1,538 research outputs found
Euclidean TSP with few inner points in linear space
Given a set of points in the Euclidean plane, such that just points
are strictly inside the convex hull of the whole set, we want to find the
shortest tour visiting every point. The fastest known algorithm for the version
when is significantly smaller than , i.e., when there are just few inner
points, works in time [Knauer and Spillner,
WG 2006], but also requires space of order . The best
linear space algorithm takes time [Deineko, Hoffmann, Okamoto,
Woeginer, Oper. Res. Lett. 34(1), 106-110]. We construct a linear space
time algorithm. The new insight is extending the
known divide-and-conquer method based on planar separators with a
matching-based argument to shrink the instance in every recursive call. This
argument also shows that the problem admits a quadratic bikernel.Comment: under submissio
Disconnected Skeleton: Shape at its Absolute Scale
We present a new skeletal representation along with a matching framework to
address the deformable shape recognition problem. The disconnectedness arises
as a result of excessive regularization that we use to describe a shape at an
attainably coarse scale. Our motivation is to rely on the stable properties of
the shape instead of inaccurately measured secondary details. The new
representation does not suffer from the common instability problems of
traditional connected skeletons, and the matching process gives quite
successful results on a diverse database of 2D shapes. An important difference
of our approach from the conventional use of the skeleton is that we replace
the local coordinate frame with a global Euclidean frame supported by
additional mechanisms to handle articulations and local boundary deformations.
As a result, we can produce descriptions that are sensitive to any combination
of changes in scale, position, orientation and articulation, as well as
invariant ones.Comment: The work excluding {\S}V and {\S}VI has first appeared in 2005 ICCV:
Aslan, C., Tari, S.: An Axis-Based Representation for Recognition. In
ICCV(2005) 1339- 1346.; Aslan, C., : Disconnected Skeletons for Shape
Recognition. Masters thesis, Department of Computer Engineering, Middle East
Technical University, May 200
Parameterized Complexity Analysis of Randomized Search Heuristics
This chapter compiles a number of results that apply the theory of
parameterized algorithmics to the running-time analysis of randomized search
heuristics such as evolutionary algorithms. The parameterized approach
articulates the running time of algorithms solving combinatorial problems in
finer detail than traditional approaches from classical complexity theory. We
outline the main results and proof techniques for a collection of randomized
search heuristics tasked to solve NP-hard combinatorial optimization problems
such as finding a minimum vertex cover in a graph, finding a maximum leaf
spanning tree in a graph, and the traveling salesperson problem.Comment: This is a preliminary version of a chapter in the book "Theory of
Evolutionary Computation: Recent Developments in Discrete Optimization",
edited by Benjamin Doerr and Frank Neumann, published by Springe
2์ฐจ์ ๊ท ์ผ ์ปค๋ฒ๋ฆฌ์ง ๊ฒฝ๋ก ๊ณํ์ ์ํ ํจ์จ์ ์๊ณ ๋ฆฌ์ฆ
