The rapid development of intelligent connected vehicles has led to widespread attention for platooning control, which is an effective method to mitigate a variety of societal issues. This paper is concerned with platooning control via a refined constant-time headway (CTH) strategy in the framework of model predictive control (MPC), where round-robin (RR) protocols are introduced to alleviate the communication burden. First, a dynamic model for platoon tracking errors is developed by using matrix transformation to account for the influence of the refined CTH strategy and the RR protocol. With the help of stability analysis, the original optimization problem involving unknown disturbances is transformed into an auxiliary MPC optimization problem to minimize its upper bound. Considering the cyclic characteristics of RR protocols, some sufficient conditions are then acquired to ensure the recursive feasibility of the MPC optimization problem. The desired controller parameters are obtained by means of an online optimization algorithm. Furthermore, the stability of platooning systems is disclosed under the developed sufficient conditions. Finally, the effectiveness of the devised control scheme is evaluated through numerical simulations.

High-fidelity 3-D face reconstruction is critical for enhancing personalized and immersive human–machine interaction experiences. However, existing methods struggle to capture the full spectrum of facial textures, particularly fine-scale details, such as wrinkles and pores, due to limitations in multiscale representation. To address this challenge, we propose a self-supervised multiscale hierarchical network to hierarchically model fine geometric details in multiple scales in this study. We design a global and local Markov random field loss and a detail perception loss to provide a global and local sensory field of view guidance for retaining fine-scale detail structure information of the face. In addition, we introduce a learnable Gabor-aware texture enhancement module to enhance the network’s sensitivity to fine textures. Extensive experiments show that the proposed method can reconstruct fine-scale details of the face and has superior performance to the state-of-the-art methods in terms of reconstruction accuracy and visual effect.

High-fidelity 4D dynamic facial avatar reconstruction from monocular video is a critical yet challenging task, driven by increasing demands for immersive virtual human applications. While Neural Radiance Fields (NeRF) have advanced scene representation, their capacity to capture high-frequency facial details, such as dynamic wrinkles and subtle textures from information-constrained monocular streams, requires significant enhancement. To tackle this challenge, we propose a novel hybrid NeRF framework, called Geometry-Aware-Transformer-Enhanced NeRF (GAT-NeRF) for high-fidelity and controllable 4D facial avatar reconstruction, which integrates the Transformer mechanism into the NeRF pipeline. GAT-NeRF synergistically combines a coordinate-aligned Multilayer Perceptron (MLP) with a lightweight Transformer module, termed as Geometry-Aware Transformer (GAT) due to its processing of multi-modal inputs containing explicit geometric priors. The GAT module is enabled by fusing multi-modal input features, including 3D spatial coordinates, 3D Morphable Model (3DMM) expression parameters, and learnable latent codes to effectively learn and enhance feature representations pertinent to fine-grained geometry. The Transformer’s effective feature learning capabilities are leveraged to significantly augment the modeling of complex local facial patterns like dynamic wrinkles and acne scars. Comprehensive experiments unequivocally demonstrate GAT-NeRF’s state-of-the-art performance in visual fidelity and high-frequency detail recovery, forging new pathways for creating realistic dynamic digital humans for multimedia applications.

ABSTRACT The NSGA‐II algorithm is widely applied in multiobjective mechanical structure optimization. In this study, the NSGA‐II algorithm was adopted to optimize the refractory lining structure of a ladle. First, a parametric model of the ladle was established using ANSYS Workbench, and the temperature and stress fields under typical operating conditions were calculated to generate the sample data required for training a BPNN prediction model. Second, to address the limitations of conventional BPNN, a genetic algorithm was employed to optimize the initial weights and thresholds. Taking the thicknesses of the working layer, permanent layer, and insulation layer as design variables, a GA‐BPNN single‐objective prediction model was developed, enabling high‐precision predictions of ladle mass, ladle volume, maximum ladle shell temperature, and maximum refractory lining stress. Finally, the NSGA‐II algorithm was utilized to solve the multiobjective optimization problem of the ladle refractory lining. In this optimization, ladle mass and capacity were imposed as constraints, while the maximum shell temperature and maximum lining stress were defined as objectives. In the simulation experiments, thermo‐mechanical coupling analysis was performed in ANSYS Workbench to generate 81 training samples and 10 test samples of temperature and stress data. The GA‐BPNN model optimized by the genetic algorithm achieved accurate predictions of ladle mass, volume, shell temperature rise, and lining stress. The results demonstrated that when the insulation, permanent, and working layers were 9.992, 83.998, and 137 mm thick, respectively, the ladle mass, volume, maximum shell temperature, and maximum lining stress reached 57,972.525 kg, 14.298 m 3 , 145.549°C, and 43.621 MPa. Under this parameter combination, the ladle exhibited optimal comprehensive performance in terms of insulation and service life. This method provides an effective approach to determining the optimal refractory lining structure of ladles, with significant implications for improving thermal performance, extending service life, and enhancing industrial economic efficiency.

