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The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of network resources in the next decades. Among different B5G use cases, the Digital Twin (DT) has been identified as a key high bandwidth-demanding use case. The creation and operation of a DT require the continuous collection of an enormous and widely distributed amount of sensor telemetry data which can overwhelm the transport layer. Therefore, the reduction in such transported telemetry data is an essential objective of smart use case operation. Moreover, deep telemetry data analysis, i.e., anomaly detection, can be executed in a hierarchical way to reduce the processing needed to perform such analysis in a centralized way. In this paper, we propose a smart management system consisting of a hierarchical architecture for telemetry sensor data analysis using deep autoencoders (AEs). The system contains AE-based methods for the adaptive compression of telemetry time series data using pools of AEs (called AAC), as well as for anomaly detection in single (called SS-AD) and multiple (called MS-AGD) sensor streams. Numerical results using experimental telemetry data show compression ratios of up to 64% with reconstruction errors of less than 1%, clearly improving upon the benchmark state-of-the-art methods. In addition, fast and accurate anomaly detection is demonstrated for both single and multiple-sensor scenarios. Finally, a great reduction in transport network capacity resources of 50% and more is obtained by smart use case operation for distributed DT scenarios.

Rolling bearings are widely used in rotating machinery in modern industry, and ensuring their stability during operation is one of the prerequisites for the overall safety of the equipment. Predicting performance degradation can play a key role in preventing accidents and extending equipment life. With the development of big data and deep learning, more trend prediction methods are emerging in the field of performance degradation prediction of rolling bearings. Therefore, this paper reviews the evaluation indicators and performance degradation prediction models for rolling bearing performance degradation prediction. The advantages and disadvantages of physical degradation indicators, and virtual degradation indicators are analyzed. It is presented to utilize the powerful feature self-extraction ability and nonlinear function characterization ability of deep learning methods to construct bearing evaluation indicators. It also analyzes the research progress of traditional performance degradation prediction models and deep learning prediction models. In this review, future developments in rolling bearing performance degradation prediction are summarized in this paper as deep learning-based, digital twin correlation, high dimensionality, and adaptive, which guide researchers and practitioners to effectively identify suitable performance degradation prediction models. [ABSTRACT FROM AUTHOR]

Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care. [ABSTRACT FROM AUTHOR]

In the face of rapidly evolving cyber threats, network-based intrusion detection systems (NIDS) have become critical to the security of industrial and robotic systems. This survey explores the specialized requirements, advancements, and challenges unique to deploying NIDS within these environments, where traditional intrusion detection systems (IDS) often fall short. This paper discusses NIDS methodologies, including machine learning, deep learning, and hybrid systems, which aim to improve detection accuracy, adaptability, and real-time response. Additionally, this paper addresses the complexity of industrial settings, limitations in current datasets, and the cybersecurity needs of cyber–physical Systems (CPS) and Industrial Control Systems (ICS). The survey provides a comprehensive overview of modern approaches and their suitability for industrial applications by reviewing relevant datasets, emerging technologies, and sector-specific challenges. This underscores the importance of innovative solutions, such as federated learning, blockchain, and digital twins, to enhance the security and resilience of NIDS in safeguarding industrial and robotic systems. [ABSTRACT FROM AUTHOR]

The prediction of photovoltaic (PV) system performance has been intensively studied as it plays an important role in the context of sustainability and renewable energy generation. In this paper, a digital twin (DT) model based on a domain-matched transformer is proposed using convolutional neural network (CNN) for domain-invariant feature extraction, transformer for PV performance prediction, and domain adaptation neural network (DANN) for domain adaptation. The effectiveness of the proposed framework is validated using a PV power prediction dataset. The results indicate an accuracy improvement of up to 39.99% in model performance. Additionally, experiments with varying numbers of timestamps demonstrate enhanced PV power prediction performance as parameters are continuously updated within the DT framework, offering a reliable solution for real-time and adaptive PV power forecasting. [ABSTRACT FROM AUTHOR]

The safe operation of nuclear power plants relies on maintaining the structural integrity of various systems and components, such as equipment-piping systems. Ageing and degradation from flow-accelerated erosion and corrosion can lead to cracks and leakages, posing risks like loss of coolant accidents (LOCA). To prevent such incidents, regular monitoring and maintenance are vital. Recent efforts to implement Artificial Intelligence (AI)-driven condition monitoring aim to enhance safety and efficiency. However, the effectiveness of simulation-based degradation detection models needs to be validated using experimental or real-time data from nuclear power plants. This research explores an AI-based monitoring framework's validity for nuclear equipment-piping systems through experimentation. A piping system is designed and subjected to harmonic excitations representing typical pump-induced vibrations in nuclear plants. Degradation levels are classified based on wall thickness loss as minor, moderate or severe. Non-uniform degradation is implemented at structural discontinuities such as the elbows. Sensor response is collected from accelerometers installed on the experimental system and its corresponding digital twin. Deep neural networks such as multilayer perceptron and convolutional neural networks are developed to detect the degraded locations and their severity. The results from the experimental data, as well as the simulated data, are compared for accuracy. [ABSTRACT FROM AUTHOR]

