QoE enhancement of the industrial metaverse based on Mixed Reality application optimization

As a comprehensive integration of many new-generation information technologies, the metaverse has become a research hotspot that has attracted much attention. As a part of the metaverse, the industrial metaverse is expected to break through the constraints of space and time and promote high-quality industrial development. The industrial metaverse is human-centric, so its quality of experience (QoE) is a key topic. As one of the enabling technologies of the industrial metaverse, Mixed Reality (MR) can seamlessly integrate virtual information with the physical world and is widely regarded as an important window to the industrial metaverse. In close integration with other enabling technologies, industrial MR applications can be seen as a path toward the realization of the industrial metaverse; thus, the optimization of industrial MR applications can effectively achieve the QoE enhancement of the industrial metaverse. Based on the analysis of existing research and the characteristics of industrial scenarios, consistency, authenticity, smoothness, and comfort are identified as the factors that influence the user experience (UX) of industrial MR applications. Specific optimization methods for industrial MR applications are proposed to improve the UX with regard to these four factors. To verify the effectiveness of the proposed methods, a QoE evaluation model of the industrial metaverse based on the fuzzy analytic hierarchy process (FAHP) is established. Moreover, an industrial metaverse prototype for longwall mining that incorporates the proposed methods is developed and its QoE is evaluated. The results show that the proposed optimization methods for industrial MR applications significantly enhance the QoE in the industrial metaverse, and can provide better services for users in industrial systems, thus better serving these systems.     

Computation and analysis of aero-optic imaging deviation of a blunt nosed aircraft with Mach number 0.5—3

Aero-optic imaging is a kind of optical effect, which describes the imaging deviation on the imaging plane. In this paper, the effect of the change of Mach number of blunt aircraft on the aero-optic imaging deviation is studied. The imaging deviations of Mach number 0.5—3 are analyzed systematically. The results show that with the increase of Mach number, imaging deviation increases gradually, and the increase rate is gradually slow. Imaging deviation slope decreases gradually with the increase of Mach number, and gradually tends to be zero, suggesting that imaging deviation is not sensitive to the change of the larger Mach number. In other words, the Mach number of smaller changes can lead to larger imaging deviation. As the Mach number of the aircraft increases, the slope of the imaging offset tends to be closer and closer to 0. When the Mach number of the aircraft increases to a certain extent, the change of the imaging offset will not have much influence. Therefore, in order to reduce the impact of flight speed on imaging migration, the aircraft should fly at a higher Mach number.     

Efficient skeleton curve-guided five-axis sweep scanning based on optimal traversing of multiple connected skeleton curves

The five-axis sweep scanning approach is an emerging surface inspection technology which could tremendously boost the inspection efficiency through working in the way of continuous scanning. While inspecting the surfaces with multiple connected skeleton curves, the topological complexity brings conflict between achieving efficient inspection and working in continuous manner. Recently, a skeleton curve-guided five-axis sweep scanning method was proposed to tackle this problem but the resulting inspection path has to inspect the entire surface in a round-trip way. The manner of round-trip inspection pulls down the entire inspection efficiency and should be avoided as much as possible. In this paper, we present an improved skeleton curve-guided five-axis sweep scanning method to generate a more efficient five-axis scanning path for the surface with multiple connected skeleton curves. The proposed method starts from the framework of existing skeleton curve-guided five-axis sweep scanning method. Under the unique kinematic requirements of efficient five-axis sweep scanning, an integer linear programming optimization approach is utilized to optimally connect the inspection paths on independent surface patches and form a shorter skeleton curve-based sweep scanning path as compared with the existing skeleton curve-guided five-axis sweep scanning method. The resulting inspection path is composed of the single-pass inspection for most of the surface and the round-trip inspection for a small part of the surface. The comparison experiments are conducted on two surfaces with multiple connected skeleton curves. Experiment results show that the proposed method significantly outperforms the method provided by the leading commercial software Apexblade and the original skeleton curve-guided five-axis sweep scanning method.     

Multiple source partial knowledge transfer for manufacturing system modelling

Transfer learning has shown its attractiveness for manufacturing system modelling by leveraging previously acquired knowledge to assist in training the target model, whereas most techniques focus on single-source transfer settings. Since there are usually multiple source domains available in practice, multi-source transfer learning is attracting more attention. Existing researches regard the source instance or the source model as the basic information granularity, which makes it difficult to reduce the global shift and the local discrepancy across domains simultaneously. Therefore, this paper presents a multiple source partial knowledge transfer method (MSPKT) for manufacturing system modelling tasks, in which partial knowledge is defined as a novel information granularity between the instance granularity and model granularity. Firstly, TSK (Takagi-Sugeno-Kang) fuzzy system is introduced as the basic learner to represent partial knowledge effectively. Then, we design a transferability measurement of partial knowledge by considering the similarity and reliability to support transfer learning with multiple source domains. Finally, a synthetic dataset and two manufacturing system datasets are used to verify the effectiveness of the proposed method.     

Sparse identification for ball-screw drives considering position-dependent dynamics and nonlinear friction

Establishing accurate dynamic models in a form that is suitable for integration with model-based control methods, is of great significance for further improving the dynamic motion control precision of ball-screw drives. However, due to the nonlinear time-varying factors such as position-dependent dynamics and nonlinear friction disturbance, it is difficult to model the dynamic characteristics of ball-screw drives accurately, concisely and efficiently. To overcome this challenge, a sparse identification method for ball-screw drives is proposed. Ball-screw drives are modeled as discrete-time linear parameter-varying systems under nonlinear friction disturbance, and two types of dictionary function libraries are designed to represent the position-dependent dynamics and nonlinear friction respectively. After constructing the regression form of the system model, a stepwise sparse regression policy is proposed to solve all the coefficients of dictionary functions. The proposed method is verified in both simulation and real environments. The results both show that by the proposed method, an accurate and linearizable dynamic model of ball-screw drives can be identified only using the data from only one global random excitation experiment covering the working stroke.     

