Search Results (196)

This paper proposes a compact, broadband, and low-profile filtering antenna designed for Sub-6 GHz communication. By applying characteristic mode analysis to the radiating elements, the operational mechanism of the antenna is clearly elucidated. The current cancellation among different radiating elements results in two radiation nulls in the primary radiation direction, effectively enhancing the filtering effect. The antenna achieves a wide operational bandwidth ($S_{11}\le -10$ dB) of 35.9% (4.3–6.4 GHz), making it highly suitable for Sub-6 GHz communication systems. Despite its compact size of 25 × 25 mm², the antenna consistently maintains stable broadside radiation patterns, with a peak gain of 6.14 dBi and a minimal gain fluctuation of less than 1 dBi at 4.6–6.45 GHz. This design ensures reliable and robust communication performance for V2V systems operating in the designated frequency band. Full article

The large-scale integration of new energy is an inevitable trend to achieve the low-carbon transformation of power systems. However, the strong randomness of wind power, photovoltaic power, and loads poses severe challenges to the safe and stable operation of systems. Existing studies demonstrate insufficient integration and handling of source-load bilateral uncertainties in wind–solar–fossil fuel storage complementary systems, resulting in difficulties in balancing economy and low-carbon performance in their energy storage configuration. To address this insufficiency, this study proposes an optimal energy storage configuration method considering source-load uncertainties. Firstly, a deterministic bi-level model is constructed: the upper level aims to minimize the comprehensive cost of the system to determine the energy storage capacity and power, and the lower level aims to minimize the system operation cost to solve the optimal scheduling scheme. Then, wind and solar output, as well as loads, are treated as fuzzy variables based on fuzzy chance constraints, and uncertainty constraints are transformed using clear equivalence class processing to establish a bi-level optimization model that considers uncertainties. A differential evolution algorithm and CPLEX are used for solving the upper and lower levels, respectively. Simulation verification in a certain region shows that the proposed method reduces comprehensive cost by 8.9%, operation cost by 10.3%, the curtailment rate of wind and solar energy by 8.92%, and carbon emissions by 3.51%, which significantly improves the economy and low-carbon performance of the system and provides a reference for the future planning and operation of energy systems. Full article

This study presents the design, fabrication, and performance assessment of a novel, small-scale (30–70 W), hybrid ocean energy system that captures energy from wave-induced heave motion using a point-absorber buoy and from ocean currents via a vertical axis water turbine (VAWT). Key innovations include a custom designed and built dual-rotor generator that accepts independent mechanical input from both subsystems without requiring complex mechanical coupling and a bi-directional mechanical motion rectifier with an overdrive. Numerical simulations using ANSYS AQWA (2024R2) and QBLADE(2.0.4) guided the design optimization of the buoy and turbine, respectively. Wave resource assessment for the Khobar coastline, Saudi Arabia, was conducted using both historical data and field measurements. The prototype was designed and built using readily available 3D-printed components, ensuring cost-effective construction. This mechanically simple system was tested in both laboratory and outdoor conditions. Results showed reliable operation and stable power generation under simultaneous wave and current input. The performance is comparable to that of existing hybrid ocean wave–current energy converters that employ more complex flywheel or dual degree-of-freedom systems. This work provides a validated pathway for low-cost, compact, and modular hybrid ocean energy systems suited for remote coastal applications or distributed marine sensing platforms. Full article

In this study, we propose a novel wideband aperture-coupled magneto-electric (ME) dipole antenna that achieves enhanced bandwidth by simultaneously leveraging ME resonance and aperture-coupled excitation. Building upon the conventional ME dipole architecture, the antenna integrates a pair of horizontal metal patches forming the electric dipole and a pair of vertical metal patches forming the magnetic dipole. A key innovation is the aperture-coupled feeding mechanism, where electromagnetic energy is transferred from a tapered microstrip line to the dipole structure through a slot etched in the ground plane. This design not only excites the characteristic ME resonances effectively but also significantly improves impedance matching, delivering a markedly broader impedance bandwidth. To validate the proposed concept, a prototype antenna was fabricated and experimentally characterized. Measurements show an impedance bandwidth of 84.48% (3.61–8.89 GHz) for $S_{11}$ ≤ −10 dB and a maximum in-band gain of 7.88 dBi. The antenna also maintains a stable, unidirectional radiation pattern across the operating band, confirming its potential for wideband applications such as 5G wireless communications. Full article