The stable flight of drones relies on Global Navigation Satellite Systems (GNSS). However, in complex environments, GNSS signals are prone to interference, leading to flight instability. Inspired by cross-view machine learning, this paper introduces the VDUAV dataset and designs the VRLM network architecture, opening new avenues for cross-view geolocation. First, to address the limitations of traditional datasets with limited scenarios, we propose the VDUAV dataset. By leveraging the virtual–real mapping of latitude and longitude coordinates, we establish a digital twin platform that incorporates 3D models of real-world environments. This platform facilitates the creation of the VDUAV dataset for cross-view drone localization, significantly reducing the cost of dataset production. Second, we introduce a new baseline model for cross-view matching, the Virtual Reality Localization Method (VRLM). The model uses FocalNet as its backbone and extracts multi-scale features from both drone and satellite images through two separate branches. These features are then fused using a Similarity Computation and Feature Fusion (SCFF) module. By applying a weighted fusion of multi-scale features, the model preserves critical distinguishing features in the images, leading to substantial improvements in both processing speed and localization accuracy. Experimental results demonstrate that the VRLM model outperforms FPI on the VDUAV dataset, achieving an accuracy increase to 83.35% on the MA@20 metric and a precision of 74.13% on the RDS metric. [ABSTRACT FROM AUTHOR]

As outlined by the International Energy Agency, 44% of carbon emissions in 2021 were attributed to electricity and heat generation. Under this critical scenario, the power industry has adopted technologies promoting sustainability in the form of smart grids, microgrids, and renewable energy. To overcome the technical challenges associated with these emerging approaches and to preserve the stability and reliability of the power system, integrating advanced digital technologies such as Digital Twins (DTs) and Artificial Intelligence (AI) is crucial. While existing research has explored DTs and AI in power systems separately, an overarching review of their combined, synergetic application in sustainable power systems is lacking. Hence, in this work, a comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The main results of this review analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity. The prominent utilisation of deep learning, supervised learning, reinforcement learning, and swarm intelligence techniques was identified as mainly constrained to power system operations and maintenance functions, along with the potential for more sophisticated AI techniques in computer vision, natural language processing, and smart robotics. This review also discovered sustainability-related objectives addressed by AI-powered DTs in power systems, encompassing renewable energy integration and energy efficiency, while encouraging the investigation of more direct efforts on sustainable power systems. [ABSTRACT FROM AUTHOR]

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions and yielding economic, environmental, and health benefits. The work organization of modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, and report to human experts in real time. This review evaluates the current state of ICT technology to determine if it supports autonomous, continuous crop monitoring. The focus is on shifting from traditional cloud-based approaches, where data are sent to remote computers for deferred processing, to a hybrid design emphasizing edge computing for real-time analysis in the field. Key aspects considered include algorithms for in-field navigation, AIoT models for detecting agricultural emergencies, and advanced edge devices that are capable of managing sensors, collecting data, performing real-time deep learning inference, ensuring precise mapping and navigation, and sending alert reports with minimal human intervention. State-of-the-art research and development in this field suggest that general, not necessarily crop-specific, prototypes of fully autonomous UGVs for continuous monitoring are now at hand. Additionally, the demand for low-power consumption and affordable solutions can be practically addressed. [ABSTRACT FROM AUTHOR]

The rapid reconstruction of the internal flow field within pressure vessel equipment based on features from limited detection points was of significant value for online monitoring and the construction of a digital twin. This paper proposed a surrogate model that combined Proper Orthogonal Decomposition (POD) with deep learning to capture the dynamic mapping relationship between sensor monitoring point information and the global flow field state during equipment operation, enabling rapid reconstruction of the temperature field and velocity field. Using POD, the order of the tested temperature field was reduced by 99.75%, and the order of the velocity field was reduced by 99.13%, effectively decreasing the dimensionality of the flow field. Our analysis revealed that the first modal coefficient of the temperature field snapshot data, after modal decomposition, had a higher energy proportion compared to that of the velocity field snapshot data, along with a more pronounced marginal effect. This indicates that more modes need to be retained for the velocity field to achieve a higher total energy proportion. By constructing a CSSA-BP model to represent the mapping relationship between the modal coefficients of the temperature and velocity fields and the data collected from the detection points, a comparison was made with the BP method in reconstructing the temperature field of a shell-and-tube heat exchanger. The CSSA-BP method yielded a maximum mean squared error (MSE) of 9.84 for the reconstructed temperature field, with a maximum mean absolute error (MAE) of 1.85. For the velocity field, the maximum MSE was 0.0135 and the maximum MAE was 0.0728. The global maximum errors for the reconstructed temperature field were 4.85%, 3.65%, and 4.29%, respectively. The global maximum errors for the reconstructed velocity field were 17.72%, 11.30%, and 16.79%, indicating that the model established in this study has high accuracy. Conventional CFD simulation methods require several hours, whereas the reconstruction model proposed here can rapidly reconstruct the flow field within 1 min after training is completed, significantly reducing reconstruction time. This work provides a new method for quickly obtaining the internal flow field state of pressure vessel equipment under limited detection points, offering a reference for online monitoring and the development of digital twins for pressure vessel equipment. [ABSTRACT FROM AUTHOR]

The synchronous operation of the digital twin model of nuclear power and the actual unit is an important guarantee for the safe operation of nuclear power. Taking the digital twin model of nuclear power steam system as the research object, the proxy model of steam system was constructed by constructing a five-layer fully connected neural network, and then the intelligent optimization algorithm was used to optimize the parameters of the proxy model to realize the automatic synchronization of the digital twin model with the actual unit, and finally an improvement scheme of the intelligent optimization algorithm was proposed and its application was verified. The results show that the proxy model can reach 99. 99% of the accuracy of the digital twin model, the call speed is increased by 5 times, the optimization results of the intelligent optimization algorithm are better than the manual debugging results, the improved scheme can effectively improve the optimization accuracy, and the automatic synchronization effect is remarkable. [ABSTRACT FROM AUTHOR]

