自适应传感器协同预测人体移动

Paul Baumann
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引用次数: 3

摘要

我的论文主要是关于人类流动性的预测。我对深入了解影响人类移动性预测算法性能的因素很感兴趣。我工作的主要贡献是:分析影响移动性预测器性能的不同因素,设计和开发一种自适应方法来检测和识别用户的相关地点,以及估计用户的瞬时可预测性。后一项贡献旨在使应用程序场景能够决定在多大程度上信任所提供的预测和移动性数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive sensor cooperation for predicting human mobility
My thesis focuses on the prediction of human mobility. I am interested in gaining a deeper understanding of the factors that influence the performance of human mobility prediction algorithms. The main contributions of my work are: the analyses of different factors that influence the performance of mobility predictors, the design and development of a self-adaptive approach for detecting and recognizing users' relevant places, and estimating users' momentary predictability. The latter contribution aims to enable the possibility for the application scenarios to decide how much to trust the provided predictions and mobility data.
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