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Digital twins are virtual renditions of the actual world that include real-world assets, connections, activities, and processes. Recent developments in technologies play a key role in advancing the digital twin concept in urban planning, designing, and monitoring. Moreover, the latest developments in remote sensing technology have resulted in accurate city-scale light detection and ranging (LiDAR) data, which can be used to represent urban objects (buildings, vegetation, roads, and utilities), enabling the creation of digital twin of urban landscapes. This study aims to build a digital twin of the University of North Texas (UNT) using LiDAR and GIS data. In this research, LiDAR point clouds are used to create 3D building and vegetation modeling along with other GIS data (bicycle racks and parking areas) in creating a digital twin model. 3D Basemap solutions of ArcGIS Pro and ArcGIS Online Scene Viewer, respectively, are used to create an initial 3D urban model and build the ultimate digital twin of UNT. The emergency management floorplans of UNT buildings are incorporated into the digital twin to increase emergency management efficiency. Moreover, solar power potential for individual buildings at UNT has been estimated using the Digital Surface Model (DSM) and integrated into the digital twin model to identify the buildings with the highest solar energy capacity. This study indicates that implementing a digital twin in a university enhances campus efficiency, safety, and sustainability, serving as a central system for a smart campus and contributing to intelligent urban growth.

The cost and effort of modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. The time required for generating a geometric Bridge Information Model, a holistic data model which has recently become known as a "Digital Twin", of an existing bridge from Point Cloud Data is roughly ten times greater than laser scanning it. There is a pressing need to automate this process. This is particularly true for the highway infrastructure sector because Bridge Digital Twin Generation is an efficient means for documenting bridge condition data. Based on a two-year inspection cycle, there is a need for at least 315,000 bridge inspections per annum across the United States and the United Kingdom. This explains why there is a huge market demand for less labour-intensive bridge documentation techniques that can efficiently boost bridge management productivity. Previous research has achieved the automatic generation of surface primitives combined with rule-based classification to create labelled cuboids and cylinders from point clouds. While existing methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with real-world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge topology is much more complicated than idealized cases. Real bridge geometries are defined with curved horizontal alignments, and varying vertical elevations and cross-sections. These characteristics increase the modelling difficulties, which is why none of the existing methods can handle reliably. The objective of this PhD research is to devise, implement, and benchmark a novel framework that can reasonably generate labelled geometric object models of constructed bridges comprising concrete elements in an established data format (i.e. Industry Foundation Classes). This objective is achieved by answering the following research questions: (1) how to effectively detect reinforced concrete bridge components in Point Cloud Data? And (2) how to effectively fit 3D solid models in the format of Industry Foundation Classes to the detected point clusters? The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in point clouds. This framework directly extracts structural bridge components and then models them without generating low-level shape primitives. Experimental results suggest that the proposed framework can perform quickly and reliably with complex and incomplete real-world bridge point clouds encounter occlusions and unevenly distributed points. The results of experiments on ten real-world bridge point clouds indicate that the framework achieves an overall micro-average detection F1-score of 98.4%, an average modelling accuracy of (C2C) ̅_Auto 7.05 cm, and the average modelling time of merely 37.8 seconds. Compared to the laborious and time-consuming manual practice, the proposed framework can realize a direct time-savings of 95.8%. This is the first framework of its kind to achieve such high and reliable performance of geometric digital twin generation of existing bridges. Contributions. This PhD research provides the unprecedented ability to rapidly model geometric bridge concrete elements, based on quantitative measurements. This is a huge leap over the current practice of Bridge Digital Twin Generation, which performs this operation manually. The presented research activities will create the foundations for generating meaningful digital twins of existing bridges that can be used over the whole lifecycle of a bridge. As a result, the knowledge created in this PhD research will enable the future development of novel, automated applications for real-time condition assessment and retrofit engineering.

The Digital Twin (DT) is a concept that is gaining more and more attention. A DT is a virtualversion of a physical product, which can be used in monitoring and developing said physical product.In this report we have discussed how a DT of a Transportable Culture Cabinet (TCC) could beimplemented, and its framework. The TCC is being developed at Q-linea and will be used for culturingblood samples for faster diagnosis of sepsis. A DT of a TCC could be used to monitor the statusof the sample and the physical product itself. Data from the TCC could also be used by the DTto detect and predict failures of the TCC such as malfunctioning batteries. The TCC can also bereferred to as a physical twin and will exist in the physical space, while the DT will exist in thevirtual space. An information processing layer (IPL) will connect the physical space and the virtualspace. A Digital Twin can either be a Digital Twin Prototype (DTP), a Digital Twin Environment(DTE), a Digital Twin Instance (DTI), or a Digital Twin Aggregate (DTA), which is an aggregationof all the DTIs. In this paper, we have solely focused on DTIs and DTAs. The Digital Twin ofthe Transportable culture cabinet will be a DTI, and by aggregating all the existing DTIs, or allDTIs belonging to the same hospital, a DTA will be created. Both DTIs and DTAs can be appliedwith different levels of complexity. Hence, we have proposed three levels of complexity of DTIs and DTAs. There are challenges of using a DT for a TCC. The user interface have to be user friendly forstaff that have little IT knowledge. Another challenge is the synchronisation between the TCC and DT.Since the TCC is mobile, a stable internet connection cannot be guaranteed. The TCC should thereforebe able to store the most important data.

