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Digital twin (DT) modeling is essential to optical fiber communication systems, particularly for enhancing system performance, controlling the system in real time, and understanding signal nonlinearity. Conventional split‐step Fourier method ‐based simulations, however, struggle with wide‐band transmissions, plagued by increasing complexity and inherent biases due to inconsistent link parameter availability. Addressing these challenges, a hybrid data‐driven and model‐driven DT approach for the wide‐band and long‐haul physical systems with various system effects is developed. The approach utilizes a neural network (NN) to capture fiber nonlinear features as well as biased perturbations as “lumped” stochastic noises while offloading the linear effects to modules described by physical models of link elements. The model, tested in a 30.5‐Tbps 1200 km fiber transmission link with 40 channels, achieves a mean Q factor error of less than 0.1 dB and a maximum runtime of 1.3 s for NN processing under various launch powers, transmission lengths, and optical signal‐to‐noise ratios. Furthermore, the study has implemented a nonlinear compensation algorithm on the DT model, yielding a consistent enhancement in experimental data. The accuracy and adaptability of the DT model underline its suitability for planning, design, and optimization within the physical optical fiber communication systems. [ABSTRACT FROM AUTHOR]

The convergence with the emerging digital twin technology has been envisaged as the looming of a smart cloud-based healthcare system. Precision clone simulation of the patients and medical service builds a bridge to the virtual medical world, enabling diagnosing, monitoring, and future prediction to offer enhanced medical services. Cloud services provider plays a crucial role in endowing a way to interlink the physical world to the virtual medical world through outsourced storage, a communication platform, reduced computational cost, and the relevance of hosting and other services over the internet offered by it. However, any alteration, deviation, or amendment of the physical patient's and its digital twin's stored data in the cloud storage has been one of the security liabilities. In this work, we propose a blockchain-assisted certificateless public auditing mechanism for the cloud-based digital twin healthcare networks to substantiate the data integrity in cloud storage. Blockchain in the proposed mechanism enables keeping the auditing process and outsourcing activities log. Meanwhile, it also enjoys the benefits of batch auditing and pairing-free operations. The proposed work is demonstrated to be provably secure and efficient in achieving security goals like storage correctness, privacy-preserving, and public verifiability. Furthermore, the computational overhead of the presented work is shown to be efficient with contrasted with some existing works. [ABSTRACT FROM AUTHOR]

The high-speed railway (HSR) in China has entered the stage of large-scale operation, and there is an urgent need for the intelligent and refined management of track structures. However, due to operating requirements and railroad clearance of HSR, it is difficult to fully grasp the service status of track structures in real time. The digital twin theory, which emphasizes the interconnection of physical and virtual worlds, provides a novel perspective for managing track structures. Therefore, this study proposes using digital twin technology to evaluate the status of track structures, specifically focusing on the continuously welded turnout (CWT) on HSR bridges. Via the refined finite element method (FEM), a virtual entity (VE) model is established to mirror the behavior and status of the physical entity (PE), a continuously monitored CWT on the Beijing-Shanghai High-speed Railway. To ensure an efficient and accurate mapping relationship between VE and PE, an improved MOPSO algorithm based on the surrogate model technique and global ranking criterion has been developed. The proposed method is applied to evaluate the performance evolution and damage status of the CWT. The results demonstrate good consistency with insitu manual inspections, suggesting that this method can accurately locate local defects in track structures, assisting in efficiently managing HSR. [ABSTRACT FROM AUTHOR]

This study examines the connection between digital twin mechanisms and digital twin uses. We examined digital twins through three dimensions: navigation; interaction; and discovery. The effects were examined by considering two different uses: enabling use and controlling use. This study found a direct and positive relationship between the interaction mechanism and the enabling and controlling uses of digital twins. The results also indicated the nonexistence of any relationship between the navigation and discovery mechanisms, and digital twins' enabling and controlling uses. The results indicate that digital twin realism exerts a negative moderating effect on the relationship between digital twins' discovery mechanism and controlling use. Managers can leverage research findings as they adopt new digital tools to support their service businesses and management needs. For example, managers can leverage research findings by using digital technologies to inspire and motivate employees to fulfil organisational objectives. [ABSTRACT FROM AUTHOR]

The multi-layer indices decision-making of complex industrial processes is the key to reducing costs and improving production efficiency. With the development of the Industrial Internet, a large number of industrial streaming data and intelligent algorithms have brought opportunities for optimizing plant-wide production indices. However, due to the strong dynamic and coupling of the production process, the intelligent system based only on the optimization algorithm cannot give practical data analysis suggestions and decision results, so a human–computer interactive visual analysis and index decision system are urgently needed. This paper combines multi-layer indices decision-making algorithms with 3D digital twin visual analysis technology to propose an intelligent decision-making system for mineral processing production indices based on 3D digital twin interactive visualization (DTIV). The DTIV system provides users a 3D digital twin modeling view from the production park, workshop, and equipment scenes. It adopts visualization technology that seamlessly integrates 3D and 2D to help users obtain indices decision input information and hidden data features from real-time stream data with different spatiotemporal data characteristics. In addition, the DTIV system also combines a multi-layer indices optimization decision-making algorithms engine and designs a human–machine interaction indices decision interface and indices decision execution visual analysis interface to improve users' production perception and decision-making ability. Through our collaboration with domain experts, carefully designed interviews, and prototype system evaluation in a beneficiation plant, the effectiveness and usability of the system have been proven. [ABSTRACT FROM AUTHOR]

