Aligning Human and Computational Evaluations of Functional Design Similarity

2021 ◽  
Author(s):  
Ananya Nandy ◽  
Kosa Goucher-Lambert
Keyword(s):  
Similarity Measures ◽  
Human Perception ◽  
Functional Similarity ◽  
Levels Of Abstraction ◽  
Product Function ◽  
Functional Models ◽  
Support Tools ◽  
Human Perceptions ◽  
Design By Analogy ◽  
High Level

Abstract Function drives many early design considerations in product development. Therefore, finding functionally similar examples is important when searching for sources of inspiration or evaluating designs against existing technology. However, it is difficult to capture what people consider to be functionally similar and therefore, if measures that compare function directly from the products themselves are meaningful. In this work, we compare human evaluations of similarity to computationally determined values, shedding light on how quantitative measures align with human perceptions of functional similarity. Human perception of functional similarity is considered at two levels of abstraction: (1) the high-level purpose of a product, and (2) a detailed view of how the product works. Human evaluations of similarity are quantified by crowdsourcing 1360 triplet ratings at each functional abstraction, and then compared to similarity that is computed between functional models. We demonstrate how different levels of abstraction and the fuzzy line between what is considered “similar” and “similar enough” may impact how these similarity measures are utilized, finding that different measures better align with human evaluations along each dimension. The results inform how product similarity can be leveraged by designers. Therefore, applications lie in creativity support tools, such as those used for design-by-analogy, or future computational methods in design that incorporate product function in addition to form.

Evaluating Quantitative Measures for Assessing Functional Similarity in Engineering Design

2021 ◽  
Vol 144 (3) ◽  
Author(s):  
Ananya Nandy ◽  
Andy Dong ◽  
Kosa Goucher-Lambert
Keyword(s):  
Engineering Design ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Functional Similarity ◽  
Cosine Similarity ◽  
Design Support ◽  
Widespread Adoption ◽  
Functional Models ◽  
Support Tools ◽  
Design By Analogy

Abstract The development of example-based design support tools, such as those used for design-by-analogy, relies heavily on the computation of similarity between designs. Various vector- and graph-based similarity measures operationalize different principles to assess the similarity of designs. Despite the availability of various types of similarity measures and the widespread adoption of some, these measures have not been tested for cross-measure agreement, especially in a design context. In this paper, several vector- and graph-based similarity measures are tested across two datasets of functional models of products to explore the ways in which they find functionally similar designs. The results show that the network-based measures fundamentally operationalize functional similarity in a different way than vector-based measures. Based upon the findings, we recommend a graph-based similarity measure such as NetSimile in the early stages of design when divergence is desirable and a vector-based measure such as cosine similarity in a period of convergence, when the scope of the desired function implementation is clearer.

A Comparison of Vector and Network-Based Measures for Assessing Design Similarity

2020 ◽  
Author(s):  
Ananya Nandy ◽  
Andy Dong ◽  
Kosa Goucher-Lambert
Keyword(s):  
Functional Model ◽  
Similarity Measures ◽  
Current Function ◽  
Surface Level ◽  
Graph Similarity ◽  
Functional Models ◽  
Design By Analogy ◽  
Computational Systems ◽  
The Impact ◽  
Target Design

Abstract In order to retrieve analogous designs for design-by-analogy, computational systems require the calculation of similarity between the target design and a repository of source designs. Representing designs as functional abstractions can support designers in practicing design-by-analogy by minimizing fixation on surface-level similarities. In addition, when a design is represented by a functional model using a function-flow format, many measures are available to determine functional similarity. In most current function-based design-by-analogy systems, the functions are represented as vectors and measures like cosine similarity are used to retrieve analogous designs. However, it is hypothesized that changing the similarity measure can significantly change the examples that are retrieved. In this paper, several similarity measures are empirically tested across a set of functional models of energy harvesting products. In addition, the paper explores representing the functional models as networks to find functionally similar designs using graph similarity measures. Surprisingly, the types of designs that are considered similar by vector-based and one of the graph similarity measures are found to vary significantly. Even among a set of functional models that share known similar technology, the different measures find inconsistent degrees of similarity — some measures find the set of models to be very similar and some find them to be very dissimilar. The findings have implications on the choice of similarity metric and its effect on finding analogous designs that, in this case, have similar pairs of functions and flows in their functional models. Since literature has shown that the types of designs presented can impact their effectiveness in aiding the design process, this work intends to spur further consideration of the impact of using different similarity measures when assessing design similarity computationally.

scholarly journals A STUDY OF ACTOR AND ACTION SEMANTIC RETENTION IN VIDEO SUPERVOXEL SEGMENTATION

2013 ◽  
Vol 07 (04) ◽  
pp. 353-375 ◽  
Author(s):  
CHENLIANG XU ◽  
RICHARD F. DOELL ◽  
STEPHEN JOSÉ HANSON ◽  
CATHERINE HANSON ◽  
JASON J. CORSO
Keyword(s):  
Large Scale ◽  
Human Perception ◽  
Large Set ◽  
Video Content ◽  
Shape Context ◽  
Large Scale Data ◽  
Human Perceptions ◽  
New Feature ◽  
Large Scale Data Processing ◽  
High Level

Existing methods in the semantic computer vision community seem unable to deal with the explosion and richness of modern, open-source and social video content. Although sophisticated methods such as object detection or bag-of-words models have been well studied, they typically operate on low level features and ultimately suffer from either scalability issues or a lack of semantic meaning. On the other hand, video supervoxel segmentation has recently been established and applied to large scale data processing, which potentially serves as an intermediate representation to high level video semantic extraction. The supervoxels are rich decompositions of the video content: they capture object shape and motion well. However, it is not yet known if the supervoxel segmentation retains the semantics of the underlying video content. In this paper, we conduct a systematic study of how well the actor and action semantics are retained in video supervoxel segmentation. Our study has human observers watching supervoxel segmentation videos and trying to discriminate both actor (human or animal) and action (one of eight everyday actions). We gather and analyze a large set of 640 human perceptions over 96 videos in 3 different supervoxel scales. Furthermore, we design a feature defined on supervoxel segmentation, called supervoxel shape context, which is inspired by the higher order processes in human perception. We conduct actor and action classification experiments with this new feature and compare to various traditional video features. Our ultimate findings suggest that a significant amount of semantics have been well retained in the video supervoxel segmentation and can be used for further video analysis.

Functional Similarity at Varying Levels of Abstraction

2010 ◽  
Author(s):  
Benjamin W. Caldwell ◽  
Gregory M. Mocko
Keyword(s):  
Clustering Algorithm ◽  
Consumer Products ◽  
Controlled Vocabulary ◽  
Functional Similarity ◽  
Concept Generation ◽  
Levels Of Abstraction ◽  
Similarity Metric ◽  
Conceptual Design Phase ◽  
Functional Models ◽  
Function Models

Function modeling is often used in the conceptual design phase as an approach to capture a form-independent purpose of a product. Current research efforts have focused on the formalization of functional models, development of function-based design repositories, and concept generation based on a quantitative functional similarity metric. In this paper, three levels of abstraction of function models are obtained by including supporting functions, excluding supporting functions, and applying abstraction rules to function models of 128 products in a design repository. The similarity of these products is computed using the Functional Basis controlled vocabulary and a matrix-based similarity metric. A matrix-based clustering algorithm is then applied to the similarity results to identify groups of similar products. A subset of these products is then studied to further compare the three levels of abstraction and to validate the results. Similarity between consumer products depends on the level of abstraction of the models, with higher levels of abstraction producing better results.

scholarly journals A Journey from basic Image Features to Lofty Human Intelligence in Content-based Image Retrieval: Motivation, Applications and Future Trends

2020 ◽  
Vol 9 (2) ◽  
pp. 990-998
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Similarity Measures ◽  
Human Perception ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Future Trends ◽  
Research Issues ◽  
Basic Image ◽  
High Level

Due to a remarkable increase in the complexity of the multimedia content, there is a cumulative enhancement of digital images both online and offline. For the purpose of retrieving images from a vast storehouse of images, there is an urgent requirement of an effectual image retrieval system and the most effective system in this domain is denoted as content-based image retrieval (CBIR) system. CBIR system is generally based on the extraction of basic image attributes like texture, color, shape, spatial information, etc. from an image. But, there exists a semantic gap between the basic image features and high-level human perception and to reduce this gap various techniques can be used. This paper presents a detailed study about the various basic techniques with an emphasis on different intelligent techniques like, the usage of machine learning, deep learning, relevance feedback, etc., which can be used to achieve a high level semantic information in CBIR systems. In addition, a detailed outline regarding the framework of a basic CBIR system, various benchmark datasets, similarity measures, evaluation metrics have been also discussed. Finally, solution to some research issues and future trends have also been given in this paper.

Representing User Activity and Product Function for Universal Design

2009 ◽  
Author(s):  
Vincent Kostovich ◽  
Daniel A. McAdams ◽  
Seung Ki Moon
Keyword(s):  
Universal Design ◽  
Design Research ◽  
Analysis Framework ◽  
Product Analysis ◽  
Research And Practice ◽  
Complex Products ◽  
Product Function ◽  
Functional Models ◽  
The Us ◽  
High Level

This paper presents a product analysis framework to improve universal design research and practice. Seventeen percent of the US population has some form of a disability. Nevertheless, many companies are unfamiliar with approaches to achieving universal design. A key element of the framework is the combination of activity diagrams and functional models. The framework is applied in the analysis of 20 pairs of products that satisfy a common high level need but differ with one product intended for fully able users and the other intended for persons with some disability or reduced functioning. Discoveries based on the analysis include the observation that differences in typical and universal products can be categorized functionally, morphologically, or parametrically different than typical products. Additionally, simple products appear to be made more accessible through parametric changes whereas more complex products require functional additions and changes.

Exploring The Effect of Visual Information Degradation on Human Perception and Performance In A Human-Telerobot System

2020 ◽  
Vol 64 (1) ◽  
pp. 1302-1307
Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall
Keyword(s):  
Visual Information ◽  
Human Perception ◽  
Low Level ◽  
Hit Rate ◽  
Word Level ◽  
Level Of Automation ◽  
Significant Difference ◽  
Human Operators ◽  
And Performance ◽  
High Level

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.

scholarly journals Model-Driven Architectural Framework towards Safe and Secure Nuclear Power Reactors

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5136
Author(s):  
Bassem Ouni ◽  
Christophe Aussagues ◽  
Saadia Dhouib ◽  
Chokri Mraidha
Keyword(s):  
Nuclear Power ◽  
Nuclear Reactors ◽  
System Development ◽  
Levels Of Abstraction ◽  
Model Driven ◽  
Case System ◽  
Architectural Framework ◽  
Performance Check ◽  
High Level ◽  
Different Levels

Sensor-based digital systems for Instrumentation and Control (I&C) of nuclear reactors are quite complex in terms of architecture and functionalities. A high-level framework is highly required to pre-evaluate the system’s performance, check the consistency between different levels of abstraction and address the concerns of various stakeholders. In this work, we integrate the development process of I&C systems and the involvement of stakeholders within a model-driven methodology. The proposed approach introduces a new architectural framework that defines various concepts, allowing system implementations and encompassing different development phases, all actors, and system concerns. In addition, we define a new I&C Modeling Language (ICML) and a set of methodological rules needed to build different architectural framework views. To illustrate this methodology, we extend the specific use of an open-source system engineering tool, named Eclipse Papyrus, to carry out many automation and verification steps at different levels of abstraction. The architectural framework modeling capabilities will be validated using a realistic use case system for the protection of nuclear reactors. The proposed framework is able to reduce the overall system development cost by improving links between different specification tasks and providing a high abstraction level of system components.

scholarly journals Carebots

2016 ◽  
Vol 138 (09) ◽  
pp. S8-S13 ◽  
Author(s):  
Thiago Marinho ◽  
Christopher Widdowson ◽  
Amy Oetting ◽  
Arun Lakshmanan ◽  
Hang Cui ◽  
...  
Keyword(s):  
Older Adults ◽  
Multidisciplinary Approach ◽  
Human Perception ◽  
The Elderly ◽  
Comfort Level ◽  
Robot System ◽  
Perceived Safety ◽  
High Level ◽  
Future Work ◽  
Flying Robots

This article demonstrates a multidisciplinary approach that proposes to augment future caregiving by prolonged independence of older adults. The human–robot system allows the elderly to cooperate with small flying robots through an appropriate interface. ASPIRE provides a platform where high-level controllers can be designed to provide a layer of abstraction between the high-level task requests, the perceptual needs of the users, and the physical demands of the robotic platforms. With a robust framework that has the capability to account for human perception and comfort level, one can provide perceived safety for older adults, and further, add expressively that facilitates communication and interaction continuously throughout the stimulation. The proposed framework relies on an iterative process of low-level controllers design through experimental data collected from psychological trials. Future work includes the exploration of multiple carebots to cooperatively assist in caregiving tasks based on human-centered design approach.

Spatial Models, Cognitive Metrics, and Majority Rule Equilibria

2009 ◽  
Vol 40 (1) ◽  
pp. 11-30 ◽  
Author(s):  
Macartan Humphreys ◽  
Michael Laver
Keyword(s):  
Empirical Research ◽  
Majority Rule ◽  
Sufficient Conditions ◽  
Spatial Models ◽  
Human Perception ◽  
City Block ◽  
Human Perceptions ◽  
Smooth Preferences ◽  
Similarity And Difference ◽  
Necessary And Sufficient

Long-standing results demonstrate that, if policy choices are defined in spaces with more than one dimension, majority-rule equilibrium fails to exist for a general class of smooth preference profiles. This article shows that if agents perceive political similarity and difference in ‘city block’ terms, then the dimension-by-dimension median can be a majority-rule equilibrium even in spaces with an arbitrarily large number of dimensions and it provides necessary and sufficient conditions for the existence of such an equilibrium. This is important because city block preferences accord more closely with empirical research on human perception than do many smooth preferences. It implies that, if empirical research findings on human perceptions of similarity and difference extend also to perceptions ofpoliticalsimilarity and difference, then the possibility of equilibrium under majority rule re-emerges.

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