A new method was proposed to address fault diagnosis by applying the digital twin (DT) high-fidelity behavior and the deep learning (DL) data mining capabilities. Subsequently, the proposed fault distribution GAN (FDGAN) was built to map virtual and physical entities for the data from the established test platform. Finally, the MobileViG was employed to validate the model and diagnose faults. The accuracy of the proposed method with training samples of 600 and 800 were 88.4% and 99.5%, respectively. These accuracies surpass those of other methods based on CycleGAN (98.86%), CACGAN (94.92%), ACGAN (86.45%), ML1D-GAN (82.33%), and transfer learning (99.38%). Therefore, with the integration of global connectivity, an innovative network structure, and training methods, FDGAN can effectively address challenges such as network degradation, limited feature extraction in small windows, and insufficient model robustness. [ABSTRACT FROM AUTHOR]

In recent years, advancements in high-resolution digital twin platforms or artificial intelligence marine forecasting have led to the increased requirements of high-resolution oceanic data. However, existing sea surface temperature (SST) products from observations often fail to meet researchers' resolution requirements. Deep learning models serve as practical techniques for improving the spatial resolution of SST data. In particular, diffusion models (DMs) have attracted widespread attention due to their ability to generate more vivid and realistic results than other neural networks. Despite DMs' potential, their application in SST spatial downscaling remains largely unexplored. Hence we propose a novel DM-based spatial downscaling model, called DIFFDS, designed to obtain a high-resolution version of the input SST and to restore most of the meso scale processes. Experimental results indicate that DIFFDS is more effective and accurate than baseline neural networks, its downscaled high-resolution SST data are also visually comparable to the ground truth. The DIFFDS achieves an average root-mean-square error of 0.1074 °C and a peak signal-to-noise ratio of 50.48 dB in the 4× scale downscaling task, which shows its accuracy. [ABSTRACT FROM AUTHOR]

Accurate defect detection is crucial for ensuring the trustworthiness of intelligent railway systems. Current approaches either rely on static ensembling of Convolutional Neural Networks (CNNs) or on single deep-learning CNN models. Traditional methods use a large volume of data to identify underlying patterns. However, training a new defect classifier with limited samples often results in overfitting and poor performance on unseen images. To overcome these challenges, we introduce a dynamic weight adaptation mechanism within an ensemble learning framework, enhanced by state-of-the-art transfer learning models (VGG-19, MobileNetV3, and ResNet50). We obtained a good test and validation accuracy of 99%, accompanied by a precision of 0.99, and recall and F1-score of 0.98. Unlike previous methods, our approach demonstrates consistency. Particularly, techniques that involve selecting predictions based on minimum loss previously required a significant number of epochs to stabilize. Data fusion techniques are even more demanding, requiring a substantially higher number of epochs for stabilization. Conversely, our proposed method achieves stable performance and rapid convergence within just 10 epochs, and it shows minimal fluctuation in the training curve (0–5 epochs). This visibly contrasts with earlier methods, which, despite reaching similar levels of accuracy, required significantly more epochs and exhibited greater fluctuations in their training curves. Through these findings, we anticipate that the proposed dynamic weight adaptation-based ensemble approach will further research and development of reusable AI-enabled solutions for rail defect classification. [ABSTRACT FROM AUTHOR]

Recently, extensive research has been conducted on generating virtual training data in a digital twin-based simulator to reduce the time and cost associated with acquiring high-quality training data necessary for autonomous driving. In this study, we propose an efficient method of generating synthetic training datasets for autonomous driving by combining real-world and virtual training data. Specifically, we propose a method of implementing a digital twin-based autonomous driving simulator, collecting large amounts of virtual training data using its camera sensor, and generating synthetic training datasets by combining virtual and real-world training data in various ratios. The effectiveness of these datasets is then validated in deep learning applications, particularly for detecting traffic lights and signal information. Validation results indicate that synthetic training datasets significantly improve deep learning performance, provided they include a sufficient amount of real-world training data to avoid class imbalance issues. We conclude that the synthetic training datasets generated using a digital twin-based simulator are cost-effective and practical for deep learning applications. [ABSTRACT FROM AUTHOR]

The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration. [ABSTRACT FROM AUTHOR]

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated. [ABSTRACT FROM AUTHOR]

Key parts in high-value equipment have critical requirements of high precision and performance. The machining processes of such parts normally involve multiple stations. Therefore, the machining quality of finished parts is an accumulated result of process chains (multi-stations, i.e. spatiodimension) and machine state conditions over different parts in batches (i.e. temporal dimension), which makes quality prediction difficult. Current quality prediction methods have no consideration of equipment state degradation (ESD) or simply investigate a single machine. To improve the prediction accuracy of machining quality, a digital twin-driven part spatio-temporal quality prediction (DT-PSTQP) framework for multi-stage machining processes (MMP) is proposed with full considerations of multi-machine processes and multi-state machine degradation. The relationship graph analysis (RGA) is used to classify continuous ESD into limited discrete states to construct MMP reconstruction module. The DT-QPL module is a collection of quality prediction models that are trained with the refined sub-datasets obtained by MMP reconstruction. The proposed framework and the three models are validated through a thin-walled part production line. The results show that the proposed framework can help to improve the quality prediction average accuracy by 18.8% compared to the traditional framework without DT-PSTQP. [ABSTRACT FROM AUTHOR]

