This paper presents a comprehensive investigation into zero-refractive index materials (ZIMs) through the application of transmission lines modeled by their inductance-capacity (L-C) representation. Using the wave concept iterative procedure (WCIP) method, the study accurately simulates the behavior of ZIMs, demonstrating their unique ability to maintain consistent phase and amplitude of electromagnetic waves across a ZIM region. Our results show that ZIMs enhance the electromagnetic directivity of a source by 30% compared to conventional materials and facilitate seamless, reflection-free transitions between waveguides of varying sections. The simulation results of the electric field E for the narrow section waveguide align closely with theoretical expectations for ZIMs, showing less than 2% deviation. These quantitative findings validate the superior performance of ZIMs in maintaining wave coherence and improving directivity. When compared to existing materials, ZIMs offer a significant improvement in transmission efficiency, with a 25% reduction in signal loss. These advancements position ZIMs as a promising solution for applications in telecommunications, radar, and wireless transmission systems, outperforming current state-of-the-art technologies.

Traditional GNSS/INS (Global Navigation Satellite Systems/Inertial Navigation Systems) coupled navigation algorithms often struggle with accuracy in GNSS-denied or challenging environments. This paper introduces a novel adaptive fusion algorithm leveraging an Adaptive Neuro-Fuzzy Inference System (ANFIS) that dynamically adjusts sensor weightings based on real-time signal quality and system performance. The core innovation lies in the real-time integration of fuzzy logic and neural network learning, enabling the system to continuously adapt and optimize its decision-making rules for navigation accuracy. A comprehensive, dynamic error source model is constructed incorporating GNSS atmospheric delays, orbit errors, and INS drift to enhance the learning-driven weight adjustment mechanism. The resulting ANFIS-based fusion strategy shows significant superiority over traditional Kalman-based methods, achieving over 90% robustness across harsh scenarios with an average execution time of 0.69 seconds, demonstrating improved adaptability, learning capability, and fault resilience in dynamic environments.

In the context of long-distance detection and obstacle occlusion, the limited observation angle of electromagnetic imaging poses significant challenges for accurate reconstruction. To address this issue, we propose a hybrid electromagnetic reconstruction algorithm based on a generative adversarial network (GAN). This algorithm utilizes the diffraction tomography (DT) method to generate an initial image, which serves as input for the GAN. Through adversarial training between the generator and the discriminator, the algorithm produces a reconstructed image with enhanced accuracy. Firstly, unlike complete learning-based reconstruction methods that rely solely on scattering field data, our approach effectively integrates both scattering characteristics and a priori information from the DT image model, thus improving the accuracy and generalizability of the neural network. Secondly, compared to other linear approximation algorithms, the DT algorithm incorporates fast Fourier transform (FFT) to enhance computational efficiency. Thirdly, this study employs a Fourier spatial data extrapolation technique to mitigate the limitations of insufficient data and improve imaging fidelity. Numerical simulations demonstrate that even at a narrow observation angle of 900, the proposed algorithm exhibits excellent reconstruction performance and notable generalization ability.

Scholars have conducted extensive research on the anchoring methods and cable systems of suspension bridges, but there is relatively less research on the analysis of steel tower construction stages. As an important supporting part of the entire bridge, the stability of steel towers during installation directly affects the safety assurance during construction. In order to comprehensively understand the dynamic characteristics of steel towers during the construction stage, this paper presents a comprehensive analysis of the construction process of steel towers in large-span suspension bridges, focusing on the roles of active and cross braces in maintaining structural stability. Utilizing advanced finite element modelling techniques in both Midas Civil and ANSYS, the study evaluates the deformation and stress responses of the steel tower under various loading conditions, including self-weight and wind loads. The findings reveal critical insights into the maximum deformation behaviors and stress distributions at different construction stages, underscoring the importance of jacking operations and the strategic installation of braces. By comparing the performance of structures with and without these braces, the research demonstrates their essential role in enhancing the overall stability and safety of the tower during construction. Furthermore, considering the influence of wind loads and crane loads on the structure in Midas Civil, the paper analyzes the changes in structural strength and stiffness, providing useful references and guidance for this project and similar engineering endeavors.

To accurately characterize the erosion phenomenon of the armature in electromagnetic railgun launches, a two-dimensional magneto-thermal-mechanical coupling model for melt-wave was developed. For the first time, a fully implicit finite volume method was employed for equation discretization, and an alternating direction implicit method was used for coupling calculations to obtain both steady-state and transient erosion characteristics of the armature. The results demonstrate that the velocity skin effect concentrates significant current at the armature tail, driving the propagation of the melt-wave. The erosion rate remains constant initially but increases significantly when variations in electrical conductivity are considered. After applying an external current, the erosion distance increases sharply with current amplitude before leveling off, and changes in the duration of current amplitude also significantly influence the erosion distance. This study provides a clear understanding of the armature’s erosion behavior, offering a solid theoretical foundation for further research on armature transition phenomenon.

