Current industrial image anomaly detection methods generally face challenges such as high dependency on anomalous samples, insufficient realism of synthetic samples, and limited perception capability for complex defects. To An industrial image anomaly detection method based on diffusion synthesis and feature mining was proposed to address these issues. Accordingly, a category-sensitive selective diffusion anomaly synthesis module was designed to generate pseudo-anomaly samples with adjustable intensity and category adaptability through controllable perturbation and category-sensitive loss, which could effectively alleviate data scarcity. Meanwhile, a multi-stage feature mining framework was constructed, including contrast-driven feature selection, multi-dimensional perception attention reconstruction, and residual refinement selection modules, enabling dynamic screening of anomaly-sensitive features and enhancement of structural details. Experimental results demonstrated that the proposed method achieved outstanding performance on the MVTec AD and MPDD datasets, with image-level AUROC scores of 99.7% and 98.4%, and pixel-level AUROC scores of 99.0% and 98.7%, respectively, validating its effectiveness and robustness.

To address the issue of inconsistent thermal stress between the upper and lower devices of the same bridge arm in converters under conditions of uneven aging or layout deviations, a novel thermal balancing method for upper and lower bridge arms based on dead-time shifting adjustment was proposed. By monitoring the on-state voltage of insulated gate bipolar transistor (IGBT) online, the real-time junction temperature of IGBTs was deduced based on the mapping relationship between the on-state voltage and junction temperature of IGBTs. Then, the temperature difference between the upper and lower devices of the same bridge arm was determined. When the thermal stress difference between the upper and lower bridge arms exceeded the threshold, the effective conduction time of the upper and lower bridge arms was adjusted by fine-tuning the position of the dead time, thereby actively regulating the power loss distribution of the upper and lower bridge arms. This achieved an increase in the loss of the bridge arm with lower temperature and a decrease in the loss of the bridge arm with higher temperature without affecting the total system loss, thus improving the thermal stress consistency of power devices in the converter and enhancing system reliability. Theoretical analysis and experimental results showed that the proposed method did not affect system efficiency and had no significant negative impact on output performance. Under typical operating conditions, the average temperature difference between the upper and lower devices was reduced by approximately 14% after adjustment, effectively improving the thermal stress consistency of the upper and lower bridge arms.

Autonomous driving simulation platforms play a vital role in the development, testing and validation of autonomous driving systems. A systematic review of the classification and key technical pathways of mainstream simulation platforms was presented, covering aspects such as environment modeling, sensor simulation, vehicle dynamics modeling, perception algorithm evaluation, V2X communication and cloud-based simulation. Core challenges and research progress related to synthetic data generation, cost-effective algorithm training, cross-domain generalization and platform scalability were analyzed, emphasizing simulation technologies based on computer vision and artificial intelligence. Future development trends were discussed. Simulation platforms are evolving toward higher realism, interactivity and closed-loop validation with the advancement of emerging technologies such as generative AI, neural rendering and multimodal learning, gradually forming a comprehensive pipeline that integrates data generation, algorithm training and performance evaluation. Simulation platforms will continue to play an essential role in enhancing the generalization capability of autonomous driving system, accelerating product deployment, and establishing standard testing and validation framework in the future.

Discrete element method (DEM) and Tavares breakage model were employed to investigate the rock fragmentation mechanism and the statistical characteristics of fragments under impact loading. Drop hammer impact crushing process of limestone specimens with different aspect ratios (1.0—2.0) and sizes (diameter 30—50 mm) was systematically simulated. Image J image processing technology was used to quantitatively analyze the fragment count, particle size distribution, and shape indices. The results showed that increasing the aspect ratio and the size significantly raised the crushing energy threshold, which led to a delay in the peak normal force, a reduction in its amplitude, and a slowdown of the overall fragmentation process. The equivalent particle size of fragments showed poor alignment with the sieve analysis curve, which indicated that the fragments commonly exhibited irregular geometric configurations such as flaky and rod-like shapes. Fragments from specimens with lower aspect ratios (1.0) and smaller sizes (diameter 30 mm) had lower roundness, more pronounced angularity, and greater surface roughness. A quantitative relationship model among geometric parameters–crushing response–fragment morphology can provide theoretical basis and regulatory pathways for the design of impact-resistant structures, the optimization of blasting parameters, and the control of particle morphology in rockfill grading.

