工业工程 ›› 2022, Vol. 25 ›› Issue (6): 120-125.doi: 10.3969/j.issn.1007-7375.2022.06.014

• 专题论述 • 上一篇    下一篇

深度学习技术下的消费者动机设计模型研究

孙斐, 陆定邦, 赵雨淋, 孙悦   

  1. 广东工业大学 艺术与设计学院,广东 广州 510006
  • 收稿日期:2022-05-25 发布日期:2022-12-23
  • 作者简介:孙斐(1974—),女,山东省人,博士研究生,主要研究方向为艺术设计理论、产业发展研究
  • 基金资助:
    广东省科技计划海外名师资助项目(2020A1414010314))

Research on the Motivation Design Model Based on Deep Learning Technology

SUN Fei, LUH Dingbang, ZHAO Yulin, SUN Yue   

  1. School of Art & Design, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-05-25 Published:2022-12-23

摘要: 提出基于深度学习技术的一种快速有效的产品分类方法MdmNet,将人的动机思维方式嵌入机器学习形成新产品设计的技术方法。运用消费者信息推理方法和加权融合模块,在基准数据集Cars上进行实验,根据生产方的产品特性提出基于多神经网络特征融合的动机需求分类,其思想在于使用MDM与多神经网络融合来增加对图像库中数据的特征提取。此方法可显著提高新产品分类的性能。与对比模型比较,其性能在准确率等指标上面均有体现。在基准数据集Cars上,当设置参数Wa为0.1,参数Wb 为0.9,MdmNet输出的Top-1、Top-5及Top-10正确率相对于设计师视角的传统算法可分别有所提高。MdmNet适用于数据量少、时间短的此类系统对象。

关键词: 动机设计模型, 深度学习, 产品分类, 新产品开发, 目标消费者

Abstract: A fast and effective product classification method based on deep learning technology, MdmNet, is proposed, which embeds human's motivation thinking mode into machine learning to form a new product design method. By using the consumer information reasoning method and the weighted fusion module, an experiment is carried out on the benchmark data set Cars. According to the product characteristics of the producer, a classification of motive requirements based on multi-neural network feature fusion is proposed. This idea is to use MDM and multi-neural network fusion to increase the feature extraction of image database data. This method can significantly improve the performance of new product classification, compared with the comparison model, its performance is reflected in the accuracy and other indicators. On the benchmark Dataset Cars, when set to 0.1 and 0.9, MdmNet outputs Top-1, Top-5, and Top-10 correct rates are higher than those of traditional algorithms from the designer's point of view. MdmNet is suitable for such system objects with small amount of data and short time.

Key words: motivation design model, deep learning, product classification, new product development, target consumers

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