ABSTRACT The ladle is a critical piece of equipment for transporting high‐temperature molten steel in the steelmaking process, and its operational performance directly impacts the quality of the final product, energy efficiency, and overall production costs. With the advancement of continuous casting and external refining technologies, the stability of ladles under high‐temperature and high‐intensity service conditions faces increasingly stringent demands, particularly as the issue of thermal stress damage to the refractory lining becomes more pronounced. Based on typical steelmaking conditions, this study establishes a multi‐stage service cycle model for a 350‐ton ladle. Utilizing a parameterized finite element approach, we conduct a coupled transient thermo‐mechanical simulation to analyze the temperature and stress fields, specifically investigating the synergistic effects of thermal expansion, temperature gradient, and ferrostatic pressure. The results demonstrate that thermal stress is the predominant factor responsible for lining damage. Notably, the thermal expansion behavior of the refractory materials exerts a significant influence on the distribution and magnitude of thermal stress within the ladle structure. In contrast, mechanical loads such as the static pressure from the molten steel contribute minimally to the overall stress state. Furthermore, the study reveals that the stress on the ladle shell significantly reduces after the refractory lining fails and expansion pressure diminishes, quantitatively highlighting the critical role of interfacial expansion constraints. This research provides a comprehensive theoretical foundation and valuable engineering insights for the optimized design and longevity enhancement of ladle lining structures.

Within the scope of location-based services and personalized recommendations, the challenges of recommending new and unvisited points of interest (POIs) to mobile users are compounded by the sparsity of check-in data. Traditional recommendation models often overlook user and POI attributes, which exacerbates data sparsity and cold-start problems. To address this issue, a novel multiplex hypergraph attribute-based graph collaborative filtering is proposed for POI recommendation to create a robust recommendation system capable of handling sparse data and cold-start scenarios. Specifically, a multiplex network hypergraph is first constructed to capture complex relationships between users, POIs, and attributes based on the similarities of attributes, visit frequencies, and preferences. Then, an adaptive variational graph auto-encoder adversarial network is developed to accurately infer the users’/POIs’ preference embeddings from their attribute distributions, which reflect complex attribute dependencies and latent structures within the data. Moreover, a dual graph neural network variant based on both Graphsage K-nearest neighbor networks and gated recurrent units are created to effectively capture various attributes of different modalities in a neighborhood, including temporal dependencies in user preferences and spatial attributes of POIs. Finally, experiments conducted on Foursquare and Yelp datasets reveal the superiority and robustness of the developed model compared to some typical state-of-the-art approaches and adequately illustrate the effectiveness of the issues with cold-start users and POIs.

In this article, a neural-network (NN)-based approach of distributed state estimation with probabilistic constraints is proposed for a class of nonlinear systems over sensor networks. For the discussed plant, the unknown nonlinear dynamics are approximated by resorting to NNs and the communication among estimators and sensors is scheduled by encoding–decoding schemes. The goal of the addressed problem is to design a distributed estimator such that, in the presence of the bounded noises, all possible errors are confined to some certain region in a predetermined probability while achieving the exponentially bounded performance in a finite time domain. In light of the matrix operation, some sufficient conditions are obtained to ensure the existence of the desired gains of estimators, which are computed by dealing with the corresponding matrix inequalities in an iterative way. The effectiveness of the proposed distributed state estimation method is verified by presenting an example of a one-track model.

Cyber-physical systems (CPSs) take on the characteristics of both multiple rates of information collection and processing and the dependency on information exchanges. The purpose of this paper is to develop a joint recursive filtering scheme that estimates both unknown inputs and system states for multi-rate CPSs with unknown inputs. In cyberspace, the information transmission between the local joint filter and the sensors is governed by an adaptive event-triggered strategy. Furthermore, the desired parameters of joint filters are determined by a set of algebraic matrix equations in a recursive way, and a sufficient condition verifying the boundedness of filtering error covariance is found by resorting to some algebraic operation. A state fusion estimation scheme that uses local state estimation is proposed based on the covariance intersection (CI) based fusion conception. Lastly, an illustrative example demonstrates the effectiveness of the proposed adaptive event-triggered recursive filtering algorithm.