The specific harmonic near the resonance point of input side was selected as the suppression object aiming at the problem that input LC filter of matrix converter was easy to cause input current resonance. A method of input filter parameter optimization and resonance suppression based on specific harmonic elimination was proposed to improve input performance. The working principle of input LC filter and conventional resonance suppression method was analyzed. Multiple constraints were used to optimize the design of filter parameters. The damping resistance was selected based on the suppression effect of specific harmonics near the resonance point, and the specific optimization design steps were given. The idea of specific harmonic elimination was introduced into the active resonance suppression method, and the strategy of using notch filter to collect the specific harmonic quantity in the filter capacitor and feed it back to the control loop was proposed. The simulation model based on Matlab/Simulink software was constructed. The simulation results showed that the total harmonic distortion of input current was reduced by 98.5% and 99.3% respectively by using the proposed input filter parameter optimization method and active resonance suppression method. The system has good stability after suppression processing.

A laboratory-scale 3 kW liquid spray burner was used to explore the dynamic characteristics of liquid spray combustion instability. The dynamic characteristics of sound pressure and flame heat release rate in the combustion chamber under different equivalence ratios were measured, and nonlinear time series analysis methods such as phase space and recurrence plot were used to study the characteristics of thermoacoustic oscillation signal. When the air flow rate of the liquid spray burner gradually increased from 4.0 L/min to 9.5 L/min, the dynamic characteristics of thermoacoustic oscillation in the combustion chamber were different. When the air flow rate was from 4.0 L/min to 5.5 L/min, the sound pressure amplitude of the combustion chamber was between 20 Pa and 30 Pa. However, when the air flow rate was 6.0 L/min, the sound pressure amplitude suddenly increased to 100 Pa. The flame presents turbulent combustion noise, limit cycle, and semi-steady state. At the same time, the increase in air volume (decrease in the equivalence ratio) will trigger thermoacoustic instability, and the turbulent combustion noise of the combustion chamber will abruptly become a limit cycle oscillation.

A news recommendation method based on Transformer and knowledge graph was proposed to increase the auxiliary information and improve the prediction accuracy. The self-attention mechanism was used to obtain the connection between news words and news entities in order to combine news semantic information and entity information. The additive attention mechanism was employed to capture the influence of words and entities on news representation. Transformer was introduced to pick up the correlation information between clicked news of user and capture the change of user interest over time by considering the time-series characteristics of user preference for news. High-order structural information in knowledge graphs was used to fuse adjacent entities of the candidate news and enhance the integrity of the information contained in the candidate news embedding vector. The comparison experiments with five typical recommendation methods on two versions of the MIND news dataset show that the introduction of attention mechanism, Transformer and knowledge graph can improve the performance of the algorithm on news recommendation.

A lightweight small target detection algorithm called RTA-YOLOv8s was proposed in order to address the challenges of complex backgrounds, small target, and limited device resources in UAV images. The RepVGG module was introduced into the backbone network to enhance feature extraction capabilities. A tri-branch attention mechanism was applied to reduce false positive and false negative rates. A dedicated small target detection head was integrated to improve detection accuracy. The WIoUv3 loss function was adopted to improve localization and robustness. The experimental results showed that the RTA-YOLOv8s algorithm achieved a mAP50 of 44.9% and detection speed of 88.5 frame per second on the VisDrone dataset. mAP50 increased by 6.1%, detection accuracy increased by 4.7%, and params reduced by 13.9% compared with YOLOv8s. The improved algorithm effectively addresses the poor detection performance in complex UAV scenes, and balances accuracy and speed. The user-friendly interface design enables result visualization, making detection tasks more intuitive and easier to operate, and is suitable for UAV target detection.

