A. James Clark School of Engineering

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Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells
(MDPI, 2019-07-29) Diao, Weiping; Saxena, Saurabh; Han, Bongtae; Pecht, Michael
Lithium-ion batteries typically exhibit a transition to a more rapid capacity fade trend when subjected to extended charge–discharge cycles and storage conditions. The identification of the knee point can be valuable to identify the more severe degradation trend, and to provide guidance when scheduling battery replacements and planning secondary uses of the battery. However, a concise and repeatable determination of a knee point has not been documented. This paper provides a definition of the knee point which can be used as a degradation metric, and develops an algorithm to identify it. The algorithm is implemented on various data cases, and the results indicate that the approach provides repeatable knee point identification.
Algorithmic Composition as a Model of Creativity
(1996-12) Jacob, Bruce
““’There are two distinct types of creativity: the flash out of the blue (inspiration? genius?), and the process of incremental revisions (hard work). Not only are we years away from modeling the former, we do not even begin to understand it. The latter is algorithmic in nature and has been modeled in many systems both musical and non-musical. Algorithmic composition is as old as music composition. It is often considered a cheat, a way out when the composer needs material and/or inspiration. It can also be thought of as a compositional tool that simply makes the composer's work go faster. This article makes a case for algorithmic composition as such a tool. The 'hard work' type of creativity often involves trying many different combinations against each other and choosing one over others. This iterative task seems natural to be expressed as a computer algorithm. The implementation issues can be reduced to two components: how to understand one's own creative process well enough to reproduce it as an algorithm, and how to program a computer to differentiate between 'good' and 'bad' music. The philosophical issues reduce to the question who or what is responsible for the music produced?
Design Space Re-Engineering for Power Minimization in Modern Embedded Systems
(2006-06-01) Yuan, Lin; Qu, Gang; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Power minimization is a critical challenge for modern embedded system design. Recently, due to the rapid increase of system's complexity and the power density, there is a growing need for power control techniques at various design levels. Meanwhile, due to technology scaling, leakage power has become a significant part of power dissipation in the CMOS circuits and new techniques are needed to reduce leakage power. As a result, many new power minimization techniques have been proposed such as voltage island, gate sizing, multiple supply and threshold voltage, power gating and input vector control, etc. These design options further enlarge the design space and make it prohibitively expensive to explore for the most energy efficient design solution. Consequently, heuristic algorithms and randomized algorithms are frequently used to explore the design space, seeking sub-optimal solutions to meet the time-to-market requirements. These algorithms are based on the idea of truncating the design space and restricting the search in a subset of the original design space. While this approach can effectively reduce the runtime of searching, it may also exclude high-quality design solutions and cause design quality degradation. When the solution to one problem is used as the base for another problem, such solution quality degradation will accumulate. In modern electronics system design, when several such algorithms are used in series to solve problems in different design levels, the final solution can be far off the optimal one. In my Ph.D. work, I develop a {\em re-engineering} methodology to facilitate exploring the design space of power efficient embedded systems design. The direct goal is to enhance the performance of existing low power techniques. The methodology is based on the idea that design quality can be improved via iterative ``re-shaping'' the design space based on the ``bad'' structure in the obtained design solutions; the searching run-time can be reduced by the guidance from previous exploration. This approach can be described in three phases: (1) apply the existing techniques to obtain a sub-optimal solution; (2) analyze the solution and expand the design space accordingly; and (3) re-apply the technique to re-explore the enlarged design space. We apply this methodology at different levels of embedded system design to minimize power: (i) switching power reduction in sequential logic synthesis; (ii) gate-level static leakage current reduction; (iii) dual threshold voltage CMOS circuits design; and (iv) system-level energy-efficient detection scheme for wireless sensor networks. An extensive amount of experiments have been conducted and the results have shown that this methodology can effectively enhance the power efficiency of the existing embedded system design flows with very little overhead.
Biologically-inspired optimal control
(2005-11-14) Shao, Cheng; Hristu, Dimitrios; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Inspired by the collective activities of ant colonies, and by their ability to gradually optimize their foraging trails, this dissertation investigates the cooperative solution of a broad class of trajectory optimization problems with various types of boundary conditions. A set of cooperative control algorithms are presented and proved to converge to an optimal solution by iteratively optimizing an initially feasible trajectory/control pair. The proposed algorithms organize a group of identical control systems by imposing a type of pair-wise interaction known as "local pursuit". The bio-inspired approach taken here requires only short-range, limited interactions between group members, avoids the need for a "global map" of the environment in which the group evolves, and solves an optimal control problem in "small" pieces, in a manner which is made precise. These features enable the application of the proposed algorithms in numerical optimization, leading to an increase of the permitting size of problems that can be solved, as well as a decrease of numerical errors incurred in ill-conditioned problems. The algorithms' effectiveness is illustrated in a series of simulations and laboratory experiments

A. James Clark School of Engineering

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