Base type selection of product service system based on convolutional neural network

机译：基于卷积神经网络的产品服务系统基础型选择

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摘要

Customer preferences for products reflect the product and service requirements of the Product Service System (PSS). Picking out and associating the proper product units and service units are the key point of configuring a product service system to meet the requirements of individual customer. Some intelligent algorithms are used in product selection, as well as in the design of products and services. However, existing selection methods are difficult to convert customer requirements into base types of PSS in highly complex mapping spaces. In order to realize the rational allocation of product service systems and provide crucial information for company staffs in product design department, we using convolutional neural networks to study the nexus of customer demand attributes and product service system base types in this paper. Through training the convolutional neural network to obtain the complex nonlinear mapping relations between customer demand attributes and product service system base types, and product service can be selected. The proposed method has been verified in the base type selection of CNC machine tools product service system.

机译：客户偏好的产品偏好反映了产品服务系统（PSS）的产品和服务要求。挑选和关联适当的产品单位和服务单元是配置产品服务系统以满足个人客户要求的关键点。一些智能算法用于产品选择，以及产品和服务的设计。但是，在高度复杂的映射空间中，现有的选择方法难以将客户需求转换为基础类型的PSS。为了实现产品服务系统的合理配置，为产品设计部门提供公司员工的关键信息，我们使用卷积神经网络研究了本文的客户需求属性的Nexus和产品服务系统基础类型。通过培训卷积神经网络来获得客户需求属性和产品服务系统基础类型的复杂非线性映射关系，可以选择产品服务。所提出的方法已经在CNC机床产品服务系统的基础类型选择中验证。

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《CIRP Conference on Industrial Product-Service Systems》|2019年|830p|共5页
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作者
Yuening Zhou; Wei Liu; Nana Chai; Beibei Fan; Zaifang Zhang;
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中图分类 TB4-53;
关键词
Product service system; Base types selection; Convolutional neural network;

机译：产品服务系统;基类型选择;卷积神经网络;

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1. Base type selection of product service system based on convolutional neural network [J] . Yuening Zhou, Wei Liu, Nana Chai, Procedia CIRP . 2019,第2期

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2. Convolutional neural networks for relevance feedback in content based image retrieval A Content based image retrieval system that exploits convolutional neural networks both for feature extraction and for relevance feedback [J] . Lorenzo Putzu, Luca Piras, Giorgio Giacinto Multimedia Tools and Applications . 2020,第37a38期

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3. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system [J] . Liu Tao, Abd-Elrahman Amr, Morton Jon, GIScience & remote sensing . 2018,第2期

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4. Base type selection of product service system based on convolutional neural network [C] . Yuening Zhou, Wei Liu, Nana Chai, CIRP Conference on Industrial Product-Service Systems . 2019

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5. Genetic Algorithm Based Convolutional Neural Network for Few Shot Learning in Disease Type Prediction on RNA-Seq Data [D] . Subramanian, Kowshik Sharan. 2020

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6. Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks [O] . Jia-xin Cai, Ranxu Zhong, Yan Li 2015

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7. Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks [O] . Jia-xin Cai, Ranxu Zhong, Yan Li 2019

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Base type selection of product service system based on convolutional neural network

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