ABSTRACT As a critical link in the steel metallurgy process, the automation level of ladle masonry directly influences the service life of the ladle and metallurgical quality. Considering that the ladle masonry manipulator is susceptible to environmental disturbance, sensor noise, system parameter variations, and other disturbances in complex environments, this paper proposes an active disturbance rejection control method based on a fixed‐time observer. A multi‐power fixed‐time extended state observer is constructed to ensure that complex disturbances converge to the equilibrium point within a fixed time, enabling accurate estimation of total system disturbances within a fixed‐time framework. Simulation results demonstrate that this method effectively overcomes the degradation of observation performance in the extended state observer caused by large initial observation errors, thereby enhancing the trajectory tracking accuracy and robustness of the manipulator.

Contemporary deep learning approaches for optical flow estimation continue to face persistent challenges in model interpretability, generalization capacity, and deployment efficiency, significantly constraining their practical implementation. This limitation becomes particularly critical in applications such as visual odometry (VO), where precise sparse point tracking supersedes the conventional emphasis on dense optical flow accuracy. Moreover, the lack of a joint framework combining keypoint detection and optical flow estimation limits sparse optical flow performance. To address these fundamental issues, we propose a novel dual-task imperative learning framework that synergistically optimizes sparse optical flow estimation (iFLOW) with adaptive keypoint detection (iPOINT). Our methodology implements an Expectation-Maximization (EM) paradigm where iFLOW and iPOINT undergo alternating optimization through a Gauss-Newton reasoning engine. This innovative architecture leverages convolutional feature advantages under the generalized feature invariance principle. The resulting imperative learning mechanism imbues our framework with enchanced interpretability and cross-domain adaptability while maintaining computational efficiency. Through comparative evaluations against classical and learning-based baselines, our ultra-compact models (0.05M parameters for iFLOW, 0.09M for iPOINT) demonstrate remarkable performance across multiple metrics (End-point Error, F1-all, VO trajectory accuracy) despite requiring only 200 training image pairs.

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 In recent years, intelligent control methods based on surface electromyographic signals (sEMG) have received extensive attention in the fields of bionic prosthetics and human‐computer interaction. Compared with high‐density sampling, sparse sampling sEMG has significant advantages due to its convenient collection, strong non‐intrusiveness and high equipment flexibility. However, it has deficiencies in spatial resolution and muscle activity information acquisition, which can easily lead to a decline in the recognition accuracy of complex gestures. In view of the limitations of sparse sEMG in feature extraction and channel information utilization, this paper proposes a hybrid neural network SERes‐L based on the attention mechanism. This model introduces the squeeze‐and‐excitation (SE) attention module in the residual unit to construct a residual attention structure to enhance the channel modeling of key information. At the same time, it combines the long short‐term memory network (LSTM) to capture the long‐term temporal dependencies in gesture actions, achieving a deep integration of spatial and temporal features. Experiments on the self‐built dataset show that SERes‐L achieves an average recognition accuracy rate of 93.90%. To verify its generalization ability, further tests were conducted on three public datasets, NinaPro DB1, DB4, and DB5. The average classification accuracy rates of the model reached 90.28%, 84.72%, and 91.87% respectively, significantly outperforming many existing mainstream methods. The above results indicate that the proposed SERes‐L architecture can effectively alleviate the challenges of insufficient utilization of channel information and inadequate feature extraction in sparse sEMG, demonstrating excellent generalization performance and practical application potential.

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.

To address the issue of diverse monitoring data types, high dimensionality, and sparse values, which significantly affect the accuracy of mechanical equipment’s remaining useful life (RUL) prediction, this study proposes a novel aircraft engine RUL prediction method utilizing a feature selection strategy. Initially, based on the monitoring data sequences from different sensors, a feature selection criterion was developed to screen data of high-contribution as inputs for the prediction model. Subsequently, a regression variational autoencoder network was constructed for latent space interpretability in feature extraction, to intuitively express the latent space mapping form and confirm the contribution of the preferred features to the prediction and the representation ability of the degraded features. Finally, the dilated causal convolution network and nested Long Short-Term Memory (LSTM) network were utilized to achieve aircraft engine RUL prediction using the C-MAPSS dataset. In comparison with existing research, this method has effectively reduced prediction errors, achieving the lowest RMSE values in the FD002 and FD004 datasets. Additionally, it has also achieved favorable outcomes in the FD001 and FD003 datasets.

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.

Summary Sliding mode control is one of the common control methods for manipulators, but the discontinuity of sliding mode control can cause jitter and vibration of the manipulator system. This article takes the Dobot Magician manipulator as the research object and constructs a simplified model of the manipulator dynamics. And the adaptive fuzzy sliding mode control method is designed in combination with fuzzy control theory, which effectively solves the jitter problem of the control torque. In the MATLAB/Simulink simulation environment analysis show that the proposed adaptive fuzzy sliding mode control method solves the jitter problem in sliding mode control with good stability, robustness and tracking performance. By combining the adaptive fuzzy sliding mode control method with the manipulator and comparing the motion time of the same motion trajectory with the PID control method in Dobot Studio, the hardware experiment results effectively verify the feasibility and effectiveness of the designed control 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.