The efficient electrochemical reduction of carbon dioxide to formate under mild conditions is a promising approach to mitigate the energy crisis, but requires the use of high-performance catalysts. The selectivity and activity of catalysts can be enhanced through multi-metal doping, further advancing the electrochemical reduction of CO₂ to formate. This study demonstrates a co-electrodeposition strategy for synthesizing SnBi electrocatalysts on pretreated copper foam substrates, systematically evaluating how the Sn²⁺/Bi³⁺ molar ratio in the electrodeposition solution and the applied current density affect the catalytic performance for CO₂-to-formate conversion. Optimal performance was achieved with a molar ratio of Sn²⁺ to Bi³⁺ of 1:0.5 and a deposition current density of 3 mA cm⁻², resulting in a formate Faradaic efficiency (FE_formate) of 97.80% at −1.12 V (vs. RHE) and a formate current density of 26.9 mA·cm⁻². Furthermore, the Sn₁Bi_0.50-3 mA·cm⁻² electrode demonstrated stable operation at the specified potential for 9 h, maintaining a FE_formate above 90%. Compared to previously reported metal catalysts, the SnBi catalytic electrode exhibits superior performance for the electrochemical reduction of CO₂ to HCOOH. The study highlights the significant impact of the metal ion molar ratio and deposition current density in the electrodeposition process on the characteristics and catalytic performance of the electrode. Full article

To address the growing demand for industrial software in the digital transformation of small and medium-sized enterprises (SMEs) in the manufacturing sector, and to ensure the stable integration and operation of multi-source heterogeneous industrial software under complex conditions—such as heterogeneous compatibility, component dependencies, and uncertainty disturbances—this study established a comprehensive evaluation index system for service composition and optimal selection (SCOS). The system incorporated key criteria including service time, service cost, service reputation, service delivery quality, and availability. Based on this, a bi-objective SCOS model was established with the goal of maximizing both quality of service (QoS) and availability. To efficiently solve the proposed model, a hybrid enhanced multi-objective Gray Wolf Optimizer (HEMOGWO) was developed. This algorithm integrated Tent chaotic mapping and a Levy flight-enhanced differential evolution (DE) strategy. Extensive experiments were conducted, including performance evaluation on 17 benchmark functions and case studies involving nine industrial software integration scenarios of varying scales. Comparative results against state-of-the-art, multi-objective, optimization algorithms—such as MOGWO, MOEA/D_DE, MOPSO, and NSGA-III—demonstrate the effectiveness and feasibility of the proposed approach. Full article

In low-detectability application scenarios such as covert reconnaissance, wildlife behavior observation, and battlefield detection, antennas not only need to have wideband performance but also require good biomimetic camouflage characteristics. To address this issue, this article proposes a leaf-shaped biomimetic flexible wideband antenna. The design concept of the antenna is inspired by the symmetrical vein structure of aquifoliaceae leaves, incorporating vein-like slots into the radiation patch to form multiple inter-slot capacitances, which improves the high-frequency resonance behavior and expands the antenna’s operating bandwidth. In addition, the traditional rectangular grounding plane is replaced with a semi-elliptical shape, optimizing the electric field distribution between the feed line and the radiation part, thereby improving impedance matching. The measured results show that the leaf-shaped antenna achieves a relative bandwidth of 100% (2.4 GHz–7.1 GHz), with its operating frequency bands covering several common communication bands such as n41, n78, n79, and ISM 5.8 GHz, with a maximum gain of 5.4 dBi. Additionally, the leaf-shaped antenna has a good resemblance to the shape of aquifoliaceae leaves. The antenna’s performance remains relatively stable with bending radii of 40 mm, 50 mm, and 60 mm, demonstrating an important role in camouflage application scenarios. Full article

In this study, a novel anode-supported solid oxide fuel cell (SOFC) comprising a Bi₂O₃-doped NiO-ScSZ anode and an ScSZ electrolyte was successfully fabricated via a low-temperature co-sintering process at 1300 °C. The incorporation of 3 wt% Bi₂O₃ effectively promoted the sintering of both the anode support and electrolyte layer, resulting in a dense, gas-tight electrolyte and a mechanically robust porous anode support. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses confirmed the formation of phase-pure, highly crystalline ScSZ with an optimized microstructure. Electrochemical performance measurements demonstrated that the fabricated cells achieved excellent power density, reaching a peak value of 0.861 W cm⁻² at 800 °C under humidified hydrogen fuel conditions. The cells maintained stable performance under dry methane operation, with a maximum power density of 0.624 W cm⁻² at 800 °C, indicating resistance to carbon deposition. Gas chromatographic analyses further revealed that the Bi₂O₃-doped NiO-ScSZ anode facilitated earlier and more stable electrochemical oxidation of methane-derived species compared with the conventional NiO-YSZ system, even under conditions of an elevated methane partial pressure. These findings demonstrate that Bi₂O₃ co-doping, combined with low-temperature co-sintering, provides an effective approach for fabricating high-performance intermediate-temperature SOFCs with enhanced structural integrity and electrochemical stability. The developed methodology presents a promising pathway toward achieving cost-effective and durable SOFC technologies. Full article

