Real-time task reallocation for large-scale UAV team in dynamic environment

2016 ◽  
Author(s):  
Fei Yan ◽  
Zhuang Shao ◽  
Zhou Zhou ◽  
Xiaoping Zhu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Dynamic Environment ◽  
Time Task ◽  
Task Reallocation

scholarly journals P055: EMS boot camp: a real-world, real-time educational experience for emergency medicine residents

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S96-S97
Author(s):  
C. Farrell ◽  
S. Teed ◽  
N. Costain ◽  
M.A. Austin ◽  
A. Willmore ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Patient Care ◽  
Real Time ◽  
Real World ◽  
Large Scale ◽  
Clinical Care ◽  
Dynamic Environment ◽  
Educational Experience ◽  
Boot Camp ◽  
Medical Control

Introduction/Innovation Concept: In 2014, Eastern Ontario paramedic services, their medical director staff and area community colleges developed an EMS Boot Camp experience to orient Queen’s University and the University of Ottawa emergency medicine residents to the role of paramedics and the challenges they face in the field. Current EMS ride-alongs and didactic classroom sessions were deemed ineffective at adequately preparing residents to provide online medical control. From those early discussions came the creation of a real-world, real-time (RWRT) educational experience. Methods: Specific challenges unique to paramedicine are difficult to communicate to a medical control physician at the other end of a telephone. The goal of this one-day educational experience is for residents to gain insight into the complexity and time sensitive nature of delivering medical care in the field. Residents are immersed as responding paramedics in a day of intense RWRT simulation exercises reflecting the common paramedic logistical challenges to delivering patient care in an uncontrolled and dynamic environment. Curriculum, Tool, or Material: Scenarios, run by paramedic students, are overseen by working paramedics from participating paramedic services. Residents learn proper use of key equipment found on an Ontario ambulance while familiarize themselves with patient care standards and medical directives. Scenarios focus on prehospital-specific clinical care issues; performing dynamic CPR in a moving vehicle, extricating a bariatric patient with limited personnel, large scale multi-casualty triage as well as other time sensitive, high risk procedures requiring online medical control approval (i.e. chest needle thoracostomy). Conclusion: EMS Boot Camp dispels preconceived biases regarding “what it’s really like” to deliver high quality prehospital clinical care. When providing online medical control in the future, the residents will be primed to understand and expect certain challenges that may arise. The educational experience fosters collaboration between prehospital and hospital-based providers. The sessions provide a reproducible, standardized experience for all participants; something that cannot be guaranteed with traditional EMS ride-alongs. Future sessions will evaluate participant satisfaction and self-efficacy with the use of a standard evaluation form including pre/post self-evaluations.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data
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