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Deep learning for automatic usability evaluations based on images: A case study of the usability heuristics of thermostats

机译：基于图像的自动可用性评估的深度学习：恒温器可用性启发式案例研究

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摘要

Thermostats are designed for increasing requirements on indoor thermal comfort. Nevertheless, they are critical devices for saving energy in buildings and households. However, when thermostats do not accomplish the usability requirements, the end-users do not save energy. Then, when a thermostat is designed or validated, one of the leading problems that must be tackled is the usability evaluation. Generally, the evaluation is based on usability heuristics that are done by experts and designers and involve a very complicated cycling process in which usability experts need to be included in the complete usability evaluation. On the other hand, there are several proposals for generating an automatic usability analysis that can be used by designers or end-users. However, they are limited by the methodologies that are implemented in the evaluation because usability evaluations necessitate a large amount of data abstraction, and the amount of processed information is enormous; As an alternative, Artificial Intelligence can help to solve this problem, especially machine learning techniques with deep learning capabilities that can reach a high level of data abstraction with a significant amount of information and implement an automatic usability evaluation based on images. Convolutional networks that are included in deep learning can classify complex problems, attain highly accurate results. This paper proposes to train a convolutional network with standard usability heuristics for evaluating usability, which is an easy method for evaluating usability in thermostats, based on images. The proposed automatic method gives excellent results for evaluating usability heuristics in the heuristic assigned. This paper provides a complete methodology, using deep learning, for automatically evaluating the usability heuristics of thermostats. (C) 2017 Elsevier B.V. All rights reserved.
机译：温控器旨在满足对室内热舒适性日益增长的要求。然而，它们是节省建筑物和家庭能源的关键设备。但是，当恒温器不能满足可用性要求时，最终用户将无法节省能源。然后，在设计或验证恒温器时，可用性评估是必须解决的主要问题之一。通常，评估是基于专家和设计人员进行的可用性启发式评估，涉及非常复杂的循环过程，其中可用性专家需要包含在完整的可用性评估中。另一方面，有一些建议可用于生成自动可用性分析，供设计人员或最终用户使用。但是，它们受到评估中实施的方法的限制，因为可用性评估需要大量的数据抽象，并且处理的信息量也很大。作为替代方案，人工智能可以帮助解决此问题，尤其是具有深度学习功能的机器学习技术，可以通过大量信息达到高水平的数据抽象，并基于图像实现自动可用性评估。深度学习中包含的卷积网络可以对复杂问题进行分类，从而获得高度准确的结果。本文提出了一种训练具有标准可用性启发式方法的卷积网络以评估可用性，这是一种基于图像的评估恒温器可用性的简便方法。所提出的自动方法为评估启发式分配中的可用性启发式方法提供了极好的结果。本文提供了使用深度学习的完整方法，可用于自动评估恒温器的可用性启发法。 （C）2017 Elsevier B.V.保留所有权利。

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  • 来源
    《Energy and Buildings》 |2018年第3期|111-120|共10页
  • 作者

    Ponce Pedro; Balderas David; Peffer Therese; Molina Arturo;

  • 作者单位

    Tecnol Monterrey, Calle Puente 222, Mexico City 14380, DF, Mexico;

    Tecnol Monterrey, Calle Puente 222, Mexico City 14380, DF, Mexico;

    Univ Calif Berkeley, Calif Inst Energy & Environm, 2087 Addison St,2nd Floor, Berkeley, CA 94708 USA;

    Tecnol Monterrey, Calle Puente 222, Mexico City 14380, DF, Mexico;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Automatic usability evaluation; Deep learning; Thermostats; Images;

    机译：自动可用性评估;深度学习;温控器;图像;

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