Automated Configuration of a Machine Simulation Based on a Modular Approach

2013 ◽  
pp. 603-612 ◽  
Author(s):  
Michael Weyrich ◽  
Frank Steden
Keyword(s):  
Modular Approach ◽  
Simulation Based ◽  
Machine Simulation

scholarly journals Sequential lidar sensor system simulation: a modular approach for simulation-based safety validation of automated driving

2020 ◽  
Vol 5 (3-4) ◽  
pp. 187-197
Author(s):  
Philipp Rosenberger ◽  
Martin Friedrich Holder ◽  
Nicodemo Cianciaruso ◽  
Philip Aust ◽  
Jonas Franz Tamm-Morschel ◽  
...  
Keyword(s):  
Simulation Models ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Modular Approach ◽  
Active Perception ◽  
Automated Driving ◽  
Test Procedures ◽  
Sequential Approach ◽  
Simulation Based ◽  
Tracking Model

Abstract Validating safety is an unsolved challenge before autonomous driving on public roads is possible. Since only the use of simulation-based test procedures can lead to an economically viable solution for safety validation, computationally efficient simulation models with validated fidelity are demanded. A central part of the overall simulation tool chain is the simulation of the perception components. In this work, a sequential modular approach for simulation of active perception sensor systems is presented on the example of lidar. It enables the required level of fidelity of synthetic object list data for safety validation using beforehand simulated point clouds. The elaborated framework around the sequential modules provides standardized interfaces packaging for co-simulation such as Open Simulation Interface (OSI) and Functional Mockup Interface (FMI), while providing a new level of modularity, testability, interchangeability, and distributability. The fidelity of the sequential approach is demonstrated on an everyday scenario at an intersection that is performed in reality at first and reproduced in simulation afterwards. The synthetic point cloud is generated by a sensor model with high fidelity and processed by a tracking model afterwards, which, therefore, outputs bounding boxes and trajectories that are close to reality.

A modular approach for simulation-based optimization of MEMS

2002 ◽  
Vol 33 (1-2) ◽  
pp. 29-38 ◽  
Author(s):  
Peter Schneider ◽  
André Schneider ◽  
Peter Schwarz
Keyword(s):  
Modular Approach ◽  
Simulation Based ◽  
Simulation Based Optimization

Modular approach for simulation-based optimization of MEMS

2000 ◽  
Author(s):  
Peter Schneider ◽  
E. Huck ◽  
S. Reitz ◽  
Sandra Parodat ◽  
Andre Schneider ◽  
...  
Keyword(s):  
Modular Approach ◽  
Simulation Based ◽  
Simulation Based Optimization

Author response for "A complex garment assembly line balancing using simulation‐based optimization"

2020 ◽  
Author(s):  
Ocident Bongomin ◽  
Josphat Igadwa Mwasiagi ◽  
Eric Oyondi Nganyi ◽  
Ildephonse Nibikora
Keyword(s):  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Author Response ◽  
Simulation Based ◽  
Simulation Based Optimization

Inquiry Learning

2009 ◽  
Vol 23 (2) ◽  
pp. 117-127 ◽  
Author(s):  
Astrid Wichmann ◽  
Detlev Leutner
Keyword(s):  
Scientific Reasoning ◽  
Inquiry Learning ◽  
Instructional Support ◽  
Test Results ◽  
Knowledge Test ◽  
Simulation Based ◽  
Scientific Experiments ◽  
Specific Support ◽  
Science Classes

Seventy-nine students from three science classes conducted simulation-based scientific experiments. They received one of three kinds of instructional support in order to encourage scientific reasoning during inquiry learning: (1) basic inquiry support, (2) advanced inquiry support including explanation prompts, or (3) advanced inquiry support including explanation prompts and regulation prompts. Knowledge test as well as application test results show that students with regulation prompts significantly outperformed students with explanation prompts (knowledge: d = 0.65; application: d = 0.80) and students with basic inquiry support only (knowledge: d = 0.57; application: d = 0.83). The results are in line with a theoretical focus on inquiry learning according to which students need specific support with respect to the regulation of scientific reasoning when developing explanations during experimentation activities.

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