Macro-informatics of cognition and its application for design

Ujiie, Yoshiki; Matsuoka, Yoh

doi:10.1016/j.aei.2008.10.004

Cited by 8 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the points should be set at adequate intervals after considering the influence of noise. Incidentally, we have conducted a study on the removal of this influence [19]. Δρ N−2…”

Section: Curvature Variationmentioning

confidence: 99%

Curvature Entropy for Curved Profile Generation

Ujiie

Katô

Sato

et al. 2012

Entropy

Self Cite

View full text Add to dashboard Cite

Abstract:In a curved surface design, the overall shape features that emerge from combinations of shape elements are important. However, controlling the features of the overall shape in curved profiles is difficult using conventional microscopic shape information such as dimension. Herein two types of macroscopic shape information, curvature entropy and quadrature curvature entropy, quantitatively represent the features of the overall shape. The curvature entropy is calculated by the curvature distribution, and represents the complexity of a shape (one of the overall shape features). The quadrature curvature entropy is an improvement of the curvature entropy by introducing a Markov process to evaluate the continuity of a curvature and to approximate human cognition of the shape. Additionally, a shape generation method using a genetic algorithm as a calculator and the entropy as a shape generation index is presented. Finally, the applicability of the proposed method is demonstrated using the side view of an automobile as a design example.

show abstract

Section: Curvature Variationmentioning

confidence: 99%

Curvature Entropy for Curved Profile Generation

Ujiie

Katô

Sato

et al. 2012

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…The variation of the instantaneous distance is comparatively lower than the radius of curvature, which shows that the geometry of the shape can be easily controlled using the instantaneous distance compared to the radius of curvature. It is worth mentioning that there are several key elements for shape designing, and mathematical formulation of the radius of curvature or instantaneous distance can be one effective approach [74].…”

Section: Quantification Based On Radius Of Curvaturementioning

confidence: 99%

Geometric Modeling and 3D Printing Using Recursively Generated Point Cloud

Tashi

Ullah

Kubo

2019

MCA

View full text Add to dashboard Cite

Previous studies have reported that a recursive process called the point cloud creation algorithm (PCA) that generates a point cloud is useful for reverse engineering a planner shape. This study elucidates the characteristics of the parameters used in the recursive process as well as its ability in geometric modeling and 3D printing of 3D shapes. In the recursive process, three constants (center point, initial distance, and initial angle) and two variables (instantaneous distance and instantaneous rotational angle) are employed. The shape-modeling characteristics of the constants and variables are elucidated using some commonly used shapes (straight-line, circle, ellipses, spiral, astroid, S-shape, and leaf-shape). In addition, the shape-modeling capability of the recursive process as a whole is quantified using two parameters called the radius of curvature and aesthetic value. Moreover, an illustrative example that shows the efficacy of the recursive process in virtual and physical prototyping of a relatively complex 3D object is presented. The results show that reverse engineering performed by the recursive-process-created point cloud is free from computational complexity compared to reverse engineering performed by the 3D-scanner-created point cloud. As such, the outcomes of this study enrich the field of reverse engineering.

show abstract

“…This paper also assumes that the induction field is the proper explanation of the human vision and applies the concept to people's recognition of product shape. There were many efforts [3,4] to quantify the shapes of the products as some mathematical methods. However, since the users' preference of product shapes are very product-oriented, quantification of the shape cannot be the direct answer to design product shapes preferred by users.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Shape Design and Its Correlation with User’s Preference

Mishima

2019

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

Quantitative evaluation of shape is one of the remaining problems in product design. Since product shape is an important feature affects users’ intention to purchase, knowing characteristics of preferred designs is helpful in designing attractive products.When a person watches some shape, a potential field named “induction field” is aroused in the brain, corresponding to the strength of the stimulation of the optic nerve. The induction field has a close relation between the strength of the impression. Value of the induction field in a certain point can be calculated based on the distance of the point from the contour line of the figure and the strength of the pixels expressed by 256 levels gray scale. Overall tendency of the induction field can be expressed by two values that are surface integral of the shape along the plane (PE), and difference of the maximum and minimum values of the strength of the induction field (DI). PE corresponds to the strength of the overall impression and DI shows the sharpness of the shape. The paper used smartphone as a case study and investigated the correlation between users’ preference with PE and DI. The result showed negative correlations. The fact suggested that relatively calm and ambiguous shape is preferred in case of smartphone design. Through this study, it was concluded that the induction field is helpful in quantifying shape designs and determining preferred designs among several design plans. This is a significant step in reviewing product design processes by engineering approaches.

show abstract

Macro-informatics of cognition and its application for design

Cited by 8 publications

References 19 publications

Curvature Entropy for Curved Profile Generation

Curvature Entropy for Curved Profile Generation

Geometric Modeling and 3D Printing Using Recursively Generated Point Cloud

Quantification of Shape Design and Its Correlation with User’s Preference

Contact Info

Product

Resources

About