Enhancing the Prediction of Artificial Lighting Control Behavior Using Virtual Reality (VR): A Pilot Study
Enhancing the Prediction of Artificial Lighting Control Behavior Using Virtual Reality (VR): A Pilot Study
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使用虚拟现实 (VR) 增强人工照明控制行为的预测：试点研究
DOI：
10.1061/9780784481301.022
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Yimin Zhu
中科院分区：
文献类型：
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作者：
Chanachok Chokwitthaya;Robert DiBiano;Sanaz Saeidi;S. Mukhopadhyay;Yimin Zhu
虚拟现实（VR）在许多研究领域都获得了声誉。在人类行为学研究领域中，它的应用越来越广泛。对于建筑物中的居住者行为研究，VR能够让研究人员研究不存在或未来建筑物中的居住者行为，以了解和改进建筑功能，满足居住者的满意度。然而，在VR中进行研究有一些限制，特别是样本量。一般来说，VR实验生成的样本数量比来自传感器的数据更少真实的环境（原位）。由于样本量较小，有时VR数据不足以准确进行进一步分析。为了克服这个问题，作者利用隐马尔可夫模型（HMM）Baum-Welch算法的潜力来统计学习VR数据的序列和结果，并重新填充足够好的合成数据。对重新填充数据的结果进行了评估，并与VR数据进行了比较，结果表明HMM精确地估计并生成了额外的VR数据。
Virtual reality (VR) has gained reputation in many research areas. It has been increasingly employed in the area of human behavior study. For the study of occupant behavior in buildings, VR is capable of allowing researchers to study occupant behavior in non-existing or future buildings to understand and refine building functions to fulfill occupants’ satisfaction. However, doing research in VR has some limitations specially the sample size. In general, VR experiment generates a fewer number of samples compared to data from sensors a real environment (in-situ). Having a small sample size, sometimes VR data are insufficient to accurately perform further analyses. To overcome this problem, the authors utilize the potential of the Hidden Morkov Model (HMM) Baum-Welch algorithm to statistically learn the sequences and outcomes of VR data and repopulate synthetic data that are good enough for applications. The outcome of repopulated data was evaluated and compared with VR data and showed that HMM precisely estimated and generated the additional VR data.