Showing papers by "James Bailey published in 2007"

Neural Network Adaptive Output Feedback Control for Intensive Care Unit Sedation and Intraoperative Anesthesia

Abstract: The potential applications of neural adaptive control for pharmacology, in general, and anesthesia and critical care unit medicine, in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery.

An Efficient Technique for Mining Approximately Frequent Substring Patterns

Abstract: Sequential patterns are used to discover knowledge in a wide range of applications. However, in many scenarios pattern quality can be low, due to short lengths or low supports. Furthermore, for dense datasets such as proteins, most of the sequential pattern mining algorithms return a tremendously large number of patterns, which are difficult to process and analyze. However, by relaxing the definition of frequency and allowing some mismatches, it is possible to discover higher quality patterns. We call these patterns Frequent Approximate Substrings or FAS-patterns and we introduce an algorithm called FAS-Miner, to handle the mining task efficiently. The experiments on real-world protein and DNA datasets show that FAS-Miner can discover patterns of much longer lengths and higher supports than standard sequential mining approaches.

Are zero-suppressed binary decision diagrams good for mining frequent patterns in high dimensional datasets?

Abstract: Mining frequent patterns such as frequent itemsets is a core operation in many important data mining tasks, such as in association rule mining. Mining frequent itemsets in high-dimensional datasets is challenging, since the search space is exponential in the number of dimensions and the volume of patterns can be huge. Many of the state-of-the-art techniques rely upon the use of prefix trees (e.g. FP-trees) which allow nodes to be shared among common prefix paths. However, the scalability of such techniques may be limited when handling high dimensional datasets. The purpose of this paper is to analyse the behaviour of mining frequent itemsets when instead of a tree data structure, a canonical directed acyclic graph namely Zero Suppressed Binary Decision Diagram (ZBDD) is used. Due to its compactness and ability to promote node reuse, ZBDD has proven very effective in other areas of computer science, such as boolean SAT solvers. In this paper, we show how ZBDDs can be used to mine frequent itemsets (and their common varieties). We also introduce a weighted variant of ZBDD which allows a more efficient mining algorithm to be developed. We provide an experimental study concentrating on high dimensional biological datasets, and identify indicative situations where a ZBDD technology can be superior over the prefix tree based technique.

Limit Cycle Stability Analysis of a Multi-Compartment Model for a Pressure-Limited Respirator and Lung Mechanics System

Abstract: Acute respiratory failure due to infection, trauma, or major surgery is one of the most common problems encountered in intensive care units and mechanical ventilation is the mainstay of supportive therapy for such patients. In this paper, we develop a general mathematical model for the dynamic behavior of a multi-compartment respiratory system in response to an arbitrary applied inspiratory pressure. Specifically, we use compartmental dynamical system theory to model and analyze the dynamics of a pressure-limited respirator and lung mechanics system, and show that the periodic orbit generated by this system is globally asymptotically stable. Furthermore, we show that the individual compartmental volumes, and hence the total lung volume, converge to steady-state end-inspiratory and end-expiratory values.

Merging dynamic functionality: integration of XML transformations

Abstract: Integration of XML data is an increasingly important problem and many methods have recently been developed. In this talk, we examine the related and more challenging task of how to integrate for XSLT programs. XSLT is a primary language for XML transformation. Program integration can be particularly important for server-side XSLT application, where it is necessary to generate a global XSLT program, that is a combination of some initial XSLT programs and operates over a newly integrated XML database. This global program should inherit as much functionality from the initial XSLT programs as possible, since designing a brand new global XSLT program from scratch could be expensive, slow and error prone, especially when the initial XSLT programs are large and/or complicated. However, it is a challenging task to develop methods to support XSLT integration and a number of difficulties need to be resolved. In this talk, we report on our recent work, which aims to develop a novel framework for semiautomatic integration of XSLT programs.