scholarly journals Developing Occupancy Grid with Automotive Simulation Environment

2020 ◽  
Vol 10 (21) ◽  
pp. 7629
Author(s):  
Paweł Markiewicz ◽  
Jakub Porębski
Keyword(s):  
Ground Truth ◽  
System Level ◽  
Control Algorithms ◽  
Data Generation ◽  
Simulation Framework ◽  
Early Evaluation ◽  
Fusion Methods ◽  
Occupancy Grids ◽  
Building Occupancy ◽  
Validation Tool

This study presents the process employed in prototyping and early evaluation of automotive perception algorithms. The data generation was performed using an automotive virtual validation tool. The off-the-shelf simulation framework used was expanded to include phenomenological sensors model that allowed for a simplified simulation of radars, lidars, and cameras. This paper extends the description of the methods for the generation of control algorithms. The work presented also includes a description of relevant data fusion methods for building occupancy grids. Results were obtained by performing a comparison of algorithm results against ground-truth. This virtual validation was used to enable early definition and verification of system-level requirements, narrow down performance assessment methods, and identify performance limitations before data from real sensors are available.

scholarly journals A flexible ChIP-sequencing simulation toolkit

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
An Zheng ◽  
Michael Lamkin ◽  
Yutong Qiu ◽  
Kevin Ren ◽  
Alon Goren ◽  
...  
Keyword(s):  
Ground Truth ◽  
Peak Calling ◽  
Simulation Framework ◽  
Experimental Conditions ◽  
Ground Truth Data ◽  
Chip Sequencing ◽  
Genome Wide ◽  
Experimental Parameters ◽  
Differential Binding ◽  
The Impact

Abstract Background A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq. Results We present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips. Conclusions ChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.

A New Simulation Framework Based on the Kepler and Scicos Open-Source Software for the Design and Qualification of Tokamak Control Algorithms: First Test Case Results

2011 ◽  
Vol 60 (2) ◽  
pp. 819-824 ◽  
Author(s):  
Oliviero Barana ◽  
Cédric Boulbe ◽  
Sylvain Brémond ◽  
Simone Mannori ◽  
Philippe Moreau ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Control Algorithms ◽  
Test Case ◽  
Simulation Framework

scholarly journals A Comparative Study of Methods for Measurement of Energy of Computing

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2204 ◽  
Author(s):  
Muhammad Fahad ◽  
Arsalan Shahid ◽  
Ravi Reddy Manumachu ◽  
Alexey Lastovetsky
Keyword(s):  
Predictive Models ◽  
Ground Truth ◽  
System Level ◽  
Average Error ◽  
Level Energy ◽  
Physical Measurements ◽  
Wide Range ◽  
External Power ◽  
On Chip ◽  
Multicore Cpus

Energy of computing is a serious environmental concern and mitigating it is an important technological challenge. Accurate measurement of energy consumption during an application execution is key to application-level energy minimization techniques. There are three popular approaches to providing it: (a) System-level physical measurements using external power meters; (b) Measurements using on-chip power sensors and (c) Energy predictive models. In this work, we present a comprehensive study comparing the accuracy of state-of-the-art on-chip power sensors and energy predictive models against system-level physical measurements using external power meters, which we consider to be the ground truth. We show that the average error of the dynamic energy profiles obtained using on-chip power sensors can be as high as 73% and the maximum reaches 300% for two scientific applications, matrix-matrix multiplication and 2D fast Fourier transform for a wide range of problem sizes. The applications are executed on three modern Intel multicore CPUs, two Nvidia GPUs and an Intel Xeon Phi accelerator. The average error of the energy predictive models employing performance monitoring counters (PMCs) as predictor variables can be as high as 32% and the maximum reaches 100% for a diverse set of seventeen benchmarks executed on two Intel multicore CPUs (one Haswell and the other Skylake). We also demonstrate that using inaccurate energy measurements provided by on-chip sensors for dynamic energy optimization can result in significant energy losses up to 84%. We show that, owing to the nature of the deviations of the energy measurements provided by on-chip sensors from the ground truth, calibration can not improve the accuracy of the on-chip sensors to an extent that can allow them to be used in optimization of applications for dynamic energy. Finally, we present the lessons learned, our recommendations for the use of on-chip sensors and energy predictive models and future directions.

Early Design Modeling and Simulation of Behaviors: Case Study of Mobile Work Machine

2009 ◽  
Author(s):  
William Brace ◽  
Eric Coatane´a ◽  
Heikki Kauranne ◽  
Matti Heiska
Keyword(s):  
Design Process ◽  
Simulation Models ◽  
Integrated Approach ◽  
Simulation Framework ◽  
Early Design ◽  
Early Evaluation ◽  
Design Structure ◽  
Qualitative Physics ◽  
Early Design Phase

The early evaluation of a proposed function structure for a product and also, the possibility to expose the potential failures related to this provides that the design process can be modeled in its entirety. However, so far there are no existed suitable models for the early phase of design process. This article presents an integrated approach aimed to explore the behaviors of concept designs in the early design phase. The approach is founded on a combination of Petri net, π-numbers, qualitative physics principles and Design Structure Matrix. The final aim is to implement this method on the SysML modeling language to integrate a simulation approach that is initially not standardized in the language. A second interest of the approach is to provide a coherent simulation framework that can be used as a reference to verify the coherency of other simulation models further in the design process.

A novel tool for ground truth data generation for video-based object classification

2015 ◽  
Author(s):  
J. S. Lopez-Villa ◽  
H. D. Insuasti-Ceballos ◽  
S. Molina-Giraldo ◽  
A. Alvarez-Meza ◽  
G. Castellanos-Dominguez
Keyword(s):  
Object Classification ◽  
Ground Truth ◽  
Data Generation ◽  
Ground Truth Data

scholarly journals Measurement validity in historical conflict data: Comparing datasets from Spain

2021 ◽  
Author(s):  
Francisco Villamil
Keyword(s):  
Political Violence ◽  
Spanish Civil War ◽  
Ground Truth ◽  
Data Availability ◽  
Data Generation ◽  
Mass Graves ◽  
Ground Truth Data ◽  
Proxy Variables ◽  
Conflict Research ◽  
Careful Assessment

Conflict research usually suffers from data availability problems, which sometimes motivates the use of use of proxy variables for violent events. But since they are usually the only alternative to measure violence patterns, there is not ground-truth data to compare them to. This limitation explains why there are no studies assessing their validity. This research note exploits a case where there are two sources on political violence: the Spanish Civil War. Comparing georeferenced mass graves and direct records of victimization, I show that the differences between these two datasets are not random but respond to different data generation processes, introducing important biases. Results highlight the need for a more careful assessment when using proxy variables for political violence.

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