This work focuses on the opportunity to use the Digital Twin of a complex system, as a Decision Support System. In studying the phenomenon of human Decision Making, the concept of Situation Awareness appears to be of primary importance when dealing with these complex systems. Given the complexity of the system to be represented in the DT, its own complexity, and the need to integrate the user's abilities to allow the acquisition of SA, the concept of reality anchor is proposed to identify the elements of the studied situation necessary for users to perceive, understand and project the situation they face. A methodology, called the Reality Anchor Methodology, has been defined to ensure the elicitation and implementation of these elements in a DT. This methodology is composed of three steps that aim (1) to elicit the reality anchors through a study of the operators' tasks and activities, (2) to design a prototype to carry out human-in-the-loop tests and (3) to validate the definition of Reality Anchors by analysing the SA, experience feedback and the activities performed during the tests. This method was applied to a case study in the oil-and-gas industry and showed the importance of the defined reality anchors. [ABSTRACT FROM AUTHOR]

The city phenomenon is dynamic, complex, and has an abundance of data. City Information Modeling (CIM) and Digital Twins (DTs) are new data-driven approaches toward monitoring, controlling, and managing cities. The main purpose of this research is to introduce and raise the concept of CIM and DTs as a necessity in the smart city era for urban planning, design, and management and to implement a practical way to achieve these concepts in the Narmak neighborhood of Tehran City (Iran) as our case study. The primary tool is CityEngine software. A method was discovered to simulate cities where the existence and access to data are severely constrained. First, a Geographic Information System (GIS) database was assembled, then it was analyzed, and the relationships between urban data were determined. Then, programming in CityEngine generated a smart three-dimensional (3D) model with all the city's information. Finally, CIM and DTs led to the production of an integrated 3D database that is smart and interactive. [ABSTRACT FROM AUTHOR]

This study presents an advanced simulated shearer machine cutting experiment system enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to accurately mirror the operational dynamics of actual mining cutters. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature, and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal rock identification research, offering significant insights into the nuances of shearer operation. [ABSTRACT FROM AUTHOR]

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

The thermal error in the spindle unit is substantial and necessitates mitigation. Current models, being predominantly static in nature, have limited efficacy in error control. Integrating digital twin technology for modeling and controlling spindle unit thermal error holds promise in enhancing the machining accuracy of machine tools. Yet, the notion of a digital twin system specifically tailored for spindle unit thermal characteristics remains uncharted territory. To navigate these challenges, this study introduces a novel digital twin system tailored for spindle unit thermal characteristics. This system is poised to revolutionize thermal error modeling and compensation by harnessing the capabilities of digital twin technology. Within this digital twin framework, both the thermal error control model and the analytical thermal characteristic model are seamlessly integrated. The control model is devised as an exponential function, utilizing operational time, inherent time constants, and both initial and equilibrium thermal errors as parameters. Delving deeper, the analytical thermal characteristic model for the spindle system is rooted in a thermal resistance network approach. This leads to a closed-loop thermal characteristic modeling process, culminating in the derivation of a steady-state thermal error. Intricate heat transfer dynamics between spindle components are dissected, and a comprehensive thermal equilibrium equation set is formulated for the spindle unit. This equation set comprehensively accounts for dynamic variations in key parameters such as preload, lubricant viscosity, thermal load intensity, thermal contact resistance, and convective coefficients. To ascertain the time constant, a meticulously designed set of thermal characteristic experiments is executed. Subsequently, the digital twin system embarks on predictive modeling of thermal errors across varied operational conditions. This prediction then forms the foundation for thermal error compensation. With the integration of the present model into the digital twin system, the results are impressive: the absolute average and maximum deviations in thermal elongation, post-error control, stand at approximately 0.40 μm and 1.24 μm, respectively. [ABSTRACT FROM AUTHOR]

Background: Integration of a patient's non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019–10–07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013–11–12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient ( ICC ) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error ( χ 2 ) of LV myocardial strain, strain rate, and cavity volume. Results: A subset of 270 parameters remained after MSM which produced high-quality DTs of all patients ( χ 2 < 1.6), but minimum parameter reproducibility was poor ( ICC min = 0.01). Iterative reduction yielded a reproducible ( ICC min = 0.83) subset of 75 parameters, including cardiac output, global LV activation duration, regional mechanical activation delay, and regional LV myocardial constitutive properties. This reduced subset produced patient-resembling DTs ( χ 2 < 2.2), while septal-to-lateral wall workload imbalance was higher for the LBBB-only DTs than for the MI-only DTs (p < 0.05). Conclusions: By applying sensitivity and identifiability analysis, we successfully determined a parameter subset of the CircAdapt model which can be used to generate imaging-based DTs of patients with LV mechanical discoordination. Parameters were reproducibly estimated using particle swarm optimization, and derived LV myocardial work distribution was representative for the patient's underlying disease substrate. This DT technology enables patient-specific substrate characterization and can potentially be used to support clinical decision making. [ABSTRACT FROM AUTHOR]