Differential geometry modeling and application of roller pose and trajectory of robot roller hemming for complex curved surface-curved edge panels

Roller hemming is a relatively new process used to achieve high-precision assembly of auto-body enclosure panels. During the process of roller hemming, accuracy of the roller pose and trajectory affects the hemming quality of the product. The traditional passive method based on robot teaching to determine the pose of the roller is inefficient and time-consuming. In these studies, we proposed an active method for solving roller pose and trajectory based on differential geometry for curved surface-curved edge geometric characteristics of auto-body enclosure panels and multi-pass reciprocating motions of the roller. Firstly, the local coordinate system of the die was constructed based on the Frenet Frame according to the normal vector of the surface of die and the tangent vector of the curved die edge. Secondly, the coordinate system of the die, diameter of the roller, TCP-RTP value, and inclination of the roller were combined to form the roller pose based on a homogeneous transformation matrix. Based on the obtained trajectory curve of the roller reference point, the equal chord deviation error method was used to analyze the roller trajectory. Finally, a roller pose and trajectory solving algorithm was developed based and implemented using PYTHON to obtain the positions and poses of the roller at several discrete reference points. ABAQUS software was subsequently utilized to complete modeling of the roller pose and trajectory. This research supports the multi-field mechanical simulation of robot roller hemming for curved surface-curved edge panels and provides support for determining roller pose and kinematic trajectory of industrial robot roller hemming for curved surface-curved edge panels.     

融合多尺度特征的复杂手势姿态估计网络

目的 基于单幅RGB图像的手势姿态估计受手势复杂性、手指特征局部自相似性及遮挡问题的影响,导致手势姿态估计准确率低。为此,提出一种面向单目视觉手势姿态估计的多尺度特征融合网络。方法 1）采用ResNet50(50-layer residual network)模块从RGB图像提取不同分辨率特征图,通过通道变换模块显式地学习特征通道间的依赖关系,增强重要的特征通道信息,弱化次要的特征通道信息。2）在全局回归模块中,通过设计节点间的连接方式融合不同分辨率特征图,以便充分利用图像的细节与整体信息。采用局部优化模块继续提取更深层的特征信息,获得手部关节点的高斯热图,以此修正遮挡等原因造成部分关节点回归不准确的问题。3）计算经通道变换模块处理后的最小特征图,通过全局池化和多层感知机处理该特征图以获得手势类别和右手相对于左手的深度。4）综合以上结果获得最终的手势姿态。结果 采用InterHand2.6M和RHD(rendered handpose dataset)数据集训练多尺度特征融合网络,评估指标中根节点的平均误差和关节点的平均误差,均低于同类方法,且在一些复杂和遮挡的场景下鲁棒性更高。在In...     

Extended state observer-based finite-region control for 2-D Markov jump systems

In this article, an extended state observer-based finite-region control scheme is presented for two-dimensional Markov jump systems with unknown mismatched disturbances. The mathematical model of the two-dimensional Markov jump systems is built on the well-known Roesser model. By establishing special recursive formulas and utilizing the 2-D Lyapunov function theory, sufficient conditions are obtained, which prove that the resultant system is finite-region bounded, if some linear matrix inequalities are achieved. Then, we provide an algorithm to solve the extended state observer-based controller gains. With the proposed control scheme, the external disturbances can be actively rejected from the system outputs. To conclude, a numerical example based on the Darboux equation is provided to demonstrate the validity and effectiveness of the devised control scheme.     

Coordinated scheduling of production and logistics for large-scale closed-loop manufacturing using Benders decomposition optimization

This paper studies coordinated scheduling of production and logistics for a large-scale closed-loop manufacturing system by integrating its manufacturing and recycling process. In addition to the forward manufacturing process, different recycling units in reverse recycling process are also studied. A decentralized network is designed to formulate the coordinated scheduling problem as a mixed integer programming model with both binary and integer variables. As the problem for closed-loop manufacturing is large-scale and computational-consuming in nature, the model is divided into integer variable sub-models and complex binary variable sub-models for preprocessing and reprocessing respectively. An iterative solution approach by Benders decomposition is developed to accelerate the solving efficiency in large-scale case by updating custom constraints. A case study is conducted to investigate the managerial implications of the decentralized network for the closed-loop manufacturing system. Computational experiments demonstrate the validity and efficiency of the proposed iterative solution approach for the large-scale scenarios.     

Railcar reallocation optimization on water-rail network under uncertain busyness

Water-rail intermodal transportation can reduce cargo losses and transportation transferring costs. However, the imbalance between the capacity of the scheduled railway network and the large container freight demand greatly reduces operational efficiency. To minimize the total transportation cost and relocation cost, a railcar reallocation stochastic optimization model is formulated to deal with uncertain congestion in the railway network. To capture the uncertain busyness and queuing pattern, a hypercube spatial queue model is embedded in the optimization model by estimating the expected queue length and waiting time. To solve the proposed nonlinear nonconcave stochastic model, an approximate hypercube based iterative algorithm is proposed. A real-world case study is presented to show the effectiveness and efficiency of the proposed method. The proposed model outperforms the comparable deterministic model in the objective value. Sensitivity analyses on the ratio of the unit waiting cost and the unit travel cost for empty cars, and the total number of freight cars show the robustness of the proposed method.