A knowledge graph-guided 3D visual question answering method was proposed to capture the implicit common-sense semantic relationships between objects in the scene. By introducing external structured knowledge, the model was effectively enhanced in both semantic understanding and reasoning ability. Specifically, key semantic entities were extracted from the question text, and a knowledge graph-guided feature enhancement module was designed to obtain knowledge features using these key semantic entities. The knowledge features were fused with visual features extracted both from the question representation and from the 3D object detection network for answer prediction. Experimental results on the ScanQA dataset showed that the proposed method outperforms existing baseline models on metrics such as EM@1 and BLEU-4.

The development and research status of YOLO algorithm in traffic object detection were systematically summarized from the perspective of the three core elements of 'people-vehicle-road' in order to comprehensively analyze the important role of YOLO (You Only Look Once) algorithm in improving traffic safety and efficiency. The commonly used evaluation indexes of YOLO algorithm were outlined, and the practical significance of these indexes in traffic scenarios was elaborately expounded. An overview of the core architecture of YOLO algorithm was provided, its development process was traced, and the optimization and improvement measures in each version iteration were analyzed. The research status and application scenarios of YOLO algorithm for traffic object detection were sorted out and discussed from the perspective of the three traffic objects 'people-vehicle-road'. The limitations and challenges of YOLO algorithm in traffic object detection were analyzed, and corresponding improvement methods were proposed. Future research focuses were anticipated, providing a research reference for the intelligent development of road traffic.

An improved lightweight small object detection method based on YOLOv8s was proposed to address the challenges of complex background interference, small object sizes, and limited device resources in unmanned aerial vehicle (UAV) image object detection. A high-resolution detection head was reconstructed to improve the sensitivity to tiny objects. A dynamic multi-scale aggregation network was designed to adaptively adjust the receptive field for scale-variant targets, while an efficient multi-scale feature deep fusion module was proposed to integrate features from different semantic levels. An efficient channel-spatial attention module was introduced to further refine fused features. Experiments on the VisDrone2021 dataset demonstrated that the proposed method achieved improvements of over 5.0% in average precision, precision at different intersection over union thresholds, and detection precision for small and medium-sized objects, with a 16.25% reduction in parameters. Tests on the DOTA dataset validated the generalization ability of the proposed model. Results demonstrate that the proposed method improves the detection performance while achieving the model light weighting, indicating its practical value for object detection in UAV aerial images.

Significant progress in multimodal large language models (MLLMs) has driven advances in visual question answering, visual understanding, and reasoning tasks, and their potential for deployment on resource-constrained edge devices is increasingly recognized. However, large model sizes and the substantial costs of deployment and inference remain major barriers to practical adoption. Optimizing MLLMs for edge devices has become a critical research direction in this field. A comprehensive survey of recent advances in optimizing MLLMs for edge deployment was presented, along with the associated challenges and development trends. The research evolution of MLLMs on edge devices was reviewed, with particular emphasis on model architecture optimization and inference scheduling strategies. In model architecture optimization, techniques including visual information compression, sparse attention, and mixture-of-experts models were specifically analyzed. System-level optimizations involving computation scheduling, hardware adaptation, compilation optimization, and cloud-edge collaboration were investigated to enhance inference efficiency and energy efficiency. Furthermore, the key challenges of these models in practical applications were discussed, and a variety of task scenarios ranging from assistive to collaborative and autonomous types were covered, categorized by the perspective of autonomy levels. Finally, current limitations were summarized and future research directions regarding standardized deployment, efficient computing and storage, and multi-modal fusion optimization were outlined.

