Search Results (178)

With the depletion of high-grade iron ore resources, the efficient utilization of low-grade iron ore has become a critical demand in the steel industry. Due to its uniform particle size and chemical composition, pelletized iron ore significantly enhances both the utilization rate of iron ore and the efficiency of metallurgical processes. This paper presents a deep learning model based on ResUNet, which integrates three-dimensional CT images obtained through industrial computed tomography (ICT) to precisely segment hematite, liquid phase, and porosity. By incorporating residual connections and batch normalization, the model enhances both robustness and segmentation accuracy, achieving F1 scores of 98.37%, 95.10%, and 83.87% for the hematite, pores, and liquid phase, respectively, on the test set. Through 3D reconstruction and quantitative analysis, the volume fractions and fractal dimensions of each component were computed, revealing the impact of the spatial distribution of different components on the physical properties of the pellets. Systematic evaluation of model robustness demonstrated varying sensitivity to different CT artifacts, with the strongest resistance to beam hardening and highest sensitivity to Gaussian noise. Multi-scale resolution analysis revealed that segmentation quality and fractal dimension estimates exhibit phase-dependent responses to resolution changes, with the liquid phase being the most sensitive. Despite these dependencies, the relative complexity relationships among phases remained consistent across resolutions, supporting the reliability of our qualitative conclusions. The study demonstrates that the deep learning-based image segmentation method effectively captures microstructural details, reduces human error, and enhances automation, providing a scientific foundation for optimizing pellet quality and improving metallurgical performance. It holds considerable potential for industrial applications. Full article

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

The article presents the results of experimental studies on the symmetrical and asymmetrical rolling process of composite laminate sheets consisting of difficult-to-deform Ti and Ni materials. Composite sheets joined by explosive welding were used for the tests. The aim of the research was to determine the impact of plastic shaping conditions in the rolling process on the quality and selected functional properties of the materials constituting the layered composite. The rolling process was carried out cold on a duo laboratory rolling mill with a roll diameter of 300 mm. During the rolling process, the influence of the rolling process conditions on the distribution of metal pressure forces on the rolls was determined, as well as the shear strength and microstructural studies of the joint area of the layered composites. As part of the conducted considerations, residual stress tests were carried out using the Barkhausen noise method. The scientific aim of the presented work was to determine the optimal conditions for the plastic processing of multi-layer Ti-Ni sheets. The results presented in the work allowed for determining the most favorable conditions for the rolling process. Full article

The efficient utilization of heavy oil is of great significance to alleviating the global energy crisis. How to efficiently convert heavy oil into high-value-added light fuel oil has become a hot issue in the field of petrochemicals. As the residual part of crude oil processing, heavy oil has a complex composition and contains polycyclic aromatic hydrocarbons, long-chain alkanes, and heteroatom compounds, which makes it difficult to process directly. Zeolite, as an important type of solid acid catalyst, has a unique pore structure, adjustable acidity, and good thermal stability. It can promote the efficient cracking and conversion of heavy oil molecules, reduce coke formation, and improve the yield and quality of light oil products. This paper systematically reviews the development status of heavy oil cracking technology, focusing on the structural characteristics, acidity regulation of zeolite catalysts, and their applications in heavy oil cracking and hydrocracking. The mechanism of the cracking reaction of polycyclic aromatic hydrocarbons and long-chain alkanes is analyzed in detail, and the catalytic characteristics and modification methods of zeolite in the reaction process are explained. In addition, this paper summarizes the main challenges faced by zeolite catalysts in practical applications, including uneven acidity distribution, limited pore diffusion, and easy catalyst deactivation, and proposes targeted development strategies. Finally, this paper looks forward to the future development direction of zeolite catalysts in the field of heavy oil cracking and upgrading reactions, emphasizes the importance of structural optimization and multi-scale characterization, and provides theoretical support and practical reference for the design and industrial application of efficient zeolite catalysts. Full article