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ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณ๊ณตํ๋ถ, 2020. 8. ๋ฐ์ข
์ฐ.Coverage path planning (CPP) is widely used in numerous robotic applications. With progressively complex and extensive applications of CPP, automating the planning process has become increasingly important. This thesis proposes an efficient CPP algorithm based on a random sampling scheme for spray painting applications. We have improved on the conventional CPP algorithm by alternately iterating the path generation and node sampling steps. This method can reduce the computational time by reducing the number of sampled nodes. We also suggest a new distance metric called upstream distance to generate reasonable path following given vector field. This induces the path to be aligned with a desired direction. Additionally, one of the machine learning techniques, support vector regression (SVR) is utilized to identify the paint distribution model. This method accurately predict the paint distribution model as a function of the painting parameters. We demonstrate our algorithm on several types of analytic surfaces and compare the results with those of conventional methods. Experiments are conducted to assess the performance of our approach compared to the traditional method.๋ณธ ๋
ผ๋ฌธ์์๋ 2์ฐจ์ ํ๋ฉด์ ๊ท ์ผ ์ปค๋ฒ๋ฆฌ์ง ๊ฒฝ๋ก ๊ณํ์ ์ค๋ช
ํ๊ณ ์ด๋ฅผ ํจ์จ์ ์ผ๋ก ํธ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ฐ๋ฆฌ๋ ๊ฒฝ๋ก ๊ณํ ๋ฌธ์ ๋ฅผ ๋ ๊ฐ์ ํ์ ๋ฌธ์ ๋ก ๋ถ๋ฆฌํ์ฌ ๊ฐ๊ฐ ํธ๋ ๊ธฐ์กด์ ๋ฐฉ์์ ๋ณด์ํ์ฌ ๋ ๊ฐ์ ํ์๋ฌธ์ ๋ฅผ ํ ๋ฒ์ ํ๋ฉด์ ๊ณ์ฐ์๊ฐ์ ์ค์ด๋ ๋ฐฉ๋ฒ์ ์ ์ํ์๋ค. ๋ํ ๊ฒฝ์ฐ์ ๋ฐ๋ผ ์ฃผ์ด์ง ๋ฒกํฐ ํ๋์ ๋๋ํ ๋ฐฉํฅ์ผ๋ก ๊ฒฝ๋ก๊ฐ ์์ฑ๋ ํ์๊ฐ ์๋๋ฐ ์ด๋ฅผ ์ํด ๊ฑฐ์ค๋ฆ ๊ฑฐ๋ฆฌ(upstream distance)์ ๊ฐ๋
์ ์ ์ํ์์ผ๋ฉฐ ์ฌํ ์ธํ์ ๋ฌธ์ (Traveling Salesman Problem)๋ฅผ ํ ๋ ์ด๋ฅผ ์ ์ฉํ์๋ค. ์ฐ๋ฆฌ๋ ์ฐจ๋ ๋์ฅ ์์ฉ๋ถ์ผ์ ๊ท ์ผ ์ปค๋ฒ๋ฆฌ์ง ๊ฒฝ๋ก ๊ณํ๋ฒ์ ์ ์ฉํ์์ผ๋ฉฐ ๋์ฅ ์์คํ
์ ๊ณ ๋ คํ์ฌ ๊ท ์ผํ ํ์ธํธ ๋๊ป๋ฅผ ๋ณด์ฅํ๋ ๋ฐฉ๋ฒ์ ๊ฐ์ด ์ ์ํ์๋ค. ๋ค ๊ฐ์ง ํ์
์ 2์ฐจ์ ๊ณก๋ฉด์ ๋ํด ์๋ฎฌ๋ ์ด์
์ ์งํํ์์ผ๋ฉฐ ๊ธฐ์กด์ ๋ฐฉ๋ฒ์ ๋นํด ๋ ์ ์ ๊ณ์ฐ์๊ฐ์ ์๊ตฌํ๋ฉด์๋ ํฉ๋ฆฌ์ ์ธ ์์ค์ ํ์ธํธ ๊ท ์ผ๋๋ฅผ ๋ฌ์ฑํจ์ ๊ฒ์ฆํ์๋ค.1 Introduction 1
1.1 Related Work 3
1.2 Contribution of Our Work 7
1.3 Organization of This Thesis 8
2 Preliminary Background 9
2.1 Elementary Differential Geometry of Surfaces in R3 10
2.1.1 Representation of Surfaces 10
2.1.2 Normal Curvature 10
2.1.3 Shape Operator 12
2.2 Traveling Salesman Problem 15
2.2.1 Definition 15
2.2.2 Variations of the TSP 17
2.2.3 Approximation Algorithm for TSP 19
2.3 Path Planning on Vector Fields 20
2.3.1 Randomized Path Planning 20
2.3.2 Upstream Criterion 20
2.4 Support Vector Regression 21
2.4.1 Single-Output SVR 21
2.4.2 Dual Problem of SVR 23
2.4.3 Kernel for Nonlinear System 25
2.4.4 Multi-Output SVR 26
3 Methods 29
3.1 Efficient Coverage Path Planning on Vector Fields 29
3.1.1 Efficient Node Sampling 31
3.1.2 Divide and Conquer Strategy 32
3.1.3 Upstream Distance 34
3.2 Uniform Coverage Path Planning in Spray Painting Applications 35
3.2.1 Minimum Curvature Direction 35
3.2.2 Learning Paint Deposition Model 36
4 Results 38
4.1 Experimental Setup 38
4.2 Simulation Result 41
4.3 Discussion 41
5 Conclusion 45
Bibliography 47
๊ตญ๋ฌธ์ด๋ก 52Maste
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