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.

Autonomous driving has gradually become a research hotspot in recent years. Visual Simultaneous Localization and Mapping (SLAM) technology can help unmanned vehicles accurately explore the environment at a lower cost, and the readability of the map can be improved by integrating target detection algorithms. However, the location and 3D shape of the object in the map were not obtained. The method of RGBD-SLAM based on object detection with two-stream YOLOv4-MobileNetv3 convolutional neural network is proposed in this paper. RGBD SLAM algorithm and target detection algorithm are combined to build an algorithm model that can generate the global sparse map and build target dense map quickly. The two-stream network is integrated to obtain 2D information about the target, and further combined with the camera pose after the front-end key frame detection of the SLAM algorithm in this paper, and the dense 3D point cloud of the target and the center point position of the object is obtained. Then, the sparse point cloud of the SLAM system and the dense point cloud of the target can be obtained. The experimental results show that the number of point clouds decreases by about 50% and the time for mapping accounts for about 60% of the global dense mapping time. The method of this paper can efficiently decrease the computational space and improve the speed of semantic mapping, which verifies its feasibility and superiority. It can be used to achieve large-area mapping and the ability to update maps during autonomous driving.

Summary With the wide application of stereovision in SLAM, object pose estimation has gradually become one of the research hotspots. This article proposes an object pose estimation for robotic grasping based on stereo vision with improved K‐D tree ICP algorithm. The feature points and feature descriptors of the point cloud of the object to be captured are extracted, and the feature template set is established. The SAC‐IA algorithm is used to carry out initial registration of the point cloud of the target, and the ICP algorithm based on K‐D tree is used for fine registration. The experimental results show that the average coincidence degree of the final registration of the proposed object pose estimation method reaches 94.1%, and the accurate 6D pose of the object to be grasped is obtained.

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.

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.

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.

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 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.