Digital Model Research Articles

Construction method and verification of digital twin mechanism model of pulmonary ventilation

Construction method and verification of digital twin mechanism model of pulmonary ventilation

3D intraoral scanning techniques support the effects of crown morphology on dental caries

BackgroundWith the development and utilization of three-dimensional (3D) intraoral scanning (IOS) technology, the morphological characteristics of teeth were quantitatively assessed. In this research, we aimed to explore the prevalence of dental caries in relation to each measurable morphological indicator of the tooth body via 3D intraoral scanning techniques.MethodsA hospital-based single-centre study was conducted at our hospital from Dec. 2021 to Apr. 2023. A total of 53 patients were involved in the study, providing complete morphological data for 79 teeth. Each patient completed an oral hygiene routine questionnaire and underwent examination by an experienced dentist to evaluate caries conditions before undergoing 3D intraoral scanning to obtain a digital dental model. Geomagic Studio 2014 was used to extract oral morphological data from the models. The acquired data were entered, cleaned and edited using Excel 2016 and subsequently exported to SPSS version 25.0 for analysis. Chi-square analysis and logistic regression analyses were employed to test the associations.ResultsAmong the participants, 33 (61.1%) were female, with a mean age of 26.52 ± 10.83 years. Significant associations were found between dental caries and the vertical distance between the distal tip and the gum (OR 14.02; 95% CI 1.80-109.07; P = 0.012), the distal lateral horizontal distance of occlusion (OR 0.40; 95% CI 0.18–0.90; P = 0.026), and the mesial horizontal distance of occlusion (OR 2.20; 95% CI 1.12–4.31; P = 0.021). The Hosmer–Lemeshow test indicated a P value of 0.33.ConclusionsThe vertical distance between the distal tip and the gum, the distal lateral horizontal distance of the occlusion and the mesial horizontal distance of the occlusion were the influencing factors for dental caries (identified as independent risk factors). We hypothesize that these factors may be associated with the physiological curvature of teeth and the role of chewing grooves in plaque formation over time. However, further studies involving larger population samples and more detailed age stratification are still needed.

Deep-FG-DSM: Fine-Grained DSM for High-resolution UAV Imagery Based on Deep Implicit Occupancy Networks

Abstract. Nowadays, DSM (Digital Surface Model) is one of most important products that has been widely applied in digital city or smart city. Over the last decades, the common way is to follow the conventional photogrammetric pipeline for generating DSM, which is often not efficient and yields noise with small geometric details lost. Inspired by the development of deep implicit occupancy network, in this paper, we presented a learning-based method for obtaining fine-grained DSM, i.e., fine Deep-FG-DSM. In particular, high-resolution UAV imagery together with the corresponding original point cloud are employed to improve DSMs preserving higher details, two heads that embed the features of images and 3D points are deployed, and a MLP (Muti-layer Perceptron) is appended to decode these embedding into continuous occupancy probabilities for predicting the existence (or not) of surface point. Our experimental results demonstrate the robustness of our model against both sparse and noisy point clouds. While generating DSMs, it retains high-frequency details from high-resolution UAV images while maintaining relatively high accuracy. For point cloud obtained after simplification with average sampling resolution of d=5m, the MAE (Mean Absolute Residual Error) is 2.15m.

Research on Monitoring and Application of Ecological Restoration Engineering in Open Pit Backfilling Mines Based on Satellite Remote Sensing Data

Abstract. The restoration and management of mining ecological environment is an important component of China's ecological civilization construction. The implementation of restoration projects is to avoid the serious impact caused by the continuous deterioration of mining ecological structure, and is a key way to maintain regional ecological security and energy and mineral security. Therefore, efficient and rapid monitoring and evaluation of the effectiveness of mining ecological restoration is an essential and important link. With the rapid development of remote sensing technology, remote sensing monitoring has become an important means to objectively, quickly, and accurately obtaining changes in the mining environment. It can provide timely and long-term monitoring of mining and backfilling conditions in mining areas. This article uses the multi temporal stereo images of the Resource 3 satellite over four years to monitor the mining and backfilling situation of the Zhungeer open-pit coal mine area in Inner Mongolia. By reconstructing the Digital Surface Model (DSM) of the open-pit mining area, the changes and thresholds of the multi temporal DSM data are statistically analyzed to extract the mining and backfilling areas, and the earthwork volume is calculated. The results showed that from 2013 to 2016, the main large-scale mining faces in the study area of Zhungeer open-pit mining area had a total mining operation of about 563.5 million cubic meters, and a total backfilling operation of about 604.29 million cubic meters; During the period from 2016 to 2018, the main large-scale mining faces in the region had a total of approximately 721.71 million cubic meters of mining operations and a total of approximately 805.42 million cubic meters of backfilling operations. The overall operational intensity increased from 2016 to 2018. The research results show that based on satellite image data, it is convenient and efficient to obtain DSM and corresponding changes of multiple time periods in mining areas. Monitoring results can provide important support for regional environmental governance and protection, as well as safety production in mining areas.

