4 citations · 13 across the 8 of their papers we have counts for
8 papers
PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data
Wang Zhou, Levente J. Klein, Siyuan Lu
An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The framework simplifies the de…
Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Rui Zhang, Conrad Albrecht, Wei Zhang +4
Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster respo…
Event Clustering & Event Series Characterization on Expected Frequency
Conrad M Albrecht, Marcus Freitag, Theodore G van Kessel +2
We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency , we introduce an $\mathca…
A clustered Gaussian process model for computer experiments
Chih-Li Sung, Benjamin Haaland, Youngdeok Hwang +1
A Gaussian process has been one of the important approaches for emulating computer simulations. However, the stationarity assumption for a Gaussian process and the intractability f…
Statistical Modeling for Spatio-Temporal Data from Stochastic Convection-Diffusion Processes
Xiao Liu, Kyongmin Yeo, Siyuan Lu
This paper proposes a physical-statistical modeling approach for spatio-temporal data arising from a class of stochastic convection-diffusion processes. Such processes are widely f…
Synthesizing simulation and field data of solar irradiance
Furong Sun, Robert B. Gramacy, Benjamin Haaland +2
Predicting the intensity and amount of sunlight as a function of location and time is an essential component in identifying promising locations for economical solar farming. Althou…
An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending
Cong Feng, Mingjian Cui, Bri-Mathias Hodge +3
Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and…
Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images
Sergiy Zhuk, Tigran Tchrakian, Albert Akhriev +2
This paper describes a new algorithm for solar energy forecasting from a sequence of Cloud Optical Depth (COD) images. The algorithm is based on the following simple observation: t…