In order to solve the issues of poor interaction, shape distortion and information loss in BIM model analysis of hydraulic engineering works, a universal digital twin hydraulic engineering lightweight technology based on BIM was proposed. The lightweight of hydraulic engineering projects was divided into five aspects of BIM modeling, data structure standardization, model extraction and transformation, model compression and transmission, model analysis and rendering. According to the characteristics of hydraulic engineering works data and business application requirements, lightweight measures were proposed in a targeted manner to give full play to the three-dimensional visualization and data performance of BIM technology and realize business collaborative application. This method was applied to the phase I of the Hanjiang to Weihe River Water Diversion Project, and the lightweight of the engineering geological model was realized by specifying the geometric configuration representation of the model, the standardized data storage space, the necessary information extraction and conversion methods of the model, the model compression tools and transmission methods, the model analysis tools and rendering methods. The number of frames per second (FPS) of the lightweight engineering geological model was increased from 10 to 55, which verified the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Human induced pluripotent stem cell‐derived cardiomyocytes (iPSC‐CMs) offer potential as an in vitro model for studying drug cardiotoxicity and patient‐specific cardiovascular disease. The inherent electrophysiological heterogeneity of these cells limits the depth of insights that can be drawn from well‐designed experiments. In this review, we provide our perspective on some sources and the consequences of iPSC‐CM heterogeneity. We demonstrate the extent of heterogeneity in the literature and explain how such heterogeneity is exacerbated by patch‐clamp experimental artifacts in the manual and automated set‐up. Finally, we discuss how this heterogeneity, caused by both intrinsic and extrinsic factors, limits our ability to build digital twins of patient‐derived cardiomyocytes. [ABSTRACT FROM AUTHOR]

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary. [ABSTRACT FROM AUTHOR]

The intelligent automated store warehouse (iZMS) research and development project was created to meet the expectations of a modern automatic store. The project concerns the development of the concept and pilot implementation of an automated store warehouse adapted to the autonomous and automatic sales of goods selected by retail chains. One of the aims of the iZMS project is to develop a scalable solution that allows for the simple adaptation of the iZMS to the needs of a potential customer, considering their requirements in terms of the quantity and variety of assortment offered within the iZMS. An important requirement in the use of the iZMS system is minimizing the customer waiting time for purchased products. This problem is related, among others, to the placement of products on the shelves of racks and will be solved in the optimizing process. Running optimization tasks requires a simulator that will mimic the features of a physical device faster than in real time to generate many proposals of the allocation of goods on storage racks in the shortest possible time and choose the best one, guaranteeing the shortest picking time of a representative basket of goods. A numerical simulator was developed to model the physical structures of food storage equipment and then simulate the sales process. Among the results obtained, the most important are the time parameters of individual operations, which will be used to optimize the placement of goods on storage racks. After analyzing the needs resulting from the usage of the iZMS system, we decided to develop a dynamic, deterministic simulator with discrete objects and perform the simulation with a controlled time increment and, in some cases, utilize elements of event-driven simulation, in which the flow of goods is simulated with first-in, first-out (FIFO) queues. Finally, verification of the numerical simulator with a physical model confirmed that it could be employed in optimization processes. [ABSTRACT FROM AUTHOR]

To improve the dynamic adaptability and flexibility of the process route during manufacturing, a dynamic optimization method of the multi-process route based on an improved ant colony algorithm driven by digital twin is proposed. Firstly, based on the analysis of the features of the manufacturing part, the machining methods of each process are selected, and the fuzzy precedence constraint relationship between machining metas and processes is constructed by intuitionistic fuzzy information. Then, the multi-objective optimization function driven by the digital twin is established with the optimization objectives of least manufacturing cost and lowest carbon emission, also the ranking of processing methods is optimized by an improved adaptive ant colony algorithm to seek the optimal processing sequence. Finally, the transmission shaft of some equipment is taken as an engineering example for verification analysis, which shows that this method can obtain a process route that gets closer to practical production. [ABSTRACT FROM AUTHOR]

Measurement models in civil engineering. A review The research project "'Last Witnesses' Measurement Models in Civil Engineering – Scientific Significance and Preservation" was funded as part of the priority program 2255 "Construction as Cultural Heritage" of the German Research Foundation (DFG) and consists of three sub‐projects divided between the University of Innsbruck, the Karlsruhe University of Applied Sciences and the Technical University of Munich. The project aimed to locate the last surviving measurement models in German‐speaking countries, document them, classify them in terms of construction history, digitise them and evaluate their state of conservation. This report is intended to provide an overview of the initial situation, the approaches, the difficulties and the results and findings achieved in the project. [ABSTRACT FROM AUTHOR]

During the transition from liquid to solid, the thermal conductivity coefficient λ of concrete decreases. Although λ of hardened concrete is well investigated, there is limited research on the transition from liquid to solid and how it depends on hydration. Currently, only simplified qualitative approaches exist for the liquid state and the transient process. An experimental method is not available. For this purpose, a test rig is designed to experimentally capture the evolution of λ for fine-grain concretes during transition. The performance of the test setup is evaluated on a characteristic high-performance concrete (HPC). The results are compared to theoretical predictions from the literature. The developed test rig is mapped in a digital twin to investigate extended boundary conditions, such as different heat sources and temperatures of the experimental setup. It allows the experiment to be repeated and optimized for different setups with little effort. The test principle is as follows: A liquid concrete sample is heated through a controlled external source, while the transient temperature distribution over the height is measured with a fiber optic sensor. The thermal conductivity is derived from the heat flux induced and the temperature distribution over an evaluation length. Experiments show that λ in the liquid state is approximately 1.4 times greater than in the solid state and exponentially decreases for the transient process. Numerical results on the digital twin indicate that the robustness of the results increases with the temperature of the heat source. Moreover, the derivation in λ turns out to be strongly dependent on the evaluation length. A length of three times the maximum grain diameter is recommended. [ABSTRACT FROM AUTHOR]