Abstract In sEMG (surface electromyography) pattern recognition, most of the research focuses on the static pattern recognition of different limbs, ignoring the importance of changing load intensity, and joint angle movement information. Traditional static qualitative pattern recognition cannot adjust the motion amplitude and load intensity, so it is of great significance to study the continuous prediction of wrist angle under different load intensities. Based on the correlation between the surface EMG signal and the joint angle signal, the article is based on the neural network to identify and predict the wrist angle under different loads continuously quantitatively. The sEMG signal in this article was collected with the approval and review of the Ethics Committee and the people's informed consent. Since qualitative pattern recognition cannot adjust the wrist movement range and the different load training intensity, the article establishes an angle prediction model based on a genetic algorithm to optimize the extreme learning machine (ELM). In addition, the article analyzes the influence of different loads on the continuous prediction accuracy of the wrist angle, realizes the continuous quantitative angle of the precise wrist prediction. Experimental analysis shows that the wrist joint angle predicted by the ELM optimized based on genetic algorithm is close to the actual angle, and the average error is about 5.96 degrees.

Summary Target localization in unknown environment is one of the development directions of mobile robots. Simultaneous localization and mapping (SLAM) can be used to build maps in unknown environments, but it has the problem of poor readability and interactivity. In this article, target detection and SLAM are combined to search and locate the target by using rich RGBD images information. The determined position in the global map is conducive to the follow‐up operation of the target by mobile robots. By establishing a local dense point cloud map of the target object, the current state of the target object is directly displayed, the readability of the map is improved, and the disadvantages of difficult understanding of the global sparse map and slow construction of the global dense map are avoided. A target localization algorithm under the framework of yolov4 is designed to apply in the process of SLAM global mapping. Our works are helpful for obtaining positions of objects in three‐dimensional space. The experimental results show that the time‐consuming of this method in dense mapping is reduced by 50%–70%, and the number of point clouds is also reduced by 60%–70%.

Abstract The manipulator workspace is an essential element in the field of manipulator research and is of great significance for manipulator motion planning. However, little research has been conducted on dividing the manipulator workspace into working subspaces. No precise division method has been proposed; the inverse kinematics of multiple solutions in manipulator trajectory planning may also cause abrupt joint changes, thus affecting the planned trajectory. The article proposes a working subspace division method for all ball‐wrist 6DOF(degree‐of‐freedom) manipulators that satisfy the Piper criterion to address the above problems. The kinematic model of the manipulator is established, and the Jacobi matrix of the manipulator is obtained. The space of joints of the manipulator is divided into unique domains containing only single inverse kinematic solutions by means of singular trajectory lines when the determinant of the Jacobi matrix is zero; The solution from the joint space to the workspace is achieved by a nonlinear mapping, which completes the partitioning of the work subspace, and each work subspace contains only unique inverse kinematic solutions. When trajectory planning is carried out from the independent area of a single workspace to the overlapping area of multiple workspaces, selecting the inverse kinematic solution in a single working subspace can effectively avoid abrupt changes in the joints of the manipulator and trajectory misalignment caused by numerous inverse solution selection problems and make the planned trajectory smooth and consistent with the operational requirements of each scene.

Summary A three‐dimensional (3D) reconstruction method of structured light based on photoelastic fringes was proposed in this research. The photoelastic fringes are produced by both simulation and a polycarbonate disk under diametric compression load. Six fringes are projected onto an object by using the six‐step phase shifting technique. Therefore, the isochromatic phase image is calculated. After phase unwrapping, the isochromatic phase image can be used for 3D reconstruction. In order to verify the effectiveness of this method, two experiment devices were built by using projector and photoelastic instrument, respectively. The results show that the fringe pattern based on photoelasticity can be used for 3D reconstruction as a structured light pattern. Compared to the simulation results, the fringes produced by load are more blurred. In order to obtain a better reconstruction result, a large load should be applied to produce dense fringes.

Abstract A manipulator is a complex electromechanical system that is nonlinear, strongly coupled, and uncertain. Achieving its precise and high‐quality trajectory control is difficult. Sliding mode control (SMC) is one of the common control methods for manipulators. However, discontinuities in SMC can cause jitter and vibration in the manipulator system, leading to a reduction in the performance of the control system. For the self‐adaptive capability jitter vibration problem of SMC, the Dobot magician manipulator is treated as the research object in this article. The dynamics equations of the manipulator are established by Lagrange method, and a simplified model of the manipulator dynamics is constructed. The method of self‐adaptive sliding mode control is proposed. Self‐adaptive parameters are added to the SMC to achieve self‐adaptive adjustment of the SMC parameters. In the MATLAB/Simulink simulation environment analysis show that the self‐adaptive SMC method has better self‐tuning ability and trajectory tracking ability than the traditional SMC, and weakens the jitter phenomenon existing in the traditional SMC.

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 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.