Thesis (M.S.)--University of Wisconsin--Madison, 1996. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaf 69).

No description available.

Thesis (Ph. D.)--West Virginia University, 1999. / Title from document title page. Document formatted into pages; contains xi, 238 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 231-238).

Background: As the requirements for the modern-day comforts are raising from day to day, the great evolution in the field of lawn-mowers is recorded. This evolution made companies produce a fleet of lawn-mowers(commercial, house-hold) for different kinds of usages. Despite the great evolution and market in this field, to the best of our knowledge, no effort was made to understand customer usage by analysis of real-time usage of lawn-mowers. This research made an attempt to analyse the real-time usage of lawn-mowers using techniques like machine learning. Objectives: The main objective of the thesis work is to understand customer usage of lawn-mowers by analysing the real-time usage data using machine learning algorithms. To achieve this, we first review several studies to identify what are the different ways(scenarios) and how to understand customer usage from those scenarios. After discussing these scenarios with the stakeholders at the company, we evaluated a suitable scenario in the case of lawn-mowers. Finally, we achieved the primary objective by clustering the usage of lawn-mowers by analysing the real-world time-series data from the Controller Area Network(CAN) bus based on the driving patterns. Methods: A Systematic literature review(SLR) is performed to identify the different ways to understand customer usage by analysing the usage data using machine learning algorithms and SLR is also performed to gain detailed knowledge about different machine learning algorithms to apply to the real-world data. Finally, an experiment is performed to apply the machine learning algorithms on the CAN bus time-series data to evaluate the usage of lawn-mowers into various clusters and the experiment also involves the comparison and selection of different machine learning algorithms applied to the data. Results: As a result of SLR, we achieved different scenarios to understand customer behaviours by analysing the usage data. After formulating the best suitable scenario for lawn-mowers, SLR also suggested the best suitable machine learning algorithms to be applied to the data for the scenario. Upon applying the machine learning algorithms after making necessary pre-processing steps, we achieved the clusters of usage of lawn-mowers for every driving pattern selected. We also achieved the clusters for different features of driving patterns that indicate the various characteristics like a change of intensity in the usage, rate of change in the usage, etc. Conclusions: This study identified customer behaviours based on their usage data by clustering the usage data. Moreover, clustering the CAN bus time-series data from lawn-mowers gave fresh insights to study human behaviours and interaction with the lawn-mowers. The formulated clusters have a great scope to classify and develop the individual strategy for each cluster formulated. Further, clusters can also be useful for identifying the outlying behaviour of users and/or individual components.