Realistic Simulation Environment Research Articles

BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping

Recent advances in machine learning and deep learning have presented new opportunities for learning to localize the origin of ventricular activation from 12-lead electrocardiograms (ECGs), an important step in guiding ablation therapies for ventricular tachycardia. Passively learning from population data is faced with challenges due to significant variations among subjects, and building a patient-specific model raises the open question of where to select pace-mapping data for training. This work introduces BOATMAP, a novel active learning approach designed to provide clinicians with interpretable guidance that progressively assists in locating the origin of ventricular activation from 12-lead ECGs. BOATMAP inverts the input–output relationship in traditional machine learning solutions to this problem and learns the similarity between a target ECG and a paced ECG as a function of the pacing site coordinates. Using Gaussian processes (GP) as a surrogate model, BOATMAP iteratively refines the estimated similarity landscape while providing suggestions to clinicians regarding the next optimal pacing site. Furthermore, it can incorporate constraints to avoid suggesting pacing in non-viable regions such as the core of the myocardial scar. Tested in a realistic simulation environment in various heart geometries and tissue properties, BOATMAP demonstrated the ability to accurately localize the origin of activation, achieving an average localization accuracy of 3.9±3.6mm with only 8.0±4.0 pacing sites. BOATMAP offers real-time interpretable guidance for accurate localization and enhancing clinical decision-making.

In-vitro testing and circuit analysis of an ultra-miniaturized biosensor for multiple medical implant systems

This paper presents an ultra-miniaturized and flexible biosensor for multiple medical implant systems, operating in the 2.4 GHz Industrial, Scientific, and Medical (ISM) band. A flexible Rogers RO Duroid 3010 material with a dielectric constant (εr) 10.2, loss tangent (δ) 0.0022 and thickness of 0.635 mm is utilised as a substrate and superstrate for the proposed biosensor. The size of an biosensor is 10 mm × 10 mm. Certain design elements are employed to accomplish a miniaturized geometry, such as a slotted radiating patch and ground plane, a flexible substrate and superstrate material. A realistic simulation environment has been utilized to evaluate the performance of the proposed biosensor in multiple tissues, encompassing both homogeneous and heterogeneous situations within different organs. This enables the examination and advancement of the practicality of several implantable applications. The proposed biosensor is validated by making an equivalent circuit. The biosensor exhibits a higher gain of −13.18 dB and a higher bandwidth of 16.52 % at the desired ISM band. To test the practicality of a proposed biosensor, in-vitro measurements have been carried out using different tissue-mimicking phantoms. Moreover, a proposed biosensor’s specific absorption rate (SAR) in multiple tissues has also been calculated. The proposed biosensor demonstrates a good correlation between simulated and measured results, which makes the biosensor an excellent choice for implantation inside multiple medical implant devices.

Performance Analysis of NSUM Estimators in Social-Network Topologies

The Network Scale-up Methods (NSUM) are methods to estimate unknown populations based on indirect surveys in which the participants provide information about aggregated data of their acquaintances. This preserves the privacy and may lead to higher participation. During the last thirty years, new NSUM estimators have emerged. However, conditions related to the design of the experiments and the robustness of the estimators have not been studied in depth, especially in a realistic simulation environment. This study aims to compare nine NSUM estimators under relevant conditions in the literature through simulation experiments. We have analyzed how the NSUM is affected by the network topology, transmission and recall errors, the distribution of the unknown subpopulation, the number and sizes of subpopulations, and sample size. This article shows that some NSUM estimators barely used are better and more robust to some conditions, especially when the network is scale-free or under barrier effects. In addition, some methods are very sensitive to recall errors. In terms of the subpopulations configuration, we observe that the number of known subpopulations usually employed is quite large and that the most common NSUM is robust to the number and sizes of the subpopulations.

Communicating robots’ intent through visual cues enhances human anticipatory behavior in human–dual robot collaboration

The present study aims at exploring the effect of communicating robots’ intent through visual cues, to the human on a complex human-robot collaborative task. Specifically, it aims to investigate (i) whether the use of such “anticipatory cues” will have a positive effect on task efficiency, human safety and collaborating fluency, (ii) the degree of this effect with varying robots’ speed and (iii) whether a retention effect will be observed after the removal of the cues. For exploring these issues, a human - dual robot industrial assembly task was designed in a Virtual Reality simulation environment and testing was carried out by 64 volunteer participants. Results showed that communicating robots’ intent through visual cues enhanced human anticipatory behavior, resulting in a significant improvement in human safety, team efficiency and collaborative fluency, in conjunction with a favorable subjective tendency towards the robots. However, the positive effect of the anticipatory cues was not found to increase with higher robot speed. Finally, the findings suggest that prior exposure to the cues made participants more confident in coordinating with the robots, even when the cues were removed from them, thus retaining their prior efficiency but with a negative effect on safety. In summary, the study provides evidence that use of anticipatory visual cues accelerates the legibility of robot movement and fosters human confidence and familiarization. The use of anticipatory cues seems promising for high-pace, non-repetitive interactions with collaborative robots or as a training aid in more repetitive human-robot collaborative tasks.

