To address the challenge of achieving surface uniformity in microcrystalline glass magnetorheological polishing (MRP), this study proposes a method for predicting and evaluating surface uniformity based on the motion trajectories of abrasive particles. First, a mathematical model of abrasive particle trajectories was established based on kinematic principles. COMSOL magnetic field simulations defined the physical boundaries of the effective polishing zone under Halbach ring arrays. The coefficient of variation (Cv) of particle trajectory density was innovatively introduced as a new metric for quantifying trajectory uniformity. Second, the numerical simulation system reveals the influence mechanisms of three key process parameters-workpiece speed, polishing disc speed, and eccentricity-on surface uniformity: increasing workpiece speed extends the abrasive particle trajectory path but tends to cause trajectories to cluster toward the workpiece center; polishing disc speed significantly affects trajectory overlap rate and distribution range; reducing eccentricity promotes dense abrasive particle distribution in the workpiece center region, thereby enhancing overall uniformity. Through systematic optimization, the study identified the optimal parameter combination: workpiece speed of 700 r/min, polishing disc speed of 60 r/min, and eccentricity of 28.6 mm. At these settings, the simulated Cv value was minimized, and the abrasive particle trajectory distribution was most uniform. Experimental validation demonstrated that under different process parameters, the trends in the coefficient of variation (Cv) of workpiece surface roughness and surface light transmittance closely matched the simulated Cv trends, confirming the validity of the established model and simulation experiments.

The video object re-identification model demonstrates significant potential for applications in criminal investigations and intelligent surveillance, and has been widely studied in recent years. However, existing methods still struggle to effectively address the issues of video frame misalignment and variations in external interference. To address these two challenges, this paper proposes a video object re-identification model based on Pyramid Alignment and ID Graph Matching (PAGM). The pyramid alignment module hierarchically aligns all frames in a tracklet, effectively mitigating video frame misalignment in long sequences while maintaining high computational efficiency. The ID graph matching module constructs graphs for the query tracklet and IDs in the gallery separately, and computes matching scores between the query graph and ID graphs. These scores guide the model in reducing the impact of external interference, thereby improving retrieval accuracy. Extensive experiments on the VVRD and Mars datasets demonstrate that our method outperforms all compared methods.

Uniform polishing of rotationally symmetric aspheric optical elements has long been a challenge, especially in cases of uneven curvature. Traditional fixed-spacing random paths often result in insufficient coverage and uneven surface quality. To address this issue, the article proposes a pseudo-random path planning method based on removal of regional overlap rates. According to the surface shape and curvature changes of the rotationally symmetric non-spherical surface, a path point set is obtained for achieving an equal overlap rate distribution. Moreover, the path search algorithm is optimized by adjusting the weights of the path points, allowing the paths to precisely converge at the predetermined positions. A two-layer acceleration architecture is established, including the inputting algorithm and the blocking strategy to improve the efficiency of path generation; The integrated angular velocity control module compensates for curvature differences, ensuring consistent removal depth at all locations. The experimental results show that the surface roughness variance obtained by the proposed method is only 2.49 x 10- 7 mu m2, which is significantly lower than the 3.25 x 10-4 mu m2 obtained by the traditional fixed-spacing random path method, thereby validating the uniform coverage and efficient path generation of this method in polishing rotationally symmetric aspherical surfaces. This research provides new theoretical insights and practical approaches for improving the surface quality of ultra-precision optical processing.

Hard and brittle materials exhibit excellent thermochemical stability, and glass-ceramics, as a typical representative, have broad application prospects in science, technology, and national defense. However, subsurface damage during processing presents significant challenges to ultra-precision machining. This study explores the mechanism of subsurface damage during magnetorheological polishing (MRP) of glass-ceramics. Using a combined approach of theoretical analysis, discrete element method (DEM) simulation, and experimental research, it thoroughly investigates how subsurface damage forms and how abrasive particle size and cutting speed affect crack propagation. The study first developed a model for the penetration depth of a single abrasive particle, based on the microstructure of glass-ceramics and particle removal mechanisms, illustrating how various parameters influence penetration depth. Next, a two-dimensional DEM model was built using PFC2D software. Through numerical experiments to calibrate mechanical parameters, the model simulated subsurface damage under different particle sizes and cutting speeds. Finally, single-factor polishing experiments on a custom-built MRP device validated the model's accuracy. The results indicate that when the abrasive particle size increases from 0.5 mu m to 3.0 mu m, the single-particle penetration depth significantly increases from 8.19 nm to 31.68 nm. Larger abrasive particles cause deeper damage to the substrate and higher microcrack density, while higher cutting speeds result in smaller crack propagation. Moderate-sized particles produce the best surface quality, and improving the rotational speeds of both the workpiece and polishing pad reduces surface roughness. This research provides essential theoretical foundations and technical support for optimizing glass-ceramic MRP processes and reducing subsurface damage, with significant implications for improving machining quality and efficiency of hard, brittle materials. It has broad application prospects in science, technology, national defense, and other fields due to its excellent thermal and chemical stability. However, the properties of glass-ceramics mainly depend on the process parameters during manufacturing.