A framework to characterize, model, and optimize water-energy systems: case study of a novel HDH system

The rapid development in water-energy systems calls for advanced modeling and optimization tools to improve the technologies throughputs and minimize their energy consumption. While having a wide footprint in multiple research areas, Artificial Intelligence applications in water-energy systems are still in early stages. There is a need for generalized models to fully characterize parametric relationships, optimize technologies' performance, and have the flexibility to apply to different complex and non-linear systems. This article describes a digital twin framework that can be used for that purpose. This framework provides fundamental understanding of the process as in process-driven models alongside with the accuracy and usability of data-driven methods. The framework's structure focused on modeling and optimization aspects of digital twin models. As a part of developing a novel humidification-dehumidification desalination technology, this framework was used to model energy consumption based on 16 different operational parameters and was accurate to within 1 % of the data. Pairwise parameters sensitivities were identified by the framework for two case studies involving the whole cycle operation and a critical subsystem of the technology. Moreover, the framework was used to optimize the technology performance through data-efficient Bayesian search methods. Different search realms were used in this process. The framework was able to identify minimum and maximum energy consumption regions by sampling only 25 data points out of possible 5157 cases.

Critical Examination of 3D Building Modelling through UAV Frame and Video Imaging

Abstract. Data capture in UAV photogrammetry is carried out using two main methodologies: frame frame-based and video video-based. Frame Frame-based data gathering is the preferred method among UAV projects because to its inherent reliability in calibration. Nonetheless, circumstances involving moving objects or occlusions inside the measured region may produce unsatisfactory results utilizing this method. I In response to these challenges, video video-based data collecting appears as a potential option, capable of creating a series of successive images that together alleviate the constraints outlined above. In this study we aim to compare the usefulness of frame and video images in building 3D models, using both oblique and vertical image orientations. Rigorous evaluations produced many outputs, including dense point clouds, digital surface models (DSM), meshes, and orthophotographs. The evaluation criteria included da ta acquisition velocity, processing efficiency, calibration precision, distortion analysis, residual plots, scale correctness, and reprojection error. The empirical results demonstrated the advantages of video video-frame captures in improving the quality of the resulting 3D models. Notably, the use of video frames resulted in a significant reduction in reprojection error by 16%, calibration residuals by 36%, distortions by up to 51%, and processing time by 27%. Thus, it seems that integrating video frames improves data gathering accuracy and speeds up processing, replacing standard frame images with video counterparts for increased efficacy.

Traditional agencies on bridges: How is digital transformation changing business models?

AbstractDigital technologies produce new strategic and organizational challenges for incumbent firms that require major digital transformation (DT) processes, leading to the evolution of business models (BMs). Contributing to the research stream on DT and BM evolution, this study explores which technologies have the most relevant effect on core BM dimensions, i.e., value proposition, value creation, and value capture, by investigating both the consequent themes and intensities of change in these dimensions. A valuable context for analysis was chosen: the advertising industry and traditional communication agencies, namely, creative and media agencies. Based on 38 in-depth interviews with senior communication professionals and qualitative content analysis, we propose a comprehensive reading of (1) the impact of specific technologies on different BM dimensions and components and (2) the themes and intensities of change in creative and media agencies’ BM dimensions. Finally, we outline an interpretative framework, identifying two different paths for incumbents: outside-in BM adaptation and inside-out BM innovation. This study therefore offers two academic contributions. First, it overcomes the vagueness of previous investigations that considered the effects of digital technologies in general terms, and it also resolves the narrow scope of research that focused only on the impact of one specific technology. Second, by deepening the understanding of the joint evolutions and intensities of change across all three BM dimensions, this study has widened the limited scope of previous contributions on one BM dimension. By doing so, it offers a comprehensive reading of BM dynamics due to DT.

