Create Simulation Scenarios Research Articles

Software-Based Simulation on a 3D Environment for Vaccination Teaching and Learning: Design Science Research.

Student training requires specific laboratories for vaccination practice, which are usually limited, and even professionals' continuing education regularly lacks proper care. Thus, new methodologies, concepts, and technologies, such as software-based simulations, are in highly demand. This work aims to develop a 3D virtual environment to support teaching activities in the vaccination room. The software-based simulation must contribute positively to teaching considering a variable set of scenarios. We applied the design science research method to guide the work. First, the concepts and opportunities were raised, which we used to build the simulation (ie, the proposed technological artifact). The development was assisted by a specialist, in which we sought to create a vaccination room according to Brazilian standards. The artifact evaluation was achieved in 2 stages: (1) an evaluation to validate the design with experts through the Delphi method; and (2) a field evaluation with nursing students to validate aspects of usability (System Usability Scale [SUS]) and technology acceptance and use (Unified Theory of Acceptance and Use of Technology version 2). We built the simulation software using the Unity game engine. An additional module was also developed to create simulation scenarios and view the students' performance reports. The design evaluation showed that the proposed solution is adequate. Students' evaluations confirm good usability (SUS score of 81.4), besides highlighting Performance Expectation as the most positively influential factor of Behavioral Intention. Effort Expectancy is positively affected by younger users. Both evaluation audiences cited the high relevance of the proposed artifact for teaching. Points for improvement are also reported. The research accomplished its goal of creating a software-based simulation to support teaching scenarios in the vaccination room. The evaluations still reveal desirable improvements and user behavior toward this kind of technological artifact.

Comprehensive Automated Driving Maneuvers under a Non-Signalized Intersection Adopting Deep Reinforcement Learning

Automated driving systems have become a potential approach to mitigating collisions, emissions, and human errors in mixed-traffic environments. This study proposes the use of a deep reinforcement learning method to verify the effects of comprehensive automated vehicle movements at a non-signalized intersection according to training policy and measures of effectiveness. This method integrates multilayer perceptron and partially observable Markov decision process algorithms to generate a proper decision-making algorithm for automated vehicles. This study also evaluates the efficiency of proximal policy optimization hyperparameters for the performance of the training process. Firstly, we set initial parameters and create simulation scenarios. Secondly, the SUMO simulator executes and exports observations. Thirdly, the Flow tool transfers these observations into the states of reinforcement learning agents. Next, the multilayer perceptron algorithm trains the input data and updates policies to generate the proper actions. Finally, this training checks the termination and iteration process. These proposed experiments not only increase the speeds of vehicles but also decrease the emissions at a higher market penetration rate and a lower traffic volume. We demonstrate that the fully autonomous condition increased the average speed 1.49 times compared to the entirely human-driven experiment.

Photorealistic simulation platform for autonomous landing of fixed-wing aircraft

To investigate the vision-based autonomous landing of fixed-wing aircraft, we propose a photorealistic simulation platform that leverages ROS, Unreal Engine, and Pixhawk 4. This platform adopts a software-in-the-loop model consisting of rendering, control, communication, and sensor modules. To achieve realistic rendering control, the seawater hydrodynamics and fixed-wing aircraft dynamics are modeled. Based on the platform, we create simulation scenarios under different weather and disturbance conditions for autonomous landing of the aircraft carrier. The platform solves the problem that datasets of related scenes are difficult to collect and experiments are difficult to carry out. To verify the developability of the platform, we design and implement a runway line feature point extraction method (vision-based pose estimation algorithm), and evaluate the performance of the method under various conditions. Experiments show that our software-in-the-loop platform enables vision-based algorithm verification and supports simulation research for the autonomous landing of fixed-wing aircraft.