Learning Outcomes of Psychomotor Domains in Welding Technology: VR Welding Kit Assessment: A Systematic Literature Review

Aim. This systematic literature review aims to analyse existing studies that have investigated the impact of VR welding kits on the development of psychomotor skills, knowledge acquisition, and overall learning experience. By synthesising findings from a variety of sources, this review seeks to provide insight into the strengths, limitations, and potential areas for further research in this domain. Method. This systematic literature review involved an extensive search of electronic databases such as PubMed, Google Scholar, and IEEE Xplore to identify relevant studies that focused on the impact of VR welding kits on psychomotor skills development and knowledge acquisition. Result. Findings from a systematic literature review reveal a range of results related to the impact of VR welding kits on psychomotor skills development and knowledge acquisition. Most of the research highlights the positive effects of utilising VR technology in welding education. VR welding kits were invented to improve learners’ psychomotor skills by providing a realistic and interactive simulation environment. Conclusion. Ongoing research in this domain will be critical to refining and integrating VR welding technology into welding education and practice, ultimately positioning the industry for a future that improves productivity, safety, and competitiveness. Cognitive Value. Allowing students to practice and refine welding techniques in a safe and controlled setting, ultimately leading to increased proficiency and confidence in practical applications. The insights gained from this review have significant implications for educators, training programs, and the welding industry.

Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment

By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on normalized advantage function reinforcement learning. It aims to boost the body's immune response against cancer cell proliferation. This control approach is applied to provide a combination of both chemotherapy and anti-angiogenic drugs for the first time without the need for complex, predefined mathematical models. It employs a model-free reinforcement learning technique that adaptively adjusts to individual patients to determine optimal drug administration with minimum injection rates. In this regard, a comprehensive and realistic simulation and training environment is employed, with the concentrations of normal cells, cancer cells, and endothelial cells, as well as the levels of chemotherapy and anti-angiogenic agents, as state variables. Furthermore, high levels of disturbances are considered in the simulation to investigate the robustness of the proposed method against probable uncertainties in the treatment process or patient parameters. A practical reward function has also been devised in alignment with medical objectives to ensure effective and safe treatment outcomes. The results demonstrate robustness and superior performance compared to the existing methods. Simulations show that the proposed approach is a dependable strategy for effectively reducing the concentration of cancer cells in the shortest duration using minimal doses of chemotherapy and anti-angiogenic drugs.

Testing Usability of a Medical Device Using Virtual Reality.

Before commercialization of a medical device, it is important to evaluate its usability. Traditional methods such as user testing to evaluate usability of medical device encountered difficulties to put participants in simulation conditions that are sufficiently realistic to be representative of real life. Virtual reality can be used to immerse participants in a high-fidelity simulation at a lower cost, but is not widely used today. This feasibility study aims to compare the results obtained between user tests in a real simulated environment and in a virtual reality environment, with feedback on the advantages and disadvantages of both conditions.

Adaptive meta-reinforcement learning for AUVs 3D guidance and control under unknown ocean currents

Given the challenges posed by underwater communication constraints, Autonomous Underwater Vehicles (AUVs) must operate with enhanced autonomy while balancing operational safety and efficiency within complex marine settings. AUVs face a 3D guidance and control challenge, requiring complex adjustments to unknown ocean currents that affect their stability, maneuverability, and Guidance-Navigation-Control system efficiency. This study introduces and implements an off-policy meta-reinforcement learning framework to address AUVs’ 3D guidance and control challenges under unknown ocean currents. Our method, APE-SAC, utilizes an inference network to identify latent variables, streamlining the adaptation to unknown ocean currents by optimizing their strength and direction. Consequently, this framework redefines the issue of guidance and control under unknown current disturbances into a manageable learning task across multiple predefined scenarios. Utilizing the 3D dynamics of the REMUS AUV, we construct meta-Markov Decision Processes to detail the comprehensive state space, action space, and robust reward function tailored to the task’s specific demands. Furthermore, we develop a highly realistic simulation environment specifically designed for comprehensive experiments. Extensive results demonstrate that, compared to deep reinforcement learning baselines, APE-SAC exhibits outstanding performance in guiding and controlling AUVs under unknown ocean currents.