In order to enhance the management and application of fault knowledge within intelligent production lines, thereby increasing the efficiency of fault diagnosis and ensuring the stable and reliable operation of these systems, we propose a fault diagnosis methodology that leverages knowledge graphs. First, we designed an ontology model for fault knowledge by integrating textual features from various components of the production line with expert insights. Second, we employed the ALBERT–BiLSTM–Attention–CRF model to achieve named entity and relationship recognition for faults in intelligent production lines. The introduction of the ALBERT model resulted in a 7.3% improvement in the F₁ score compared to the BiLSTM–CRF model. Additionally, incorporating the attention mechanism in relationship extraction led to a 7.37% increase in the F₁ score. Finally, we utilized the Neo4j graph database to facilitate the storage and visualization of fault knowledge, validating the effectiveness of our proposed method through a case study on fault diagnosis in CNC machining centers. The research findings indicate that this method excels in recognizing textual entities and relationships related to faults in intelligent production lines, effectively leveraging prior knowledge of faults across various components and elucidating their causes. This approach provides maintenance personnel with an intuitive tool for fault diagnosis and decision support, thereby enhancing diagnostic accuracy and efficiency. Full article

Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). However, conventional solutions are deficient in two aspects. Firstly, they lack the necessary feature information. Secondly, they are incapable of performing open-circuit fault diagnosis of the modular multilevel converter with the requisite degree of accuracy. To solve this problem, an intelligent diagnosis method is proposed to integrate the modal time–frequency diagram and FFT-CNN-BiGRU-Attention. By selecting the phase current and bridge arm voltage as the core fault parameters, the particle swarm algorithm is used to optimize the Variational Modal Decomposition parameters, and the fault signal is decomposed and reconstructed into sensitive feature components. The reconstructed signals are further transformed into modal time–frequency diagrams via continuous wavelet transform to fully retain the time–frequency domain features. In the model construction stage, the frequency–domain features are first extracted using the fast Fourier transform (FFT), and the local patterns are captured through a combination with a convolutional neural network; subsequently, the timing correlations are analyzed using bidirectional gated loop cells, and the Attention Mechanism is introduced to strengthen the key features. Simulations show that the proposed method achieves 98.63% accuracy in locating faulty insulated gate bipolar transistors (IGBTs) in the sub-module, with second-level real-time response capability. Compared with the recently published scheme, it maintains stable performance under complex working conditions such as noise interference and data imbalances, showing stronger robustness and practical value. This study provides a new idea for the intelligent operation and maintenance of power electronic devices, which can be extended to the fault diagnosis of other power equipment in the future. Full article

Insulator defect detection is important in ensuring power systems’ safety and stable operation. To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extraction from high-entropy details to low-entropy semantics, an improved YOLOv8 target detection network for insulator defects based on bidirectional weighted feature fusion was proposed. A C2f_DSC feature extraction module was designed to identify more insulator tube features, an EMA (encoder–modulator–attention) mechanism and a BiFPN (bidirectional weighted feature pyramid network) fusion layer in the backbone network were introduced to extract different features in complex environments, and EIOU (efficient intersection over union) as the model’s loss function was used to accelerate model convergence. The CPLID (China Power Line Insulator Dataset) was tested to verify the effectiveness of the proposed algorithm. The results show its model size is only 6.40 M, and the mean accuracy on the CPLID dataset reaches 98.6%, 0.8% higher than that of the YOLOv8n. Compared with other lightweight models, such as YOLOv8s, YOLOv6, YOLOv5s, and YOLOv3Tiny, not only is the model size reduced, but also the accuracy is effectively improved with the proposed algorithm, demonstrating excellent practicality and feasibility for edge devices. Full article

