論文

The overall shape features that emerge from combinations of shape elements, such as "complexity" and "order", are important in designing shapes of industrial products. However, controlling the features of shapes is difficult and depends on the experience and intuition of designers. Among these features, "complexity" is said to have an influence on the "beauty" and "preference" of shapes. This research proposed a Gaussian curvature entropy as a "complexity" index of a curved surface shape. The proposed index is calculated based on Gaussian curvature, which is obtained by the sampling and quantization of a curved surface shape and validated by the sensory evaluation experiment while using two types of sample shapes. The result indicates the correspondence of the index to perceived "complexity" (the determination coefficient is greater than 0.8). Additionally, this research constructed a shape generation method that was based on the index as a car design supporting apparatus, in which the designers can refer many shapes generated by controlling "complexity". The applicability of the proposed method was confirmed by the experiment while using the generated shapes.

In this study, we defined the order of closed planar curves as a characteristic to reduce the human effort for recognizing the shape. We propose an order index counting mirror and rotational symmetries, along with a calculation method using the autocorrelation function of curvature functions. To confirm the effectiveness of the proposed index, we conducted the experiments using two sample groups of closed curved shapes: the shapes used in Birkhoff's study and silhouettes of humidifiers. The coefficient of determination between the proposed index and sensory evaluation values of these samples were greater than 0.70, supporting the applicability of the proposed index. Additionally, this study calculated the Eysenck's beauty index as the product of the proposed order index and conventional complexity index, and confirmed the correlation between the index and sensory evaluation values of beauty against same closed shape samples. This suggests the possibility to evaluate an industrial design requirement quantitatively, and contributes to develop the automatic shape generation in product design.

In recent years, with the popularization of 3D-CAD, various curved surface shapes have been used in the styling of industrial products. However, evaluation and control of shape features are done in trial and error based on the experience and intuition of the designer, causing the inefficiency of styling design. Therefore, this study aims to support the styling design by quantifying cognitive characteristics in curved surface shape. First, this research proposed total absolute curvature to quantify “complexity”, which is one of the shape features related to “beauty”, and described its geometric meaning as the total area of the Gauss map on the curved surface shape. Next, the tangible calculation method is shown by applying it to the curved surface shape represented by a triangular polygon mesh. Then, sensory evaluation experiments were carried out using three types of shapes generated by extrusion, rotation, and emergent shape generation system to confirm the applicability of the proposed quantification method. Finally, the result revealed that the logarithmic approximation between the proposed index and human perceived “complexity”.

Purpose: The purpose of this paper is to propose the selection guide of the multi-objective optimization methods for the ergonomic design. The proposed guide enables designers to select an appropriate method for optimizing the human characteristics composed of the engineering characteristics (e.g. users’ height, weight and muscular strength) and the physiological characteristics (e.g. brain wave, pulse-beat and myoelectric signal) in the trade-off relationships. Design/methodology/approach: This paper focuses on the types of the relationships between engineering or physiological characteristics and their psychological characteristics (e.g. comfort and usability). Using these relationships and the characteristics of the multi-objective optimization methods, this paper classified them and constructed a flow chart for selecting them. Findings: This paper applied the proposed selection guide to a geometric design of a comfortable seat and confirmed its applicability. The selected multi-objective optimization method optimized the contact area of seat back (engineering characteristic associated with the comfortable fit of the seat backrest) and the blood flow volume (physiological characteristic associated with the numbness in the lower limb) on the basis of each design intent such as a deep-vein thrombosis after long flight. Originality/value: Because of the lack of the selection guide of the multi-objective optimization methods, an inappropriate method is often applied in industry. This paper proposed the selection guide applied in the ergonomic design having a lot of the multi-objective optimization problem.

An appropriate level of arousal induces positive emotions, and a high arousal potential may provoke negative emotions. To explain the effect of arousal on emotional valence, we propose a novel mathematical framework of arousal potential variations in the dual process of human cognition: automatic and controlled. A suitable mathematical formulation to explain the emotions in the dual process is still absent. Our model associates free energy with arousal potential and its variations to explain emotional valence. Decreasing and increasing free energy consequently induce positive and negative emotions, respectively. We formalize a transition from the automatic to the controlled process in the dual process as a change of Bayesian prior. Further, we model emotional valence using free energy increase (FI) when one tries changing one's Bayesian prior and its reduction (FR) when one succeeds in recognizing the same stimuli with a changed prior and define three emotions: “interest,” “confusion,” and “boredom” using the variations. The results of our mathematical analysis comparing various Gaussian model parameters reveals the following: (1) prediction error (PR) increases FR (representing “interest”) when the first prior variance is greater than the second prior variance, (2) PR decreases FR when the first prior variance is less than the second prior variance, and (3) the distance between priors’ means always increases FR. We also discuss the association of the outcomes with emotions in the controlled process. The proposed mathematical model provides a general framework for predicting and controlling emotional valence in the dual process that varies with viewpoint and stimuli, as well as for understanding the contradictions in the effects of arousal on the valence.