Sample Size Reduction Method Based on Data Fusion for Freeways with Fixed Detectors

This research investigates the required minimum number of probe vehicles (PVs) necessary to report the real-time travel speed and travel time with the desired statistical accuracy for freeways equipped with fixed detectors. The field data used were collected from the Freeway Performance Measurement System from 7:00 to 10:00 a.m. on May 28, 2014, during a typical weekday. With corridor information and traffic volume data, this study uses VISSIM to create simulation scenarios to generate PV data and loop data for further analysis. Then the average relative errors of travel time by setting are compared with various PV percentages in traffic on different links. The least squares errors of the fused speed data are also compared with the PV and loop data. Through analysis, the study reaches the following results. When the probe sample size reaches a certain level (3% in the testing environment), the estimated errors are reduced gently with the increasing number of PVs. To ensure that the link is traversed by at least one PV in one sample interval, at least 5-min, 5- to 10-min, and 20- to 25-min sampling intervals are required when the PV percentages are 3%, 2%, and 1%, respectively. The fusion of PV data and loop data can increase the accuracy of estimation results. About 3% of PVs are sufficient to collect highly accurate data for general freeways; 2% is enough to obtain the same or higher accuracy for the same road with fixed detectors.

The Simulated Clinical Environment as a Platform for Refining Critical Thinking in Nursing Students: A Pilot Program

Aim. This research explores the use of clinical simulations as a platform for stimulating critical thinking in nursing students. Background. This study takes place in an associate degree nursing program with a clinical group of nursing students. Method. Utilizing a faculty partnership approach, information was presented to the clinical group. Students were divided into small groups to create simulation scenarios. Results. Students were able to appreciate complexity of care and research differential diagnoses as they applied to care and management of changes in client condition scenarios. Conclusions. Use of a small-group teaching method to develop, implement, and critique clinical simulation scenarios facilitated critical thinking and clinical judgment of nursing students.

Diabetes: Models, Signals and control

Diabetes and its complications impose significant economic consequences on individuals, families, health systems, and countries. The control of diabetes is an interdisciplinary endeavor, which includes significant components of modeling, signal processing and control. Models: first, I will discuss the minimal (coarse) models which describe the key components of the system functionality and are capable of measuring crucial processes of glucose metabolism and insulin control in health and diabetes; then, the maximal (fine-grain) models which include comprehensively all available knowledge about system functionality and are capable to simulate the glucose-insulin system in diabetes, thus making it possible to create simulation scenarios whereby cost effective experiments can be conducted in silico to assess the efficacy of various treatment strategies – in particular I will focus on the first in silico simulation model accepted by FDA as a substitute to animal trials in the quest for optimal diabetes control. Signals: I will review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the crucial role of models to enhance the interpretation of their time-series signals, and on the opportunities that they present for automation of diabetes control. Control: I will review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, I will discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers.

Aurora Road Network Modeler

Aurora object-oriented framework is a set of generic Java classes and interfaces used as base objects in applications that model flow networks. A network consists of directed links, nodes and monitors. Nodes can be complex, being networks themselves. Controllers on nodes control flows coming from the input links. Monitors are special objects whose purpose is to monitor the state of given links and feed the observed data to controllers or generate events. Events change network configuration or inputs and are triggered at specified times. They can be user-defined, or generated by monitors based on the data observed. Aurora Road Network Modeler (RNM) is a tool set for operational planning and management of travel corridors (freeways and surrounding arterials), which is based on Aurora object-oriented framework. Currently, it consists of three graphical applications with intuitive user interface: Simulator runs macroscopic traffic simulations, allowing the user to create simulation scenarios by means of events; Configurator is used to build road network configurations from scratch or edit the existing ones; and GIS Importer extracts road information from the GIS .shp and .dbf files and saves it in the XML format of Aurora configuration file. Aurora RNM is open source and can be freely downloaded from the internet.

Intelligent behaviour-based team UUVs cooperation and navigation in a water flow environment

This paper considers the problem of intelligent behaviour-based team unmanned underwater vehicles (UUVs) cooperation and navigation, especially in a water flow environment. Animals often have behaviour which aims to maintain them living as groups. We learn from animals’ typical group behaviour and develop behaviour-based rules for team cooperation of UUVs. We create simulation scenarios in which a team of vehicles cooperate to track a target in a water current environment. This paper customises several behaviour-based rules to satisfy the requirement of the desired scenarios. We use fuzzy logic controllers to set different priority weights for each rule on-line according to the situation that the vehicles meet. The decision of the vehicle's next step steering direction is made by the combination of these rules multiplied by the relative priority weights. The line-of-sight guidance law is modified as the navigation rule in a water flow environment. The dynamic manoeuvring model of a real small UUV, SUBZERO III, is used in the simulation. The simulation results indicate that the entire system is successful in reaching the target without any collision within the scenario. The different trajectories and travel times are compared and discussed when normal and modified line-of-sight guidance rules are applied.