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Lari Melander

Big Data Fusion in Forest Harvesters

DSII Graduate
Research is focusing on developing a new industrial internet concept in forest harvesting domain for utilizing better the data available from various sources. The main idea is to aggregate collected Big Data, such as harvester’s CAN bus data and other multi-dimensional information used in wood procurement, using modern machine learning and data fusion methods.

Research includes also development of new sensor systems for estimating the key properties of the forest environment. Main interest is to create a system that would efficiently offer organized positioned data for optimizing wood procurement.
Studied
2016
2019
Industry partner
Academic supervisor
Jarmo Hämäläinen
Risto Ritala

Research is focusing on developing a new industrial internet concept in forest harvesting domain for utilizing better the data available from various sources. The main idea is to aggregate collected Big Data, such as harvester’s CAN bus data and other multi-dimensional information used in wood procurement, using modern machine learning and data fusion methods.

Research includes also development of new sensor systems for estimating the key properties of the forest environment. Main interest is to create a system that would efficiently offer organized positioned data for optimizing wood procurement.

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