Video summarization (VS) technologies can automatically extract key frames with effective information and thus can help to quickly identify the events or speed up the decision-making process, especially for accidents. With the fast development of deep learning technologies, many generative adversarial network (GAN)- and reinforcement learning (RL)-based unsupervised VS methods have been developed in recent years. However, these methods could suffer from the problems of unstable training and difficulty of reward function formulation, respectively. To this end, we present an unsupervised VS method called diffusion model of feature fusion (DMFF) in this article, which consists of a diffusion module (DM), a feature extraction and compression module (FECM), and a coarse-fine frame selector (CFFS). DM is designed to avoid the training instability problem caused by GAN’s alternate training generator and discriminator. FECM is used to extract and compress video features. CFFS is designed to capture both low-level and high-level features between frames to handle complex and diverse accident videos. Then, high-level local and global features are fused to generate a multigrained final frame score. Experiments on two widely used benchmark datasets, SumMe and TVSum, demonstrate the effectiveness and superiority of the proposed network to the state-of-the-art methods, and the training is more stable.

Autonomous driving has gradually become a research hotspot in recent years, but the robustness of loopback detection in complex environments such as dynamic and weak textures needs to be improved. A semantic loopback detection method is proposed based on instance segmentation and visual SLAM to make sufficient use of semantic information in autonomous driving. The proposed method combines image segmentation and visual SLAM (Simultaneous Localization and Mapping) to construct a semantic SLAM system. What’s more, a data association method that combines semantic and geometric information is proposed to improve the traditional loopback detection method by using semantic information to increase the accuracy of loopback detection. The result of experiment on the TUM public dataset shows that the loopback detection accuracy of the improved loopback detection method is higher than that of the bag-of-words method in all four datasets, and our proposed algorithm can effectively improve the accuracy of loopback detection of the SLAM system in general.

Summary As the development of deep learning and the continuous improvement of computing power, as well as the needs of social production, target detection has become a research hotspot in recent years. However, target detection algorithm has the problem that it is more sensitive to large targets and does not consider the feature‐feature interrelationship, which leads to a high false detection or missed detection rate of small targets. An small target detection method (C‐SSD) based on improved SSD is proposed, that replaces the backbone network VGG‐16 of the SSD network with the improved dense convolution network (C‐DenseNet) network to achieves further feature fusion through fast connections between dense blocks. The Introduction of residuals in the prediction layer and DIoU‐NMS further improves the detection accuracy. Experimental results demonstrate that C‐SSD outperforms other networks at three different image scales and achieves the best performance of 83. A 8% accuracy on the PASCAL VOC2007 test set, proving the effectiveness of the algorithm. C‐SSD achieves a better balance of speed and accuracy, showing excellent performance in rapid detection of small targets.

Abstract With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human‐computer interaction more natural and flexible, bringing richer interactive experience to teaching, on‐board control, electronic games, etc. In order to perform robust recognition under the conditions of illumination change, background clutter, rapid movement, partial occlusion, an algorithm based on multi‐level feature fusion of two‐stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains RGB‐D images to establish a gesture database. At the same time, data enhancement is performed on training and test sets. Then, a model of multi‐level feature fusion of two‐stream convolutional neural network is established and trained. Experiments result show that the proposed network model can robustly track and recognize gestures, and compared with the single‐channel model, the average detection accuracy is improved by 1.08%, and mean average precision (mAP) is improved by 3.56%. The average recognition rate of gestures under occlusion and different light intensity was 93.98%. Finally, in the ASL dataset, LaRED dataset, and 1‐miohand dataset, recognition accuracy shows satisfactory performances compared to the other method.

Summary The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down‐sampling of low‐level features and up‐sampling of deconvolution of high‐level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small‐scale target.

SUMMARY With the promotion of smart grid construction work, the use of high‐precision and high‐efficiency substation inspection robot has become the development trend of substation inspection. A multi‐scale feature fusion meter target detection algorithm is proposed to address the problems of low efficiency and susceptibility to surrounding environmental factors by the traditional manual meter reading method. Kinecct is used to acquire color images of substation meters with different backgrounds, light intensities, and angles to build a substation meter dataset. Based on the complementarity and correlation of multi‐scale features, an SSD target detection model with multi‐scale feature fusion is established, and the performance of the algorithm is tested on the constructed dataset, and comparative experiments are conducted to verify the effectiveness of the algorithm for target detection accuracy improvement.

