Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning
المؤلفون المشاركون
Yoon, Yourim
Kim, Yong-Hyuk
Kim, Na-Young
Im, Hyo-Hyuc
Choi, Reno K. Y.
Ha, Ji-Hun
Sim, Sangjin
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-07-25
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones.
The method proposed in this study conducts clustering and regression analysis with time domain classification.
Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km2, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user’s mobility, prior to the expectation-maximization (EM) clustering.
Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR).
The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering.
For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP.
It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Kim, Yong-Hyuk& Ha, Ji-Hun& Yoon, Yourim& Kim, Na-Young& Im, Hyo-Hyuc& Sim, Sangjin…[et al.]. 2016. Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099820
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Kim, Yong-Hyuk…[et al.]. Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1099820
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Kim, Yong-Hyuk& Ha, Ji-Hun& Yoon, Yourim& Kim, Na-Young& Im, Hyo-Hyuc& Sim, Sangjin…[et al.]. Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099820
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1099820
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر