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.     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.     

A digital twin in transportation: Real-time synergy of traffic data streams and simulation for virtualizing motorway dynamics

The introduction of digital twins is expected to fundamentally change the technology in transportation systems, as they appear to be a compelling concept for monitoring the entire life cycle of the transport system. The advent of widespread information technology, particularly the availability of real-time traffic data, provides the foundation for supplementing predominated (offline) microscopic simulation approaches with actual data to create a detailed real-time digital representation of the physical traffic. However, the use of actual traffic data in real-time motorway analysis has not yet been explored. The reason is that there are no supporting models and the applicability of real-time data in the context of microscopic simulations has yet to be recognized. Thus, this article focuses on microscopic motorway simulation with real-time data integration during system run-time. As a result, we propose a novel paradigm in motorway traffic modeling and demonstrate it using the continuously synchronized digital twin model of the Geneva motorway (DT-GM). We analyze the application of the microscopic simulator SUMO in modeling and simulating on-the-fly synchronized digital replicas of real traffic by leveraging fine-grained actual traffic data streams from motorway traffic counters as input to DT-GM. Thus, the detailed methodological process of developing DT-GM is presented, highlighting the calibration features of SUMO that enable (dynamic) continuous calibration of running simulation scenarios. By doing so, the actual traffic data are directly fused into the running DT-GM every minute so that DT-GM is continuously calibrated as the physical equivalent changes. Accordingly, DT-GM raises a technology dimension in motorway traffic simulation to the next level by enabling simulation-based control optimization during system run-time that was previously unattainable. It, thus, forms the foundation for further evolution of real-time predictive analytics as support for safety–critical decisions in traffic management. Simulation results provide a solid basis for the future real-time analysis of an extended Swiss motorway network.     

数字孪生贾鲁河在工程施工期防洪度汛中的应用

为满足贾鲁河综合治理工程施工期防洪度汛精细化管理的需求,确保工程安全的同时能够充分发挥流域防洪效益,数字孪生技术被应用于在建水利工程施工度汛精准风险分析、预警和防汛预案制定中。数字孪生贾鲁河以自建采集L3级和共享L1和L2级数据为“算据”底板,以模型平台和知识平台为“算法”支撑,以信息化基础设施为“算力”依托,搭建具有预报、预警、预演、预案等“四预”功能的流域防洪应用。在贾鲁河流域基础资料收集及数字化场景搭建和模型构建基础上,以流域降雨量级超100年一遇、约50年一遇、约20年一遇设置了三类预演情景模拟洪水,统计施工期工程存在的漫堤风险、淹没风险、冲刷风险、洪水倒灌风险、桥梁阻水风险、堤外积水风险等六类风险并制定防洪度汛措施。数字孪生贾鲁河确保了工程2022年安全度汛,总结了河道治理工程施工期洪水风险类型和定量评估方法,形成了在建工程复杂环境条件下的地形勘测、洪水模拟、风险预警、应对措施制定的完整解决方案。     

Digital twin-assisted fault diagnosis system for robot joints with insufficient data

The robot joint is an important component of the construction robot, and its fault diagnosis can ensure the exact execution of building jobs, stable operation, and timely prevention of probable safety mishaps. However, deep learning-based fault diagnosis needs a multitude of measured fault data, which is difficult to obtain for various reasons. To solve the problem of insufficient data, a digital twin-assisted fault diagnosis system for robot joints is proposed. First, a simplified dynamics model of the robot joint is developed to generate the virtual entity data which can be used as the X-domain data for the digital twin model. Second, a CycleGAN-based digital twin model is proposed to map the virtual entity (X-domain) data to the physical entity (Y-domain) utilizing only a small amount of measured data. In the end, a test-rig for the robot joint is built to simulate the robot's working conditions, and the CNN-ResNet classifier is utilized to verify the effectiveness of the simulated data generated by the digital twin model. The results show that the fault diagnosis accuracy can be increased from 32.5% to 98.86% utilizing only 400 sets of measured data.     

基于数字孪生的虚拟仿真系统研究与应用

为解决全实物平台开发环境搭建困难、不便于测试与维护等问题,在研究PowerPC 处理器的实时嵌入式开发的基础上,采用数字孪生技术的设计思想,完成了一种拥有故障模拟与注入、协同仿真与模块化编程等功能的超实时虚拟仿真系统;提出了一种全数字仿真系统的设计与实现方法,基于数字孪生技术把硬件主板上的处理器以及外围设备虚拟化,集成到数字模型当中;各模型之间相互协同控制,实现CPU控制外部设备与动力学模型等设备之间的通信,以及数据收发、内存读写、串口输出等行为,最终实现与物理设备相同的功能;另外,采用了虚实结合的方法验证了运行结果的准确性,加快了软件执行效率,便于快速全面的系统开发与测试,从而更好地运用到各个领域当中。     

MoS2势垒层磁性隧道结的温度-偏压特性研究

MoS₂是一种具有特殊能带结构的二维半导体材料,当层数较少时,其带隙会随层数显著减小。因此,基于MoS₂势垒层的磁性隧道结会展现出更丰富的物理特性。文章通过理论计算分别得到了单层、双层、三层以及五层MoS₂势垒层磁性隧道结的温度-偏压相图,研究了铁磁电极半交换劈裂能对相图特性的影响。计算结果表明：单层和三层MoS₂势垒层磁性隧道结适合应用于低温器件中。其中,单层MoS₂势垒层磁性隧道结在高功率工作环境下具有优异的性能。双层MoS₂势垒层磁性隧道结的优化区域位于室温和低偏压区,因此适用于信息存储领域。五层MoS₂势垒层磁性隧道结可通过调节铁磁电极参数使其工作在较宽的功率范围内。上述研究结果为MoS₂势垒层磁性隧道结的应用奠定了坚实的理论基础。     

Flexible Nonvolatile Transistor Memory with Solution‐Processed Transition Metal Dichalcogenides

Nonvolatile field‐effect transistor (FET) memories containing transition metal dichalcogenide (TMD) nanosheets have been recently developed with great interest by utilizing some of the intriguing photoelectronic properties of TMDs. The TMD nanosheets are, however, employed as semiconducting channels in most of the memories, and only a few works address their function as floating gates. Here, a floating‐gate organic‐FET memory with an all‐in‐one floating‐gate/tunneling layer of the solution‐processed TMD nanosheets is demonstrated. Molybdenum disulfide (MoS₂) is efficiently liquid‐exfoliated by amine‐terminated polystyrene with a controlled amount of MoS₂ nanosheets in an all‐in‐one floating‐gate/tunneling layer, allowing for systematic investigation of concentration‐dependent charge‐trapping and detrapping properties of MoS₂ nanosheets. At an optimized condition, the nonvolatile memory exhibits memory performances with an ON/OFF ratio greater than 10⁴, a program/erase endurance cycle over 400 times, and data retention longer than 7 × 10³ s. All‐in‐one floating‐gate/tunneling layers containing molybdenum diselenide and tungsten disulfide are also developed. Furthermore, a mechanically‐flexible TMD memory on a plastic substrate shows a performance comparable with that on a hard substrate, and the memory properties are rarely altered after outer‐bending events over 500 times at the bending radius of 4.0 mm.