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being. [ABSTRACT FROM AUTHOR]

Wind turbines (WT) are a clean renewable energy source that have gained popularity in recent years. Gearboxes are complex, expensive, and critical components of WT, which are subject to high maintenance costs and several stresses, including high loads and harsh environments, that can lead to failure with significant downtime and financial losses. This paper focuses on the development of a digital twin-based approach for the modelling and simulation of WT gearboxes with the aim to improve their design, diagnosis, operation, and maintenance by providing insights into their behavior under different operating conditions. Powerful commercial computer-aided design tools (CAD) and computer-aided engineering (CAE) software are embedded into a computationally efficient multi-objective optimization framework (modeFrontier) with the purpose of maximizing the power density, compactness, performance, and reliability of the WT gearbox. High-fidelity models are used to minimize the WT weight, volume, and maximum stresses and strains achieved without compromising its efficiency. The 3D CAD model of the WT gearbox is carried out using SolidWorks (version 2023 SP5.0), the Finite Element Analysis (FEA) is used to obtain the stresses and strains, fields are modelled using Ansys Workbench (version 2024R1), while the multibody kinematic and dynamic system is analyzed using Adams Machinery (version 2023.3, Hexagon). The method has been successfully applied to different case studies to find the optimal design and analyze the performance of the WT gearboxes. The simulation results can be used to determine safety factors, predict fatigue life, identify potential failure modes, and extend service life and reliability, thereby ensuring proper operation over its lifetime and reducing maintenance costs. [ABSTRACT FROM AUTHOR]

Precisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is proposed as a solution to reduce financial and time losses for vehicle manufacturers by streamlining the expensive and time-consuming processes of designing and implementing electric vehicle types and road assessments. The use of digital twins to monitor, evaluate, and optimize vehicle performance based on real-time road data is increasingly crucial in the DT concept. In this study, the digital twin of the CERYAN brand vehicle model has been employed to compare the performance of different motor types (PMSM, PMSM Brushless, BLDC/PMSM Brushless, and BLDC) in terms of energy consumption and acceleration at various inclination angles, utilizing real-time road data. According to the World Motorcycle Test Cycle (WMTC) standards, the motor type with the best performance parameters was determined as a PMSM brushless motor (5 kW). The main superior aspects of the proposed motor type are to achieve a 25% higher range than the BLDC Motor, 30% better grade ascending capability than the PMSM Motor, and 26% lower energy consumption than the PMSM brushless motor (6 kW). [ABSTRACT FROM AUTHOR]

The digital twin is driving the machine manufacturing and processing workshop to change in the direction of digital intelligence and service. Aiming at the application requirements of virtual simulation monitoring of typical CNC machine tools for the unified interaction and integration of processing and production process data, this paper proposes a development architecture of virtual simulation monitoring and processing process optimization system for CNC machine tools that integrate data, model, communication, and optimization. The data semantic format and data communication are normalized by designing the OPC UA information model of CNC machine tools, modular construction of a three-dimensional digital model, and interactive mapping technology of OPC UA server address space. Virtual simulation visualization and monitoring of CNC machine tools are realized by integrating synchronous simulation modeling, collision detection, and viewpoint control technologies. Building upon this foundation, the control process of the CNC machine tool machining cell is optimized using ECRS and lean production methods. The application focuses on a typical flexible manufacturing cell (FMC) in a machine tool processing and manufacturing workshop. The development of the virtual simulation visualization monitoring system for FMC addresses challenges such as heterogeneous data interaction, sharing, and integration difficulties across multiple heterogeneous equipment. The system successfully fulfills all required functions, and the optimization of the CNC machine tool machining unit's control process has enhanced equipment utilization and productivity. This solution effectively supports the realization of intelligent manufacturing services, including standardized data-driven digital twins. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of modern manufacturing, the pursuit of efficiency, reliability, and optimal performance has prompted the integration of cutting-edge technologies. Among these, digital twins (DT) have emerged as transformative tools, offering a virtual representation of physical processes, systems, and equipment. This paper delves into the pivotal role of digital twins in advancing the monitoring and supervision of manufacturing processes, focusing specifically on process digital twin (PDT) and its application in the domain of froth flotation in minerals processing. Our data-driven digital twin, replicating the behavior of a flotation cell, was developed using a combination of industrial and simulation data anchored by Artificial Neural Networks. This approach provides precise process emulation of the flotation process. Industrial evaluations of the AI model within the Digital Shadow demonstrated an overall 94% accuracy in estimating insightful information regarding the flotation cell operations with 2 s in response time. This research significantly contributes to the practical implementation of digital twins in industrial processes, highlighting their potential to revolutionize process control and enhance efficiency in the industrial sector. [ABSTRACT FROM AUTHOR]

