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The thermal errors in ball screws present complex spatial-temporal characteristics and exhibit persistent long-term memory, significantly affecting the machining accuracy of the whole machine tool in applications. Deep learning has emerged as a promising approach to predict these errors using thermal data. However, the scarcity of extensive thermal datasets, typically due to the prohibitive costs and time required for experimental acquisition, hampers the comprehensive exploitation of spatial-temporal and long-term memory attributes. Moreover, the current thermal error compensation system does not support the deployment of deep learning-based thermal error models due to its weak real-time performance. These limitations lead to suboptimal prediction accuracy and inadequate error compensation. Addressing this challenge, a novel spatial-temporal interactive integration network is proposed, and this is a sophisticated model that synergistically blends a time memory gate and a spatial-temporal fusion gate within an advanced attention-based spatial-temporal graph convolutional framework. Our network ingeniously leverages spatial data to anchor long-term memory while employing temporal data for selective memory feature extraction, facilitating a refined integration of spatial-temporal features. Concurrently, the integration of gated recurrent units and a time attention layer meticulously extracts and enhances temporal features, bolstered by our innovative time memory gate, which is adept at handling small-sample scenarios and refining thermal error predictions. Critically, the digital twin system for thermal error compensation is constructed to improve the system's real-time performance. The integration of this network into the digital twin system marks a pivotal advancement in thermal error compensation. Our empirical results underscore the system's remarkable robustness and superior predictive accuracy, demonstrating a significant reduction in positioning and machining errors (over 90% and 80% respectively) even with constrained thermal data inputs. These advancements not only delineate a substantial leap in predictive accuracy but also underscore our contribution to reducing operational costs and enhancing the efficacy of machine tools. [Display omitted] • Spatial-temporal fusion gate is proposed to extract spatiotemporal features. • STIIN is proposed to extract spatiotemporal features of thermal error. • Digital twin system for thermal error compensation is established. • STIIN model is embedded into digital twin system. • Robust compensation is realized with small-volume thermal information data. [ABSTRACT FROM AUTHOR]

A digital twin model based on superpixel features is established to solve the problem of noise and similar gray values between foreground and background of pellet images. With superpixel as the basic unit of segmentation, the influence of single pixel on segmentation results is reduced, and allows for higher segmentation accuracy. The gray-level co-occurrence matrix is used to represent the superpixel characteristic information, and the color moment and gray level distribution are combined to comprehensively characterize the superpixel. Through principal component analysis and correlation analysis, The feature compression of superpixel is realized, and the computational efficiency is improved. The superpixel binary classification data set is built, and the multi-dimensional feature information of superpixel is extracted as input vector to train the binary classification model of SVM, and the image segmentation problem is transformed into foreground and background classification problem. A multi-scale superpixel segmentation boundary optimization method is proposed to further refine the boundary region of foreground and background. A four-neighborhood search algorithm is proposed to reduce the missegmentation rate of edge superpixels. Experimental results show that the accuracy of the proposed method can reach 95.87%, the precision of image edge segmentation is high, and the foreground and background of granular image are accurately segmented. The digital twin model is established which can provide basis for the subsequent visual analysis and decision. • An image segmentation model based on superpixel features is established. • A superpixel feature extraction method that fuses texture features and global features is proposed. • An optimization algorithm for foreground and background edge of superpixel is proposed. [ABSTRACT FROM AUTHOR]

This work proposes a new method for the detection, localization, and classification of grid-connected photovoltaic (PV) array faults. Line-to-line, open-module, shorted-module, open-string, and shorted-string faults as well as partial shading conditions are studied. The proposed method has two stages, which are (1) detection and localization of faults and (2) classification of faults. In the first stage, detection and localization are performed using a digital twin (DT) by analyzing the current ratio of each PV array. The measured DC (direct current) power after the operation of the DC/DC boost converter in the physical object is firstly converted into a 2-dimensional image using a recurrence plot (RP) and is then inputted to the second stage. A deep learning-based shifted windows (swin) transformer optimized by particle swarm optimization (PSO) is used in the classification stage, eliminating the need for model tuning by trial-and-error. A PV system of ten arrays with 49 kW is studied. The coefficients of determination (R2) between the results of the digital object (digital twin) and the physical object for different scenarios demonstrate the accuracy and success of the digital twin. R2 values of 0.99988 for varying irradiation with constant temperature and 0.97923 for constant irradiation with varying temperature indicate strong correlations between the digital and physical objects, further confirming the applicability of the digital twin in PV fault detection. The comparative evaluation of the PSO-optimized swin transformer against classical machine learning algorithms and state-of-the-art convolutional neural networks reveals the superior performance of the proposed method. It achieves an outstanding classification accuracy of 98.55%, demonstrating its ability to effectively classify various types of PV faults. The results of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, which measures the trade-off between true positive rate (TPR) and false positive rate (FPR), further illustrate the effectiveness of the proposed method for PV fault classification. • A model-based digital twin is used to detect and locate faults in a PV system. • Shifted Window Transformer with few trained variables is used to classify PV faults. • Topology of deep learning model is optimized by particle swarm optimization. [ABSTRACT FROM AUTHOR]