Summary Instance segmentation is a challenging task that requires both instance‐level and pixel‐level prediction and it has a wide range of applications in autonomous driving, video analysis, scene understandingand so on. The currently dominant instance segmentation methods have excellent accuracy, but they are slow, and the processing speed will be even less satisfactory if the input is a large‐scale image. In order to improve the efficiency and accuracy of instance segmentation of large‐scale images, this article modifies the backbone network based on YOLACT network, adds a multi‐information fusion module and provides an improved BiFPN method to achieve multi‐scale feature fusion, while adding two branches to the first level detector RetinaNet to achieve instance segmentation. The network model is tested on Cityscapes dataset and the results of the experiments show that the improved instance segmentation network in this article improves the accuracy while ensuring the speed of segmentation. The optimized network model size was reduced by 17% compared to YOLACT, and the mAP, mAP50, and mAP75 were improved by 18.3%, 32.1%, and 24.6%, respectively.

Summary With the continuous development of sensor technology, the acquisition cost of RGB‐D images is getting lower and lower, and gesture recognition based on depth images and Red‐Green‐Blue (RGB) images has gradually become a research direction in the field of pattern recognition. However, most of the current processing methods for RGB‐D gesture images are relatively simple, ignoring the relationship and influence between its two modes, and unable to make full use of the correlation factors between different modes. In view of the above problems, this paper optimizes the effect of RGB‐D information processing by considering the independent features and related features of multi‐modal data to construct a weight adaptive algorithm to fuse different features. Simulation experiments show that the method proposed in this paper is better than the traditional RGB‐D gesture image processing method and the gesture recognition rate is higher. Comparing the current more advanced gesture recognition methods, the method proposed in this paper also achieves higher recognition accuracy, which verifies the feasibility and robustness of this method.

Summary Recently, most deep convolutional neural networks used for image super‐resolution have achieved impressive performance on ideal datasets. However, these methods always fail in real‐world super‐resolution, and the results are blurred and structurally deformed. In this paper, a multiscale generative adversarial network (MGAN) is proposed to alleviate these issues. The model's multiscale loss function can effectively reduce the solution space and obtain the best features to reconstruct the image. The degraded framework based on kernel estimation and noise injection is mainly applied to obtain LR images that share the same domain with real‐world pictures. Moreover, the gradient branch is presented to provide other structural priors for SR processing. Simultaneously, to obtain better visual effects, LPIPS is used for perceptual losses instead of Visual Geometry Group (VGG). The competitive results show that our MGAN model outperforms the state‐of‐the‐art methods, resulting in lower noise and better visual quality, and reflects the superiority in image structure restoration.

Abstract Robot grasping technology is a hot spot in robotics research. In relatively fixed industrialized scenarios, using robots to perform grabbing tasks is efficient and lasts a long time. However, in an unstructured environment, the items are diverse, the placement posture is random, and multiple objects are stacked and occluded each other, which makes it difficult for the robot to recognize the target when it is grasped and the grasp method is complicated. Therefore, we propose an accurate, real‐time robot grasp detection method based on convolutional neural networks. A cascaded two‐stage convolutional neural network model with course to fine position and attitude was established. The R‐FCN model was used as the extraction of the candidate frame of the picking position for screening and rough angle estimation, and aiming at the insufficient accuracy of the previous methods in pose detection, an Angle‐Net model is proposed to finely estimate the picking angle. Tests on the Cornell dataset and online robot experiment results show that the method can quickly calculate the optimal gripping point and posture for irregular objects with arbitrary poses and different shapes. The accuracy and real‐time performance of the detection have been improved compared to previous methods.

Summary For meeting the comprehensive requirements of “super insulation,” “lightweight,” and “longevity” of the contemporary ladle, this article designs a new kind of lightweight ladle with heat preservation and longevity performance, and based on steady‐state analysis method and numerical simulation technology, the comparison of temperature distribution between new lightweight and traditional ladle under typical operating modes is made and analyzed. The simulation results of temperature field prove that the performance of heat preservation in new kind of lightweight ladle has been improved obviously from the two aspects of ladle shell temperature and molten steel file rate. At the same time, the simulation results of stress field indicate that the stress of designed lightweight ladle reduced and distributed more evenly, which is conducive to prolonging the work time in‐service of the ladle. Finally, based on the field test, the simulation is proved to be effective, and the designed ladle structure achieves the expected purpose.