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New Similarity Measures between Polylines with Applications to Morphing and Polygon Sweeping

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TLDR
Two new related metrics, the geodesic width and the link width, for measuring the “distance” between two nonintersecting polylines in the plane are introduced and used to solve two problems: Compute a continuous transformation that “morphs” one polyline into another polyline and construct a corresponding morphing strategy.
Abstract
We introduce two new related metrics, the geodesic width and the link width, for measuring the "distance" between two nonintersecting polylines in the plane. If the two polylines have n vertices in total, we present algorithms to compute the geodesic width of the two polylines in O(n 2 log n) time using O(n 2) space and the link width in O(n 3 log n) time using O(n 2) working space where n is the total number of edges of the polylines. Our computation of these metrics relies on two closely related combinatorial strutures: the shortest-path diagram and the link diagram of a simple polygon. The shortest-path (resp., link) diagram encodes the Euclidean (resp., link) shortest path distance between all pairs of points on the boundary of the polygon. We use these algorithms to solve two problems:

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Citations
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Journal ArticleDOI

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

TL;DR: Experimental results demonstrate the effectiveness of the proposed hybrid prognostics approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.
Journal Article

Triangulating a simple polygon in linear time

TL;DR: A deterministic algorithm for triangulating a simple polygon in linear time is given, using the polygon-cutting theorem and the planar separator theorem, whose role is essential in the discovery of new diagonals.
Proceedings ArticleDOI

Exact algorithms for partial curve matching via the Fréchet distance

TL;DR: This paper presents the first exact algorithm that runs in polynomial time to compute fδ(P, Q), the partial Frechet similarity between P and Q, under the L1 and L∞ norms.
Book ChapterDOI

Fréchet distance for curves, revisited

TL;DR: In this article, the problem of computing the Frechet distance between polygonal curves was revisited, focusing on the discrete distance between vertices, and the complexity of the algorithm was shown to be quadratic in the worst case and lower in practice.
Journal ArticleDOI

Pitch angle control for a small-scale Darrieus vertical axis wind turbine with straight blades (H-Type VAWT)

TL;DR: In this paper, an intelligent pitch angle controller using a multilayer perceptron artificial neural network (MLP-ANN) was proposed for the Darrieus vertical axis wind turbine (H-type VAWT).
References
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Journal ArticleDOI

Comparing images using the Hausdorff distance

TL;DR: Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented and it is shown that the method extends naturally to the problem of comparing a portion of a model against an image.
Journal ArticleDOI

Computing the fréchet distance between two polygonal curves

TL;DR: As a measure for the resemblance of curves in arbitrary dimensions the authors consider the so-called Frechet-distance, which is compatible with parametrizations of the curves, and for polygonal chains P and Q consisting of p and q edges an algorithm of runtime O(pq log( pq))) measuring the Frechet Distance.
Journal ArticleDOI

Applying Parallel Computation Algorithms in the Design of Serial Algorithms

TL;DR: It is pointed out that analyses of parallelism in computational problems have practical implications even when multi-processor machines are not available, and a unified framework for cases like this is presented.
Journal Article

Triangulating a simple polygon in linear time

TL;DR: A deterministic algorithm for triangulating a simple polygon in linear time is given, using the polygon-cutting theorem and the planar separator theorem, whose role is essential in the discovery of new diagonals.
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