In unmanned aerial vehicle (UAV) aerial imagery scenarios, challenges such as small target size, compact distribution, and mutual occlusion often result in missed detections and false alarms. To address these challenges, this paper introduces YOLO-MARS, a small target recognition model that incorporates a multi-level attention residual mechanism. Firstly, an ERAC module is designed to enhance the ability to capture small targets by expanding the feature perception range, incorporating channel attention weight allocation strategies to strengthen the extraction capability for small targets and introducing a residual connection mechanism to improve gradient propagation stability. Secondly, a PD-ASPP structure is proposed, utilizing parallel paths for differentiated feature extraction and incorporating depthwise separable convolutions to reduce computational redundancy, thereby enabling the effective identification of targets at various scales under complex backgrounds. Thirdly, a multi-scale SGCS-FPN fusion architecture is proposed, adding a shallow feature guidance branch to establish cross-level semantic associations, thereby effectively addressing the issue of small target loss in deep networks. Finally, a dynamic WIoU evaluation function is implemented, constructing adaptive penalty terms based on the spatial distribution characteristics of predicted and ground-truth bounding boxes, thereby optimizing the boundary localization accuracy of densely packed small targets from the UAV viewpoint. Experiments conducted on the VisDrone2019 dataset demonstrate that the YOLO-MARS method achieves 40.9% and 23.4% in the mAP50 and mAP50:95 metrics, respectively, representing improvements of 8.1% and 4.3% in detection accuracy compared to the benchmark model YOLOv8n, thus demonstrating its advantages in UAV aerial target detection. Full article

The acquisition of approaching vessels’ information is a critical technological challenge for maritime risk warning and intelligent collision avoidance decision-making. This paper proposes a method for autonomously identifying types of approaching vessels based on an improved YOLOv8 model and ship light features, aiming to infer the propulsion mode, size, movement, and operational nature of the approaching vessels in real-time through the color, quantity, and spatial distribution of lights. Firstly, to address the challenges of the small target characteristics of ship lights and complex environmental interference, an improved YOLOv8 model is developed: The dilation-wise residual (DWR) module is introduced to optimize the feature extraction capability of the C2f structure. The bidirectional feature pyramid network (BiFPN) is adopted to enhance multi-scale feature fusion. A hybrid attention transformer (HAT) is employed to enhance the small target detection capability of the detection head. This framework achieves precise ship light recognition under complex maritime circumstances. Secondly, 23 spatio-semantic feature indicators are established to encode ship light patterns, and a multi-viewing angle dataset is constructed. This dataset covers 36 vessel types under four viewing angles (front, port-side, starboard, and stern viewing angles), including the color, quantity, combinations, and spatial distribution of the ship lights. Finally, a two-stage discriminative model is proposed: ECA-1D-CNN is utilized for the rapid assessment of the viewing angle of the vessel. Deep learning algorithms are dynamically applied for vessel type determination within the assessed viewing angles. Experimental results show that this method achieves high determination accuracy. This paper provides a kind of technical support for intelligent situational awareness and the autonomous collision avoidance of ships. Full article

The egg-shaped pressure shell, an essential component of manned submersibles, has garnered significant attention from researchers. However, the fabrication of such shells, particularly the welding process used to connect petals or frustums into a shell blank, has raised several concerns. This study investigates the distribution of welding residual stresses in a multi-segment frustum-assembled egg-shaped shell using a thermal–elastic–plastic method under an instantaneous heat source. A numerical model for a 12-segment frustum-welded egg-shaped shell is developed, and welding simulations are performed. The model’s boundary conditions are defined by cyclic symmetry, with a mesh element size of 2 mm to enhance computational efficiency. The results are validated through experimental tests. The findings indicate that the residual stress around the weld is tensile, while compressive stress is present on both sides of the weld. The length of the generatrix and the relative inclination angle significantly affect the distribution and overlap of circumferential residual stress, whereas axial residual stress primarily influences its magnitude. Finally, a simplified numerical model of the egg-shaped shell is proposed, with its simulation results showing good agreement with the distribution of welding residual stresses on the shell surface. This study provides valuable insights for optimizing the welding process of egg-shaped pressure shells in manned submersibles. Full article

Infrared ship images have low resolution and limited recognizable features, especially for small targets, leading to low accuracy and poor generalization of traditional detection methods. To address this, we design a prior knowledge auxiliary loss for leveraging the unique brightness distribution of infrared ship images, we construct a joint feature extraction module that sufficiently captures context awareness, channel differentiation, and global information, and then we propose a prior-knowledge- and joint-feature-extraction-based YOLO (PJ-YOLO) for use in detecting infrared ships. Additionally, a residual deformable attention module is designed to integrate multi-scale information, enhancing detail capture. Experimental results on the SFISD and InfiRray Ships datasets demonstrate that the proposed PJ-YOLO achieves state-of-the-art detection performance for infrared ship targets. In particular, PJ-YOLO achieves improvements of 1.6%, 5.0%, and 2.8% in mAP₅₀, mAP₇₅, and mAP_50:95 on the SFISD dataset, respectively. Full article