The installation and commissioning of production units are crucial for establishing efficient production systems. However, current approaches frequently face challenges such as lengthy commissioning cycles, low productivity, and limited intelligence. This study aims to leverage the synergistic capabilities of augmented reality (AR) technology and digital twin technology in the context of Industry 4.0 to address the challenges faced in production commissioning. Therefore, to achieve intelligent commissioning, optimize the installation and commissioning process of production systems and increase productivity. This paper proposes a novel virtual-real fusion commissioning method utilizing AR for production commissioning. Initially, a digital twin modeling and process planning simulation method is introduced for virtual-real fusion production units; secondly, real-time commissioning and fusion display of the production process are enabled through AR technology; lastly, the approach is applied to the operation and commissioning of the motor rotor–embedded wire production unit. The results demonstrate that the system can simulate the entire production logic and operation even in the absence of certain equipment while visualizing the production process and product status. Furthermore, compared to traditional physical production commissioning, this method has achieved a 43.6% reduction in the commissioning cycle and resulted in space cost savings of 21.8%. Subsequent applications validate the soundness of the proposed modeling and fusion commissioning approach and provide practical theoretical and methodological guidance for digital twin modeling and production commissioning in production systems. [ABSTRACT FROM AUTHOR]

The increasing personalized product demands bring reformation to the manufacturing paradigm. Traditional manufacturing systems seldom analyze and give feedback on the data collected during production. The bottleneck between the physical and digital worlds of manufacturing systems is the lack of interoperability. In this paper, a digital twin-based self-organizing manufacturing system (DT-SOMS) is presented under the individualization paradigm. On the basis of the interconnection between smart workpieces and smart resources via decentralized digital twin models, a decentralized self-organizing network is established to achieve intelligent collaboration between tasks and resources. The mechanism of job-machine optimal assignment and adaptive optimization control is constructed to improve the capabilities of reconfiguration and responsiveness of the DT-SOMS. An implement case is designed to illustrate that the proposed DT-SOMS can realize synchronized online intelligence in the configuration of resources and response to disturbances. [ABSTRACT FROM AUTHOR]

To solve the problems of strong uncertainty, dynamics, and high complexity during the operation process in a discrete manufacturing shop floor that produces a variety of variable-volume products, a data-driven smart management and control framework of a digital twin shop floor (DTS) is proposed. Its implementation process is analyzed. Five key tasks are illustrated in detail: (1) the construction of a shop floor digital twin (DT) model from the multi-dimensional multi-scale perspective; (2) data acquisition and management technology in a DTS; (3) the real-time data-driven synchronous modeling of the shop floor operating status; (4) the model- and data-driven online prediction of the shop floor operation status; and (5) the multi-agent-based operation decision of a DTS. In addition, for products that are complex to produce on the assembly shop floor, a DT-based smart management and control system named the DT-VPPC is developed. The effectiveness of the proposed method is verified by a specific application example on an assembly shop floor. [ABSTRACT FROM AUTHOR]

Traditional data-driven fault diagnosis methods require a massive amount of data to train diagnosis models. However, the complex and coupled structure of CNC machine tools makes it difficult to obtain enough usable data. Current data generation methods ignore actual operating conditions and have imbalance, which reduces the accuracy of fault diagnosis. To tackle these problems, this paper presents a digital twin-driven fault diagnosis method for CNC machine tools. Firstly, a digital twin model of a CNC machine tool is established and validated. Then, a twin model library is constructed to include multiple twin models under different fault status. A model data fusion method is presented, using the decision tree algorithm Classification and Regression Tree (CART) to train a model selector and actual sensor data as input to select the optimal model from the library and realize fault diagnosis with the model. Finally, taking the CNC machine tool spindle as an example, the stiffness deterioration of the spindle during operation is effectively diagnosed, which verifies the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The digital twin (DT) technology is currently considered a key technology for the digital representation of real-world systems. The application of DT technology in smart manufacturing can provide accurate model support for the analysis of mechatronic equipment applications based on model simulations. However, for such a mechanics-electric-hydraulic-control coupled complex system of mechatronics equipment, how to quickly and effectively construct its consistent multi-domain DT mechanism model has become the biggest obstacle to the wide application of DT technology in this field. Therefore, based on the synthesis of existing model construction methods for mechatronics equipment, this paper proposes multi-domain, multi-level, parametric, and consistent mechatronics equipment DT mechanism model construction guidelines. Based on the proposed model construction guidelines, a DT mechanism model construction process for mechatronics equipment is given. Finally, a consistent multi-domain DT mechanism model of computer numerical control machine tools (CNCMT) is constructed by Simscape, to verify the feasibility of the proposed method. The solutions in this paper provide a reliable, rapid, and consistent mechanism model along with construction guidelines and theoretical systems for engineers or researchers that use DT technology to solve specific application problems. Also, the implementation of the virtual commissioning application case in this paper provides application guidance for service analysis based on the DT model. [ABSTRACT FROM AUTHOR]

As an important part of the real economy, manufacturing industry plays a major role in the whole human society. Smart manufacturing has become a strategic issue for many countries. Smart manufacturing puts forward higher requirements for the intelligence of shop-floor production process, product operation and maintenance, logistics and supply chain, which are inseparable from the support of advanced communication technology. As a new generation of mobile communication technology, 5G plays an important role in many areas of smart manufacturing with the characteristics of high bandwidth, low latency, and massive connectivity. This paper first analyzed the communication requirements for machine-to-machine, manufacturing Internet of Things, cyber-physical system-based manufacturing, logistics and supply chain, industrial Internet platform and digital twin–driven manufacturing. Based on the requirements, the research and application progress of 5G in manufacturing are investigated from the above six aspects. In addition, this paper proposed relevant future research hotspots for the further integration of 5G and the above-mentioned six areas of smart manufacturing. [ABSTRACT FROM AUTHOR]