A Comparative Model of Thermal Performances Between a Modern and Classic Habitat for A Hot and Humid Tropical Climate: Case of Guinea

The objective of this research is to carry out a comparative study between a classic habitat and a modern habitat in order to understand which of the two is more efficient for a humid tropical area. To do this, a digital modeling of the heat transfer phenomena in the different components of the habitat was highlighted for a habitat made of stabilized earth bricks, a roof made of transparent tiles, false ceilings made of rectangular panels containing phase change material, concrete floor and a classic habitat made from local materials is presented. To show the reliability of this study, we validated our habitat model integrating phase change material through an experimental study carried out by researchers B. ZIVKOVIC and I. FUJII. A study on the temperature distributions at the level of certain components of the habitat done, and the influence of the variation of certain parameters was highlighted to know the thermal behavior of the two habitats. Furthermore, a comparative study was performed between the interior and exterior temperatures of the south walls of habitats incorporating PCM and without PCM, which are the components most exposed to solar radiation whose maximum values are respectively 33°C and 34, 5°C and 33.15°C against 47°C. Keywords: Performances, thermal, bioclimatic, PCM, Tropical climate, Guinea.

InSAR Digital Surface Model Refinement by Block Adjustment with Horizontal Constraints

Abstract. Interferometric Synthetic Aperture Radar (InSAR) technology is an important method to generate digital surface model (DSM). The past studies on space-borne derived DSM most focused on the elevation correction, due to the relative low resolution of DSM product. As a large number of high-resolution satellite data emerge, the horizontal discrepancies are needed to be considered. This paper proposes a DSM block adjustment method with horizontal constraints, aimed at eliminating the horizontal errors that exist between multiple DSM scenes with overlaps, achieving high precision and consistency in both the horizontal and vertical dimensions. Using ICESat-2 ATL08 point clouds as absolute elevation control and a reference DSM for horizontal control, the adjustment equations are constructed based on the constraint of tie points and controls. The experiment selects 7 image pairs of China TH2-01 SAR satellite, corresponding ICESat-2 ATL08 point and AW3D30 as reference DEM. The block adjustment results show that the proposed method improves the absolute vertical accuracy from 3.78 m to 2.56 m and reduces the average horizontal standard deviation between the InSAR derived DSMs and the reference AW3D30 from 15.31 m to 9.08 m.

Large-scale DSM registration via motion averaging

Abstract. Generating wide-area digital surface models (DSMs) requires registering a large number of individual, and partially overlapped DSMs. This presents a challenging problem for a typical registration algorithm, since when a large number of observations from these multiple DSMs are considered, it may easily cause memory overflow. Sequential registration algorithms, although can significantly reduce the computation, are especially vulnerable for small overlapped pairs, leading to a large error accumulation. In this work, we propose a novel solution that builds the DSM registration task as a motion averaging problem: pair-wise DSMs are registered to build a scene graph, with edges representing relative poses between DSMs. Specifically, based on the grid structure of the large DSM, the pair-wise registration is performed using a novel nearest neighbor search method. We show that the scene graph can be optimized via an extremely fast motion average algorithm with O(N) complexity (N refers to the number of images). Evaluation of high-resolution satellite-derived DSM demonstrates significant improvement in computation and accuracy.

Hazard Assessment of Debris Flow: A Case Study of the Huiyazi Debris Flow

The Bailong River Basin is situated at the northeastern edge of the Qinghai–Tibet Plateau and the western transition zone of the Loess Plateau, characterized by steep terrain and heavy rainfall. This area experiences frequent occurrences of debris flows, posing serious threats to towns and construction projects. Focusing on the Huaiyazigou debris flow in the Bailong River Basin, numerical simulations of debris flow processes were conducted using Digital Surface Model (DSM) data with a resolution of 5 m × 5 m for various recurrence periods. The simulation results indicate that the debris flow develops rapidly along the gully after formation, decelerating and beginning to deposit upon reaching the cement plant area near the mouth of the gully, eventually merging into the Bailong River. The primary destructive modes of debris flow disasters encompass impact and burial. When encountering buildings, their flow characteristics manifest as deposition and diversion. A debris flow hazard classification model, based on intensity and recurrence periods, was established according to Swiss and Austrian standards, dividing the hazard into low, medium, and high levels. This method generated a debris flow hazard zone map, offering guidance for risk prevention and monitoring. This research demonstrates that using high-precision Digital Surface Models (DSM) can accurately represent the digital information of debris flow gully terrains and buildings. During the simulation process, it realistically reflects the characteristics of the debris flow movement, allowing for the more precise delineation of hazard zones.