Traditional fault diagnosis methods for rolling bearings rely on nemerous labeled samples, which are difficult to obtain in engineering applications. Moreover, when unseen fault categories appear in the test set, these models fail to achieve accurate diagnoses, as the fault categories are not represented in the training data. To address these challenges, a zero-shot fault diagnosis model for rolling bearings is proposed, which realizes knowledge transfer from seen to unseen categories by constructing attribute information, thereby reducing the dependence on labeled samples. First, an attribute method Discrete Label Embedding Method (DLEM) based on word embedding and envelope analysis is designed to generate fault attributes. Fault features are extracted using the Swin Transformer model. Then, the attributes and features are input into the constructed model Distribution Consistency and Multi-modal Cross Alignment Variational Autoencoder (DCMCA-VAE), which is built on Convolutional Residual SE-Attention Variational Autoencoder (CRS-VAE). CRS-VAE replaces fully connected layers with convolutional layers and incorporates residual connections with the Squeeze-and-Excitation Joint Attention Mechanism (SE-JAM) for improved feature extraction. The DCMCA-VAE also incorporates a reconstruction alignment module with the proposed distribution consistency loss $L_{WT}$ and multi-modal cross alignment loss function $L_{MCA}$. The reconstruction alignment module is used to generate high-quality features with distinguishing information between different categories for classification. In the face of multiple noisy datasets, this model can effectively distinguish unseen categories and has stronger robustness than other models. The model can achieve 100% classification accuracy on the SQ dataset, and more than 85% on the CWRU dataset when unseen and seen categories appear simultaneously with noise interference. Full article

Weathered crust karst reservoirs with intricately interconnected fractures and caves are common but challenging enhanced oil recovery (EOR) targets. This paper investigated the remaining oil distribution rules, formation mechanisms, and EOR methods through physical experiments on acrylic models resembling the geological features of weathered crust reservoirs. Acrylic models with precision dimensions and morphologies were fabricated using laser etching technology. By comparing experiments under different cave filling modes and production well locations, it was shown that a higher cave filling extent led to poorer bottom water flooding recovery due to stronger flow resistance but slower rising water cut owing to continued production from the filling medium. Multi-well water and gas injection achieved higher incremental oil recovery by alternating injection–production arrangements to establish new displacement channels and change drive energy. Gas injection recovered more attic remaining oil from upper cave regions, while subsequent water injection helped wash the residual oil in the filling medium. The findings reveal the significant effects of fracture cave morphological configuration and connectivity on remaining oil distribution. This study provides new insights and guidance for EOR design optimization catering to the unique features of weathered crust karst fractured vuggy reservoirs. Full article

The detection and optimization of ocean environmental noise anomalies play a crucial role in enhancing the safety of marine engineering applications and ecological protection. Current anomaly detection methods for ocean environmental noise often suffer from issues of accuracy and robustness. To address these challenges, this paper first proposes an end-to-end framework that combines time–frequency information and expert gating, significantly improving the precision of noise sequence generation. Secondly, a Gamma distribution-based residual analysis method for anomaly detection is designed, enhancing the robustness of anomaly detection. Finally, an anomaly optimization module is developed to improve data quality, enabling efficient noise anomaly detection and optimization. Our experimental results demonstrate that the proposed model significantly outperforms traditional models in multi-frequency noise prediction, with strong robustness in anomaly detection and high generalization performance. The proposed framework offers a novel approach for analyzing the causes of noise anomalies and optimizing models. Additionally, the research outcomes provide efficient technical support for deep-sea exploration, equipment optimization, and environmental protection. Full article

Multipath is a source of error that limits the Global Navigation Satellite System (GNSS) positioning precision in short baselines. The tightly combined model between systems increases the number of observations and enhances the strength of the mathematical model owing to the continuous improvement in GNSS. Multipath mitigation of the multi-GNSS tightly combined model can improve the positioning precision in complex environments. Interoperability of the multipath hemispherical map (MHM) models of different systems can enhance the performance of the MHM model due to the small multipath differences in single overlapping frequencies. The adoption of advanced sidereal filtering (ASF) to model the multipath for each satellite brings computational challenges owing to the characteristics of the multi-constellation heterogeneity of different systems; the balance efficiency and precision become the key issues affecting the performance of the MHM model owing to the sparse characteristics of the satellite distribution. Therefore, we propose a modified MHM method to mitigate the multipath for single-frequency multi-GNSS tightly combined positioning. The method divides the hemispherical map into 36 × 9 grids at 10° × 10° resolution and then searches with the elevation angle and azimuth angle as independent variables to obtain the multipath value of the nearest point. We used the k-d tree to improve the search efficiency without affecting precision. Experiments show that the proposed method improves the mean precision over ASF by 10.20%, 10.77%, and 9.29% for GPS, BDS, and Galileo satellite single-difference residuals, respectively. The precision improvements of the modified MHM in the E, N, and U directions were 32.82%, 40.65%, and 31.97%, respectively. The modified MHM exhibits greater performance and behaves more consistently. Full article

