Dynamic Data Distribution of High Level Architecture Based on Publication and Subscription Tree

2006 ◽  
pp. 532-543
Author(s):  
Yintian Liu ◽  
Changjie Tang ◽  
Chuan Li ◽  
Minfang Zhu ◽  
Tao Zeng
Keyword(s):  
Data Distribution ◽  
High Level Architecture ◽  
Dynamic Data ◽  
High Level

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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