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as "flickering" in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy. [ABSTRACT FROM AUTHOR]

This research aimed to develop a dataset of acoustic images recorded by a forward-looking sonar mounted on an underwater vehicle, enabling the classification of unexploded ordnances (UXOs) and objects other than unexploded ordnance (nonUXOs). The dataset was obtained using digital twin simulations performed in the Gazebo environment utilizing plugins developed within the DAVE project. It consists of 69,444 sample images of 512 × 399 resolution organized in two classes annotated as UXO and nonUXO. The obtained dataset was then evaluated by state-of-the-art image classification methods using off-the-shelf models and transfer learning techniques. The research included VGG16, ResNet34, ResNet50, ViT, RegNet, and Swin Transformer. Its goal was to define a base rate for the development of other specialized machine learning models. Neural network experiments comprised two stages—pre-training of only the final layers and pre-training of the entire network. The experiments revealed that to obtain high accuracy, it is required to pre-train the entire network, under which condition, all the models achieved comparable performance, reaching 98% balanced accuracy. Surprisingly, the highest accuracy was obtained by the VGG model. [ABSTRACT FROM AUTHOR]

In order to achieve the causal diagnosis of life health of high-speed spindle system, a digital twin (DT)–driven prediction method is proposed based on the real-time monitoring of thermal characteristics. The finite element method is used to achieve the DT for thermal characteristics through real time correcting the thermal boundaries using the correction models. The long short-term memory recurrent neural network (LSTM-RNN) is used to predict the heat generations of bearings and motor; the prediction results are used to diagnose the health status according to the domain and threshold models. A heat change rate–based causal diagnosis model is proposed to judge the fault sources. The spindle wear and fault experiments are carried out to verify the effectiveness of the proposed DT system. The experimental results show that the DT accuracy of thermal characteristics exceeds 95%, and the proposed DT system can successfully monitor the health status of the spindle system. [ABSTRACT FROM AUTHOR]

With the rapid development of modern power systems, the structure and operation of distribution networks are becoming increasingly complex, demanding higher levels of intelligence and digitization. Digital twin, as a virtual cutting-edge technique, can effectively reflect the operational status of distribution networks, offering new possibilities for real-time monitoring, optimization and other functions for distribution networks. Building efficient and accurate models is the foundation of enabling a digital twin of distribution networks. This paper proposes a digital twin operating system for distribution networks with renewable energy based on robust state estimation and deep learning-based renewable energy prediction. Furthermore, the identification and correction of possible bad or missing data based on deep learning are also included to purify the input data for the digital twin system. A digital twin test platform is also proposed in the paper. A case study and evaluations based on a real-time digital simulator are carried out to verify the accuracy and real-time performance of the established digital twin system. In general, the proposed method can provide the basis and foundation for distribution network management and operation, as well as intelligent power system operation. [ABSTRACT FROM AUTHOR]

There has been significant effort and a growing need to develop innovative and costeffective solutions for real-time monitoring and operation of value chains and supply chains, especially to enhance the predictability and optimisation of complex production systems for a better adaptation to disruptions and market fluctuations as well as improved sustainability. This is particularly important when taking into account the impacts and emerging advancements of smart agriculture, smart manufacturing, Digital Twins, and Industry 5.0, where data-driven solutions and AI-enabled decision-making play an important role for improving real-time monitoring, quality control and management, and operational efficiency. This study presents a conceptual framework for integrating Digital Twins into a smart agriculture platform, focusing on the real-time monitoring and operation of the coffee value chain and supply chain, to demonstrate the potential of Digital Twins in advancing smart agriculture and digital supply chains. [ABSTRACT FROM AUTHOR]

In the age of Industry 4.0 and smart manufacturing, traditional production processes are undergoing significant changes due to the integration of advanced technologies like automation, robotics, big data analytics, and machine learning, which are enhancing productivity and manufacturing efficiency. The center of this evolution is the introduction of the digital twin (DT), the digital replicas of physical assets that blend real-time data with advanced analytics and simulations. Ensuring synchronization between the digital replica and its physical counterpart is crucial for successfully implementing digital twins. This paper addresses the challenge of achieving and quantifying synchronization within DTs, focusing on replicating physical system behavior and measuring deviations or delays. The study delves into the critical aspects of synchronization within digital twin applications, focusing on its implications for a robotic assembly system. The research successfully harnessed YOLOv8 to facilitate real-time event tracking and synchronization characterization, highlighting the potential of object detection deep-learning models in enhancing synchronization accuracy and, consequently, the efficiency and reliability of manufacturing processes. [ABSTRACT FROM AUTHOR]

