Thesis (Ph. D.)--Naval Postgraduate School, 1986. / Includes bibliographical references (p. 121-126).

Thesis--Delft. / N75-23073. Summary in Dutch. Also published as Delft Hydraulics Laboratory Publication no. 131 and as Delft Hydraulics Laboratory Report M918, part 2. Photocopy. Springfield, Va., National Technical Information Service, 1975. Bibliography: leaves 240-246.

Thesis (Ph. D.)--Naval Postgraduate School, 1993. / Includes abstract. Includes bibliographical references.

Inaug.-diss.--Leipzig. / Vita.

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Includes bibliographical references. Available also in an electronic version.

Thesis (Ph. D.)--University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 77-80)

Digital applications and devices record data over time to enable the users and managers to monitor their activity. Errors occur in data, including the time series data, for various reasons including software system failures and human errors. The problem of identifying errors, also referred to as anomaly detection, in time series data is a well studied topic by the data management and systems researchers. Such data are often recorded in dynamic environments where a change in the standard or the recording hardware can result in different and novel patterns arising in the data. Such novel patterns are caused by what is referred to as concept drifts. Concept drift occurs when there is a pattern change in the statistical properties of the data, e.g. the distribution of the data, over time. The problem of identifying anomalies in time series data recorded and stored in dynamic environments has not been extensively studied. In this study, we focus on this problem. We propose and implement a unified framework that is able to identify drifts in univariate time series data and incorporate information gained from the data to train a learning model that is able to detect anomalies in unseen univariate time series data. / Thesis / Master of Science (MSc)

Off-target deposition of herbicides to non-target plant species has been extensively studied and well documented over time. Off-target movement can often be detrimental to plant growth and yield. The geography of Mississippi is favorable for off-target herbicide deposition due to many crops existing in close proximity to differing crops such as soybeans (Glycine max L.) and rice (Oryza sativa L.). Therefore, research was conducted in each of three locations in Mississippi in 2016 and 2017 to determine if enhanced ALS-tolerant soybeans could be used to mitigate off-target deposition of rice herbicides penoxsulam and bispyribac-sodium. Results indicate that this technology (specifically BOLT soybean) can be utilized if herbicide residue is below 1/16X of the full labeled rate and is not deposited at V3 or early reproductive growth stages (R1-R4).

With the development of transgenic crops resistant to auxin herbicides will come an increase in the use of these herbicides for weed control. This new technology will greatly aid growers that have glyphosate-resistant weeds such as Palmer amaranth (Amaranthus palmeri S. Wats) in their fields. A challenge will be with farmers that choose not to use this new technology and have susceptible crops on their farm or adjoining farms. Auxin herbicides such as 2, 4-D and dicamba are well-documented as being very injurious to susceptible crops, even at low doses. It is for this reason that research is being conducted to compare the differences in the amount of particle drift with hooded boom sprayers compared to open boom sprayers. Along with this research, various droplet sizes will also be analyzed and compared between the two sprayers.

[pt] Em ambientes dinâmicos, os modelos de dados tendem a ter desempenho insatisfatório uma vez que a distribuição subjacente dos dados muda. Este fenômeno é conhecido como Concept Drift. Em relação a este tema, muito esforço tem sido direcionado ao desenvolvimento de métodos capazes de detectar tais fenômenos com antecedência suficiente para que os modelos possam se adaptar. No entanto, explicar o que levou ao drift e entender suas consequências ao modelo têm sido pouco explorado pela academia. Tais informações podem mudar completamente a forma como adaptamos os modelos. Esta dissertação apresenta uma nova abordagem, chamada Detector de Drift Interpretável, que vai além da identificação de desvios nos dados. Ele aproveita a estrutura das árvores de decisão para prover um entendimento completo de um drift, ou seja, suas principais causas, as regiões afetadas do modelo e sua severidade. / [en] In a dynamic environment, models tend to perform poorly once the underlying distribution shifts. This phenomenon is known as Concept Drift. In the last decade, considerable research effort has been directed towards developing methods capable of detecting such phenomena early enough so that models can adapt. However, not so much consideration is given to explain the drift, and such information can completely change the handling and understanding of the underlying cause. This dissertation presents a novel approach, called Interpretable Drift Detector, that goes beyond identifying drifts in data. It harnesses decision trees’ structure to provide a thorough understanding of a drift, i.e., its principal causes, the affected regions of a tree model, and its severity. Moreover, besides all information it provides, our method also outperforms benchmark drift detection methods in terms of falsepositive rates and true-positive rates across several different datasets available in the literature.