Skiving has potential for gear machining, but its cutting force fluctuates greatly, its cutting temperature is high during processing, and it has the characteristics of thermomechanical coupling. It is difficult for traditional methods to dynamically predict the thermomechanical coupling during the skiving process, whose efficiency and stability cannot be guaranteed. Aiming to solve these problems, a digital twin (DT)-based dynamic method is proposed to predict thermomechanical coupling in the skiving process. Considering the time-varying characteristics and coupling of the cutting force and temperature, a multi-physical modeling method dual-driven by mechanism and data is proposed to establish a thermomechanical coupling DT (TMDT) model of the skiving process. The dynamic consistency of the skiving process between the digital and physical spaces is realized. Principal component analysis (PCA) and an extreme learning machine (ELM) are used to reduce the order of the TMDT, the reduced-order model is trained using the skiving big data, and a relationship mapping model of the cutting parameters and the cutting force and temperature is established to realize the dynamic prediction of the cutting force and temperature. The effectiveness of the proposed method is verified through gear skiving experiments. This research has important theoretical guiding significance to realize efficient and stable skiving processing. [ABSTRACT FROM AUTHOR]

Manufacturing service composition is a key technology in service-oriented manufacturing systems. Service correlation is a mix-order correlation, which is supposed to be defined as adjacent-order correlation (AO-C) and non-adjacent-order correlation (NAO-C). The existing works mainly focus on AO-C without considering NAO-C, and constantly lead to the failure of composite service execution path (CSEP). In this paper, with the support of digital twin, firstly the non-uniform transitivity of correlation from AO-C to NAO-C is analyzed. Then, the basic model of AO-C, multi-order model of NAO-C, and its relevancy degree formula are proposed based on workflow and modular design. Meanwhile, a perception method based on improved Apriori algorithm is designed and the relevant supporting data is collected by digital twin technology, so as to percept AO-C relevancy degree and calculate the relevancy degree of mix-order correlation in CSEP in the proposed AO-C and NAO-C models. Finally, a case study of magnetic bearing manufacturing service composition is conducted to verify the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

The increase in port scale and business complexity has led to an increased demand for comprehensive and lean control on ports. The current operation mode is facing the bottleneck of the increasingly significant production efficiency and performance. Digital twin (DT) technology realizes holographic visual management and control patterns using cyber-physical fusion and promotes the transformation of a port to an intelligent operation mode. In this paper, the framework of a digital twin application system is proposed based on the analysis of business characteristics of large-scale comprehensive ports. Construction methods and technologies such as digital twin modeling, global ubiquitous perception, data mapping, and model fusion are analyzed. With regard to the construction needs of Qingdao Port's digital twin system, this paper presents a case study and illustrates the overall design process and function of the digital twin system for typical terminals. The system realizes the intelligent operation of the port with the core functions of three-dimensional visual monitoring and optimal dispatching based on real-time perception data. This paper serves as a feasible reference for future intelligent development of large ports and the application of digital twin technology. [ABSTRACT FROM AUTHOR]

Background: For the static loading test in the aerospace field, conventional strain field reconstruction methods relying on finite element analysis (FEA) or test data are difficult to meet the accuracy requirements of test monitoring. Objective: This study aims to construct a high-accuracy strain field for real-time test monitoring. Methods: An improved strain field reconstruction method based on digital twin (DT) named as DT-SFRM is proposed. The DT is built by data fusion of FEA results and test data, which combines the benefits of these data. The FEA conducted before formal test provides approximate strain field distribution, and the strain gauges data with high accuracy are used to modify FEA strain fields in real time. After that, the real-time DT is used to determine the possible risk regions of test articles. Finally, a large opening cylindrical shell (LOCS) buckling test is conducted to validate the advantages of DT-SFRM. Results: Results show that the accuracy of DT-SFRM is much higher and less affected by the nonlinearity of test data than that of conventional methods. Compared with the time cost by conventional real-time FEA (about 50 min), the DT method only takes 9s to reconstruct strain field, and the possible risk regions predicted by DT-SFRM are more consistent with test buckling regions of LOCS than conventional methods. Conclusions: The DT-SFRM is validated to have a higher accuracy and better monitoring effect, and it is more suitable for test monitoring of complex structures. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the application and impact of digital twin (DT) technology in bolstering the reliability of Floating Offshore Wind Turbines (FOWTs) and their supporting platforms. Within the burgeoning domain of offshore wind energy, this study contextualises the need for heightened reliability measures in FOWTs and elucidates how DT technology serves as a transformative tool to address these concerns. Analysing the existing scholarly literature, the review encompasses insights into the historical reliability landscape, DT deployment methodologies, and their influence on FOWT structures. Findings underscore the pivotal role of DT technology in enhancing FOWT reliability through real-time monitoring and predictive maintenance strategies, resulting in improved operational efficiency and reduced downtime. Highlighting the significance of DT technology as a potent mechanism for fortifying FOWT reliability, the review emphasises its potential to foster a robust operational framework while acknowledging the necessity for continued research to address technical intricacies and regulatory considerations in its integration within offshore wind energy systems. Challenges and opportunities related to the integration of DT technology in FOWTs are thoroughly analysed, providing valuable insights into the role of DTs in optimising FOWT reliability and performance, thereby offering a foundation for future research and industry implementation. [ABSTRACT FROM AUTHOR]

