A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Design Tools for Dynamic, Data-Driven, Stream Mining Systems(2015) Sudusinghe, Kishan Palintha; Bhattacharyya, Shuvra S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The proliferation of sensing devices and cost- and energy-efficient embedded processors has contributed to an increasing interest in adaptive stream mining (ASM) systems. In this class of signal processing systems, knowledge is extracted from data streams in real-time as the data arrives, rather than in a store-now, process later fashion. The evolution of machine learning methods in many application areas has contributed to demands for efficient and accurate information extraction from streams of data arriving at distributed, mobile, and heterogeneous processing nodes. To enhance accuracy, and meet the stringent constraints in which they must be deployed, it is important for ASM systems to be effective in adapting knowledge extraction approaches and processing configurations based on data characteristics and operational conditions. In this thesis, we address these challenges in design and implementation of ASM systems. We develop systematic methods and supporting design tools for ASM systems that integrate (1) foundations of dataflow modeling for high level signal processing system design, and (2) the paradigm on Dynamic Data-Driven Application Systems (DDDAS). More specifically, the contributions of this thesis can be broadly categorized in to three major directions: 1. We develop a new design framework that systematically applies dataflow methodologies for high level signal processing system design, and adaptive stream mining based on dynamic topologies of classifiers. In particular, we introduce a new design environment, called the lightweight dataflow for dynamic data driven application systems environment (LiD4E). LiD4E provides formal semantics, rooted in dataflow principles, for design and implementation of a broad class of stream mining topologies. Using this novel application of dataflow methods, LiD4E facilitates the efficient and reliable mapping and adaptation of classifier topologies into implementations on embedded platforms. 2. We introduce new design methods for data-driven digital signal processing (DSP) systems that are targeted to resource- and energy-constrained embedded environments, such as unmanned areal vehicles (UAVs), mobile communication platforms, and wireless sensor networks. We develop a design and implementation framework for multi-mode, data driven embedded signal processing systems, where application modes with complementary trade-offs are selected, configured, executed, and switched dynamically, in a data-driven manner. We demonstrate the utility of our proposed new design methods on an energy-constrained, multi-mode face detection application. 3. We introduce new methods for multiobjective, system-level optimization that have been incorporated into the LiD4E design tool described previously. More specifically, we develop new methods for integrated modeling and optimization of real-time stream mining constraints, multidimensional stream mining performance (e.g., precision and recall), and energy efficiency. Using a design methodology centered on data-driven control of and coordination between alternative dataflow subsystems for stream mining (classification modes), we develop systematic methods for exploring complex, multidimensional design spaces associated with dynamic stream mining systems, and deriving sets of Pareto-optimal system configurations that can be switched among based on data characteristics and operating constraints.Item A COMPARISON BETWEEN DATA-DRIVEN AND PHYSICS OF FAILURE PHM APPROACHES FOR SOLDER JOINT FATIGUE(2010) Jaai, Rubyca; Pecht, Michael G; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Prognostics and systems health management technology is an enabling discipline of technologies and methods with the potential of solving reliability problems that have been manifested due to complexities in design, manufacturing, environmental and operational use conditions, and maintenance. Over the past decade, research has been conducted in PHM to provide benefits such as advance warning of failures, enable forecasted maintenance, improve system qualification, extend system life, and diagnose intermittent failures that can lead to field failure returns exhibiting no-fault-found symptoms. While there are various methods to perform prognostics, including model-based and data-driven methods, these methods have some key disadvantages. This thesis presents a fusion prognostics approach, which combines or ―fuses together‖ the model based and data-driven approaches, to enable increasingly better estimates of remaining useful life. A case study using an electronics system to illustrate a step by step implementation of the fusion approach is also presented. The various benefits of the fusion approach and suggestions for future work are included.