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data.

Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

A nuclear power plant digital twin for developing robot navigation and interaction

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.

Validation of a Tool-Based Visual Anorectal Examination Advanced Simulator for the Early Detection of Colorectal Cancer.

Rectal examination through proctoscopy or rigid sigmoidoscopy is a common investigation in clinical practice. It is an important diagnostic tool for the workup and management of anorectal pathologies. Performing the examination can be daunting not only for patients but also for junior doctors. There are associated risks with the procedure, such as pain, diagnostic failure, and perforation of the bowel. Simulation-based training is recognised as an important adjunct in clinical education. It allows students and doctors to practice skills and techniques at their own pace in a risk-free environment. These skills can then be transferred to and developed further in clinical practice. There is extensive research published regarding the role of simulation-based training in endoscopy, however, we identified no published study regarding simulation-based training in rigid sigmoidoscopy or proctoscopy. This study aims to establish the initial face, content, and construct validity of a tool-based visual anorectal examination advanced simulator model for proctoscopy and rigid sigmoidoscopy. This innovative, highly realistic simulated environment aims to enhance the training of healthcare professionals and improve the efficiency of detecting and diagnosing distal colorectal disease.

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications

Object recognition, localization, and tracking play a role of primordial importance in computer vision applications. However, it is still an extremely difficult task, particularly in scenarios where objects are attended to using fast-moving UAVs that need to robustly operate in real time. Typically the performance of these vision-based systems is affected by motion blur and geometric distortions, to name but two issues. Gimbal systems are thus essential to compensate for motion blur and ensure visual streams are stable. In this work, we investigate the advantages of active tracking approaches using a three-degrees-of-freedom (DoF) gimbal system mounted on UAVs. A method that utilizes joint movement and visual information for actively tracking spherical and planar objects in real time is proposed. Tracking methodologies are tested and evaluated in two different realistic Gazebo simulation environments: the first on 3D positional tracking (sphere) and the second on tracking of 6D poses (planar fiducial markers). We show that active object tracking is advantageous for UAV applications, first, by reducing motion blur, caused by fast camera motion and vibrations, and, second, by fixating the object of interest within the center of the field of view and thus reducing re-projection errors due to peripheral distortion. The results demonstrate significant object pose estimation accuracy improvements of active approaches when compared with traditional passive ones. More specifically, a set of experiments suggests that active gimbal tracking can increase the spatial estimation accuracy of known-size moving objects, under conditions of challenging motion patterns and in the presence of image distortion.

Preparing Pre-service Teachers to Teach Reading Strategies within a Mixed Reality Simulation Environment

As a language teacher, teacher educator and researcher, to me the name Rebecca Oxford has always been synonymous with language learning strategies across the world and specifically the theme of its teachability. Rebecca has always stated that the language teacher’s role includes strategy instruction even if it achieves only partial success. Research has focused almost exclusively on how reading comprehension strategies, the focus of this study, can be taught to learners, but the preparedness of pre-service teachers to teach reading strategies has seldom been the focus within initial teacher education programs. The purpose of this study was to determine participants’ perceptions of teaching within a mixed reality simulation environment with a specific focus on the explicit instruction of reading comprehension strategies. A case study approach was utilized, and ten second-year pre-service teachers enrolled for the Bachelor of Education (B.Ed) degree as well as one mentor teacher and one teacher educator were purposefully selected to participate in the study. The results indicated that the themes corresponded with the activities during the different stages of a mixed reality simulation action review cycle and included show and tell, collaborative brainstorming, deconstruction of the core teaching practice, anxiety and disbelief, pause and redo, coaching and feedback and focused reflection.

Object Manipulation in Marine Environments using Reinforcement Learning

Performing intervention tasks in the maritime domain is crucial for safety and operational efficiency. The unpredictable and dynamic marine environment makes the intervention tasks such as object manipulation extremely challenging. This study proposes a robust solution for object manipulation from a dock in the presence of disturbances caused by sea waves. To tackle this challenging problem, we apply a deep reinforcement learning (DRL) based algorithm called Soft. Actor-Critic (SAC). SAC employs an actor-critic framework; the actors learn a policy that minimizes an objective function while the critic evaluates the learned policy and provides feedback to guide the actor-learning process. We trained the agent using the PyBullet dynamic simulator and tested it in a realistic simulation environment called MBZIRC maritime simulator. This simulator allows the simulation of different wave conditions according to the World Meteorological Organization (WMO) sea state code. Simulation results demonstrate a high success rate in retrieving the objects from the dock. The trained agent achieved an 80 percent success rate when applied in the simulation environment in the presence of waves characterized by sea state 2, according to the WMO sea state code.