The development of monitoring and diagnostic methods for electrical machines requires the use of transient models capable of operating in real time and producing signal signatures with high precision. In this context, coupled-circuit models offer numerous advantages due to their speed of execution and accuracy. They have been successfully employed to create real-time digital twins of electrical machines. The main challenge of this modeling method lies in the preparation of the model, which involves numerous preliminary calculations and takes time to identify all its parameters. This is particularly due to the variation in inductances based on the rotor position. To determine these inductance values with great precision, the classical approach involves using finite-element field calculation software. However, the computation time quickly becomes an issue due to the large number of values to calculate and simulations to perform. This article introduces an innovative experimental approach to identify a coupled-circuit model and develop a digital twin of a wound-rotor induction machine. This method relies solely on simple electrical measurements and tests conducted at extremely low rotation speeds (1 rpm) to obtain inductance variations as a function of the rotor position. By employing this technique, the need for analytical models or finite-element field calculation simulations, which typically require precise knowledge of the machine's geometry and materials, is circumvented. The measurement processing employs optimization methods to extract the inductances as a function of the rotor position, which are then used as input data for the coupled-circuit model. The final parameters are specific to each machine and replicate all its manufacturing imperfections such as eccentricity and geometric or winding defects. This experimental identification method significantly enhances the model's accuracy and reduces the usually required preliminary calculation time in a finite-element-based identification process. [ABSTRACT FROM AUTHOR]

A new method based on a digital twin is proposed for fault diagnosis, in order to compensate for the shortcomings of the existing methods for fault diagnosis modeling, including the single fault type, low similarity, and poor visual effect of state monitoring. First, a fault diagnosis test platform is established to analyze faults under constant and variable speed conditions. Then, the obtained data are integrated into the Unity3D platform to realize online diagnosis and updated with real-time working status data. Finally, an industrial test of the digital twin model is conducted, allowing for its comparison with other advanced methods in order to verify its accuracy and application feasibility. It was found that the accuracy of the proposed method for the entire reducer was 99.5%, higher than that of other methods based on individual components (e.g., 93.5% for bearings, 96.3% for gear shafts, and 92.6% for shells). [ABSTRACT FROM AUTHOR]

Tracking polarization in real‐time is a long‐term challenge. The conventional heuristic and gradient‐descent‐based algorithms for polarization tracking lack efficiency and interpretability. To resolve the problem, a white‐box digital twin modeling the entire polarization tracking system is derived by calculating with Stokes vectors and Mueller matrices. Moreover, the real‐time polarization tracking enabled by the white‐box digital twin is experimentally demonstrated, which is over 7 times faster than the commonly used stochastic parallel gradient descent (SPGD) on average. The adoption of digital twin allows the algorithm to bypass the loop of perturbation, sampling, and adjusting over the real system, thereby significantly reducing the sample times and recovery time. The proposed white‐box digital‐twin‐based algorithm has strong interpretability and high efficiency, which has substantial potential to become a standard approach to achieve real‐time polarization tracking. [ABSTRACT FROM AUTHOR]

The building of a knowledge graph within the water conservancy sector can offer structured knowledge support for digital twin water conservancy projects. Based on the forecast and scheduling, engineering safety and business rule data of Xiaolangdi and Wanjiazhai key water control projects on the Yellow River, an entity recognition and relationship extraction model was used to build a water conservancy knowledge graph for digital twin engineering (KG4DT), and the graph was visualized and displayed. It determined the entity classification system and relationship classification system with the key water control project as the core, and built the pattern layer of KG4DT. It used a secondary development annotation platform to perform semi-automatic annotation and manual proofreading on unstructured text and built the data layer of KG4DT. Taking the flood control, sedimentation reduction, water supply and power generation businesses of the Xiaolangdi and Wanjiazhai key water control projects on the Yellow River as the main line, it combined the technologies such as AC automaton to design knowledge retrieval and automatic generation of contingency plans, which could provide intelligent decision-making support for engineering operations. [ABSTRACT FROM AUTHOR]

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today. [ABSTRACT FROM AUTHOR]

The current digital transformation in manufacturing has a strong impact on the competencies needed by manufacturing companies. This leads to evolving requirements for digital training in industrial engineering courses. The concept of virtual labs in academic environments can be instrumental in teaching new digital skills through practical experience. This paper aims to examine the requirements and essential factors involved in designing virtual labs, proposing a framework to meet the requirements of virtual lab activities, integrating didactic and research purposes while examining the requirements and essential factors involved in designing virtual labs digital twin as an enabling technology. The framework is the result of the analysis of the technical aspects related to immersive technologies, such as extended reality, together with insights gathered from interviews and pilot testing conducted in workshops involving students, teachers, and lab managers from three institutions implementing virtual labs. The outcomes of this study include the digital model of four manufacturing labs as virtual labs that are openly available for academic purposes. This showcases a commitment towards offering quality and inclusive engineering education through cuttingedge virtual technologies. [ABSTRACT FROM AUTHOR]

The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from "regular maintenance" and "post-event maintenance" to "predictive maintenance". In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of "combining the real with the virtual and controlling the real". Firstly, a digital twin system structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom. [ABSTRACT FROM AUTHOR]

