基于RFID软件的屏蔽:在不同环境条件下进一步方法的实现

Mattia Neroni, A. Rizzi, Giovanni Romagnoli, Mirco Rosa
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引用次数: 2

摘要

基于软件屏蔽(SBS)在我们之前题为“基于软件屏蔽的不同商店区域实时库存计数:时尚零售的可行性分析”的工作中获得了令人鼓舞的结果,在本文中,我们通过探索某些周围方面的影响,如(i)隔墙,(ii)标签密度,进行了更详细的探讨。我们还提出了替代算法,而不是在以前的工作中分析的逻辑回归。,启发式算法,神经网络(NN),卷积神经网络(CNN),并引入参考标签来增强这些方法。结果表明,逻辑回归和CNN是最准确的模型。它们之间的选择可能取决于应用程序上下文:第一种不太可靠,但更容易实现,而第二种是更复杂和完整的机器学习模型。在环境条件下,RFID标签的密度和配置是影响最大的方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RFID software-based shielding: Implementation of further approaches under varying surrounding conditions
Given the promising results obtained by the Software-Based Shielding (SBS) in our previous work entitled “Software-based shielding for real-time inventory count in different store areas: a feasibility analysis in fashion retail”, in this paper, we go into more detail by exploring the effect of certain surrounding aspects, such as (i) the partition wall, and (ii) the density of tags. We also propose alternative algorithms other than logistic regression analysed in the previous work –i.e., a heuristic algorithm, a Neural Network (NN), a Convolutional Neural Network (CNN), and the introduction of reference tags to enhance these approaches. The results show that the logistic regression and the CNN are the most accurate models. The choice between them might depend on the application context: the first is less reliable but much simpler to implement, while the second is a more complex and complete machine learning model. Concerning the environmental conditions, the density and disposition of RFID tags appear the aspects with the greatest impact.
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