An effective MBSE approach for constructing industrial robot digital twin system

Recently, the rapid development of digital twin (DT) technology has been regarded significant in Cyber-physical systems (CPS) promotion. Scholars are focusing on the theoretical architecture and implementing applications, in order to establish a high-fidelity, dynamic, and full-lifecycle DT model and achieve a deep fusion of real and virtual. As a typical complex system with multi-disciplines, multi-physics, and multi-domain characteristics, industrial robot (IR) involves various processes and elements from the two other levels of the system: components and production lines. Their complex relationships lead to a huge challenge to build a comprehensive DT model. Current researchers usually concentrates on single-layer services because of limited construction methodology, which results in enormous isolated models, and leads to low reusable system blocks, finite scalability, and high costs of design, adjustment, upgrade, and maintenance. To address these issues, a standardized methodology and a hierarchical, modular, and generic architecture are proposed to depict comprehensive and variable industrial robot digital twin (IRDT). Firstly, the ontology information model is presented by analyzing variable factors systematically. Then, model-based system engineering (MBSE) based methodology is introduced, including construction process and variants management. After modeling process of three levels (problem domain, solution main, and implementation domain) and four viewpoints (requirement, structure, behavior, and parameter), a generic architecture of IRDT is constructed and a feature-based variants management method is described. Besides, a six-axis IRDTS is implemented to illustrate the mapping of logical architecture and physical system as a multi-level elements and processes representation example. And the steps of numerical evaluations consist of system delay and derivation. Finally, results show the effectiveness and the potential of the proposed theoretical methodology for constructing IRDTS and other industrial applications.     

A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation

The combination of Augmented Reality (AR) and Digital Twin (DT) has begun to show its potential nowadays, leading to a growing research interest in both academia and industry. Especially under the current human-centric trend, AR embraces the potential to integrate operators into the new generation of Human Cyber–Physical System (HCPS), in which DT is a pillar component. Some review articles have focused on this topic and discussed the benefits of combining AR and DT, but all of them are limited to a specific domain. To fill the gap, this research conducts a state-of-the-art survey (till 17-July-2022) from the AR-assisted DT perspective across different sectors of the industrial field, covering a total of 118 selected publications. Firstly, application scenarios and functions of AR-assisted DT are summarized by following the engineering lifecycle, among which production process, service design, and Human–Machine Interaction (HMI) are hot topics. Then, improvements specifically brought by AR are analyzed according to three dimensions, namely virtual twin, hybrid twin, and cognitive twin, respectively. Finally, challenges and future perspectives of AR-assisted DT for futuristic human-centric industry transformation are proposed, including promoting product design, robotic-related works, cyber–physical interaction, and human ergonomics.     

An Efficient One-Arrow-Two-Hawks Strategy Achieves High Efficiency and Stable Batch Variance for Benzodifuran-based Polymer Solar Cells

With the development of organic solar cells (OSCs), the high-performance and stable batch variance are becoming a new challenge for designing polymer donors. To obtain high photovoltaic performance, adopting polymers with high molecular weight as donors is an ordinary strategy. However, the high molecular weight need to subtly control the reaction time and state, inevitably caused batch-to-batch variations. Herein, a strategy of steric effect is applied to benzodifuran (BDF)-based polymer by introducing different positions of Cl atom, producing two polymers PBDFCl-1 and PBDFCl-2. The more twisted side chains conformation not only achieve the control of moderate molecular weight for PBDFCl-2, but also easily form molecular stacking through adopting BDF unit and maintain sufficient polymeric crystallinity. Due to the optimized stacking mode and good blend miscibility, PBDFCl-2-based device exhibitsa more elegant power conversion efficiency (PCE) of 17.00% compared to PBDFCl-1-based device. This is the highest efficiency record for BDF-based binary OSCs. Meanwhile, the PCE device variation of the different molecular weights for PBDFCl-2 is little, indicating the reduction of the batch variation. Therefore, smartly using steric effect of Cl atom in strong crystalline BDF unit can form efficient molecular stacking regulations and realize the coordination of high-performance and stable batch variance.     

Proximity-Induced Fully Ferromagnetic Order with Eightfold Magnetic Anisotropy in Heavy Transition Metal Oxide CaRuO3

Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO₃ in (001)-(LaMnO₃/CaRuO₃) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω⁻¹ cm⁻¹ and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La_0.67Sr_0.33MnO₃. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO₃ layers are in a fully ferromagnetic state (∼0.8 μ_B/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO₃ and CaIrO₃ (∼0.1 μ_B/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO₃/CaRuO₃) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage.     

