the residual block. Then, to perceive global information on the long-range skip connections, a frequency-modulation masked-attention module (FMM) is used to interact with multi-layer features from the encoder. FMM contains two crucial elements: (a) a mask attention (MA) mechanism for injecting broad contextual feature efficiently to promote full-level semantic correlation and focus on salient regions, and (b) a channel-wise frequency modulation module (CFM) for enhancing the most informative frequency components and suppressing useless ones. Extensive experiments on three benchmark datasets (e.g., SIRST, NUDT-SIRST, IRSTD-1k) demonstrate that FM-Net achieves superior detection performance. Full article

This article considers a fluxgate magnetometer (FM) that operates based on a new physical principle. The authors analyze how the alternating electric charge potential of a cylindrical metal electrode impacts the structure of a cylindrical permanent magnet made of composite-conducting ferrite. They demonstrate that this impact and permanent magnet structure initiate the emergence of polarons with oscillating magnetism. This causes significant changes in the entropy of indirect exchange and the related sublattice magnetism fluctuations that ultimately result in the generation of circularly polarized spin waves at the spin wave resonance frequency that are channeled and evolve in dielectric ferrite waveguides of the FM. It is demonstrated that these moving spin waves have an electrodynamic impact on the measuring FM coils on the macro-level and perform parametric modulation of the magnetic permeability of the waveguide material. This results in the respective variations of the changeable magnetic field, which is also registered by the measuring FM coils. The authors considered a generalized flow of the physical processes in the FM to obtain a detailed representation of the operating functions of the FM. The presented experimental results for the proposed FM in the field meter mode confirm its operating parameters (±40 μT—measurement range, 0.5 nT—detection threshold). The usage of a cylindrical metal electrode as a source of exciting electrical change instead of a conventional multiturn excitation coil can significantly reduce temperature drift, simplify production technology, and reduce the unit weight and size. Full article

Ultra-wide swath synthetic aperture radar (SAR) systems are of great significance for applications such as terrain measurement and ocean monitoring. In conventional SAR systems, targets echo from the near-range and far-range of an observed swath mutually overlap, and the blind ranges are caused by those that the radar cannot receive while it is transmitting. Therefore, the swath of conventional SAR systems is limited due to their range ambiguity as well as the transmitted pulse blockage. With the development of waveform diversity, range ambiguity can be suppressed by radar waveform design with a low-range sidelobe without increasing the system’s complexity when compared to the scan-on-receive (SCORE) based on digital beamforming (DBF) technique. Additionally, by optimizing the pulse repetition interval (PRI) variation strategy, the negative impact of blind range on imaging can be reduced. Based on these technologies, this paper proposes a theoretical architecture to achieve an ultra-wide swath SAR imaging system via chaotic frequency modulation (FM) signals and a random pulse repetition interval variation strategy without increasing the antenna area. By transmitting time-variant chaotic-FM signals, the interference between targets in the near range and far range can be reduced by the corresponding match filters. Furthermore, random pulse repetition intervals increase the irregularity and aperiodicity of the blind ranges to further improve the imaging quality. Simulation results demonstrate that the proposed wide-swath SAR system has better performance compared to classical SAR systems. Full article

Planetary gearbox vibrations exhibit complex amplitude modulation (AM) and frequency modulation (FM) characteristics. The spectral correlation (SC) can reveal the cyclostationarity of rotating machinery signals, but previous studies have modeled gearbox signals as impulse response signals rather than AM–FM signals. This paper presents a fault diagnosis method for planetary gearboxes via SC based on the AM–FM model. Firstly, the theoretical expression for the spectral correlation characteristics of AM-FM signals is derived, showing that their demodulation features consist of discrete and grouped points rather than continuous vertical lines, and demonstrating the capability of SC to reveal the cyclostationarity of AM–FM signals. Then, the theoretical SC characteristics of planetary gearbox vibration signals under gear localized fault conditions are derived in closed form, providing theoretical guidance for fault diagnosis. The theoretical derivations are validated experimentally, and the localized faults on the ring, sun, and planet gears are successfully diagnosed. Full article

For the double-sided inductor–capacitor–capacitor (DS-LCC) compensation topology, the parametric deviation of compensation elements results in the mismatch between the resonant frequency and operating frequency. Furthermore, this mismatch leads to the loss of the load-independent constant output characteristics. Therefore, an innovative design approach based on the reduction in the capacitance ratio is proposed to attain the load-independent constant current under the parametric deviation. With the presented method, simply by reducing the compensation capacitor ratio, the load-independent constant current output characteristics can be preserved, and fluctuations in the transmission gain caused by the parametric deviation are minimized. This implies that when the constant transmission gain is achieved by the frequency modulation (FM) control, the required FM range can be reduced. Finally, from the experimental results, in the load range of 3 Ω to 33 Ω, compared to the high capacitance ratio, the load-independent constant current characteristics can be maintained at the low capacitance ratio. In addition, without parametric deviation, the transmission efficiencies at different capacitance ratios are basically the same at 93.5% and 94.2%, respectively. However, the transmission efficiencies under different parametric deviations at the low capacitance ratio are 87.4% and 84.9%, but only 73.9% and 68.2% at the high capacitance ratio. Full article