The quasi-intermittent vibration assisted swing cutting (QVASC) has demonstrated excellent performance in improving the machining ability of difficult to machine materials such as Ti6AL4V. This machining technique also offers the benefits of reversing friction, lowering cutting force, and enhancing surface quality as compared to elliptical vibration assisted turning (EVC). Additionally, the distinct quasi-intermittent deflection feature can successfully lower the surface residual height and enhance processing quality. At present, the modeling of cutting specific energy mainly focuses on the macroscopic scale of ordinary cutting methods and materials. There is limited research on modeling the cutting specific energy of QVASC titanium alloy at the micrometer scale. This article proposes a comprehensive specific cutting energy (SCE) model considering size effects, material elastic recovery, temperature evolution, and chip friction, based on the unique quasi-intermittent cutting characteristics of QVASC and the microstructure characteristics of Ti6AL4V. Through finite element simulation analysis and single factor comparative experiments, the effects of different cutting methods, undeformed cutting thickness, cutting speed, tool swing frequency and amplitude on cutting specific energy were studied. The change mechanism of SCE is analyzed. The results show that QVASC can reduce the equivalent plastic strain and the maximum principal stress, which is helpful to reduce the cutting specific energy. At small cutting thicknesses, most of the energy is wasted in extruding the material rather than in shear removal, which results in a higher SCE. The minimum SCE occurs during the transition of the cutting mechanism from ploughing to shearing. Furthermore, the fluctuating patterns of theoretical and experimental SCE under various cutting thicknesses, cutting speeds, tool swing frequencies and amplitudes are consistent with maximum errors of 9.3 %, 12.3 %, 9.1 %, and 9.2 % respectively. All are within the acceptable range, which proves the validity of the theoretical model. This study is helpful to understand the energy dissipation in the cutting process of QVASC.

SiCp/Al composites have traditionally proven difficult to manufacture due to poor surface quality, excessive tool wear, and subsurface damage. It has been established that ultrasonic elliptical vibration cutting (UEVC) technology is effective at increasing the materials machinability. However, there are currently no relevant investigations on the formation of the subsurface microstructure of SiCp/Al composites under UEVC conditions. To determine the removal method of SiCp/Al composites under UEVC, this work first conducts comparative tests to extensively investigate the correlations between chip morphology, tool wear and surface/subsurface morphology in conventional cutting (TC) and UEVC. Electron backscatter diffraction (EBSD) technique is then employed to investigate the evolution of the subsurface microstructure of SiCp/Al composites. According to the study, UEVC may successfully reduce mechanical stress during cutting, prevent material morphing, and prevent grain over-refinement. Compared to TC, UEVC can effectively suppress subsurface micro-damage and reduce residual stress in the subsurface by controlling the fracturing of SiC particles and their direction of fracture. Furthermore, it can reduce the texture strength of the Al matrix from 6 to 1.96, significantly weakening the deformation texture, effectively preventing dislocations from slipping and propagating, and reducing the creation of surface defects.

Vessel Re-Identification (Re-ID) has become an increasingly critical task for maritime security due to the rapid growth in the number of ships and the current lack of effective identification methods. Specifically, vessel Re-ID faces significant challenges in data collection and often encounters significant inter-class similarity, which complicates the classification and Re-ID processes. To address these challenges, we construct a large-scale vessel Re-ID dataset and propose a novel vessel Re-ID method based on part-whole hierarchies, which addresses the issue of local feature misalignment and reduces computational cost. Additionally, we introduce a comprehensive Re-ID computation framework tailored to meet the demands of industrial applications, ensuring highly accurate identity determination for the query target. Our proposed method demonstrates superior performance on the Rank@n metric, significantly outperforming current state-of-the-art techniques. These results highlight the method’s great potential for real-world applications, offering both efficiency and high precision in vessel Re-ID tasks.

