Mechanical Engineering

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    Artificial Intelligence-Related Research Funding by the U.S. National Science Foundation and the National Natural Science Foundation of China
    (IEEE, 2020-10-06) Abadi, Hamidreza Habibollahi Najaf; He, Zhou; Pecht, Michael
    For the United States and China, artificial intelligence (AI) algorithms, methods, and applications are considered key to a nation's economic competitiveness and security. This paper investigates funding by the U.S. National Science Foundation and National Natural Science Foundation of China from 2010 to 2019, including the key institutions and universities that received AI awards, and the key AI disciplines and applications of focus in the research. Comparisons between the U.S. National Science Foundation and the National Natural Science Foundation of China, including the number of published papers as a result of the awards, are also presented.
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    PRIVACY IN DISTRIBUTED MULTI-AGENT COLLABORATION: CONSENSUS AND OPTIMIZATION
    (2018) Gupta, Nirupam; Chopra, Nikhil; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Distributed multi-agent collaboration is an interactive algorithm that enables agents in a multi-agent system (MAS) to achieve pre-defined collaboration objective in a distributed manner, such as agreeing upon a common value (commonly referred as distributed consensus) or optimizing the aggregate cost of the MAS (commonly referred as distributed optimization). Agents participating in a typical distributed multi-agent collaboration algorithm can lose privacy of their inputs (containing private information) to a passive adversary in two ways. The adversary can learn about agents' inputs either by corrupting some of the agents that are participating in the collaboration algorithm or by eavesdropping the communication links between the agents during an execution of the collaboration algorithm. Privacy of the agents' inputs in the former case is referred as internal privacy, and privacy of the agents' inputs in the latter case is referred as external privacy. This dissertation proposes a protocol for preserving internal privacy in two particular distributed collaborations: distributed average consensus and distributed optimization. It is shown that the proposed protocol can preserve internal privacy of sufficiently well connected honest agents (agents that are not corrupted by the adversary) against adversarial agents (agents that are corrupted by the adversary), without affecting the collaboration objective. This dissertation also investigates a model-based scheme, as an alternative to cryptographic encryptions, for external privacy in distributed collaboration algorithms that can be modeled as linear time-invariant networked control systems. It is demonstrated that the model-based scheme preserves external privacy, without affecting the collaboration objective, if the system parameters of the networked control system, that equivalently models the distributed collaboration algorithm, satisfy certain conditions. Unlike cryptographic encryptions, the model-based scheme does not rely on secure generation and distribution of keys amongst the agents for guaranteeing external privacy.
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    Studying the Efficacy of and Developing Data-Driven Real-Time Clinical Decision Support Systems for Hypotension Detection
    (2016) Yapps, Bryce Anthony; Hahn, Jin-Oh; Reisner, Andrew T; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Critically ill patients admitted into intensive care units are prone to reoccurring episodes of sustained hypotension. Prolonged durations of hypotension are correlated to, and potentially cause, permanent body-wide damage to patients if not properly treated, which may result in death. Currently, typical care for the management of hypotension in the critically ill is reactive and delayed, perhaps due to clinical inertia. The purpose of this study is to describe the current problem that is faced in critical care through a retrospective analysis and introduce candidate models that may be used as clinical informatics systems for preemptive hypotension detection to aid clinicians and nurses providing care in the fast-paced clinical environment. The clinical performance of the models is quantified and the efficacy of implementation of these models is discussed.