基于多模态图像融合的早期蕈样肉芽肿识别

早期蕈样肉芽肿（Mycosis fungoid, MF）可表现为红斑鳞屑性皮损,很难从银屑病及慢性湿疹等良性炎症性皮肤病中鉴别出来。本文提出了一种基于多模态图像融合的早期蕈样肉芽肿识别方法。该方法基于皮肤镜图像和临床图像,采用ResNet18网络提取单模态图像的特征;设计跨模态的注意力模块,实现两种模态图像的特征融合;并且设计自注意力模块提取融合特征中的关键信息,改善信息冗余,从而提高蕈样肉芽肿智能识别的准确度。实验结果表明,本文所提出的智能诊断模型优于对比算法。将本文模型应用于皮肤科医生的实际临床诊断,通过实验组医生和对照组医生平均诊断准确率的变化证实了本文模型能够有效提升临床诊断水平。     

UAV imagery based potential safety hazard evaluation for high-speed railroad using Real-time instance segmentation

Potential safety hazards (PSHs) along the track needs to be inspected and evaluated regularly to ensure a safe environment for high-speed railroad operations. Other than track inspection, evaluating potential safety hazards in the nearby areas often requires inspectors to patrol along the track and visually identify potential threads to the train operation. The current visual inspection approach is very time-consuming and may raise safety concerns for the inspectors, especially in remote areas. Using the unmanned aerial vehicle (UAV) has great potential to complement the visual inspection by providing a better view from the top and ease the safety concerns in many cases. This study develops an automatic PSH detection framework named YOLARC (You Only Look at Railroad Coefficients) using UAV imagery for high-speed railroad monitoring. First, YOLARC is equipped with a new backbone having multiple available receptive fields to strengthen the multi-scale representation capability at a granular level and enrich the semantic information in the feature space. Then, the system integrates the abundant semantic features at different high-level layers by a light weighted feature pyramid network (FPN) with multi-scale pyramidal architecture and a Protonet with residual structure to precisely predict the track areas and PSHs. A hazard level evaluation (HLE) method, which calculates the distance between identified PSH and the track, is also developed and integrated for quantifying the hazard level. Experiments conducted on the UAV imagery of high-speed railroad dataset show the proposed system can quickly and effectively turn UAV images into useful information with a high detection rate and processing speed.     

Stakeholder requirement evaluation of smart industrial service ecosystem under Pythagorean fuzzy environment for complex industrial contexts: A case study of renewable energy park

This study focuses on ways to systematically evaluate stakeholder requirements when developing a smart industrial service ecosystem (SISE) in a complex industrial context. The SISE development requires considering the service requirement from both the complex industrial context and service ecosystem manners. This study proposes a systematic framework for stakeholder requirement evaluation in SISE. The first part of the framework is the industrial context-viable system model with ecological thinking (IC-VESM) to elicit the service requirements for the SISE, which facilitates a systematic analysis of the service value proposition and service requirement elicitation in the operational lifecycle of an entire industrial context. This second part of the framework proposes a method for evaluating service requirements that is both feasible and systematic. This is achieved by combining the Fuzzy Kano and AHP methods in a Pythagorean fuzzy (PF) environment. The PF Kano computes the categories and determines the weights of service requirements from a consumer perspective, while the PF AHP hierarchically analyzes the service requirements and provides pairwise comparison paths for design experts. Finally, an illustrative case study in a renewable energy context was used to demonstrate the feasibility and effectiveness of the methodology. The proposed theoretical model provides more reliable and systematic outcomes than traditional methods when eliciting service requirements and evaluating complex smart industrial service solutions. The study has practical implications by providing useful insights for companies to recognize key smart service requirements in complex industrial contexts and to improve sustainable development.     

“信息获取与处理”领域2022年度国家自然科学基金项目申请与资助情况综述

该文为国内科技工作者了解2022年度“信息获取与处理”领域基础研究队伍、主要研究方向及重大类项目立项资助等情况,对本年度国家自然科学基金委员会F01申请代码下信息获取与处理领域的面上项目、青年科学基金项目、地区科学基金项目、重点项目、优秀青年科学基金项目和国家杰出青年科学基金项目等项目类型进行了统计与分析,给出了自然科学基金各类项目申请中涉及面最广的面上项目和青年科学基金项目申请与资助的依托单位、申请代码分布,介绍了本领域按科学问题属性分类的评审试点和“负责任、讲信誉、计贡献”评审机制试点工作,展望了领域“十四五”期间重点发展方向。