A method for hierarchical weighted fitting of regular grid DSM with discrete points

Abstract. A Digital Surface Model (DSM) is a crucial spatial geographic information data used to describe the shape of the earth’s surface in Geographic Information Systems (GIS). DSM is the core data used in terrain analysis in GIS. A regular grid DSM is generally generated by interpolating a large number of discrete point clouds. This paper proposes a method of using a hierarchical weighted strategy to fit a regular grid DSM with discrete points. This method uses a pyramid hierarchical strategy to refine the target regular grid from one grid with finer parameters of 3*3, until the nth level (the interval of the grid is equal to the expected interval), and then gradually places the discrete point cloud into the corresponding grid by weighted averaging, and uses the result of this level as the initial value of the next level. This algorithm can avoid the problem of low efficiency in retrieving a large number of discrete point clouds, and the indirect interpolation method not considering the contribution of distant neighboring point clouds. The operation of point cloud data is a stream operation, which does not require consideration of the topological information of point clouds, and has simple operation and no additional memory consumption. It is especially suitable for the production of regular grid DSM with massive point clouds. To verify the effectiveness of this method, the article selected six typical terrain data such as high mountains, mountains, hills, plains, urban areas, and lakes for experiments. The results show that compared with the construct-TIN method for producing DSM, this method has very good processing accuracy and processing efficiency.

Fusion method for digital twin model of a production line

Fusion method for digital twin model of a production line

Consistent land use and land cover classification across 20 years of various high-resolution images for detecting soil sealing in Murcia, Spain

Artificial human-induced soil sealing has numerous negative consequences. The extent of impervious surfaces is a key indicator of the location and intensity of human activity; however, it is also proof of damage to the natural environment as a result of the sealing and modification of ecosystems. Remote sensing techniques can help detect and monitor changes in land use and cover over an extended period. However, the limited availability of consistent satellite images with high spatiotemporal resolutions covering several decades poses major challenges for achieving high overall classification accuracy. An accurate methodology for the multitemporal detection of artificial land cover classes was developed and applied to a case study of the metropolitan area of Murcia (Spain) with its challenging landscape conditions due to the frequent presence of bare soil. For this purpose, a variety of high-resolution satellite images from SPOT 5, Rapid Eye, and PlanetScope covering a period of 20 years were used. To improve the automated detection of built-up areas, the reflectance values of the images, normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and a building surface digital model were used as inputs for the supervised classification model. We applied a random forest algorithm to non-public, high-resolution images in the Google Earth Engine (GEE) as a processing environment to identify eight target land cover classes. The results show that the proposed methodology leads to a substantial improvement, after including the indices and the digital building model, in the overall accuracy (from 93.16 to 95.97%) and in all classes. This improvement was significant for the artificial classes and was particularly noticeable for the built-up areas (from 91.1 to 95.64%) because their confusion with bare soil was considerably reduced. This work demonstrates the effectiveness of the building-surface digital model as a tool for training the classification model, as it reduces uncertainty in confusion with other spectrally similar classes and its applicability to multisource imagery.

Controlling chaos using edge computing hardware

Machine learning provides a data-driven approach for creating a digital twin of a system – a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often, the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection. Here, we show that a nonlinear controller based on next-generation reservoir computing can tackle a difficult control problem: controlling a chaotic system to an arbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devices. Furthermore, the model only requires 25.0 ±7.0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 7.0$$\\end{document} nJ per evaluation, well below other algorithms, even without systematic power optimization. Our work represents the first step in deploying efficient machine learning algorithms to the computing “edge.”

Flood plain inundation modeling with explicit description of land surface macrostructures

Although high-resolution digital surface model (DSM) data derived from lidar surveys can describe land surface macrostructures like trees and buildings, digital terrain model (DTM) data obtained by filtering out these macrostructures are commonly used in flood inundation models. In the present study, it is shown for the first time that DSM data can be used directly in flood inundation models by employing automatically-extracted ridges as breaklines for the generation of geomorphologically-informed meshes (GIMs). Even under the simplifying assumption of impermeable macrostructures, especially when GIM refinement is applied, the use of DSM data in preference to DTM data leads to significant improvement in flood predictions. By comparing simulations and observations for a real flood inundation, it is found that the direct use of 1-m DSM data in place of the related DTM data leads to a 42% improvement in predicted flood area, a 36% improvement in predicted flood areal position, and a 25% improvement in predicted times of travel.