Significance: Deep learning enables label-free all-optical biopsies and automated tissue classification. Endoscopic systems provide intraoperative diagnostics to deep tissue and speed up treatment without harmful tissue removal. However, conventional multi-core fiber (MCF) endoscopes suffer from low resolution and artifacts, which hinder tumor diagnostics. Aim: We introduce a method to enable unpixelated, high-resolution tumor imaging through a given MCF with a diameter of around 0.65 mm and arbitrary core arrangement and inhomogeneous transmissivity. Approach: Image reconstruction is based on deep learning and the digital twin concept of the single-reference-based simulation with inhomogeneous optical properties of MCF and transfer learning on a small experimental dataset of biological tissue. The reference provided physical information about the MCF during the training processes. Results: For the simulated data, hallucination caused by the MCF inhomogeneity was eliminated, and the averaged peak signal-to-noise ratio and structural similarity were increased from 11.2 dB and 0.20 to 23.4 dB and 0.74, respectively. By transfer learning, the metrics of independent test images experimentally acquired on glioblastoma tissue ex vivo can reach up to 31.6 dB and 0.97 with 14 fps computing speed. Conclusions: With the proposed approach, a single reference image was required in the pre-training stage and laborious acquisition of training data was bypassed. Validation on glioblastoma cryosections with transfer learning on only 50 image pairs showed the capability for high-resolution deep tissue retrieval and high clinical feasibility. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, integrating Digital Twin (DT) technology stands out as a critical challenge for enhancing manufacturing processes and productivity. The combination of DT and Artificial Intelligence (AI) provides a significant benefit for improving processes in real-time. Industries are actively researching these technologies to keep pace with the rapid evolution of technology, utilizing virtual representations for efficient real-time monitoring and control. The present paper proposes a new approach that relies on DT technology for monitoring, optimizing manufacturing processes and enabling quality control through Deep learning (DL). The proposed methodology involves creating a digital replica of the physical system and utilizing DL models for quality control purposes. This approach improves automation and productivity while maintaining high levels of quality assurance in factories. DL is deployed within the DT for gathering data from the physical system and making predictions regarding product quality. The approach is illustrated by considering an experimental industrial prototype. The results obtained are particularly intriguing, demonstrating heightened predictive accuracy in assessing product quality and real-time issue resolution. Overall, the findings underscore the significant and interesting impact of DT technology with DL on manufacturing processes in the context of Industry 4.0. [ABSTRACT FROM AUTHOR]

Magnetic field is one of the basic data for constructing a transformer digital twin. The finite element transient simulation takes a long time and cannot meet the real-time requirements of a digital twin. According to the nonlinear characteristics of the core and the timing characteristics of the magnetic field, this paper proposes a fast calculation method of the spatial magnetic field of the transformer, considering the nonlinear characteristics of the core. Firstly, based on the geometric and electrical parameters of the single-phase double-winding test transformer, the corresponding finite element simulation model is built. Secondly, the key parameters of the finite element model are parametrically scanned to obtain the nonlinear working condition data set of the test transformer. Finally, a deep learning network integrating a convolutional neural network (CNN) and a long short-term memory network (LSTM) is built to train the mapping relationship between winding voltage, current, and the spatial magnetic field so as to realize the rapid calculation of the transformer magnetic field. The results show that the calculation time of the deep learning model is greatly shortened compared with the finite element model, and the model calculation results are consistent with the experimental measurement results. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of robotics technology, robot coupling simulation has become a highly concerned and researched field. Robot coupling simulation involves the interaction between multiple robots or between robots and the environment, and multiple aspects need to be considered. In this case, digital twin technology provides a new solution and tool for robot coupling simulation. This article aims to study the application of digital twin technology in robot coupling simulation, explore the basic knowledge of digital twin technology and big data technology in robot system modeling, motion simulation, and control simulation, and recommend using algorithms such as deep learning, convolutional neural networks, and recurrent neural networks for digital twin modeling and prediction. This article also conducts six axis robot experiments to verify the effectiveness and feasibility of digital twin technology in robot coupling simulation. Finally, this article summarizes the advantages and limitations of digital twin technology in robot system simulation, and proposes the future development trend of digital twin technology in robot system simulation. [ABSTRACT FROM AUTHOR]

This review focuses on the definitions, modalities, applications, and performance of various aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this regard, the context around Industry 4.0 and even aspirations for Industry 5.0 are discussed. The many definitions and interpretations of DTs in this domain are first summarized. Subsequently, their adoption and performance levels for rotating and industrial machineries for manufacturing and lifetime performance are observed, along with the type of validations that are available. A significant focus on integrating fundamental operations of the system and scenarios over the lifetime, with sensors and advanced machine or deep learning, along with other statistical or data-driven methods are highlighted. This review summarizes how individual aspects around DTs are extremely helpful for lifetime design, manufacturing, or decision making even when a DT can remain incomplete or limited. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

As a robust real-time mapping technology for virtual reality, Digital Twin (DT) offers significant potential in the Internet of Things (IoT) context. When combined with IoT, DT enables excellent online monitoring and management capabilities for physical entities. Additionally, this collaboration has led to the development of a smart monitoring system that leverages DT and deep learning technologies to provide a user-friendly monitoring platform and enable real-time anomaly detection. In detail, we first establish a DT-based monitoring platform to ensure the mapping of physical entities to virtual environments. Furthermore, we employ a deep learning-based anomaly detection method for real-time monitoring. The system is implemented in a smart laboratory environment to evaluate our approach. Based on the collected data, our method shows excellent performance in anomaly detection. Experimental results demonstrate the effectiveness of our strategy in achieving the stated goals. In conclusion, leveraging deep learning and DT techniques, this solution offers a robust and reliable solution for IoT active monitoring systems, facilitating highly relevant and valuable real-world applications. [ABSTRACT FROM AUTHOR]

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable. [ABSTRACT FROM AUTHOR]