Few-shot action recognition aims to enable models to quickly learn new action categories from a limited number of labeled samples, addressing the challenge of data scarcity in real-world applications. Current research primarily faces three core challenges: (1) temporal modeling, where models are prone to interference from irrelevant static background information and struggle to capture the essence of dynamic action features; (2) visual similarity, where categories with subtle visual differences are difficult to distinguish; and (3) the modality gap between visual-textual support prototypes and visual-only queries, thereby complicating alignment within a shared embedding space. To address these challenges, this paper proposes a CLIP-SPM framework, which includes three components: (1) the Hierarchical Synergistic Motion Regularization (HSMR) module, which aligns deep and shallow motion features to improve temporal modeling by reducing static background interference; (2) the Semantic Prototype Modulation (SPM) strategy, which generates query-relevant text prompts to bridge the modality gap and integrates them with visual features, enhancing the discriminability between similar actions; and (3) the Prototype-Anchor Dual Modulation (PADM) method, which refines support prototypes and aligns query features with a global semantic anchor, improving consistency across support and query samples. Comprehensive experiments across standard benchmarks, including Kinetics, SSv2-Full, SSv2-Small, UCF101, and HMDB51, demonstrate that our CLIP-SPM achieves competitive performance under 1-shot, 3-shot, and 5-shot settings. Extensive ablation studies and visual analyses further validate the effectiveness of each component and its contributions to addressing the core challenges.The source code and models are publicly available at GitHub.

SiCp/Al is widely used in the manufacturing of high-end equipment, but it is difficult to process and poor in surface quality. Conversely, Quasi-intermittent Vibration Assisted Swing Cutting (QVASC) combines the intermittent cutting mechanism of EVC (Elliptical Vibration Cutting) while effectively reducing cutting forces via its unique offset swing motion. This study undertakes detailed analysis of the kinematics and friction characteristics intrinsic to QVASC. A predictive model for cutting temperature is subsequently developed, incorporating the influence of reinforcing particles. This model primarily ascribes the generated heat to two sources: the shear heat source and the frictional heat source, further examining resultant temperature distributions in the tool, workpiece, and chip alongside corresponding heat partition ratios. Single-factor experiments were conducted with spindle speed, cutting depth, frequency, amplitude, and feed amount as parameters. Theoretical and experimental cutting temperatures are compared, revealing consistent trends. Calculated average relative errors for these parameters are 4.65%, 9.50%, 3.32%, 2.58%, and 3.81% respectively. These errors lie within acceptable limits, validating the theoretical model's accuracy. Consequently, this research provides valuable insights into the QVASC cutting mechanism and offers an efficient approach for determining optimal cutting parameters.

Continuous fiber-reinforced metal matrix composites (CFMMCs) hold significant application potential in aerospace due to their high specific strength and thermal resistance. However, their structural characteristics-brittle fibers coexisting with a ductile matrix-make them prone to fiber breakage, interfacial damage, and high cutting forces during conventional machining. These forces are extremely difficult to predict, and the removal mechanism is exceptionally complex. Based on the periodic contact-disengagement motion characteristics of ultrasonic elliptical vibration cutting (UEVC), it effectively reduces cutting forces and improves surface quality, demonstrating significant advantages in machining difficult-to-cut materials. To this end, this paper systematically investigates the removal mechanism, surface formation patterns, and cutting-force modeling of a typical silicon carbide fiber-reinforced aluminum-matrix composite (SiCf/Al). A cutting force model applicable to continuous fiber-reinforced metal matrix composites was established from an energy perspective, incorporating both phase structure and material structure considerations. This model was validated through cutting force experiments. The error of the cutting force model decreases with increasing cutting depth. At a cutting depth of 5 mu m, the errors of the tangential force (Ft) and normal force (Fn) compared to experimental values were 18.5% and 18%, respectively. When the cutting depth reached 25 & micro;m, the theoretical values of Fn and Ft deviated from experimental values by -7.1% and - 6.8%, respectively. The theoretical predictions and experimental results showed good consistency in their trends. By analyzing and comparing the surface topography of UEVC and CC machining methods using finite element simulations and experiments, it was found that the surface quality of both methods deteriorated with increasing cutting depth. However, UEVC demonstrated a reduction in surface damage to the material. The material removal behavior exhibits a phased evolution from a plastic-deformation-dominated stage to a fiber-brittle fracture-dominated stage. UEVC effectively mitigates stress concentration, suppresses macroscopic fiber fracture, and improves surface quality. This study aims to comprehensively reveal the abrasive machining characteristics of CFMMCs from aspects including surface morphology, material removal mechanisms, and cutting force prediction. The findings provide a theoretical basis for precision machining and process parameter optimization of continuous fiber-reinforced metal-matrix composites.

