Machine Vision: A Multi-Disciplinary Systems Engineering Problem

1988 ◽  
Author(s):  
Donald G. Bailey
Keyword(s):  
Machine Vision ◽  
Systems Engineering ◽  
Engineering Problem

Evaluating the Systems Engineering Problem Management Process for Industrial Manufacturing Problems

2016 ◽  
Vol 19 (2) ◽  
pp. 133-145 ◽  
Author(s):  
Dennis A. Perry ◽  
Bill Olson ◽  
Paul Blessner ◽  
Timothy D. Blackburn
Keyword(s):  
Systems Engineering ◽  
Engineering Problem ◽  
Management Process ◽  
Problem Management ◽  
Industrial Manufacturing ◽  
Manufacturing Problems

Study on the System Optimization Method for Supporting Structure of Urban Tunnel

2012 ◽  
Vol 256-259 ◽  
pp. 1230-1234
Author(s):  
Ang Li ◽  
Jing Bo Su ◽  
Cai Hua Shen ◽  
Guo Jian Shao ◽  
Sheng Yong Ding ◽  
...  
Keyword(s):  
Systems Engineering ◽  
Score Function ◽  
System Optimization ◽  
Optimization Method ◽  
Engineering Problem ◽  
Test Method ◽  
Tunnel Support ◽  
Safety Control ◽  
Supporting Structure ◽  
Non Linear

The tunnel project is a systems engineering problem. And it is very necessary to study on the system optimization method for supporting structure in order to promote the research about intelligent optimization of safety control for tunnel project. In this paper, the analytic hierarchy process is used to establish system model of supporting structure for urban tunnel and the SCORE function test method for non-linear drift (i.e., non-linear impact of factors) is also applied to simplify the model. This idea and method for the optimization of retaining structural system have important significance for optimization theory building of the tunnel support structure system.

Technology Characterization Models and Their Use in Systems Design

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
Robert R. Parker ◽  
Edgar Galvan ◽  
Richard J. Malak
Keyword(s):  
Decision Making ◽  
Systems Engineering ◽  
Systems Design ◽  
Computational Effort ◽  
Engineering Problem ◽  
Component Technology ◽  
System Level ◽  
Support Vector ◽  
Engineering Project ◽  
Set Based Design

Prior research suggests that set-based design representations can be useful for facilitating collaboration among engineers in a design project. However, existing set-based methods are limited in terms of how the sets are constructed and in their representational capability. The focus of this article is on the problem of modeling the capabilities of a component technology in a way that can be communicated and used in support of system-level decision making. The context is the system definition phases of a systems engineering project, when engineers still are considering various technical concepts. The approach under investigation requires engineers familiar with the component- or subsystem-level technologies to generate a set-based model of their achievable technical attributes, called a technology characterization model (TCM). Systems engineers then use these models to explore system-level alternatives and choose the combination of technologies that are best suited to the design problem. Previously, this approach was shown to be theoretically sound from a decision making perspective under idealized circumstances. This article is an investigation into the practical effectiveness of different TCM representational methods under realistic conditions such as having limited data. A power plant systems engineering problem is used as an example, with TCMs generated for different technical concepts for the condenser component. Samples of valid condenser realizations are used as inputs to the TCM representation methods. Two TCM representation methods are compared based on their solution accuracy and computational effort required: a Kriging-based interpolation and a machine learning technique called support vector domain description (SVDD). The results from this example hold that the SVDD-based method provides the better combination of accuracy and efficiency.

Modeling Information Gathering Decisions in Systems Engineering Projects

2014 ◽  
Author(s):  
Chuck Hsiao ◽  
Richard Malak
Keyword(s):  
Systems Engineering ◽  
Information Gathering ◽  
Engineering Problem ◽  
Value Functions ◽  
Amount Of Information ◽  
Course Of Action ◽  
Resource Requirements ◽  
Markov Decision ◽  
The Cost ◽  
Engineering Projects

Decisions in systems engineering projects commonly are made under significant amounts of uncertainty. This uncertainty can exist in many areas such as the performance of subsystems, interactions between subsystems, or project resource requirements such as budget or personnel. System engineers often can choose to gather information that reduces uncertainty, which allows for potentially better decisions, but at the cost of resources expended in acquiring the information. However, our understanding of how to analyze situations involving gathering information is limited, and thus heuristics, intuition, or deadlines are often used to judge the amount of information gathering needed in a decision. System engineers would benefit from a better understanding of how to determine the amount of information gathering needed to support a decision. This paper introduces Partially Observable Markov Decision Processes (POMDPs) as a formalism for modeling information-gathering decisions in systems engineering. A POMDP can model different states, alternatives, outcomes, and probabilities of outcomes to represent a decision maker’s beliefs about his situation. It also can represent sequential decisions in a compact format, avoiding the combinatorial explosion of decision trees and similar representations. The solution of a POMDP, in the form of value functions, prescribes the best course of action based on a decision maker’s beliefs about his situation. The value functions also determine if more information gathering is needed. Sophisticated computational solvers for POMDPs have been developed in recent years, allowing for a straightforward analysis of different alternatives, and determining the optimal course of action in a given situation. This paper demonstrates using a POMDP to model a systems engineering problem, and compares this approach with other approaches that account for information gathering in decision making.

scholarly journals No cure no pay: How to contract for software services

2007 ◽  
Vol 4 (1) ◽  
pp. 29-41 ◽  
Author(s):  
Tom Gilb
Keyword(s):  
Systems Engineering ◽  
Supplier Management ◽  
Systems Development ◽  
Engineering Problem ◽  
Development Work ◽  
Project Failure ◽  
Project Failures ◽  
Corrective Actions ◽  
Small Increments ◽  
Software Services

Contractual motivation is needed to avoid costly project failures and improve the delivery of stakeholder value. Only if the supplier management is made to feel the pain of project failure will it strive to avoid it. The current culture of rewarding failure, by paying for systems development work regardless of the product delivered, must be altered. Such contractual motivation must be supported by quantitative requirements and evolutionary delivery. Quantitative requirements allow project progress and success to be measured enabling monitoring and testing for contractual compliance Evolutionary delivery (that is, delivering early high value in small increments and using feedback from deliverables to determine future increments) allows early reporting of the ability of systems development to deliver and so enables any required corrective actions. Note: This paper specifically addresses the software problem, but the ideas most likely apply to the wider systems engineering problem to some interesting degree as well .

Abstraction: An alternative neurocognitive account of recognition, prediction, and decision making

2020 ◽  
Vol 43 ◽  
Author(s):  
Valerie F. Reyna ◽  
David A. Broniatowski
Keyword(s):  
Decision Making ◽  
Software Engineering ◽  
Systems Engineering ◽  
Evidence Based ◽  
Extensive Literature ◽  
Trace Theory ◽  
Fuzzy Trace Theory ◽  
Abstract Representations ◽  
Technical Systems

Abstract Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.

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