2927089
New results in decentralized adaptive backstepping stabilization of nonlinear interconnected systems
In this paper, the results of stabilizing a large scale nonlinear systems with uncertain dynamic interactions and unmodelled dynamics depending on both subsystem inputs and outputs in [9] and [10] are extended to highly nonlinear systems. Certain modifications on standard adaptive backstepping controllers are proposed to compensate for the effects of interactions from other subsystems in designing local controllers.
20468001
Adaptive backstepping control of uncertain systems with unknown input time-delay
In this paper, we establish the robustness of adaptive controllers designed using the standard backstepping technique with respect to unmodeled dynamics involving unknown input time delay. While noting that some results on robust stabilization of non-minimum phase systems using the backstepping technique are available, we realize that the standard adaptive backstepping technique has only been shown applicable to unknown minimum phase systems. Another significance of our result is to enable the class of systems stablizable by adaptive backstepping controllers to cross the boundary of minimum phase systems, since systems with input time delay belong to non-minimum phase systems. Moreover, the L"2 and L"~ norms of the system output are also established as functions of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters.
12878597
Pattern Mining for Information Extraction Using Lexical, Syntactic and Semantic Information: Preliminary Results
A method is being developed to mine a text corpus for candidate linguistic patterns for information extraction. The candidate patterns can be used to improve the quality of extraction patterns constructed by a pseudosupervised learning method--an automated method in which the system is provided with a high quality seed pattern or clue, which is used to generate a training set automatically. The study is carried out in the context of developing a system to extract disease-treatment information from medical abstracts retrieved from the Medline database. In an earlier study, the Apriori algorithm had been used to mine a sample of sentences containing a disease concept and a drug concept, to identify frequently occurring word patterns to see if these patterns could be used to identify treatment relations in text. Word patterns and statistical association measures alone were found to be insufficient for generating good extraction patterns, and need to be combined with syntactic and semantic constraints. In this study, we explore the use of syntactic, semantic and lexical constraints to improve the quality of extraction patterns.
90043
A new approach to design robust backstepping controller for MIMO nonlinear systems with input unmodeled dynamics
In this paper, a new scheme inspired by the idea of decentralized control methodology is proposed to design output feedback controllers for a class of MIMO nonlinear systems with input unmodeled dynamics. With dynamic normalization and backstepping approach, the destabilizing effects of unmodeled dynamics depending on inputs are compensated and global uniform boundedness of all the signals in the closed loop system is established for sufficiently weak input unmodeled dynamics.
13324501
Local Structure Comparison of Proteins
Abstract Protein local structure comparison aims to recognize structural similarities between parts of proteins. It is an active topic in bioinformatics research, integrating computer science concepts in computational geometry and graph theory with empirical observations and physical principles from biochemistry. It has important biological applications, including protein function prediction. In this chapter, we provide an introduction to the protein local structure comparison problem including challenges and applications. Current approaches to the problem are reviewed. Particular consideration is given to the discovery of local structure common to a group of related proteins. We present a new algorithm for this problem that uses a graph-based representation of protein structure and finds recurring subgraphs among a group of protein graphs.
5116928
Multi-guarded safe zone: An effective technique to monitor moving circular range queries
Given a positive value r, a circular range query returns the objects that lie within the distance r of the query location. In this paper, we study the circular range queries that continuously change their locations. We present an efficient and effective technique to monitor such moving range queries by utilising the concept of a safe zone. The safe zone of a query is the area with a property that while the query remains inside it, the results of the query remain unchanged. Hence, the query does not need to be re-evaluated unless it leaves the safe zone. The shape of the safe zone is defined by the so-called guard objects. The cost of checking whether a query lies in the safe zone takes k distance computations, where k is the number of the guard objects. Our contributions are as follows. 1) We propose a technique based on powerful pruning rules and a unique access order which efficiently computes the safe zone and minimizes the I/O cost. 2) To show the effectiveness of the safe zone, we theoretically evaluate the probability that a query leaves the safe zone within one time unit and the expected distance a query moves before it leaves the safe zone. Additionally, for the queries that have diameter of the safe zone less than its expected value multiplied by a constant, we also give an upper bound on the expected number of guard objects. This upper bound turns out to be a constant, that is, it does not depend either on the radius r of the query or the density of the objects. The theoretical analysis is verified by extensive experiments. 3) Our thorough experimental study demonstrates that our proposed approach is close to optimal and is an order of magnitude faster than a naïve algorithm.
12965386
A Note on the Relationships Between the Generalized Bernoulli and Euler Polynomials
1. IntroductionIn many contexts, matrix representation of a particular counting se-quence is considered, and such a representation provides a powerful com-putational tool for deriving identities and explicit formulas related to thesequence, see, for example [3, 7].Recently, Cheon [2] rederived several known properties and relationshipsinvolving the classical Bernoulli polynomials
1194502
Energy-Aware Adaptive Watermarking for Real-Time Image Delivery in Wireless Sensor Networks
A secure image transmission mechanism is necessary when malicious intruders intend to access and modify content delivery over wireless networks. To assure data integrity, authentication is required for multimedia data delivered over wireless image sensor networks. Watermarking technique is an effective vehicle to assert and assure the image data authentications. There have been recent works reported on watermarking, but few with the consideration of energy cost in terms of the data communication and processing, which is a key constraint to many embedded systems and wireless sensor networks. Most watermarking systems only target at minimizing watermarked image distortion and increasing robustness at the source coding site for lossy image processing. The watermarked image distortion caused by error-prone wireless environments during transmission has not been fully considered. In this paper, an innovative energy-aware adaptive watermarking scheme for realtime image delivery is proposed in wireless multimedia sensor networks. This new scheme allocates network resource to protect the watermarked image transmission while embedding watermark coding redundancies into the images. Dynamic watermark thresholds are applied to be adaptive to the network condition (packet loss ratio) and the inter-frame correlation is exploited to reduce processing delay. The simulation results show that the proposed adaptive watermark system can achieve considerable energy efficiency and assure the data integrity.
15381526
A general framework for adaptive and online detection of web attacks
Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we propose a novel general framework for adaptive and online detection of web attacks. The general framework can be based on any online clustering methods. A detection model based on the framework is able to learn online and deal with "concept drift" in web audit data streams. Str-DBSCAN that we extended DBSCAN to streaming data as well as StrAP are both used to validate the framework. The detection model based on the framework automatically labels the web audit data and adapts to normal behavior changes while identifies attacks through dynamical clustering of the streaming data. A very large size of real HTTP Log data collected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness.