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.

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.

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 Implementasi Algoritma TF-IDF Pada Pengukuran Kesamaan Dokumen

2017 ◽  
Vol 1 (1) ◽  
pp. 53 ◽  
Author(s):  
Sri Andayani ◽  
Ady Ryansyah
Keyword(s):  
Similarity Measure ◽  
Processing Time ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Vector Representation ◽  
Document Collection ◽  
Pdf Format ◽  
Word Format

Documents similarity measure is a time consuming problem. The large amount of documents and the large number of pages per document are causing the similarity measures to becomes a complicated and hard job to do manually. In this research, a system that can automatically measuring similarity between documents is built by implementing TF-IDF. Measurements are carried by first creating a vector representation of documents being compared. This vector representation containing the weight of each term in the documents. After that, the similarity value are calculated using cosine similarity. The finished system can carry out comparison of documents in pdf or word format. Document comparison can be done using all the chapters in the report, or just a few selected chapters that are considered significant. Based on experiment, it can be concluded that TF-IDF needs at least three documents to be available in the document collection being processes. The test of correlation shows that for document in pdf format, there is a significant correlation between the amount of characters in the document with the processing time.

scholarly journals Similarity measures for Fermatean fuzzy sets and its applications in group decision-making

2022 ◽  
Vol 11 (2) ◽  
pp. 167-180
Author(s):  
Laxminarayan Sahoo
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Similarity Measure ◽  
Group Decision Making ◽  
Group Decision ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Future Research ◽  
Uncertain Information

The intention of this paper is to propose some similarity measures between Fermatean fuzzy sets (FFSs). Firstly, we propose some score based similarity measures for finding similarity measures of FFSs and also propose score based cosine similarity measures between FFSs. Furthermore, we introduce three newly scored functions for effective uses of Fermatean fuzzy sets and discuss some relevant properties of cosine similarity measure. Fermatean fuzzy sets introduced by Senapati and Yager can manipulate uncertain information more easily in the process of multi-criteria decision making (MCDM) and group decision making. Here, we investigate score based similarity measures of Fermatean fuzzy sets and scout the uses of FFSs in pattern recognition. Based on different types of similarity measures a pattern recognition problem viz. personnel appointment is presented to describe the use of FFSs and its similarity measure as well as scores. The counterfeit results show that the proposed method is more malleable than the existing method(s). Finally, concluding remarks and the scope of future research of the proposed approach are given.

scholarly journals A New Semantic Similarity Measure Based On Ontology for Movie Rate Prediction

2019 ◽  
Vol 8 (3) ◽  
pp. 6756-6762
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Experimental Evaluation ◽  
Pearson Correlation ◽  
Similarity Measures ◽  
Similarity Score ◽  
Cosine Similarity ◽  
Semantic Similarity Measure ◽  
Rate Prediction ◽  
Target User

A recommendation algorithm comprises of two important steps: 1) Predicting rates, and 2) Recommendation. Rate prediction is a cumulative function of the similarity score between two movies and rate history of those movies by other users. There are various methods for rate prediction such as weighted sum method, regression, deviation based etc. All these methods rely on finding similar items to the items previously viewed/rated by target user, with assumption that user tends to have similar rating for similar items. Computing the similarities can be done using various similarity measures such as Euclidian Distance, Cosine Similarity, Adjusted Cosine Similarity, Pearson Correlation, Jaccard Similarity etc. All of these well-known approaches calculate similarity score between two movies using simple rating based data. Hence, such similarity measures could not accurately model rating behavior of user. In this paper, we will show that the accuracy in rate prediction can be enhanced by incorporating ontological domain knowledge in similarity computation. This paper introduces a new ontological semantic similarity measure between two movies. For experimental evaluation, the performance of proposed approach is compared with two existing approaches: 1) Adjusted Cosine Similarity (ACS), and 2) Weighted Slope One (WSO) algorithm, in terms of two performance measures: 1) Execution time and 2) Mean Absolute Error (MAE). The open-source Movielens (ml-1m) dataset is used for experimental evaluation. As our results show, the ontological semantic similarity measure enhances the performance of rate prediction as compared to the existing-well known approaches.

Quantitative Measures for Design by Analogy

2000 ◽  
Author(s):  
Daniel A. McAdams ◽  
Kristin L. Wood
Keyword(s):  
Similarity Measure ◽  
Quantitative Measure ◽  
Layout Design ◽  
Functional Similarity ◽  
Original Design ◽  
Design By Analogy

Abstract In this paper a quantitative measure for design-by-analogy is developed. This measure is based on the functional similarity of products. By using this product similarity measure, designers are able to formalize and quantify design-by-analogy techniques during concept and layout design. The similarity measure and its application is clarified and validated through a case study. The case study is the original design of a pickup winder.

Effects of Dichotomous Lexical Stimuli in Concept Generation

2008 ◽  
Author(s):  
I. Chiu ◽  
L. H. Shu
Keyword(s):  
Engineering Design ◽  
Think Aloud ◽  
Generation Process ◽  
Concept Generation ◽  
Design Support ◽  
Positive Correlation ◽  
Support Tools ◽  
The Relationship

The relationship between language and reasoning motivates us to study the use of language within engineering design. This paper describes our continued investigation of language as stimuli for concept generation. Specifically we investigate dichotomous lexical stimuli that are related to the problem in either a disagreeing, incongruent manner or in an agreeing, congruent manner. This is a follow-up investigation where we extend previous experiments to include both congruent and incongruent stimuli to enable comparison of differences between designer behavior and concepts. A between-subjects think-aloud experiment was performed where participants were presented with a problem and asked to generate concepts to address the problem. Half the participants were provided with incongruent stimuli and the remaining were provided with congruent stimuli. Participants provided with incongruent stimuli used the stimulus words as verbs more often than the participants provided with congruent stimuli. Verbs possess several properties desirable for use as design stimuli including the increased introduction of new lexicalized concepts to the concept generation process. When two independent raters scored the concepts, there was a positive correlation between the raters that concepts developed with incongruent stimuli were more novel. Understanding the effects of different lexical stimulus types on concept generation contributes to the development of design support tools that exploit the relationship between language and reasoning to increase design novelty.

scholarly journals The Intuitionistic Fuzzy Linguistic Cosine Similarity Measure and Its Application in Pattern Recognition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Donghai Liu ◽  
Xiaohong Chen ◽  
Dan Peng
Keyword(s):  
Similarity Measure ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Intuitionistic Fuzzy ◽  
Linguistic Term ◽  
Cosine Similarity Measure ◽  
Linguistic Scale Function ◽  
Cosine Similarity Measures ◽  
Fuzzy Linguistic ◽  
Interval Valued

We propose the cosine similarity measures for intuitionistic fuzzy linguistic sets (IFLSs) and interval-valued intuitionistic fuzzy linguistic sets (IVIFLSs), which are expressed by the linguistic scale function based on the cosine function. Then, the weighted cosine similarity measure and the ordered weighted cosine similarity measure for IFLSs and IVIFLSs are introduced by taking into account the importance of each element, and the properties of the cosine similarity measures are also given. The main advantage of the proposed cosine similarity measures is that the decision-makers can flexibly select the linguistic scale function depending on the actual semantic situation. Finally, we present the application of the cosine similarity measures for intuitionistic fuzzy linguistic term sets and interval-valued intuitionistic fuzzy linguistic term sets to pattern recognition and medical diagnosis, and the existing cosine similarity measures are compared with the proposed cosine similarity measures by the illustrative example.

scholarly journals Cosine Similarity Measure of Complex Fuzzy Sets and Robustness of Complex Fuzzy Connectives

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Wenping Guo ◽  
Lvqing Bi ◽  
Bo Hu ◽  
Songsong Dai
Keyword(s):  
Similarity Measure ◽  
Fuzzy Set ◽  
Fuzzy Inference ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Invariance Properties ◽  
Cosine Similarity Measure ◽  
Cosine Similarity Measures ◽  
Complex Valued ◽  
Fuzzy Connectives

Complex fuzzy set (CFS), as a generalization of fuzzy set (FS), is characterized by complex-valued membership degrees. By considering the complex-valued membership degree as a vector in the complex unit disk, we introduce the cosine similarity measures between CFSs. Then, we investigate some invariance properties of the cosine similarity measure. Finally, the cosine similarity measure is applied to measure the robustness of complex fuzzy connectives and complex fuzzy inference.

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