Preparing the infrastructure for analyzing, recognizing, and characterizing microorganisms in the Metaverse can transform the fields of biology, medicine, and drug discovery. Accordingly, the realization of digital twins of microorganisms, such as viruses, fungi, algae, bacteria, protozoa, archaea, and multicellular animal parasites, can streamline the applicability of the Metaverse and similar emerging technologies like Cyber-Physical Healthcare Systems (CPHS). This is why a new approach to the digital twinning of bacteria has been presented in this research. This method of digital twinning can revolutionize the research and study of bacteria because it allows us to separate useful and harmful bacterial species, increasing the efficiency of treatment noticeably. This innovative method can easily be used by clinics, medical centers, or even by private users physically and virtually and can be adapted to every centralized or decentralized Metaverse Platform. To determine the proper treatment, biologists have always tried to identify the correct bacterial species that prompted a bacterial infection. They use various indicators, such as the bacterial cell's shape and the size of the colony formed by the bacteria, to classify different types of bacteria with different biochemistries and shapes. However, it is challenging because of the extensive similarities between some species. For instance, such similarities exist between Staphylococcus aureus and Staphylococcus saprophyticus, which has caused numerous false diagnostic reports by operators. Wrong species reports bring about treatment failure and increase antibiotic resistance issues. Therefore, the digital twins of bacteria cover all of their identifiable characteristics and overcome many limitations to study them. In this approach, DTL techniques, including MobileNetV2, EfficientNetV2-S, and ResNet-50, have been employed to build digital twins of bacteria species. A hybrid dataset was used for training and evaluation. Among the models, EfficientNetV2-S exhibited the best performance, with a validation accuracy of 99.58% and a test accuracy of 99.33%. The results showed the ability of deep learning models to make bacteria digital twins based on image processing from different labs, thus assisting experts in speeding up the process and reducing diagnostic errors. In addition, in the mispredicted cases, the correct species was among the first three choices of the model. Therefore, not only can experts use DTL approaches to speed up the digital twin realization of microorganisms in the Metaverse, but they can also use these methods to reduce diagnostic errors. • Introducing a technique to streamline digital twinning of microorganisms using CNN. • Describing machine learning digital twins of microorganisms in the Metaverse. • Presenting an exact, economical digital twinning approach via smartphone photography. • Providing a comprehensive bacteria species dataset for future method development. [ABSTRACT FROM AUTHOR]

In many hybrid flow shop production environments, dynamic events (DEs) often occur and seriously impact production schedules. However, the traditional model rarely considers DEs, which makes the model seriously inconsistent with the actual situation. In addition, to reduce carbon emissions, there is an urgent need to carry out green optimization scheduling of production workshops, especially in emerging digital workshops. First, this paper proposes a dynamic digital-twin-based Multi-Objective Hybrid Flowshop Green Scheduling Model with DEs (MOHFGSM-DEs) to fill the above gaps. The MOHFGSM-DEs model's bi-objectives are the makespan minimization and digital workshop total energy consumption minimization. In addition, the MOHFGSM-DEs model takes account of representative DEs in the actual dynamic production process, including controlled processing time, device dynamic reconfiguration events, and workpiece reworking events, which makes it more adaptive in practical production. Secondly, an Adaptive Multi-Objective Dynamic Harris Hawks Optimizer (AMODHHO) is presented with DEs parameters perceived by the digital twin of the workshop, adjusting the scheduling scheme adaptively in real time to address the MOHFGSM-DEs model efficiently. AMODHHO combines a nonlinear optimization strategy on balancing the exploration and exploitation of Harris Hawks Optimizer (HHO) and introduces the crossover operator of Genetic Algorithm (GA) to improve its optimal global ability. Moreover, a digital-twin-driven dynamic encoding methodology containing many optimization strategies is designed based on problem-specific characteristics. Finally, numerical experiments and application case comparisons are performed among AMODHHO, SPEA2, and NSGA-II. Results show that AMODHHO is superior to SPEA2 and NSGA-II regarding comprehensive performance. Therefore, the proposed model and AMODHHO are feasible to solve the real-world MOHFGSM-DEs adaptively. Furthermore, it performs dynamic adaptive adjustment for the actual scheduling schemes according to the real-time DEs the digital twin perceives. • A dynamic digital-twin-based multi-objective hybrid flow green scheduling model with dynamic events (DEs) (MOHFGSM-DEs). • MOHFGSM-DEs adaptively optimize the energy consumption and production efficiency of digital production line under DEs. • An adaptive multi-objective Harris Hawks Optimizer (AMODHHO) by combining DEs sensed by digital-twin and multiple strategies. • Compared with SPEA2 and NSGA-II, results of experiments and applications prove AMODHHO's effectiveness. • MOHFGSM-DEs and AMODHHO avoid unplanned shutdowns of digital-twin-based unmanned production lines. [ABSTRACT FROM AUTHOR]