Zirconia ceramics are often used in electronics, aerospace, biomedicine, and other fields because of their excellent mechanical and optical properties; however, as they are hard and brittle materials, they are highly susceptible to cracking and chipping during processing. Ultrasonic elliptical vibratory-assisted cutting (UEVC) is a promising ceramic processing technology that addresses existing problems in materials processing. In this study, the critical depth of cut ( h_c ) of zirconia ceramics was predicted using two models, focusing on the influence of the circular edge of the tool and tool front angle in the actual machining process. Subsequently, a model was established based on the specific cutting energy to predict the h_c of zirconia ceramics in UEVC machining. A simulation software was used to simulate the variable depth of zirconia ceramics using the constitutive improved Johnson-Holmquist ceramic (JH-2) model. Finally, the relationship between the cutting speed and h_c of zirconia ceramics under conventional cutting (CC) and UEVC machining was investigated using scribing experiments. The results showed that the h_c of zirconia ceramics decreased nonlinearly with increasing cutting speed. The h_c of zirconia under CC is 0.8 μm, whereas the h_c values of zirconia under UEVC machining are 1.79, 1.75, 1.45, and 1.3 μm with a maximum increment of 124

Most existing person re-identification (ReID) methods focus on improving retrieval accuracy by refining features, which fails to balance accuracy with inference efficiency. We observe that query difficulty varies: global features suffice for simple cases, while fine-grained part features are required for challenging cases, such as occlusion. However, current methods typically use the same feature extraction network for all queries, which may limit accuracy on difficult queries or waste computational resources on easier ones. To address this, we propose a two-stage Coarse-to-Fine Dynamic Retrieval mechanism that adaptively allocates resources based on query difficulty. For "easy" queries, only global features are used in the coarse stage, and inference terminates early. For "hard" queries, part features are extracted in the fine stage for detailed matching. To further reduce computational costs, we introduce Mixture of Experts for part feature extraction, where a router assigns patches to part experts using topology annotations, and only activates body-relevant experts, enabling accurate part identification with significant computation reduction. Extensive experiments demonstrate that our method achieves competitive performance while significantly reducing computational costs compared to state-of-the-art methods.

A cutting force prediction model for elliptical vibration-assisted cutting (EVC) cutting SiCp/Al composite materials was established. The flow stress of SiCp/Al was calculated based on the Johnson-Cook model, and the dynamic cutting force was decomposed into chip formation force, frictional force, ploughing force, and particle fracture force, according to the cutting characteristics at different stages of the EVC cycle were studied separately and decomposed along the cutting speed direction, depth of cut direction, and feed direction to obtain the prediction model for cutting forces in three directions. Orthogonal tests were designed to investigate the cutting parameters (cutting speed, depth of cut, and feed) by means of extreme difference analysis. The deviations between the experimental and predicted values of cutting forces in the three directions are compared through cutting experiments, and the reasons are analyzed to verify the validity of the established cutting force prediction model.

As an effective strategy to address urban traffic congestion, traffic flow prediction has gained attention from Federated-Learning (FL) researchers due FL's ability to preserving data privacy. However, existing methods face challenges: some are too simplistic to capture complex traffic patterns effectively, and others are overly complex, leading to excessive communication overhead between cloud and edge devices. Moreover, the problem of single point failure limits their robustness and reliability in real-world applications. To tackle these challenges, this paper proposes a new method, CMBA-FL, a Communication-Mitigated and Blockchain-Assisted Federated Learning model. First, CMBA-FL improves the client model's ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device. Second, to reduce the communication overhead during federated learning, we introduce a verification method based on parameter update consistency, avoiding unnecessary parameter updates. Third, to mitigate the risk of a single point of failure, we integrate consensus mechanisms from blockchain technology. To validate the effectiveness of CMBA-FL, we assess its performance on two widely used traffic datasets. Our experimental results show that CMBA-FL reduces prediction error by 11.46%, significantly lowers communication overhead, and improves security.