Summary: In order to improve the security and reliability of power industry control systems, research proposes the design of intelligent industrial control systems based on digital twins. This design combines the digital twin multilevel reconstruction method to construct an intelligent power control system structure, and proposes a system safety verification range design and evaluation indicators. The experimental data illustrates that the production time of the deep learning digital twin system reconstruction algorithm is 19.1% less than that of the reconstruction algorithm based on differential evolution; and when the number of nodes and the type and number of twins change, the algorithm proposed in the study takes less time and is more in line with the actual needs of the situation. The intelligent power industry control system based on digital twins has a score of no less than 9.5 in four aspects, indicating that the overall effectiveness of the system is good. The results indicate that this method can improve the safety and reliability of the system, ensure the stable operation of the power system, and provide technical support for the widespread application of intelligent power industry control systems. [ABSTRACT FROM AUTHOR]

Digital transformation is omnipresent in our daily lives and its impact is noticeable through new technologies, like smart devices, AI-Chatbots or the changing work environment. This digitalization also takes place in product development, with the integration of many technologies, such as Industry 4.0, digital twins or data-driven methods, to improve the quality of new products and to save time and costs during the development process. Therefore, the use of data-driven methods reusing existing data has great potential. However, data from product design are very diverse and strongly depend on the respective development phase. One of the first few product representations are sketches and drawings, which represent the product in a simplified and condensed way. But, to reuse the data, the existing sketches must be found with an automated approach, allowing the contained information to be utilized. One approach to solve this problem is presented in this paper, with the detection of principle sketches in the early phase of the development process. The aim is to recognize the symbols in these sketches automatically with object detection models. Therefore, existing approaches were analyzed and a new procedure developed, which uses synthetic training data generation. In the next step, a total of six different data generation types were analyzed and tested using six different one- and two-stage detection models. The entire procedure was then evaluated on two unknown test datasets, one focusing on different gearbox variants and a second dataset derived from CAD assemblies. In the last sections the findings are discussed and a procedure with high detection accuracy is determined. [ABSTRACT FROM AUTHOR]

The rapid development of advanced technologies provides futuristic option to enhance product value with digital services, which the concept of Smart Product-Service System (SPSS) was proposed. SPSS focuses on usage data, the whole lifecycle, and the iterative optimisation. A large amount of data and context information can be collected during the usage stage, which can be used to elicit requirements to guide further design. How to realise the requirement elicitation with cognitive intelligence based on usage data is the key step for design optimisation. Therefore, this research proposes a cognitive intelligence-enabled requirements elicitation framework to help designers promote the design optimisation process. First, the digital twin-driven data collection architecture of SPSS is proposed by considering multi-sources and multi-stages. Second, the behaviour of SPSS is modelled based on Auto-Encoder which is constructed using deep learning to cognise SPSS performance. Third, the corresponding behaviour model is interpreted based on Shapley Addictive exPlanations (SHAP) to discover key factors with cognitive intelligence. Finally, the domain knowledge graph is used to generate the optimisation requirements for further design. To demonstrate the feasibility and advantages of the proposed framework, this study adopts the case study of wind turbines for illustration and verification. [ABSTRACT FROM AUTHOR]

Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS 2023 conference data challenge to test anomaly detection algorithms. A survey of the suggested techniques is provided, demonstrating that traditional signal processing techniques interestingly outperform deep learning algorithms in this case. Of the 11 participating groups, only those that used traditional approaches achieved good results on most of the channels. Additionally, we introduce a signal processing anomaly detection algorithm and meticulously compare it to a standard deep learning anomaly detection algorithm using data from the HUMS 2023 challenge and simulated signals. The signal processing algorithm surpasses the deep learning algorithm on all tested channels and also on simulated data where there is an abundance of training data. Finally, we present a new digital twin that enables the estimation of the remaining useful life of the tested gear from the HUMS 2023 challenge. [ABSTRACT FROM AUTHOR]

The safe and steady operation of hydropower generation systems is crucial for electricity output in the grid. However, hydropower stations have complicated interior structures, making defect detection difficult without disassembly inspections. The application of digital modeling to hydropower stations will effectively promote the intelligent transformation of hydropower stations as well as reduce the maintenance costs of the system. This study provides a model of the power generating and transmission system for hydropower plants, with an emphasis on primary equipment and measured data. The model utilizes PSCAD to digitalize state response in hydropower plants with various short-circuit faults. The fault information is identified and learned using the Adaptive Time–Frequency Memory (AD-TFM) deep learning model. It is demonstrated that our proposed method can effectively obtain the fault information through radio frequency identification (RFID). The accuracy of the traditional method is 0.90, while the results for AD-TFM show a fault classification accuracy of 0.92, which is more than enough to identify multiple fault types compared to the existing methods. [ABSTRACT FROM AUTHOR]

Urban water management faces new challenges due to the rise of digital solutions and abundant data, leading to the development of data-centric tools for decision-making in global water utilities, with AI technologies poised to become a key trend in the sector. This paper proposes a novel methodology for optimal sensor placement aimed at supporting the creation of a digital twin for water infrastructure. A significant innovation in this study is the creation of a metamodel to estimate pressure at consumption nodes in a water supply system. This metamodel guides the optimal sensor configuration by minimizing the difference between estimated and observed pressures. Our methodology was tested on a synthetic case study, showing accurate results. The estimated pressures at each network node exhibited low error and high accuracy across all sensor configurations tested, highlighting the potential for future development of a digital twin for water distribution systems. [ABSTRACT FROM AUTHOR]

