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We used a 50Hz sampling frequency to collect tri-axle acceleration from the cows. For the traditional Machine learning approach, we segmented the data to calculate features, selected the important features, and applied machine learning algorithms for classification. We compared the performance of various models and found a robust model with relatively low computation and high accuracy. For the deep learning approach, we designed an end-to-end trainable Convolutional Neural Networks (CNN) to predict activities for given segments, applied distillation, and quantization to reduce model size. In addition to the fixed window size approach, we used CNN to predict dense labels that each data point has an individual label, inspired by semantic segmentation. In this way, we could have a more precise measurement for the composition of activities. Summarily, physically monitoring the well-being of crowded animals is labor-intensive, so we proposed a solution for timely and efficient measuring of cattle’s daily activities using wearable sensors and machine learning models. / M.S. / Animal agriculture has intensified over the past several decades, and animals are managed increasingly as large groups. This group-based management has significantly increased productivity. However, animals are often located remotely on large expanses of pasture, which makes continuous monitoring of daily activities to assess animal health and well-being labor-intensive and challenging [37]. Remote monitoring of animal activities with wireless sensor nodes integrated with machine learning algorithms is a promising solution. The machine learning models will predict the activities of given accelerometer segments, and the pre-dicted result will be uploaded to the cloud. The challenges would be the limitation in power consumption and computation. To propose a precise measurement of individual cattle in the herd, we experimented with several different types of machine learning methods with different advantages and drawbacks in performance and efficiency.

We introduce recurrent collective classification (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more directly minimize the loss function. In our experiments, this direct loss minimization translates to improved accuracy and robustness on real network data. We demonstrate the robustness of RCC in settings where local classification is very noisy, settings that are particularly challenging for ICA. As a new way to train generative models, generative adversarial networks (GANs) have achieved considerable success in image generation, and this framework has also recently been applied to data with graph structures. We identify the drawbacks of existing deep frameworks for generating graphs, and we propose labeled-graph generative adversarial networks (LGGAN) to train deep generative models for graph-structured data with node labels. We test the approach on various types of graph datasets, such as collections of citation networks and protein graphs. Experiment results show that our model can generate diverse labeled graphs that match the structural characteristics of the training data and outperforms all baselines in terms of quality, generality, and scalability. To further evaluate the quality of the generated graphs, we apply it to a downstream task for graph classification, and the results show that LGGAN can better capture the important aspects of the graph structure. / Doctor of Philosophy / Graphs are one of the most important and powerful data structures for conveying the complex and correlated information among data points. In this research, we aim to provide more robust and accurate models for some graph specific tasks, such as collective classification and graph generation, by designing deep learning models to learn better task-specific representations for graphs. First, we studied the collective classification problem in graphs and proposed recurrent collective classification, a variant of the iterative classification algorithm that is more robust to situations where predictions are noisy or inaccurate. Then we studied the problem of graph generation using deep generative models. We first proposed a deep generative model using the GAN framework that generates labeled graphs. Then in order to support more applications and also get more control over the generated graphs, we extended the problem of graph generation to conditional graph generation which can then be applied to various applications for modeling graph evolution and transformation.

This research develops a methodology for enhancing the performance of a precision computer numerically controlled (CNC) machine tool. The ability to precisely maintain the desired relative position between the cutting tool and the workpiece along the cutting trajectory has a major impact on the dimensional accuracy of the finished part. It is important to ensure that the workpiece geometry satisfies tolerances before removing it from the machine tool. Traditional manufacturing procedures do not catch bad parts until the post-process inspection stage, when the part has already been removed from the setup. Subsequent attempts at re-machining require that the workpiece be re-fixtured back on the machine which often introduces more error into the process. The objective of this research is to develop a methodology that integrates pre-process calibration and process-intermittent gaging to enhance the ability of a two-axis vertical turning center to cut a circular arc. The developed methodology is straightforward and integrates the usage of commercially available instrumentation such as the ball bar and on-machine probe for error identification, prediction, and compensation.

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Thesis (Ph.D.)--Tufts University, 2004. / Advisers: Haris Doumanidis; Anil Saigal; Nikos Fourligkas. Submitted to the Dept. of Mechanical Engineering. Includes bibliographical references (leaves 72-73). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 102-108). Also available in electronic version.

Thesis (M.S.)--University of Wisconsin--Madison, 1983. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 113-114).

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Fathianathan, Mervyn; Committee Member: Melkote, Shreyes; Committee Member: Paredis, Chris

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2007. / Includes bibliographical references (p. 97-100).