Rapid synthesis of high-level architecture-based heterogeneous simulation: a model-based integration approach

SIMULATION ◽  
2011 ◽  
Vol 88 (2) ◽  
pp. 217-232 ◽  
Author(s):  
Graham Hemingway ◽  
Himanshu Neema ◽  
Harmon Nine ◽  
Janos Sztipanovits ◽  
Gabor Karsai
Keyword(s):  
High Level Architecture ◽  
Rapid Synthesis ◽  
Model Based ◽  
Integration Approach ◽  
High Level

scholarly journals Affective-Model Based High-Level Controller for Human-Robot Applications

2012 ◽  
Vol 41 ◽  
pp. 812-818
Author(s):  
Shahrul Nairn Sidek ◽  
Elliana Ismaif ◽  
Nor Anija Jalaludin
Keyword(s):  
Model Based ◽  
High Level

scholarly journals The Design of an Instructional Model Based on Connectivism and Constructivism to Create Innovation in Real World Experience

2018 ◽  
Vol 11 (3) ◽  
pp. 12 ◽  
Author(s):  
Kanokrat Jirasatjanukul ◽  
Namon Jeerungsuwan
Keyword(s):  
Real World ◽  
Model Evaluation ◽  
Sampling Technique ◽  
Instructional Model ◽  
Model Design ◽  
Research Results ◽  
Purposive Sampling ◽  
Evaluation Form ◽  
Model Based ◽  
High Level

The objectives of the research were to (1) design an instructional model based on Connectivism and Constructivism to create innovation in real world experience, (2) assess the model designed–the designed instructional model. The research involved 2 stages: (1) the instructional model design and (2) the instructional model rating. The sample consisted of 7 experts, and the Purposive Sampling Technique was used. The research instruments were the instructional model and the instructional model evaluation form. The statistics used in the research were means and standard division. The research results were (1) the Instructional Model based on Connectivism and Constructivism to Create innovation in Real World Experience consisted of 3 components. These were Connectivism, Constructivism and Innovation in Real World Experience and (2) the instructional model rating was at a high level (=4.37, S.D.=0.41). The research results revealed that the Instructional Model Based on Connectivism and Constructivism to Create Innovation in Real World Experience was a model that can be used in learning, in that it promoted the creation of real world experience innovation.

A High Level Architecture-based Medical Simulation System

SIMULATION ◽  
1999 ◽  
Vol 73 (5) ◽  
pp. 281-287 ◽  
Author(s):  
Mikel D. Petty ◽  
Piotr S. Windyga
Keyword(s):  
Simulation System ◽  
Medical Simulation ◽  
High Level Architecture ◽  
High Level

Simulation and Implementation of HLA-Based Branch Predictor of Multi-Pipeline Processor

2012 ◽  
Vol 204-208 ◽  
pp. 4952-4957
Author(s):  
Ji Hua Ye ◽  
Qi Xie ◽  
Yao Hong Xiahou
Keyword(s):  
Simulation System ◽  
New Method ◽  
High Level Architecture ◽  
Hit Rate ◽  
Branch Predictor ◽  
The Right ◽  
High Level ◽  
Two Sides

Researched how the multi-pipeline processor accelerates the running of thread ,found that when the branch predictor facing the random branch instruction, the hit rate will become very low, so bring out a new method that using the free pipeline to accelerate the running of branch instruction. If the right prediction from branch predictor is less than 70% and there is a free pipeline, then using two pipelines to run the two sides of a branch instruction at the same time. In order to test the new method, the HLA (High Level architecture) architecture-based simulation system is established, the results show that the new method can really reduce the time when processing the random branch instructions.

scholarly journals An Environment for Indoor Testing and Diagnosis of Drones using Co-simulation

2019 ◽  
Author(s):  
Renato Ricardo Abreu ◽  
Thyago Oliveira ◽  
Leydson Silva ◽  
Tiago Nascimento ◽  
Alisson Brito
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Detection Rate ◽  
Failure Detection ◽  
Indoor Environments ◽  
Decision Tree Algorithm ◽  
High Level Architecture ◽  
Testing Environment ◽  
Testing Tool ◽  
High Level ◽  
Require Reliability

Operations with Unmanned Aerial Vehicles (UAVs) require reliability to execute missions. With the correct diagnostic, it is possible to predict vehicle failure during or before the flight. The objective of this work is to present a testing tool, which analyzes and evaluates drones during the flight in indoor environments. For this purpose, the framework Ptolemy II was extended for communication with real drones using the High-Level Architecture (HLA) for data exchanging and synchronization. The presented testing environment is extendable for other testing routines and is ready for integration with other simulation and analysis tools. In this paper, two failure detection experiments were performed, with a total of 20 flights for each one, which 80\% were used to train a decision tree algorithm, and the other 20% flights to test the algorithm in which one of the propellers had an anomaly. The failure rate or detection rate was 70\% for the first experiment and 90% for the second one.

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