Effect Of Laser Coherence And System Design On FMCW Multiplexed Sensor System Performance

1990 ◽  
Author(s):  
Denise M. Francis
Keyword(s):  
System Design ◽  
System Performance ◽  
Sensor System

High Current 270 VDC Contactor and Current Sensor System Design and Development

1998 ◽  
Author(s):  
Sean Sande ◽  
Karl Kitts ◽  
Robert Shanebrook ◽  
Dennis Timm ◽  
Tuan N. Tran
Keyword(s):  
System Design ◽  
Sensor System ◽  
Current Sensor ◽  
High Current ◽  
Design And Development ◽  
Sensor System Design

scholarly journals Characteristics of Changes of Transportation Performance for Pairs of Critical Edges

2018 ◽  
Vol 20 (3) ◽  
pp. 84-87
Author(s):  
Peter Czimmermann ◽  
Michal Kohani
Keyword(s):  
System Design ◽  
Public Service ◽  
System Performance ◽  
Service System ◽  
Transportation Network ◽  
Service System Design ◽  
The Creation ◽  
Random Failures

When the robustness of a public service system design is tested, we can often use scenarios where possible random failures can occur and they can influence the time the service is accessible which is provided for system users. The construction of a suitable scenario is based on the choice of links of the transportation network which influence the system performance in a substantial way. In such scenarios one or multiple arcs can be affected by this failure. In our contribution we present characteristics of pairs of critical arcs that can be used to develop an algorithm for the creation of critical scenarios.

Co-Evolution of Communication and System Performance in Engineering Systems Design: A Stochastic Network-Behavior Dynamics Model

2021 ◽  
Author(s):  
Ashish M. Chaudhari ◽  
Erica L. Gralla ◽  
Zoe Szajnfarber ◽  
Jitesh H. Panchal
Keyword(s):  
System Design ◽  
System Performance ◽  
The Other ◽  
Model Parameters ◽  
Stochastic Network ◽  
Network Behavior ◽  
Dynamics Model ◽  
Behavior Dynamics ◽  
Technical Factors ◽  
Student Subjects

Abstract The socio-technical perspective on engineering system design emphasizes the mutual dynamics between interdisciplinary interactions and system design outcomes. How different disciplines interact with each other depends on technical factors such as design interdependence and system performance. On the other hand, the design outcomes are influenced by social factors such as the frequency of interactions and their distribution. Understanding this co-evolution can lead to not only better behavioral insights, but also efficient communication pathways. In this context, we investigate how to quantify the temporal influences of social and technical factors on interdisciplinary interactions and their influence on system performance. We present a stochastic network-behavior dynamics model that quantifies the design interdependence, discipline-specific interaction decisions, the evolution of system performance, as well as their mutual dynamics. We employ two datasets, one of student subjects designing an automotive engine and the other of NASA engineers designing a spacecraft. Then, we apply statistical Bayesian inference to estimate model parameters and compare insights across the two datasets. The results indicate that design interdependence and social network statistics both have strong positive effects on interdisciplinary interactions for the expert and student subjects alike. For the student subjects, an additional modulating effect of system performance on interactions is observed. Inversely, the total number of interactions, irrespective of their discipline-wise distribution, has a weak but statistically significant positive effect on system performance in both cases. However, excessive interactions mirrored with design interdependence and inflexible design space exploration reduce system performance. These insights support the case for open organizational boundaries as a way for increasing interactions and improving system performance.

Understanding the Impact of Decision Making on Robustness During Complex System Design: More Resilient Power Systems

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Joseph R. Piacenza ◽  
Kenneth John Faller ◽  
Mir Abbas Bozorgirad ◽  
Eduardo Cotilla-Sanchez ◽  
Christopher Hoyle ◽  
...  
Keyword(s):  
Complex System ◽  
Decision Making ◽  
System Design ◽  
Robust Design ◽  
System Performance ◽  
System Level ◽  
Individual Risk ◽  
Cascading Failure ◽  
The Impact ◽  
Complex System Design

Abstract Robust design strategies continue to be relevant during concept-stage complex system design to minimize the impact of uncertainty in system performance due to uncontrollable external failure events. Historical system failures such as the 2003 North American blackout and the 2011 Arizona-Southern California Outages show that decision making, during a cascading failure, can significantly contribute to a failure's magnitude. In this paper, a scalable, model-based design approach is presented to optimize the quantity and location of decision-making agents in a complex system, to minimize performance loss variability after a cascading failure, regardless of where the fault originated in the system. The result is a computational model that enables designers to explore concept-stage design tradeoffs based on individual risk attitudes (RA) for system performance and performance variability, after a failure. The IEEE RTS-96 power system test case is used to evaluate this method, and the results reveal key topological locations vulnerable to cascading failures, that should not be associated with critical operations. This work illustrates the importance of considering decision making when evaluating system level tradeoffs, supporting robust design.

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