In these challenging times caused by the COVID-19, technology that leverages Artificial Intelligence potential can help people cope with the pandemic. For example, people looking to perform physical exercises while in quarantine. We also find another opportunity in the widespread adoption of mobile smart devices, making complex Artificial Intelligence (AI) models accessible to the average user. Taking advantage of this situation, we propose a Smart Coaching experience on the Edge with our Digital Twin Coaching (DTC) architecture. Since the general population is advised to work from home, sedentarism has become prevalent. Coaching is a positive force in exercising, but keeping physical distance while exercising is a significant problem. Therefore, a Smart Coach can help in this scenario as it involves using smart devices instead of direct communication with another person. Some researchers have worked on Smart Coaching, but their systems often involve complex devices such as RGB-Depth cameras, making them cumbersome to use. Our approach is one of the firsts to focus on everyday smart devices, like smartphones, to solve this problem. Digital Twin Coaching can be defined as a virtual system designed to help people improve in a specific field and is a powerful tool if combined with edge technology. The DTC architecture has six characteristics that we try to fulfill: adaptability, compatibility, flexibility, portability, security, and privacy. We collected training data of 10 subjects using a 2D pose estimation model to train our models since there was no dataset of Coach-Trainee videos. To effectively use this information, the most critical pre-processing step was synchronization. This step synchronizes the coach and the trainee’s poses to overcome the trainee's action lag while performing the routine in real-time. We trained a light neural network called “Pose Inference Neural Network” (PINN) to serve as a fine-tuning architecture mechanism. We improved the generalist 2D pose estimation model with this trained neural network while keeping the time complexity relatively unaffected. We also propose an Angular Pose Representation to compare the trainee and coach's stances that consider the differences in different people's body proportions. For the PINN model, we use Random Search Optimization to come up with the best configuration. The configurations tested included using 1, 2, 3, 4, 5, and 10 layers. We chose the 2-Layer Neural Network (2-LNN) configuration because it was the fastest to train and predict while providing a fair tradeoff between performance and resource consumption. Using frame synchronization in pre-processing, we improved 76% on the test loss (Mean Squared Error) while training with the 2-LNN. The PINN improved the R2 score of the PoseNet model by at least 15% and at most 93% depending on the configuration. Our approach only added 4 seconds (roughly 2% of the total time) to the total processing time on average. Finally, the usability test results showed that our Proof of Concept application, DTCoach, was considered easy to learn and convenient to use. At the same time, some participants mentioned that they would like to have more features and improved clarity to be more invested in using the app frequently. We hope DTCoach can help people stay more active, especially in quarantine, as the application can serve as a motivator. Since it can be run on modern smartphones, it can quickly be adopted by many people.

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COVID-19 has led to a surge in the adoption of digital transformation in almost every sector. Digital health and well-being are no exception. For instance, now people get checkupsvia apps or websites instead of visiting a physician. The pandemic has pushed the health-care sector worldwide to advance the adoption of artificial intelligence (AI) capabilities.Considering the demand for AI in supporting the well-being of an individual, we presentthe real-life diagnosis as a digital twin(DT) diagnosis using machine learning. The MachineLearning (ML) technology enables DT to offer a prediction. Although several attemptsexist for predicting disease using ML and a few attempts through ML of DT frameworks,those do not deal with disease risk prediction. In addition, most of them deal with singledisease prediction after the occurrence and rely only on clinical test data like- ECG report,MRI scan, etc.To predict multiple disease/disease risks, we propose a dynamic machine learning algo-rithm (MLA) selection framework and a dynamic testing method. The proposed frameworkaccepts heterogeneous electronic health records (EHRs) or digital health status as datasetsand selects suitable MLA upon the highest similarity. Then it trains specific classifiers forpredicting a specific disease/disease risk. The dynamic testing method for prediction isused for predicting several diseases.We described three use cases: non-communicable disease(NCD) risk prediction, mentalwell-being prediction, and COVID-19 prediction. We selected diabetes, risk of diabetes,liver disease, thyroid, risk of stroke as NCDs, mental stress as a mental health issue, andCOVID-19. We employed seven datasets, including public and private datasets, with adiverse range of attributes, sizes, types, and formats to evaluate whether the proposedframework is suitable to data heterogeneity. Our experiment found that the proposed methods of dynamic MLA selection could select MLA for each dataset at cosine similarityscores ranging between 0.82-0.89. In addition, we predicted target disease/disease risks atan accuracy ranging from 94.5% to 98%.To verify the performance of the framework-selected predictor, we compared the accuracy measures individually for each of the three cases. We compared them with traditionalML disease prediction work in the literature. We found that the framework-selected algorithms performed with good accuracy compared to existing literature.

