Solution space exploration of volumetric surveillance using a general taxonomy

2013 ◽  
Author(s):  
Naeem Ahmad ◽  
Muhammad Imran ◽  
Khursheed Khursheed ◽  
Najeem Lawal ◽  
Mattias O'Nils
Keyword(s):  
Space Exploration ◽  
Solution Space

scholarly journals A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

2019 ◽  
Vol 1 (1) ◽  
pp. 11-20
Author(s):  
Marija Majda Perisic ◽  
Tomislav Martinec ◽  
Mario Storga ◽  
John S Gero
Keyword(s):  
Computational Study ◽  
Space Exploration ◽  
Solution Space ◽  
Problem Solution ◽  
Design Team ◽  
Design Teams ◽  
Agent Based Model ◽  
Problem Space ◽  
Agent Based ◽  
The Impact

AbstractThis paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results.

Rate of Variety and its Relation to Creativity Factors

2018 ◽  
Author(s):  
Morgan B. Weaver ◽  
Caleb Bennetts ◽  
Benjamin W. Caldwell
Keyword(s):  
Problem Solving ◽  
Quantitative Study ◽  
Significant Positive Correlation ◽  
Confounding Factor ◽  
Space Exploration ◽  
Solution Space ◽  
Problem Solving Skills ◽  
Positive Correlation ◽  
The Relationship

Individual designers demonstrate different styles of ideation in conceptual design. These styles have been quantified and described primarily through protocol, think-aloud studies that examine a designer’s thought sequence during ideation. In this paper, we examine ideation style with an outcome-based approach, examining style on a continuum of rate of variety, or solution space exploration rate. We investigate the relationship between this exploration rate and creativity factors of quality and novelty using a quantitative study of problem-solving skills. We found a significant positive correlation between broad-search style and novelty and a significant positive correlation between detail-search style and quality of ideas. These correlations are in agreement with protocol studies found in literature. We also identified quantity of ideas as a possible confounding factor and discuss potential improvements to these types of studies.

Automatically Transforming Object-Oriented Graph-Based Representations Into Boolean Satisfiability Problems for Computational Design Synthesis

2013 ◽  
Vol 135 (10) ◽  
Author(s):  
Clemens Münzer ◽  
Bergen Helms ◽  
Kristina Shea
Keyword(s):  
Space Exploration ◽  
Solution Space ◽  
Object Oriented ◽  
Computational Design ◽  
Building Blocks ◽  
Boolean Satisfiability ◽  
Design Synthesis ◽  
Alcohol Production ◽  
Systematic Solution ◽  
Computational Design Synthesis

Ever since computers have been used to support human designers, a variety of representations have been used to encapsulate engineering knowledge. Computational design synthesis (CDS) approaches utilize this knowledge to generate design candidates for a specified task. However, new approaches are required to enable systematic solution space exploration. This paper presents an approach that combines a graph-based object-oriented knowledge representation with first-order logic and Boolean satisfiability. This combination is used as the foundation for a generic automated approach for requirement-driven computational design synthesis. Available design building blocks and a design task defined through a set of requirements are modeled in a graph-based environment and then automatically transferred into a Boolean satisfiability problem and solved, considering a given solution size. The Boolean solution is automatically transferred back to the graph-based domain. The method is validated through two case studies: synthesis of automotive powertrains and chemical process synthesis for ethyl alcohol production. The contribution of the paper is a new method that is able to determine if an engineering task is solvable for a given set of synthesis building blocks and enables systematic solution space exploration.

Kernel scheduling techniques for efficient solution space exploration in reconfigurable computing

2001 ◽  
Vol 47 (3-4) ◽  
pp. 277-292 ◽  
Author(s):  
Rafael Maestre ◽  
Fadi Kurdahi ◽  
Milagros Fernandez ◽  
Roman Hermida ◽  
Nader Bagherzadeh ◽  
...  
Keyword(s):  
Reconfigurable Computing ◽  
Efficient Solution ◽  
Space Exploration ◽  
Solution Space

Goal-Oriented Inverse Design (GoID) of Feedstock Filament for Fused Deposition Modeling

2021 ◽  
Author(s):  
Angshuman Deka ◽  
Anand Balu Nellippallil ◽  
John Hall
Keyword(s):  
Fused Deposition Modeling ◽  
Space Exploration ◽  
Specific Property ◽  
Solution Space ◽  
Material Composition ◽  
Inverse Design ◽  
Structure Property ◽  
Target Property ◽  
Fused Deposition ◽  
Deposition Modeling

Abstract Additive manufacturing (AM) can produce complex geometrical shapes and multi-material parts that are not possible using typical manufacturing processes. The properties of multi-material AM parts are often unknown. For multi-material parts made using Fused Deposition Modeling (FDM), these properties are driven by the filament. Acquiring the properties of the products or the filament necessitates experiments that can be expensive and time-consuming. Thus, there is a need for simulation-based design tools that can determine the multi-material properties of the filament by exploring the complex process-structure-property (p-s-p) relationship. In this paper, we present a Goal-Oriented Inverse Design (GoID) method to produce feedstock filament for FDM process with specific property goals. Using this method, the designers connects the structure and property in the p-s-p relationship by identifying satisficing material composition for specific property goals. The filament properties identified in the problem are percentage elongation, tensile strength, and Young’s Modulus. The problem is formulated using a generic decision-based design framework, Concept Exploration Framework. The solution space exploration for satisficing solutions is performed using the compromise Decision Support Problem (cDSP). The forward information flow is first established to generate the necessary mathematical relationships between the composition and the property goals. Next, the target property goals of the filament are set. The cDSP is used for solution space exploration to identify satisficing solutions for material composition for the target property goals. While the results are interesting, the focus of our work is to demonstrate, and refine, the goal-oriented, inverse design method for the AM domain.

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