A traffic scene perception algorithm (SDFormer++) based on the principle of cross-task bidirectional feature interaction for autonomous driving in urban street scenarios was proposed by leveraging the explicit and implicit correlations between the semantic segmentation tasks and the depth estimation tasks to improve the overall performance of traffic scene perception algorithms. An interaction-gated linear unit was added into the cross-task feature extraction stage to form high-quality task-specific feature representations. A multi-task feature interaction module that used the bidirectional attention mechanism was constructed to enhance the initial task-specific features by utilizing the feature information of shared cross-domain tasks. A multi-scale feature fusion module was designed to integrate information at different levels to obtain fine high-resolution features. Experimental results on the Cityscapes dataset showed that the algorithm achieved a mean intersection over union (mIoU) of 82.4% for pixel segmentation, a root mean square error (RMSE) of 4.453 for depth estimation, an absolute relative error (ARE) of 0.130 for depth estimation, and an average distance estimation error of 6.0% for five typical traffic participants, all of which outperformed the existing mainstream multi-task algorithms such as InvPT++ and SDFormer.

A railway foreign object detection algorithm for complex weather conditions based on an improved RT-DETR, named FRP-DETR, was proposed. A feature complementary mapping module (FCM) and Pzconv units were introduced, which built complementary paths between shallow spatial details and deep semantic information to compensate for the limitations of single-scale feature representation, with the two components working collaboratively to enhance the perception capabilities of small and edge targets. A railway perception modulation fusion module (RMFM) was introduced, which was based on adaptive channel attention and spatial modulation mechanisms to enhance the model’s response to key semantic information in railway scenes. The original downsampling module was replaced with pinwheel-shaped convolution (PSConv), which enhanced edge texture extraction through multi-directional asymmetric padding and separable convolution. A railway foreign object detection dataset containing four weather conditions was constructed based on the SaMam style transfer method, by transferring sunny railway foreign object images to rainy, foggy, and snowy scenes. Experimental results showed that compared to the original RT-DETR-R18 model, this method achieved improvements of 1.65 percentage points and 3.4 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, with a 63.5% reduction in parameter count and an inference speed of 88 frames per second. The results verified that FRP-DETR achieved high accuracy, lightweight design, and real-time performance in railway foreign object detection under complex weather conditions.

A lightweight micro-expression recognition method based on optical flow and convolutional vision Transformer was proposed to solve the problems of short duration, low motion intensity and insufficient sample size of micro-expressions. The optical flow and optical strain of human faces between the onset frame and the apex frame were extracted to highlight the movement of facial muscles, thereby effectively reducing the texture interference and lowering the feature dimension. The adversarial domain adaptation method based on identity domain was adopted to further remove the irrelevant components in the micro-expression features by making full use of the subjects’ labels. A lightweight multi-stage CNN-Transformer hybrid model named MiER-CvT, including the convolutional embedding layer, the convolutional Transformer block and the SeqSoftmax layer, was constructed to enhance the model’s capabilities of local representation and information integration for micro-expressions. The experimental results showed that the proposed method achieved a UF1 score of 0.9171 and a UAR score of 0.9192 on the MEGC 2019 dataset, and the parameter number and computational complexity of MiER-CvT were 7.5 M and 0.1 G, respectively. Compared with the existing methods, such as MiMaNet, the proposed method has the advantages of high precision and light weight.

To address the challenge of difficult identification of concrete cracks in extremely dark environments, a concrete crack recognition method based on biomimetic tactile technology was proposed. By simulating the tactile perception mechanism of organisms, a tactile sensing system and a deep learning model were constructed to achieve crack detection in dark environments. A high-sensitivity silicone tactile probe was designed and combined with a multi-light source imaging system and the Lambertian reflection model, and the three-dimensional texture of cracks was reconstructed through elastomer deformation and light-shadow gradient changes, overcoming the reliance on visual information in dark environments. On this basis, a tactile segmentation network for concrete cracks (TSCC-Net) was constructed. Through the collaboration of a semantic perception extractor, mutual fusion module, and auxiliary supervision module, precise segmentation of cracks in tactile images was achieved. Experimental results showed that TSCC-Net achieved an IOU of 86.3% in tactile image crack recognition, with a model parameter size of 12.86 MB and an inference speed of 114.5 fps, significantly outperforming traditional visual models. In particular, TSCC-Net demonstrated stronger robustness in narrow cracks and areas with complex textures compared to models such as U-Net, Segformer, and DeepLabV3+.