Effective detection of the contours of cloud masks and estimation of their distribution can be of practical help in studying weather changes and natural disasters. Existing deep learning methods are unable to extract the edges of clouds and backgrounds in a refined manner when detecting cloud masks (shadows) due to their unpredictable patterns, and they are also unable to accurately identify small targets such as thin and broken clouds. For these problems, we propose MDU-Net, a multiscale dual up-sampling segmentation network based on an encoder–decoder–decoder. The model uses an improved residual module to capture the multi-scale features of clouds more effectively. MDU-Net first extracts the feature maps using four residual modules at different scales, and then sends them to the context information full flow module for the first up-sampling. This operation refines the edges of clouds and shadows, enhancing the detection performance. Subsequently, the second up-sampling module concatenates feature map channels to fuse contextual spatial information, which effectively reduces the false detection rate of unpredictable targets hidden in cloud shadows. On a self-made cloud and cloud shadow dataset based on the Landsat8 satellite, MDU-Net achieves scores of 95.61% in PA and 84.97% in MIOU, outperforming other models in both metrics and result images. Additionally, we conduct experiments to test the model’s generalization capability on the landcover.ai dataset to show that it also achieves excellent performance in the visualization results. Full article

The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which have demonstrated their capability to detect significant short-term and ionospheric anomalies preceding earthquake events. In this study, the IPI method was applied to infrared satellite observation data for the first time, and a new algorithm for extracting short-term and imminent anomalies from infrared earthquakes was explored based on the IPI method, from which we obtained the MIPI (Modified Improved Pattern Informatics Method). Using 1° × 1° nighttime Outgoing Longwave Radiation (OLR) data from NOAA_18 satellites of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC) of the United States, the evolution of OLR anomalies before the Ridgecrest Ms 6.9 earthquake in the United States on 6 July 2019 as recorded by the China Earthquake Networks Center (CENC) and the Maduo Ms 7.4 earthquake in China on 21 May 2021 as recorded by the China Earthquake Networks Center (CENC) were studied. In order to make the IPI method suitable for the calculation of OLR data, two modifications were made to the IPI algorithm: (1) the quartile method was applied for automatically determining the abnormal changes in the OLR observation data and they were used as the input data instead of ionospheric data; (2) the standard deviation of the multi-year OLR residual data of each grid was used instead of the maximum anomaly index used in the original method to re-assign and obtain the relative anomaly index, and finally the anomaly evolution time series diagram was drawn. The results show the following: (1) The MIPI method can effectively extract short-term and imminent OLR anomalies prior to earthquakes. (2) Short-term and imminent OLR anomalies appeared about two weeks before each earthquake and lasted until the earthquake occurrence, disappearing after the earthquake. During this process, the anomalies exhibited a certain evolutionary trend. (3) The short-term and imminent OLR anomalies prior to each earthquake were distributed near the epicenter or near the seismogenic fault, about 200 KM away from the epicenters. The above results are similar to the spatiotemporal evolution characteristics of seismic infrared short-term anomalies previously studied, which indicates that the MIPI method can effectively extract seismic infrared anomalies and might provide a practical method for the extraction of seismic infrared short-term and imminent anomalies. Full article

In automatic driving, accurate prediction of vehicle trajectory is the key to achieve automatic driving, and multi-vehicle joint trajectory prediction has become an important part of modern human-computer interaction systems such as automatic driving. In order to better predict vehicle trajectories, we propose a new residual diffusion model to infer the joint distribution of future multi-vehicle trajectories. This approach has several major advantages. First, the model is able to learn multiple probability distributions from trajectory data to obtain potential outcomes for vehicles to multiple future trajectories. Secondly, in order to integrate the motion characteristics of multiple vehicles in the same scene, we use the method of combining the reference denoising and multiple residual denoising to improve the model performance and prediction speed. Finally, on this basis, a general trajectory constraint function is introduced, so that the generated trajectories of multiple vehicles will not collide with each other. We perform a rich experimental comparison of various existing methods on the NGSIM dataset and demonstrate that the proposed algorithm achieves a 26% improvement on $mAP$. Full article