This article presents a novel approach to calibrating the digital twin of a laboratory mill used for copper ore milling. By integrating computer vision techniques for real-time data extraction and employing DualSPHysics simulations for various milling scenarios, including balls only, balls with ore, and balls with slurry, we achieve a high degree of accuracy in matching the digital twin's behavior with actual mill operations. The calibration process is detailed for mills with three different diameters, highlighting the adjustments in simulation parameters necessary to account for the absence of ore. Understanding the dynamics between the suspension within the mill and the operation of the grinders is crucial for the future improvement of the grinding process. This knowledge paves the way for optimizing the process, not only in terms of the quality of the end product but primarily in terms of energy efficiency. A profound understanding of these interactions will enable engineers and technologists to design mills and grinding processes in a way that maximizes efficiency while minimizing energy consumption. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) is increasingly employed in various domains, notably enhancing learning and experiences in cultural heritage (CH). This study examines the effects of gamified and non-gamified VR experiences within virtual museum environments, highlighting the concept of a digital twin and its focus on cultural heritage. It explores how these VR modalities affect visitor motivation, engagement, and learning outcomes. For this purpose, two versions were developed: a gamified virtual reality version incorporating interactive gaming elements like achievements, profiles, leaderboards, and quizzes and a non-gamified virtual reality version devoid of these elements. This study, using an experimental design with 76 participants (38 in each group for the gamified and non-gamified experiences), leverages the Wieng Yong House Museum's digital twin and its fabric collection to assess the educational and experiential quality of virtual museum visits. The findings indicate that while gamification significantly boosts the reward dimension of visitor engagement, its influence is most pronounced in the effort dimension of motivation; however, its impact on learning outcomes is less marked. These insights are instrumental for integrating VR and gamification into museum environments. [ABSTRACT FROM AUTHOR]

In view of the issues such as opaque data and monitoring blind spots in industrial robots, study was conducted on physical industrial robots, leading to the development of a digital twin robot monitoring system. Firstly, modeling of virtual robots was accomplished from three dimensions: geometric, physical, and logical. Secondly, based on the open platform communications united architecture (OPC UA), data collection and communication for robots were implemented. User display interfaces were designed to achieve real-time visualization of data. Finally, through robot simulation experiments, the relative position errors of the robot end effector in the X, Y, and Z coordinates were determined to be 0.48%, 0.32%, and 0.27%, respectively. The maximum error in joint angles of the robot, verified through synchronized experiments using a digital twin robot monitoring system, was found to be 0.31°. The experimental results demonstrate that the method can achieve motion data monitoring for digital twin industrial robots, reducing the risk of accidents and failures in the production process. It provides insights and directions for the development of intelligent industrial robots. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop a global terrain and altitude map by combining a digital twin model and deep learning technique on Florida's coastal area. Utilizing USGS data, we are able to represent diverse landforms while ensuring the accuracy of elevation changes. In order to mitigate projection distortions, we rescaled 5000 map segments worldwide, ensuring that key geographical features are included. We segment the terrain into seven distinct classes: Water, Grassland, Forest, Hills, Desert, Mountain, and Tundra. The map features are enhanced by median filtering and each class is color-coded. Random parameters were introduced in overlapping image sets in order to ensure variety and prevent redundancy. On these seven terrain classes, the U-Net network is used to perform segmentation tasks. In order to monitor the performance of the model, we implemented cross-validation. The model's effectiveness is demonstrated by robust ROC curve analysis and high AUC values, which indicate accurate terrain categorization. Using deep learning methods and satellite imagery from Google Earth, the primary objective is to develop a digital twin of Florida's coastline. The digital twin serves as both a physical and simulation model, accurately resembling real-world locations. In addition to the achievement of detailed terrain mapping, this approach is likely to have significant applications in environmental monitoring and urban planning as well. In terms of reliability and performance, the digital twin model is expected to be a significant advancement in the field of geographical information systems. [ABSTRACT FROM AUTHOR]

This article introduces a new approach for validating directional overcurrent protection schemes in ring-topology electrical distribution systems with distributed energy resources (DERs). The proposed protection scheme incorporates overcurrent and directional functions and addresses DER-induced challenges such as variable short circuit levels. This study employs real-time and offline simulations to evaluate the performance of the protection coordination scheme using a digital twin under DER-supplied loads. The utilization of digital twins offers the possibility to simulate different scenarios, providing real-time responses to dynamic changes and allowing for informed decision-making in response to disturbances or faults. This study aims to present a new approach to validate the performance of the proposed protection scheme when the load is entirely supplied by DERs, highlighting issues such as false trips and protection system blindness resulting from changes in short circuit currents. The results show a breakdown in the coordination of the protection scheme during the fault conditions, demonstrating the effectiveness of digital twins in validating the protection scheme's performance. Performing an analysis in the electromagnetic transient (EMT) domain improves the validation and refines the results. [ABSTRACT FROM AUTHOR]

With the intent to further increase production efficiency while making human the centre of the processes, human-centric manufacturing focuses on concepts such as digital twins and human–machine collaboration. This paper presents enabling technologies and methods to facilitate the creation of human-centric applications powered by digital twins, also from the perspective of Industry 5.0. It analyses and reviews the state of relevant information resources about digital twins for human–machine applications with an emphasis on the human perspective, but also on their collaborated relationship and the possibilities of their applications. Finally, it presents the results of the review and expected future works of research in this area. [ABSTRACT FROM AUTHOR]

With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher R 2 values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading. [ABSTRACT FROM AUTHOR]