609 research outputs found

    T-stress determination using thermoelastic stress analysis

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    T-stress and mixed-mode stress intensity factors have been determined experimentally using thermoelastic stress analysis and using a finite element method. Pure mode I, strong mixed-mode I and II, and interacting cracks have been used as the case studies. A new technique has been proposed to identify the crack tip from thermoelastic images. It has also been shown that using three terms of Williams's stress field formulation to determine the T-stress, yields a more accurate solution than using only the first two terms of the expansion

    Matching Subsequences in Trees

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    Given two rooted, labeled trees PP and TT the tree path subsequence problem is to determine which paths in PP are subsequences of which paths in TT. Here a path begins at the root and ends at a leaf. In this paper we propose this problem as a useful query primitive for XML data, and provide new algorithms improving the previously best known time and space bounds.Comment: Minor correction of typos, et

    Fast Searching in Packed Strings

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    Given strings PP and QQ the (exact) string matching problem is to find all positions of substrings in QQ matching PP. The classical Knuth-Morris-Pratt algorithm [SIAM J. Comput., 1977] solves the string matching problem in linear time which is optimal if we can only read one character at the time. However, most strings are stored in a computer in a packed representation with several characters in a single word, giving us the opportunity to read multiple characters simultaneously. In this paper we study the worst-case complexity of string matching on strings given in packed representation. Let mnm \leq n be the lengths PP and QQ, respectively, and let σ\sigma denote the size of the alphabet. On a standard unit-cost word-RAM with logarithmic word size we present an algorithm using time O\left(\frac{n}{\log_\sigma n} + m + \occ\right). Here \occ is the number of occurrences of PP in QQ. For m=o(n)m = o(n) this improves the O(n)O(n) bound of the Knuth-Morris-Pratt algorithm. Furthermore, if m=O(n/logσn)m = O(n/\log_\sigma n) our algorithm is optimal since any algorithm must spend at least \Omega(\frac{(n+m)\log \sigma}{\log n} + \occ) = \Omega(\frac{n}{\log_\sigma n} + \occ) time to read the input and report all occurrences. The result is obtained by a novel automaton construction based on the Knuth-Morris-Pratt algorithm combined with a new compact representation of subautomata allowing an optimal tabulation-based simulation.Comment: To appear in Journal of Discrete Algorithms. Special Issue on CPM 200

    Revisiting the Problem of Searching on a Line

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    We revisit the problem of searching for a target at an unknown location on a line when given upper and lower bounds on the distance D that separates the initial position of the searcher from the target. Prior to this work, only asymptotic bounds were known for the optimal competitive ratio achievable by any search strategy in the worst case. We present the first tight bounds on the exact optimal competitive ratio achievable, parameterized in terms of the given bounds on D, along with an optimal search strategy that achieves this competitive ratio. We prove that this optimal strategy is unique. We characterize the conditions under which an optimal strategy can be computed exactly and, when it cannot, we explain how numerical methods can be used efficiently. In addition, we answer several related open questions, including the maximal reach problem, and we discuss how to generalize these results to m rays, for any m >= 2

    Crack paths under mixed mode loading

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    Long fatigue cracks that initially experience mixed mode displacements usually change direction in response to cyclic elastic stresses. Eventually the cracks tend to orient themselves into a pure mode I condition, but the path that they take can be complex and chaotic. In this paper, we report on recent developments in techniques for tracking the crack path as it grows and evaluating the strength of the mixed mode crack tip stress field

    Structured Text Retrieval Models

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    Structured text retrieval models provide a formal definition or mathematical framework for querying semistructured textual databases. A textual database contains both content and structure. The content is the text itself, and the structure divides the database into separate textual parts and relates those textual parts by some criterion. Often, textual databases can be represented as marked up text, for instance as XML, where the XML elements define the structure on the text content. Retrieval models for textual databases should comprise three parts: 1) a model of the text, 2) a model of the structure, and 3) a query language [4]: The model of the text defines a tokenization into words or other semantic units, as well as stop words, stemming, synonyms, etc. The model of the structure defines parts of the text, typically a contiguous portion of the text called element, region, or segment, which is defined on top of the text modelâ\u80\u99s word tokens. The query language typically defines a number of operators on content and structure such as set operators and operators like â\u80\u9ccontaining â\u80\u9d and â\u80\u9ccontained-by â\u80\u9d to model relations between content and structure, as well as relations between the structural elements themselves. Using such a query language, the (expert) user can for instance formulate requests like â\u80\u9cI want a paragraph discussing formal models near to a table discussing the differences between databases and information retrievalâ\u80\u9d. Here, â\u80\u9cformal models â\u80\u9d and â\u80\u9cdifferences between databases and information retrieval â\u80\u9d should match the content that needs to be retrieved from the database, whereas â\u80\u9cparagraph â\u80\u9d and â\u80\u9ctable â\u80\u9d refer to structural constraints on the units to retrieve. The features, structuring power, and the expressiveness of the query languages of several models for structured text retrieval are discussed below. HISTORICAL BACKGROUND The STAIRS system (Storage and Information Retrieval System), which was developed at IBM already in the late 1950â\u80\u99s allowed querying both content and structure. Much like todayâ\u80\u99s On-line Public Access Catalogues, it wa

    Faster Approximate String Matching for Short Patterns

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    We study the classical approximate string matching problem, that is, given strings PP and QQ and an error threshold kk, find all ending positions of substrings of QQ whose edit distance to PP is at most kk. Let PP and QQ have lengths mm and nn, respectively. On a standard unit-cost word RAM with word size wlognw \geq \log n we present an algorithm using time O(nkmin(log2mlogn,log2mlogww)+n) O(nk \cdot \min(\frac{\log^2 m}{\log n},\frac{\log^2 m\log w}{w}) + n) When PP is short, namely, m=2o(logn)m = 2^{o(\sqrt{\log n})} or m=2o(w/logw)m = 2^{o(\sqrt{w/\log w})} this improves the previously best known time bounds for the problem. The result is achieved using a novel implementation of the Landau-Vishkin algorithm based on tabulation and word-level parallelism.Comment: To appear in Theory of Computing System
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