A novel detection framework integrating automatic fluid target annotation and a lightweight YOLOv8n model was proposed, aiming at addressing the challenges of low annotation efficiency and insufficient model adaptability in real-time debris flow monitoring. An open-source debris flow dataset of 8 064 images was built. This dataset was achieved using multi-scale dynamic capture frames, which leverage fluid motion continuity, along with adaptive frame extraction and an improved binary classification model. To enhance the extraction of dynamic debris flow features and improve the irregular boundary localization, the LGSA module and C2f_GhostNetV2 structure were developed. The Shape-IoU loss function was also introduced into the model architecture. Experimental results demonstrated that the proposed YOLOv8-Mudslide model achieved an mAP@0.5 of 86.7%, which was 4.7% higher than that of the baseline model, and the detection speed reached 230.89 frame/s. This case provides reliable technical support for the real-time monitoring of debris flow disasters, and its framework can be further extended to other intelligent detection fields of fluid targets.

A collaborative control approach integrating multi-agent proximal policy optimization (MAPPO) and maximum pressure control (MPC) was developed to address traffic control challenges at mixed traffic intersections involving connected autonomous vehicles (CAVs), human-driven vehicles (HDVs), and pedestrians. A hierarchical state space and an action space were designed through the formulation of a decentralized partially observable Markov decision process (Dec-POMDP). Reward functions considering the balance among safety, traffic efficiency, phase switching frequency, and regulatory compliance were introduced. The proposed model was validated under low, medium, and high traffic flow conditions using a centralized training with decentralized execution framework on the SUMO simulation platform. Experimental results demonstrated that the proposed MAPPO-MPC method significantly improved the throughput (by up to 44.3%) while reducing the queue length (by up to 49.3%), average vehicle delay (by up to 43.2%), and pedestrian waiting time (by up to 43.53%). Moreover, the model exhibited more substantial performance advantages when the CAV penetration rate exceeded 50%, outperforming the traditional Webster-based methods and the baseline models.

A non-destructive identification method for the dynamic performance of the solenoid switching valve (SSV) based on multi-order current derivative characteristics was proposed, aiming at the problem that the moving process of the valve spool inside the SSV was difficult to detect accurately. Considering the effect of the solenoid’s temperature rising on the coil resistance, a mathematical model among the coil inductance derivative, current derivative and spool moving velocity was established. The matching relationship between the current derivative and spool moving state was explored based on the experimental magnetization curve, and the influence of the permeability derivative on the judgment for each switching state of the SSV was analyzed. Theoretical analysis deduced that the convex point and concave point on the opening current derivative curves corresponded to the critical opening moment and fully opened moment of the SSV, and the upper and lower turning points on the closing current derivative curves matched the critical closing moment and fully closed moment of the SSV. Experimental results showed that the maximum measurement deviations of opening time and closing time were 2.40% and 3.08%, respectively, and the maximum measurement deviation of total switching time in a single cycle was 1.49%.

The interaction model between the shield and the soil was simplified into the elastic half-space hole problem in order to analyze the contact stress between shield shell and soil during shield tunneling. The shield tunneling was simplified into radial, vertical and horizontal displacement boundary modes. Then a method of the additional contact stress between the shield shell and soil was proposed by using the complex variable function method, and the method was verified by three-dimensional numerical simulation. The sensitivity analysis of relevant parameters was conducted by using the method. Results show that the additional contact stress appears a number of gradually increasing stress peaks with the increase of the relative displacement of the machine and soil, and the corresponding peak stresses are approximately the same when the displacements in different modes are the same. When the displacement boundary condition is the same, the smaller Poisson's ratio and the larger elastic modulus of soil are, the greater the additional contact stress at the extreme point is, while the burial depth has little effect on the mechanical - soil additional contact stress. The distribution function of additional stress coefficient around the shield shell was defined, and the balance equations of force and moment increment of the shield were established. The calculations show that the horizontal posture adjustment load, vertical posture adjustment load, yaw moment of the shield machine are proportional to the change of attitude angle, and the longitudinal posture adjustment load is less affected by the change of attitude angle.