Integrating Open Knowledge and Administrative Management in the Digital Transformation Model of Education Institutions: An Effective Approach

This paper explores the integration of Open Knowledge and Administrative Management into the Digital Transformation Model within educational institutions, focusing on the National Academy of Education Management and three secondary schools in Hanoi. Through a comprehensive methodology involving data collection, analysis, and system development, the study examines the effectiveness of this integrated approach in enhancing educational outcomes. The findings reveal significant improvements in knowledge dissemination, resource allocation, and administrative efficiency following the implementation of the integrated model. By harnessing open knowledge resources and streamlining administrative processes, educational institutions have been able to optimize teaching and learning practices, facilitate collaboration among stakeholders, and promote innovation in education. Furthermore, the study highlights the importance of ongoing research, evaluation, and professional development initiatives to sustain the momentum of this integrated approach. By investing in capacity building, promoting collaboration, and embracing emerging technologies, educational institutions can adapt to the evolving needs of learners and prepare them for success in the digital age. Overall, the integration of Open Knowledge and Administrative Management represents a promising pathway towards educational excellence and equity. Through collective efforts and a commitment to continuous improvement, educational stakeholders can drive meaningful change and empower learners to thrive in an increasingly complex and interconnected world.

Improved shape-from-template method with perspective space constraints for disappearing features

AbstractInferring the 3D surface shape of a known template from 2D images captured by a monocular camera is a challenging problem. Due to the severely underconstrained nature of the problem, inferring shape accurately becomes particularly challenging when the template exhibits high curvature, resulting in the disappearance of feature points and significant differences between the inferred and actual deformations. To address this problem, this paper proposes a concise and innovative approach that utilizes a physical simulator incorporating the object’s material properties and deformation law. We utilize a view frustum space constructed from the contours of a monocular camera image to effectively restrict the physically-based free motion of the template. Additionally, we employ mesh denoising techniques to ensure the smoothness of the surface following deformation. To evaluate our shape inference results, we utilize a ground truth 3D point cloud generated from multiple viewpoint images. The results demonstrate the superior performance of our approach compared to other methods in accurately inferring deformations, particularly in scenarios where feature points are unobservable. This method carries significant practical implications across diverse domains, including virtual reality, digital modeling, and medical surgery training.

Are digital servitization-based Circular Economy business models sustainable? A systemic what-if simulation model

Manufacturing companies are struggling with the implementation of Circular Economy, especially due to the uncertainty regarding its potential sustainability benefits. In particular, and despite digital servitization is advocated by several studies as a way to achieve environmental gains, circular business models based on digital servitization are not always sustainable due to burden shifting and unexpected consequences which are difficult to assess before implementation. This is particularly relevant for the Electrical and Electronics Equipment industry, which suffers structural weaknesses such as the dependance on critical raw materials and an increasing waste generation. However, literature lacks models and tools able to address the complexity inherent in the systemic micro-macro perspective envisioned by Circular Economy, while studies that quantitatively assess the sustainability impacts and trade-offs of digital servitization-based circular scenarios are limited. This article aims to develop a better understanding of how the sustainability impacts of circular and servitized scenarios can be assessed and quantified at the economic, environmental, and social level, adopting a systemic perspective through the development of a what-if simulation model. The model is implemented in a spreadsheet tool and applied to a digital servitization-based Circular Economy scenario inspired by the case of a company offering long-lasting, high-efficient washing machines as-a-service. Results show that digital servitization can actually lead to a win-win-win situation with net positive effects to the environment, the society, and the economy. This result is based on the joint application of product design for digitalization and life extension, pay-per-use business models, and product reuse. These results are robust within a significant range of key parameters values. Practitioners and policymakers may use the model to support the evaluation of different circular and servitized scenarios before implementation.

A Physics-Based Model-Data-Driven Method for Spindle Health Diagnosis, Part II: Dynamic Simulation and Validation

Abstract Mathematical modeling of bearing faults, worn tool holder taper contact interface, and unbalance are presented and integrated into a digital dynamic model of spindles in Part I of this paper. These faults lead to changes in preload and dynamic stiffness over time, consequently resulting in observable vibrations. This paper predicts the vibrations of a spindle at a particular measurement location by simulating the presence of a specific fault or multiple faults during spindle rotation. The vibration spectra generated by the digital spindle model at the spindle speed and its harmonics, the changes in the natural frequencies, and dynamic stiffnesses are correlated to faults with experimental validations. The simulated vibration spectrums are later used in training an artificial neural network for fault condition monitoring presented in Part III of the paper.