Characterization of task allocation techniques in data centers based on information theory

We present a comprehensive characterization of task allocation techniques in data centers based on information theory, focusing specifically on Shannon Entropy and Statistical Complexity. This study investigates the potential benefits and limitations of using information theory-based integrated into different task allocation techniques. We conduct experiments using a realistic simulation environment. We evaluate the number of tasks allocated over time and the evolution of queue size over time. For this purpose, we used the Google Dataset. This trace represents 29 days’ of information on a cluster of about 12.5k machines. It contains detailed information on job submissions, task execution, resource usage, and scheduling decisions. Our findings demonstrate the potential of information theory-based to identify the task allocation process using different strategies.

Digital twin for human-robot collaboration enhancement in manufacturing systems: Literature review and direction for future developments

The integration of robots in manufacturing systems known as Human Robot Collaboration (HRC) can provide relevant opportunities in terms of productivity, safety and quality control although there are still many challenges such as integration costs, complexity, safety and workforce concerns, skills gaps and regulatory compliance. Digital Twins (DTs), which are virtual replicas of physical systems, are being explored as a promising solution to address these challenges. Therefore, this paper delves into the potential DTs-based approaches for HRC in manufacturing systems with a focus on their ability to provide a realistic simulation environment for testing and optimizing collaboration strategies. The first part of the study involves a comprehensive literature review devoted to understand how the scientific community perceives the application of DTs for HRC in manufacturing systems and the main trends of this research area are identified. The second part of the study presents a detailed analysis of the currently available commercial simulation software solutions for developing DT models in manufacturing systems for HRC scenarios. The analysis encompassed various aspects such as: the software's capabilities in human and robot simulation, ergonomic and time measurements analysis, data exchange and interoperability with the physical world as well as with other software. The results of the analysis are meant to provide practical insights to researchers and practitioners interested in implementing DTs to enhance HRC in manufacturing systems.

Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System Subject to Parameter Uncertainty

This article studies how parametric uncertainties affect the cooperative manipulation of a cable-suspended beam-shaped load by means of two aerial robots not explicitly communicating with each other. In particular, this article sheds light on the impact of the uncertain knowledge of the model parameters available to an established communicationless force-based controller. First, we find the closed-loop equilibrium configurations in the presence of the aforementioned uncertainties, and then, we study their stability. Hence, we show the fundamental role played in the robustness of the load attitude control by the internal force induced in the manipulated object by nonvertical cables. Furthermore, we formally study the sensitivity of the attitude error to such parametric variations, and we provide a method to act on the load position error in the presence of uncertainties. Eventually, we validate the results through an extensive set of numerical tests in a realistic simulation environment, including underactuated aerial vehicles and sagging-prone cables, and through hardware experiments.

Emerging Frontiers in Surgical Training: Progress in 3D Printed Gel Models

With the increasing complexity and difficulty of surgical procedures, there is an urgent need for empirical and high-fidelity surgical teaching tools. Whereas in the past, teaching tools included cadavers and animals, today, with the development of three-dimensional (3D) printing technology, the creation of patient-specific organ models is possible. This technology provides an effective solution for preoperative rehearsal, thus providing surgery residents with a more realistic simulation environment. This study aimed to provide an overview of the use of personalized 3D printing in various types of surgery through a scoping review, outline their bottlenecks, and provide an outlook. Significant advancements have been made in 3D-printed gel organ models for surgical applications. However, future advancements require interdisciplinary collaborations, medical–engineering integration and novel techniques. Addressing challenges in preservation, instrument response, ultrasound performance and mechanical properties is crucial. Enhancing these aspects will improve the capabilities of organ models, benefiting both medical practitioners and patients.

Analysis of UTM Tracking Performance for Conformance Monitoring via Hybrid SITL Monte Carlo Methods

Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results.

An optimization algorithm based on forward recursion with applications to variable horizon MPC

We consider optimization algorithms designed for variable horizon model predictive control. Traditionally, such problems are considered intractable for real-time applications that require fast computations, as they need to solve multiple optimal control problems with varying horizons at each sampling instance. The main contribution is an algorithm that efficiently solves multiple optimal control problems with different prediction horizons in a recursive manner. This algorithm has been successfully implemented and integrated into the OSQP solver, resulting in a real-time controller that is both fast and reliable. To assess the effectiveness of the approach, we conducted evaluations in both a realistic simulation environment and on real hardware during outdoor flight experiments. Specifically, we focused on two distinct rendezvous maneuvers for autonomous landings of unmanned aerial vehicles. The results obtained from these evaluations further validate the practicality and efficacy of the proposed algorithm.