Aiming at the difficult problem of predicting the running state of the rotor of a Control Rod Drive Mechanism (CRDM) in a floating nuclear reactor, this paper proposes a Remaining Useful Life (RUL) prediction method based on Variational Mode Decomposition and Bidirectional Long Short-Term Memory (VMD-BiLSTM). Firstly, a bench experiment of the CRDM is carried out to collect the full operational cycle (full-stroke) vibration signals of the CRDM. Secondly, the collected data are decomposed based on the VMD, and the typical vibration signals at different stages of the experiment are used to verify this method and comprehensively mine the degradation characteristics. At the same time, the time-frequency domain feature analysis is carried out on the original vibration data, and the changing trends of the extracted features are carefully analyzed. Five feature quantities closely related to the degradation trend of the rotor of the CRDM are screened out, and the corresponding health indicators are constructed in combination with the stroke. Finally, the life prediction of the rotor of the CRDM is realized through the BiLSTM method. Then, the comparison experiments with other methods are carried out, and the experimental results show that the method proposed in this paper has high accuracy and reliability and can effectively solve the RUL prediction problem of CRDM, which provides a strong support to ensure the safe and stable operation of floating nuclear reactors. Full article

In the rapidly advancing digital era, the demand for miniaturized and high-performance electronic devices is increasing, particularly in applications such as wireless communication, unmanned aerial vehicles, and healthcare devices. Radio-frequency microelectromechanical systems (RF MEMS), particularly RF MEMS switches, play a crucial role in enhancing RF performance by providing low-loss, high-isolation switching and precise signal path control in reconfigurable RF front-end systems. Among different configurations, electrothermally actuated bistable lateral RF MEMS switches are preferred for their energy efficiency, requiring power only during transitions. This paper presents a novel approach to improve the RF performance of such a switch through structural modifications and machine learning (ML)-driven optimization. To enable efficient high-frequency operation, the H-clamp structure was re-engineered into various lateral configurations, among which the I-clamp exhibited superior RF characteristics. The proposed I-clamp switch was optimized using an eXtreme Gradient Boost (XGBoost) ML model to predict optimal design parameters while significantly reducing the computational overhead of conventional EM simulations. Activation functions were employed within the ML model to improve the accuracy of predicting optimal design parameters by capturing complex nonlinear relationships. The proposed methodology reduced design time by 87.7%, with the optimized I-clamp switch achieving −0.8 dB insertion loss and −70 dB isolation at 10 GHz. Full article

Background: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfunctioning, so its timely fault prediction and accurate diagnosis are imperative. Traditional fault detection methods rely on manual feature extraction, limiting their ability to identify complex faults, and deep learning methods suffer from unstable recognition accuracy. To address these issues, a three-class fault detection method for water pumps based on a convolutional neural network, transformer, and bidirectional gated recurrent unit (CNN-transformer-BiGRU) is proposed here. Methods: It first uses the continuous wavelet transform to convert one-dimensional vibration signals into time–frequency images for input into a CNN to extract the time-domain and frequency-domain features. Next, the transformer enhances the model’s hierarchical learning ability. Finally, the BiGRU captures the forward/backward feature information in the signal sequence. Results: The experimental results show that this method’s accuracy in fault detection is 91.43%, significantly outperforming traditional machine learning models. Using it improved the accuracy, precision, and recall by 1.86%, 1.97%, and 1.86%, respectively, relative to the convolutional neural network and long short-term memory (CNN-LSTM) model. Conclusions: Hence, the proposed model has superior performance indicators. Applying it to aquaculture systems can effectively ensure their stable operation. Full article

This work presents a novel dual-polarized antenna array tailored for Internet of Things (IoT) applications, specifically designed to operate in the millimeter-wave (mm-wave) spectrum within the frequency range of 30–60 GHz. Leveraging printed ridge gap waveguide (PRGW) technology, the antenna ensures robust performance by eliminating parasitic radiation from the feed network, thus significantly enhancing the reliability and efficiency required by IoT communication systems, particularly for smart cities, autonomous vehicles, and high-speed sensor networks. The proposed antenna achieves superior radiation characteristics through a cross-shaped magneto-electric (ME) dipole backed by an artificial magnetic conductor (AMC) cavity and electromagnetic bandgap (EBG) structures. These features suppress surface waves, reduce edge diffraction, and minimize back-lobe emissions, enabling stable, high-quality IoT connectivity. The antenna demonstrates a wide impedance bandwidth of 24% centered at 30 GHz and exceptional isolation exceeding 40 dB, ensuring interference-free dual-polarized operation crucial for densely populated IoT environments. Fabrication and testing validate the design, consistently achieving a gain of approximately 13.88 dBi across the operational bandwidth. The antenna’s performance effectively addresses the critical requirements of emerging IoT systems, including ultra-high data throughput, reduced latency, and robust wireless connectivity, essential for real-time applications such as healthcare monitoring, vehicular communication, and smart infrastructure. Full article