24 research outputs found
Deep Learning Anomaly Detection Using Edge AI
Deep learning anomaly detection is an evolving field with many real-world applications. As more and more devices continue to be added to the Internet of Things (IoT), there is an increasing desire to make use of the additional computational capacity to run demanding tasks. The increase in devices and amounts of data flooding in have led to a greater need for security and outlier detection. Motivated by those facts, this thesis studies the potential of creating a distributed anomaly detection framework. While there have been vast amounts of research into deep anomaly detection, there has been no research into building such a model in a distributed context. In this work, we propose an implementation of a distributed anomaly detection system using the TensorFlow library in Python and three Nvidia Jetson AGX Xavier deep learning modules. The key objective of this study is to determine if it is practical to create a distributed anomaly detection model without a significant loss of accuracy on classification. We then present an analysis of the performance of the distributed system in terms of accuracy and runtime and compare it to a similar system designed to run on a single device. The results of this study show that it is possible to build a distributed anomaly detection system without a significant loss of accuracy using TensorFlow, but the overall runtime increases for these trials. This proves that it is possible to distribute anomaly detection to edge devices without sacrificing accuracy, and the runtime can be improved with further research
The Macroecology of Sustainability
Global consumption rates of vital resources suggest that we have surpassed the capacity of the Earth to sustain current levels, much less future trajectories of growth in human population and economy
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Final report. Technology innovation for global change: The role of assessment, R and D, and regulation
Through the research carried out under this grant, we have made considerable progress in addressing our fundamental research question: How and under what conditions can government stimulate radical technological innovation? More specifically, we have analyzed three pathways through which government may influence the decisions by firms to invest in radical technological innovation: technological opportunism (supply-push policies), regulatory responsiveness (demand-pull policies) and anticipatory action (assessments and information policy). We have produced several written documents, as well as made several presentations of our work. We are now working on a book based on this research, which we will have to a publisher in 2002. We are also pursuing other opportunities for dissemination of the results, including both presentations and articles in the academic and policy press