The accidental leakage of gaseous hydrogen from hydrogen refueling station facilities has the potential to result in a large fire and explosion catastrophe. The ability to forecast the spatiotemporal evolution of hydrogen plumes in real-time is crucial for monitoring the distribution of plume concentrations and mitigating the risk of fire and explosion. The utilization of deep learning has been extensively employed in a diverse range of spatiotemporal forecasting tasks. However, these approaches encounter limitations in accurately quantifying uncertainty when predicting spatiotemporal concentrations and plume boundaries. This research paper introduces a novel model called DPSTNN_H 2 Evolution, which is a deep probabilistic spatial-temporal neural network designed for forecasting the spatiotemporal evolution of hydrogen plumes. The benchmark dataset is constructed by the implementation of numerical simulations pertaining to the inadvertent leak of hydrogen within a hydrogen refueling station. The findings of the study indicate that incorporating quantified uncertainty information might enhance the precision of plume boundaries and the resilience of plume concentrations when predicting the spatiotemporal development of hydrogen. By employing Monte Carlo sampling with a sample size of m = 100 and a dropout rate of p = 0.1 , the model demonstrates the ability to provide real-time inference for 10 instances of plumes, while maintaining a high level of accuracy with R2 = 0.9829. In comparison to the current state-of-the-art model, the proposed model demonstrates superior accuracy and robustness in forecasting the spatiotemporal evolution of hydrogen plumes. In general, our proposed model has the potential to serve as a dependable option for the development of a digital twin for emergency management of hydrogen refueling stations. • Probabilistic spatial-temporal neural network model for hydrogen plume evolutions is proposed. • Normalized uncertainty contours and uncertainty intervals to improve accuracy and robustness. • Numerical experiment of hydrogen leakage and dispersion in refueling station are conducted. • Optimal hyper-parameters of the proposed model are determined • Comparison between the proposed model with point-estimation based model is conducted [ABSTRACT FROM AUTHOR]

Modern cyber–physical production systems provide advanced solutions to enhance factory throughput and efficiency. However, monitoring its behaviour and performance becomes challenging as the complexity of a manufacturing system increases. Artificial Intelligence (AI) provides techniques to manage not only decision-making tasks but also to support monitoring. The integration of AI into a factory can be facilitated by a reliable Digital Twin (DT) that enables knowledge-based and data-driven approaches. While computer vision and convolutional neural networks (CNNs) are crucial for monitoring production systems, the need for extensive training data hinders their adoption in real factories. The proposed methodology leverages the Digital Twin of a factory to generate labelled synthetic data for training CNN-based object detection models. Regarding their position and state, the focus is on monitoring entities in manufacturing systems, such as parts, components, fixtures, and tools. This approach reduces the need for large training datasets and enables training when the actual system is unavailable. The trained CNN model is evaluated in various scenarios, with a real case study involving an industrial pilot plant for repairing and recycling Printed Circuit Boards (PCBs). [ABSTRACT FROM AUTHOR]

The education sector is a rapidly growing global infrastructure for human resources. It is an essential and sensitive data-containing sector where sensitive private information about the user is included in the data. The incorporation of digital twins (DT) into virtual representations is used in higher education to enhance data analytic performance. However, the DT-enabled industry generates enormous amounts of data, making it challenging to manage security and privacy. The management of personal data by human resources involved in critical and resource-constrained tasks has increased since the globalization of Industry 5.0 and the higher education sector. Managing the security and privacy of users' private data through global data collection methods poses significant challenges. We present a framework for delivering decentralized data processing and learning in blockchain-based industry 5.0, with a use-case for higher education that integrates blockchain and deep learning (DL). The framework begins by introducing a new DT model that makes creating a virtual environment to model and replicate security-critical processes in higher education easier. Second, we propose a higher education data analysis model, so that blockchain-based data are ready for the fully functional higher education DT model. At last, the security and cost analyses of the proposed system are presented. Long short-term memory (LSTM) is used for the deep learning model. A custom Python-based blockchain with a proof-of-authentication consensus algorithm is used to implement the blockchain. Our proposed system is designed in such a way that it includes the higher education sector with the concepts of blockchain technology, digital twin, and Industry 5.0. The efficiency and throughput results of the blockchain show that the proposed system is better than the digital twin-based developed systems. The novelty of our proposed system is that educational blockchain with Industry 5.0 is conceptualized and implemented with deep learning models. [ABSTRACT FROM AUTHOR]

The benefits of having digital twins of urban buildings are numerous. However, a major difficulty encountered in their creation from airborne LiDAR point clouds is the effective means of accurately reconstructing significant occlusions amidst point density variations and noise. To bridge the noise/sparsity/occlusion gap and generate high fidelity 3D building models, we propose APC2Mesh which integrates point completion into a 3D reconstruction pipeline, enabling the learning of dense geometrically accurate representation of buildings. Specifically, we leveraged complete points generated from occluded ones as input to a linearized skip attention-based deformation network for 3D mesh reconstruction. In our experiments, conducted on 3 different scenes, we demonstrate that: (1) Compared to SoTA methods like NDF, Points2Poly, Point2Mesh, and a few others, APC2Mesh ranked second in positional RMSE and first in directional RSME, with error magnitudes of 0.0134 m and 0.1581, respectively. This indicates the efficacy of APC2Mesh in handling the challenges of airborne building points of diverse styles and complexities. (2) The combination of point completion with typical deep learning-based 3D point cloud reconstruction methods offers a direct and effective solution for reconstructing significantly occluded airborne building points. As such, this neural integration holds promise for advancing the creation of digital twins for urban buildings with greater accuracy and fidelity. Our source code is available at https://github.com/geospatial-lab/APC2Mesh. [ABSTRACT FROM AUTHOR]