Metal matrix composites (MMC) are widely used in in modern industry due to their excellent mechanical properties and at the same time mechanical properties, while at the same time, due to their internal high-hardness particles, precision machining is a major challenge, and effective machining of composites is proposed; laser-assisted machining (LAM), and in the laser-assisted cutting, the temperature, as an important parameter in the cutting process, the accurate prediction of the becomes an urgent problem. Excessive temperature will produce thermal cracks, excessive thermal stress, instantaneous oxidation, etc., and the use of pulsed lasers increases the difficulty of temperature control. In order to changes in the temperature field in the LAM process, we use the principle of temperature superposition to principle of temperature superposition to predict the temperature of pulsed laser irradiation and establish a two-dimensional finite element model, taking into account the laser irradiation, cohesive unit heat transfer, particle crushing warming, as well as the influence of thermal radiation and thermal convection, combined with experiments to show that the model and the simulation can accurately predict the warming process and the change of the temperature, and the errors were 9.83, 11.24, 10.86, and 8.82

SiCp/Al composites are critical materials in aerospace optical applications. Nevertheless, the presence of SiC particles causes undesirable surface deformation and defects during the cutting process. Molecular dynamics simulation of conventional and laser assisted cutting of SiCp/Al were performed. The influences of laser power density, cutting depth, and cutting speed on tangential force, machined surface quality, workpiece material buildup, stress distribution, and dislocation were investigated. The results demonstrate that increasing laser power density leads to greater atomic stacking, which consequently enhances the thermal softening effect. The pulsed laser's energy dissipation process reduces undesirable thermal deformation, while its thermal effect mitigates deformation caused by particle extrusion and inhibits the formation of dislocations and slippage in the aluminum matrix. While the cutting depth rises, the surface roughness shows a gradual linear increase, and while the cutting depth coincides with the laser's heat-affected area, a flatter machined surface is derived. The results clarify the cutting mechanism in laser-assisted machining (LAM), highlighting its potential to improve both surface integrity and machining efficiency.

Glass ceramics with ultra-low expansion, low dielectric loss, high strength, low density and other characteristics, in the field of optical precision machinery has a wide range of applications. However, there are still problems such as cumbersome processing, high calibration cost and low processing efficiency. This study investigates a pulsed laser synchronous enhanced servo-turning technique (s-LAST), which uses pulsed laser irradiation to excite a target surface to induce thermal damage to assist fast tool servo for curved surface machining. The feasibility of laser irradiation to weaken the glass-ceramic surface to facilitate material removal was investigated in terms of both material removal and damage formation mechanism. The results show that laser assistance can lead to softening of surface craters and stimulate partial microcrack expansion, contributing to material removal and surface quality improvement. The significant reduction in roughness and cutting forces and the more complete curvature of the surfaces compared to conventional FTS demonstrate the advantages and necessity of laser irradiation-assisted FTS processing of glass-ceramics.

Glass-ceramic is widely used in aerospace, bionics, optics and other fields with low expansion and high strength and low dielectric loss, but its hardness and brittleness as well as its high strength have been classified as a kind of difficult-to-process materials. In this paper, free-form machining of glass-ceramic is performed by combining a pulsed laser with fast tool servo (FTS) technology to utilize laser-induced irradiation effects. During pulsed laser synchronously enhanced servo-turning (s-LAST), the material removal process as well as the selection range of operating temperatures were first determined by means of finite elements. The cutting forces on hard and brittle glass-ceramic materials during laser-assisted manufacturing (LAM) were predicted by developing an analytical force model that takes into account the thermal manufacturing damage caused by laser irradiation, using the discretisation method as well as the shear and ploughing effects associated with laser heating. The s-LAST experiments were performed on a selected ultra-low expansion glass-ceramic (Zerodur), and both the modelling and experimental results were validated, with laser irradiation leading to a 12% reduction in cutting force, and an average principal cutting force of 3.124 N was recorded.