<p>The identification of the vocation of an unknown heavy-duty
vehicle is valuable to parts manufacturers who may not have otherwise access to
this information on a consistent basis. This study proposes a methodology for
vocation identification that is based on clustering techniques. Two clustering algorithms are considered: K-Means
and Expectation Maximization. These algorithms are used to first construct the
operating profile of each vocation from a set of vehicles with known vocations.
The vocation of an unknown vehicle is then determined using different
assignment methods.</p>
<p> </p>
<p>These methods fall under two main categories: one-versus-all
and one-versus-one. The one-versus-all approach compares an unknown vehicle to
all potential vocations. The one-versus-one approach compares the unknown
vehicle to two vocations at a time in a tournament fashion. Two types of tournaments
are investigated: round-robin and bracket. The accuracy and efficiency of each
of the methods is evaluated using the NREL FleetDNA dataset.</p>
<p> </p>
<p>The study revealed that some of the vocations may have
unique operating profiles and are therefore easily distinguishable from others.
Other vocations, however, can have confounding profiles. This indicates that
different vocations may benefit from profiles with varying number of clusters. Determining
the optimal number of clusters for each vocation can not only improve the
assignment accuracy, but also enhance the computational efficiency of the
application. The optimal number of clusters for each vocation is determined
using both static and dynamic techniques. Static approaches refer to methods
that are completed prior to training and may require multiple iterations. Dynamic
techniques involve clusters being split or removed during training. The results
show that the accuracy of dynamic techniques is comparable to that of static
approaches while benefiting from a reduced computational time.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/13318007 |
| Date | 07 January 2021 |
| Creators | Daniel Patrick Kobold Jr (9719936) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Vocation_Clustering_for_Heavy-Duty_Vehicles/13318007 |
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