Enhancing the vibration resistance of micro-electro-mechanical systems (MEMS) resonators in complex environments is a critical issue that urgently needs to be addressed. This paper presents a chip-scale locally resonant phononic crystal (LRPnC) plate based on a folded helical beam structure. Through finite element simulation and theoretical analysis, the bandgap characteristics and vibration suppression mechanisms of this structure were thoroughly investigated. The results show that the structure exhibits a complete bandgap in the frequency range of 9.867-14.605 kHz, and the bandgap can be effectively tuned by adjusting the structural parameters. Based on this, the influence of the number of unit cell layers on the vibration reduction performance was further studied, and a finite periodic LRPnC plate was constructed. Numerical studies have shown that the LRPnC plate can achieve more than -30 dB of vibration attenuation within the bandgap and effectively suppress y-direction coupling vibrations caused by x-direction propagating waves. In addition, its chip-scale size and planar structure design provide new ideas and methods for the engineering application of phononic crystal technology in the field of MEMS vibration isolation.

This paper presents a method for fabricating a low-cost, highly reproducible miniature optical fiber Fabry-Perot (FP) sensor based on a polydimethylsiloxane (PDMS) end-cap structure. The FP cavity end-cap is formed by the optical fiber end-face and a PDMS droplet deposited onto it. The PDMS deposition is achieved by immersing the fiber end into pre-cured PDMS at a fixed speed, a process requiring careful control of PDMS viscosity and surface tension. By leveraging PDMS’s excellent thermal expansion coefficient, Poisson’s ratio, and other parameters, this method achieves high reproducibility via viscosity-optimized pre-curing, enhanced sensitivity for temperature measurements, and significant cost reduction versus commercial counterparts. Fiber FP sensors are increasingly widely used in biomedical and precision detection fields owing to their significant advantages, including small size, light weight, high sensitivity, and immunity to electromagnetic interference. In the fabrication of fiber FP sensors, using polymer materials is an effective technical approach. These polymers can be applied as coatings on the optical fiber end-face or as interlayer materials embedded between fibers to form the FP cavity structure, which not only significantly improves the overall sensor performance, but also enhances its sensitivity to changes in temperature, pressure, and refractive index. In the final part of this study, we successfully validated the exceptional performance of the PDMS end-cap based fiber FP sensor in detecting different temperatures conditions. Experimental results demonstrate a temperature sensitivity of 0.752 nm/℃ for sensors with a 60-μm PDMS end-cap, further confirming the sensor’s reliability and efficiency in practical applications.

The precise acquisition of the quality characteristic parameters of large aircraft directly affects its performance characteristics. For large aircrafts such as missiles and rockets with internal fillings, traditional measurement methods involving large-angle tilting or rotation may pose safety risks. In light of the characteristics of large aircraft and in combination with existing measurement methods, we design a mass and centroid measurement method based on four-point support and small-angle tilting, and develop a set of mass and centroid testing system. This method obtains the intersection point of the gravity action line in the product coordinate system through coordinate transformation in two postures, thereby obtaining the three-dimensional centroid of the aircraft. We first elaborate on the principle of this method in detail, then introduce the composition of the equipment, and analyze the structural stress of key components. Finally, experimental verification and uncertainty analysis are carried out. Experimental verification shows that the maximum deviation of the mass measurement accuracy is less than 0.02%, the centroid measurement accuracy in the X direction is ±0.15 mm, in the Y direction it is ±0.21 mm, and in the Z direction it is ±0.19 mm.

Grating fringe projection 3D measurement techniques are extensively applied in various fields. However, in high dynamic range scenarios with significant surface reflectivity variations, uneven greyscale distribution may lead to phase errors and poor reconstruction results. To address this problem, an adaptive fringe projection method is introduced. The method involves projecting two sets of dark and light fringes onto the object, enabling the full-field projection intensity map to be generated adaptively based on greyscale analysis. First, dark fringes are projected onto the object to extend exposure time as long as possible without causing overexposure in the image. Subsequently, bright fringes are projected under the same exposure settings to detect overexposed pixels, and the greyscale distribution of these overexposed points from the previous dark fringe projection is analyzed to calculate the corresponding projection intensities. Finally, absolute phase information from orthogonal fringes is used for coordinate matching, enabling the generation of adaptive projection fringe patterns. Experiments on various high dynamic range objects show that compared to conventional fringe projection binocular reconstruction method, the proposed algorithm achieves complete reconstruction of high dynamic range surfaces and shows robust performance against phase calculation errors caused by overexposure and low modulation.

Despite its remarkable performance on natural images, the segment anything model (SAM) lacks domain-specific information in medical imaging. and faces the challenge of losing local multi-scale information in the encoding phase. This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM) to address the issues above. Firstly, based on the SAM, a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field, thereby supplying the Vision Transformer(ViT) branch in SAM with enriched local multi-scale contextual information. At the same time, a multiaxial Hadamard product module (MHPM) is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference. Subsequently, a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM. Finally, to obtain smaller input image size and to mitigate overlapping in patch embeddings, the size of the input image is reduced from 1 024×1 024 pixels to 256×256 pixels, and a multidimensional information adaptation component is developed, which includes feature adapters, position adapters, and channel-spatial adapters. This component effectively integrates the information from small-sized medical images into SAM, enhancing its suitability for clinical deployment. The proposed model demonstrates an average enhancement ranging from 0.038 7 to 0.319 1 across six objective evaluation metrics on BUSI, DDTI, and TN3K datasets compared to eight other representative image segmentation models. This significantly enhances the performance of the SAM on medical images, providing clinicians with a powerful tool in clinical diagnosis.

The objective of this study is to address semantic misalignment and insufficient accuracy in edge detail and discrimination detection, which are common issues in deep learning-based change detection methods relying on encoding and decoding frameworks. In response to this, we propose a model called FlowDual-PixelClsObjectMec (FPCNet), which innovatively incorporates dual flow alignment technology in the decoding stage to rectify semantic discrepancies through streamlined feature correction fusion. Furthermore, the model employs an object-level similarity measurement coupled with pixel-level classification in the PixelClsObjectMec (PCOM) module during the final discrimination stage, significantly enhancing edge detail detection and overall accuracy. Experimental evaluations on the change detection dataset (CDD)and building CDD demonstrate superior performance, with F1 scores of 95.1% and 92.8%, respectively. Our findings indicate that the FPCNet outperforms the existing algorithms in stability, robustness, and other key metrics.

Sparse representation has been highly successful in various tasks related to image processing and computer vision. For ancient mural image inpainting, traditional group sparse representation models usually lead to structure blur and line discontinuity due to the construction of similarity group solely based on the Euclidean distance and the randomness of dictionary initialization. To address the aforementioned issues, an improved curvature Gabor transform and group sparse representation (CGabor-GSR) model for ancient Dunhuang mural inpainting is proposed. To begin with, mutual information is introduced to weight the Euclidean distance, and then the weighted Euclidean distance acts as a new standard of similarity group. Subsequently, to mitigate the randomness of dictionary initialization, a curvature Gabor wavelet transform is proposed to extract the features and initialize the feature dictionary with dimension reduction based on principal component analysis (PCA). Ultimately, singular value decomposition (SVD) and split Bregman iteration (SBI) can be used to resolve the CGabor-GSR model to reconstruct the mural images. Experimental results on Dunhuang mural inpainting demonstrate tha the proposed CGabor-GSR achieves a better performance than compared algorithms in both objective and visual evaluation.

This paper presents an enhanced version of the correlation-driven dual-branch feature decomposition framework (CDDFuse) for fusing low- and high-exposure images captured by the G400BSI sensor. We introduce a novel neural long-term memory (NLM) module into the CDDFuse architecture to improve feature extraction by leveraging persistent global feature representations across image sequences. The proposed method effectively preserves dynamic range and structural details, and is evaluated using a new metric, the ATEF dynamic range preservation index (ATEF-DRPI). Experimental results on a G400BSI dataset demonstrate superior fusion quality, with ATEF-DRPI scores of 0.90, a 12.5% improvement over that of the baseline CDDFuse (0.80), indicating better detail retention in bright and dark regions. This work advances image fusion techniques for extreme lighting conditions, offering improved performance for downstream vision tasks.

The traditional train positioning methods suffer from inadequate accuracy and high maintenance costs, rendering them unsuitable for the development requirements of lightweight and intelligent train positioning technology. To address these restraints, the BeiDou navigation satellite system/strapdown inertial navigation system (BDS/SINS) integrated train positioning system based on an adaptive unscented Kalman filter (AUKF) is proposed. Firstly, the combined denoising algorithm (CDA) and Lagrange interpolation algorithm are introduced to preprocess the original data, effectively eliminating the influence of noise signals and abnormal measurements on the train positioning system. Secondly, the innovation theory is incorporated into the unscented Kalman filter (UKF) to derive the AUKF, which accomplishes an adaptive update of the measurement noise covariance. Finally, the positioning performance of the proposed AUKF is contrasted with that of conventional algorithms in various operation scenes. Simulation results demonstrate that the average value of error calculated by AUKF is less than 1.5 m, and the success rate of positioning touches 95.0%. Compared to Kalman filter (KF) and UKF, AUKF exhibits superior accuracy and stability in train positioning. Consequently, the proposed AUKF is well-suited for providing precise positioning services in variable operating environments for trains.

The power-electronics-based DC microgrid system composed of new energy sources in railway field has low inertia, weak damping characteristics, and the voltage fluctuation microgrid systems caused by the power disturbance of solar. In order to improve the inertia of the DC microgrid system, a virtual DC generator technology is adopted in the interface converter of photovoltaic (PV) power generation unit, so that it has the external characteristics of DC generator. However, the influence of PV maximum power point tracking(MPPT) is not considered in the traditional virtual DC generator control. Therefore, an improved control strategy for virtual DC generator is proposed, and its small signal model is established to analyze the influence of inertia and damping coefficient on stability. The results show that the proposed method effectively weakens the impact on DC bus voltage when the output of PV power unit changes suddenly, which improves the stability of the microgrid. Meanwhile, the correctness and feasibility of the method are verified.

