An Improved Cobweb Grey Target Decision-Making Model for Multiple Kansei Images Based on Variable Weight Theory

Abstract

The use of constant weights reduces the accuracy of cognitive evaluation results, and the current design decision-making methods ignore the relationships between Kansei images. To solve these problems, an improved cobweb grey target decision-making method for multiple Kansei images based on variable weight theory is proposed. We take a hand-held electric drill as an example for exploration. First, according to the initial weight relationships of Kansei images, variable weight theory is used to identify the Kansei image variable weights of samples, and the variable weight comprehensive evaluation results for each sample are obtained. Then, based on the correlation and angle of the Kansei images, a cobweb diagram is drawn to represent the Kansei image relationship of each sample. Combined with the cobweb grey target decision-making model for multiple Kansei images, an improved cobweb grey target decision-making method for multiple Kansei images is constructed. The decision coefficients of 10 samples are obtained as 0.0567, 0, 0.0205, 0.0478, 0.0155, 0.0272, 0.0292, 0.0402, 0.0155 and 0.0470. Through the comparison and ranking of the decision coefficients, sample 2 is determined to be the relatively optimal design reference sample. Finally, the constructed model is compared with the cobweb grey target decision-making model for multiple Kansei images and the technique for order preference by similarity to ideal solution (TOPSIS). The difference coefficients of the three methods are obtained, namely, 0.5627, 0.4957 and 0.3613. The results show that the difference coefficient of the proposed method is the largest, and it can reflect the decision-making thinking of designers and improve the discrimination among the decision-making results to a certain extent.

Maneuvering Decision Making Based on Cloud Modeling Algorithm for UAV Evasion–Pursuit Game

“…The prerequisite for a UAV to make aerial pursuit maneuvering decisions is for it to be able to perform an assessment of the initial pursuit situation. The first step of such assessment is to obtain information on the situation [8]. Then, the UAV obtains the pursuit game state information through sensing sensors and the information interaction network; then, it obtains the pursuit game state information space, which is composed of the pursuit game state information set after filtering.…”

“…Yet another study [7] optimized control using multilevel influence diagrams for rolling control. One study [8] proposed to apply an adaptive pseudo-parallel genetic algorithm to solve the maneuvering decisionmaking problem. However, there is a big gap between the decision-making methods in the above studies and actual aerial pursuit, and it is difficult to perform situational assessment in high-speed changing aerial pursuit situations.…”