It has become increasingly important to introduce the Digital Twin in additive manufacturing as it is perceived as a promising step forward in its development and a vital component of Industry 4.0. Digital Twin is an up-to-date representation of a real asset in operation. The aim of this thesis is to develop a Digital Twin of a hydro test rig. Digital Twins are created by developing and simulating mathematical models, which should be integrated and validated. A downscale turbine test rig in which its runner and drafttube are replicates of the Porjus U9 turbine. This test rig is located in the John-Fieldlaboratory of the Division of Fluid and Experimental Mechanics at Luleå University of Technology (LTU). A mathematical model of the test rig has been made in the MATLAB environment Simulink. The test rig itself has components such as a Kaplan turbine, hydraulic pump, magnetic braking system, rotor, and a flow meter in a closed loop system. It is known that some test rig parameters are unknown, and so two methods have been used to optimize these parameters during the validation of the mathematical model. Optimization means finding either the maximum or the minimum of the target function with a particular set of parameters. An optimization of seven total parameters was made for the mathematical model in Simulink. The parameters were optimized using two different methods: Fmincon in MATLAB and Bayesian Optimization, a machine learning tool. Due to the fact that Fmincon could only find local minima and get stuck in that area, it could not reach the global minima. In contrast, Bayesian Optimization produced better results for minimizing the cost function and finding the global minima. / AFC4Hydro

<p dir="ltr">McAlester Army Ammunition Plant (MCAAP) was established in 1943 as the U.S Naval Ammunition Depot. In World War II, MCAAP played a crucial part in supplying ammunition for the war efforts. Today, MCAAP is home to nearly 45,000 acres of land, producing almost all the bombs for the Army, Air Force, and Navy.</p><p dir="ltr">In November of 2023, the Army launched their 15-year modernization plan for their Organic Industrial Base (OIB). The plan aims to modernize facilities, processes, and the workforce to bring the OIB into the 21^st century. The Army’s OIB consists of 17 arsenals, depots, and ammunition plants, including MCAAP.</p><p dir="ltr">This thesis optimizes the operational variables of the U.S. Air Force’s Mark-84 production process at MCAAP. Using software (AnyLogic) to construct a Digital Twin of the existing process provides insights into the current operational dynamics, enabling a deep understanding of the system’s inefficiencies. Then, utilizing this understanding and the capabilities of the Digital Twin, we offer targeted recommendations for process improvement. This study aims not only to enhance the Mark-84 production process, but also to demonstrate the transformative potential of Digital Twins in optimizing manufacturing operations.</p>

Introduction The paradigm shift in manufacturing that Industry 4.0 brings forth with new advanced technologies and the rapid growth of sensing and controlling technologies enable further visualization and optimization that can contribute to achievingimproved decision-making in manufacturing. A significant new capability is the ability to construct a Digital Twinthat connects the physical and virtual space. However, there are still confusion and obscurity regarding what Digital Twinis and how it can becreated and then used to create value for the company. Therefor the purpose of the thesis is to examine how manufacturing companies can utilize the implementation of Digital Twinand assess Digital Twinin a shop-floor. ➢RQ1: How can DT be beneficial to increase business value in a manufacturing company? ➢RQ2: What changes need to be done in the shop-floor to implement Digital Twin? Methodology A literature review was conducted to provide previous researchand contextwithin the area of Digital Twin. A multiple-case studywas performed at three case companies to gain meaningful insight from a real-world perspective, semi-structured interviews, dialogs, and observations were conductedat the case companies. The analysis was then performed by examining similarities, and dissimilarities between theoretical and empirical data, as well as opportunities in theoretical findings that correspond with challenges in empirical findings. Frame of Reference The literature review increased the authors’ understanding of the research topic and gave context to the concept of Digital Twin. The review is mainly focused on the Digital Twintechnologyand how it is constructed, as well as the applicationsareas. Empirical Findings The empirical findings provide an overview of boththe current and future state of the case companies in relation to organizational, operational, and technological factors. Additionally, it provides a deeper understanding of how shop-floor management is designed at one of the case companies. Analysis The combination of the Frame of Reference and Empirical Findings contributewith important insight on the potential benefits that can be created through the utilizationof Digital Twin, as well as what is requiredin the shop-floor to enable implementation ofDigital Twin. Conclusions The value that can be created utilizing Digital Twinis outlinedand a clearer definition is proposed to avoid misunderstandings and confusion. Requirements that need to be achieved for a successful implementation arecovered as well. A future recommendation is measuring resources and effort in relation to the created value of a Digital Twin.