Perovskite solar cells (PSCs) incorporating 2D/3D heterostructures have exhibited remarkable improvements in both power conversion efficiency and operational stability. Nevertheless, the prevalent spin-coating fabrication technique presents formidable challenges for scalable manufacturing processes. Herein, we present a blade-coating compatible methodology for fabricating high-performance 2D/3D PSCs utilizing a low-volatility t-amyl alcohol (t-AmOH)-dimethylformamide (DMF) mixed solvent system. Through systematic materials characterization and comprehensive device performance analysis, we demonstrate that this approach facilitates uniform spatial distribution of butylammonium iodide (BAI) organic spacers, thereby promoting the formation of a high-quality 2D/3D perovskite architecture characterized by enhanced crystallinity and substantially reduced defect density. The optimized device achieves a champion power conversion efficiency of 22.25% while demonstrating exceptional operational stability, retaining 83% of its initial performance after prolonged exposure under ambient conditions (45% relative humidity) for 1 000 h.

Aiming at node deployment in the monitoring area of the field observation instrument network in the cold and arid regions, we propose a virtual force algorithm based on Voronoi diagram (VFAVD), which adopts probabilistic sensing model that is more in line with the actual situation. First, the Voronoi diagram is constructed in the monitoring area to determine the Thiessen polygon of each node. Then, the virtual force on each node is calculated, and the node update its position according to the direction and size of the total force, so as to achieve the purpose of improving the network coverage rate. The simulation results show that the proposed algorithm can effectively improve the coverage rate of the network, and also has a good effect on the coverage uniformity.

This study aims to discuss the propagation characteristics of seismic Lamb wave and surface wave in a new radial gradient supercell seismic metamaterial (RGSSM). Different from the traditional seismic metamaterials with simple unit cells, the RGSSM consists of the supercells arranged periodically along the radial direction, and these supercells are composed of five kinds of unit cells with gradient filling rates. The dispersion curve and attenuation spectrum of the Lamb wave in RGSSM are studied with the finite element method combined with the supercell technology, generating a very low-frequency ultra-wide bandgap of 3.98-20 Hz, and a forbidden band generated by the localized modes forms multi-harmonic oscillators inside the supercell. Furthermore, it is found that the Young’s modulus of the soil is more sensitive to the effect of bandgap. In terms of surface wave, the RGSSM can produce rapid attenuation in the low frequency range of 5.2-8.5 Hz. Finally, a 3D model is designed to demonstrate the shielding performance of RGSSM against the seismic surface wave. The proposed RGSSM provides a new idea for the seismic isolation of ultra-wide and low-frequency seismic waves.

To apply the advantages of deep learning in recognizing two-dimensional(2D) images to three-phase inverter fault diagnosis, a three-phase inverter fault diagnosis model based on gramian angular field(GAF) combined with convolutional neural network(CNN) was proposed. Since the current signals of the inverter in different working states are different, the images formed by the time series encoding are also different, which enables the image recognition technology to be used for time series classification to identify the fault current signal of the inverter. Firstly, the one-dimensional(1D) inverter fault current signal was converted into a 2D image through the GAF. Next, the CNN model suitable for inverter fault diagnosis was input to realize the detection, classification and location of inverter fault. The simulation results show that the recognition accuracy of this method is 99.36% under different noisy data. Compared with other traditional methods, it has higher accuracy and reliability, and stronger anti-noise interference capability and robustness in dealing with noisy data. Therefore, it is an effective fault diagnosis method for inverters.

To study the combustion performance of aluminum-based micro-cell composite fuel aluminum@ammonium perchlorate(Al@AP), in hydroxyl-terminated polybutadiene (HTPB) solid propellant, the Al@AP was added to HTPB solid propellant instead of Al powder and part of AP. Firstly, the ignition and energy performance of Al@AP were investigated and the effects of Al@AP on the combustion, process and mechanical properties of HTPB solid propellant were studied by means of sphere explosion test system, adiabatic oxygen bomb calorimeter test, standard test engine test, residual active Al test, viscosity test, and tensile test. In addition, the combustion mechanism of Al@AP in HTPB solid propellant was analyzed. The results indicate that Al@AP composites offer faster ignition response than simple physical blends, and the heat of HTPB solid propellant increases from 7 385 J·g^-1 to 7 834 J·g^-1 when 21.3% Al@AP was used instead of aluminium powder. The amount of residue decreases from 3.88% to 2.10% in mass fraction, the content of active Al in residue decrease from 6.14% to 2.57%, and the particle size d₅₀ of residue decrease from 298 μm to 62 μm. The combustion efficiency of HTPB solid propellant improves from 94.0% to 94.6%. The mechanical and process properties of HTPB propellant containing Al@AP can satisfy the application.