Effective monitoring of road fixtures is essential for urban safety and functionality. However, traditional inspections are time-consuming, costly, and error prone, while current automated solutions struggle with high initial setup costs, limited flexibility preventing wide adaptation, and reliance on centralized processing that can delay response times. This study introduces an edge AI-based remote road fixture monitoring system which automatically and continuously updates the information of the road digital twin (DT). The main component is a small-sized edge device consisting of a camera, GPS, and IMU sensors designed to be installed in typical cars. The device captures images, detects the fixture, and estimates their location by employing deep learning and feature matching. This information is transmitted to a dedicated cloud server and represented on a user-friendly user interface. Experiments were conducted to test the system's performance. The results showed that the device could successfully detect the fixture and estimate their global coordinates. Outputs were marked and shown on the road DT, proving the integrated and smooth operation of the whole system. The proposed Edge AI device demonstrated that it could significantly reduce the data size by 80–84% compared to traditional methods. With a satisfactory object detection accuracy of 65%, the system effectively identifies traffic poles, stop signs, and streetlights, integrating these findings into a digital twin for real-time monitoring. The proposed system improves road monitoring by cutting down on maintenance and emergency response times, increasing the ease of data use, and offering a foundation for an overview of urban road fixtures' current state. However, the system's reliance on the quality of data collected under varying environmental conditions suggests potential improvements for consistent performance across diverse scenarios. [ABSTRACT FROM AUTHOR]

Evaluation of surface distress is an important aspect of pavement management. The most common practice to assess surface distress is to develop a pavement condition index (PCI), with ASTM-PCI being the most widely used in evaluating flexible pavements. Traditional PCI evaluation methods rely on labour-intensive, manual inspections, leading to significant time consumption. In recent years, real-time visualization and crowdsourcing have been explored, but their potential has yet to be fully realized. Integrating real-time visualization through GIS technology offers immediate insights into pavement conditions, aiding prompt decision-making. Crowdsourcing allows a broader community to contribute to condition reporting, enhancing data accuracy and coverage. This study aims to develop an artificial intelligence (AI)-based method for road condition assessment from crowd-sourced images. A deep-learning object detection model is utilized for precise crack detection and classification. The model is trained to recognize various distress types accurately and quantify attributes crucial for determining the PCI. The developed model is then integrated into a web-based platform accessible through mobile phones and dash cameras, allowing real-time capture and classification of cracks. The study demonstrates that the automated methodology significantly enhances PCI estimation efficiency, with a high correlation between semi-automated and automated methods. Stakeholders can benefit from deep learning and automation in pavement distress detection, aiding informed decision-making through crowdsourcing data. Future work includes the detection of subclasses within crack types based on severity and the creation of digital twins for public assets. Overall, this study highlights the transformative potential of AI and crowdsourcing in improving pavement management. [ABSTRACT FROM AUTHOR]

To facilitate the integration of autonomous unmanned air vehicles (UAVs) in day-to-day life, it is imperative that safe navigation can be demonstrated in all relevant scenarios. For UAVs using a navigational protocol driven by artificial neural networks, training and testing data from multiple environmental contexts are needed to ensure that bias is minimised. The reduction in predictive capacity when faced with unfamiliar data is a common weak point in trained networks, which worsens the further the input data deviates from the training data. However, training for multiple environmental variables dramatically increases the man-hours required for data collection and validation. In this work, a potential solution to this data availability issue is presented through the generation and evaluation of photo-realistic image datasets from a simulation of 3D-scanned physical spaces which are theoretically linked in a digital twin (DT) configuration. This simulation is then used to generate environmentally varied iterations of the target object in that physical space by two contextual variables (weather and daylight). This results in an expanded dataset of bicycles that contains weather and time-varied components of the same images which are then evaluated using a generic build of the YoloV3 object detection network; the response is then compared to two real image (night and day) datasets as a baseline. The results reveal that the network response remained consistent across the temporal axis, maintaining a measured domain shift of approximately 23% between the two baselines. [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]

Due to various factors such as aging, natural environment erosion, and man-made destruction, architectural heritage has formed various diseases and cracks, especially in pathology cracks, which are the most typical masonry–timber architectural heritages, directly affecting the structural stability of masonry–timber buildings. This paper uses artificial intelligence and architecture and other multi-disciplinary research methods, taking James Jackson Gymnasium, a famous masonry–timber architectural heritage in Wuhan, as an example, using 3D laser scanning technology to obtain disease details and crack data of architectural heritage, using a Mask R-CNN model to detect crack area, using an FCN model to identify and calculate single cracks, and finally summarizing the type, location, and characteristics of cracks, analyzing the causes of cracks, and then putting forward corresponding hierarchical restoration strategies. The research results build a set of detection and repair systems of masonry–timber architectural heritage pathology cracks, which provide a set of accurate and objective pathology cracks data for architectural heritage protection and repair, and provide a reference for architectural heritage repair. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinear interaction between the flow field and blades, the prediction of turbine energy acquisition capacity is still quite complex, the traditional methods take too long time to evaluate. In this article, the digital twin model + CFD simulation + monitor data are used for turbine energy efficiency assessment. A self-designed vertical wave flow turbine (VWFT) is taken as the research object, the prediction takes into account the coupling of the VWFT for motion and blade rotation, which is in good agreement with the monitor data at sea. The simulations show that the torque, thrust force and lateral force flow rate data can be loaded from the database into the digital model. If those data are not in the database, the interpolation method is used along with deep learning of recurrent neural network to obtain the energy harvesting parameters, all the error is no more than 10%. [ABSTRACT FROM AUTHOR]