In the field of remote sensing, the process of segmentation and classification of satellite images is a challenging task attributable to different types of target detection. There are problems in recognizing a target and clutter region. Then, there is a necessity to consider these problems regarding the classification of satellite image using an effectual approach. In this approach, a deep learning dependent automated segmentation, detection, and classification of satellite images is carried with artificial intelligence methods. Initially, the input image is preprocessed, segmented using Edge-ROI and YOLO v3 based segmentation in which the parameter is tuned by means of multi-heuristic tuna swarm optimization (MH-TSO) approach and is classified using hybrid multivariate improved residual network (ResNet) and U-Net classifier approach. The stage of Edge-ROI segmentation and YOLO v3 based segmentation is employed to extract regions. The preprocessing is carried using median average filtering along with adaptive histogram equalization. A scheme of deep learning-based multivariate improved residual neural network for classification of satellite images is proposed effectively. The proposed technique performance is estimated for three kinds of dataset, namely Salinas, Pavia University, and Indian Pines satellite image datasets, and the results obtained are shown, which proves the efficiency of the suggested mechanism.

This paper presents an enhanced pre-tuned particle swarm optimization (PT-PSO) algorithm for fault compensation in conformal array antennas, addressing both complete element failures and faulty phase shifters. Unlike conventional PSO, which initializes particles randomly across the entire search space (often requiring more iterations and risking local minima), PT-PSO introduces a pre-tuning mechanism that arranges initial amplitudes and phases to guide convergence. Combined with non-dominated sorting, this approach improves multi-objective optimization efficiency by reducing the search space and minimizing local minima, enabling rapid convergence to near-optimal excitation weights. To validate its effectiveness, a 1x8 X-band cylindrical conformal patch array was designed and simulated in HFSS. Results show that PT-PSO successfully restores first sidelobe levels (FSLL) and peak gain under both complete and partial failure scenarios, ensuring accurate pattern recovery. Compared to non-dominated PSO and convex optimization, PT-PSO achieves similar pattern quality with significantly lower computational complexity. The proposed method is particularly applicable to radar and wireless communication systems, where maintaining beam integrity under hardware faults is critical.

This research employs finite element analysis for simulation and calculation to investigate the causes of tooth wear in coal mining machine transmission gears and how it affects gear transmission performance. Utilising Hertz theory, we calculated the maximum contact stress and Hertz half-width during the gear transmission process. A three-dimensional geometric model of the coal mining machine’s right rocker transmission system was created using Pro/E software, which was subsequently analyzed with ANSYS/LS-DYNA for gear meshing. The wear quantity on the gear tooth surface was determined using the Archard wear model, disregarding the effects of lubricant and gear tooth temperature rise. Our simulations revealed the wear distribution and changes in wear amount across various tooth surface wear models under different operating conditions. Notably, we found that wear quantity is directly correlated with tooth wear range and contact stress, with significant wear occurring at the top and root of the tooth. Furthermore, we conducted laser cladding remanufacturing experiments, optimizing process parameters to enhance wear resistance and fatigue strength. The microstructure of the remanufactured tooth face exhibited homogeneity and a lower friction factor than new surfaces. This study offers novel insights into the wear mechanisms of coal mining machine gears. It demonstrates the effectiveness of laser cladding technology in enhancing gear performance, providing practical implications for the design and maintenance of gear systems in harsh operating environments.

This work presents a quad-band metamaterial-loaded cavity-backed substrate integrated waveguide (SIW) MIMO antenna engineered for sub-6 GHz communication standards such as 5G and WLAN, as well as X-band applications. The use of a cavity-backed SIW architecture enables reduced fabrication complexity and straightforward integration with planar circuits, supporting compact and efficient antenna design. The antenna structure incorporates a modified rectangular split ring resonator (RSRR) slot along with an open-ended rectangular slot embedded within the SIW cavity-backed radiator. This configuration generates four resonant bands operating at 2.4, 3.3, 5.0, and 7.0 GHz. To enhance radiation characteristics, modified RSRR-based metamaterial unit cells are placed along the y-direction in front of the radiating elements. These cells contribute significantly to gain enhancement and enable circular polarization at the designated frequencies. The proposed antenna demonstrates realized gains of 5 dB, 8 dB, 6 dB, and 5 dB at the respective bands, supported by a consistent radiation efficiency of approximately 88%. The antenna also exhibits a stable unidirectional radiation pattern across all operating frequencies, making it suitable for directional multi-port MIMO configurations. To suppress inter-element interference, a cavity-backed parasitic structure is introduced, effectively reducing mutual coupling between radiators. Comprehensive MIMO performance analysis is carried out using standard metrics, including envelope correlation coefficient (ECC), total active reflection coefficient (TARC), channel capacity loss (CCL), and mean effective gain (MEG), confirming strong isolation and diversity capability. Experimental validation aligns closely with simulation results, establishing the proposed antenna’s reliability and potential for use in high-performance, multi-band wirelesssystems.

This study presents a novel pattern reconfigurable antenna structure designed for wireless communication systems. The proposed antenna operates at a center frequency of 7.2 GHz, which is particularly significant for 5G and future 6G communication systems. This frequency band is suitable for high-speed data transmission and enhances user experience by improving signal quality. The antenna can provide various beam steering capabilities, making it adaptable for multiple applications, including mobile communication systems configurations. The design methodology involves using a circularly grounded monopole antenna. The equivalent circuit models for the RF switches in both open and closed states are provided, facilitating the analysis of the antenna’s electrical characteristics. Simulation and measurement results demonstrate that the antenna maintains a reflection coefficient (|S11|) below -10 dB across a bandwidth of approximately 500 MHz, confirming its wideband performance. The findings indicate that the proposed antenna structure is not only efficient but also adaptable to various operational conditions, making it a promising candidate for future wireless communication applications.