Wyniki wyszukiwania - BazTech

1

Tree structures for adaptive control space in 3D meshing

Jurczyk T., Głut B.

Computer Science

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2016

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Vol. 17 (4)

541--560

EN

The article presents a comparison of several octree- and kd-tree-based struc- tures used for the construction of control space in the process of anisotropic mesh generation and adaptation. The adaptive control space utilized by the authors supervises the construction of meshes by providing the required metric information regarding the desired shape and size of elements of the mesh at each point of the modeled domain. Comparative tests of these auxiliary struc- tures were carried out based on different versions of the tree structures with respect to computational and memory complexity as well as the quality of the generated mesh. Analysis of the results shows that kd-trees (not present in the meshing literature in this role) offer good performance and may become a reasonable alternative to octree structures.

2

Lokalizacja robota mobilnego w czasie rzeczywistym na podstawie danych ze skanera laserowego

Sankowski D., Jeżewski S., Łaski M.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

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2010

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T. 14, z. 3/1

501-513

PL

W niniejszej publikacji przedstawiono opis algorytmów wykorzystanych do lokalizacji robota mobilnego w czasie rzeczywistym. Główny nacisk położono na optymalizacje algorytmu poszukiwania najbliższego sąsiada, wykorzystywanego przez metody lokalizacji. Autorzy skupią się na najbardziej powszechnym sposobie lokalizacji, jakim jest ICP (Iterative Closest Points).

EN

This papers presents description of algorithms used in real time mobile robot localization procedure. Main problem is to optimize nearest neighbour algorithm used by localization method using characteristics of data received from laser rangefinder. The Iterative Closest Point (ICP) algorithm has been used, as it is a classical method in localization problem.

3

Some Symmetry Based Classifiers

Saha S., Bandyopadhyay S.

Fundamenta Informaticae

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2009

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Vol. 90, nr 1-2

107-123

EN

In this paper, a novel point symmetry based pattern classifier (PSC) is proposed. A recently developed point symmetry based distance is utilized to determine the amount of point symmetry of a particular test pattern with respect to a class prototype. Kd-tree based nearest neighbor search is used for reducing the complexity of point symmetry distance computation. The proposed point symmetry based classifier is well-suited for classifying data sets having point symmetric classes, irrespective of any convexity, overlap or size. In order to classify data sets having line symmetry property, a line symmetry based classifier (LSC) along the lines of PSC is thereafter proposed in this paper. To measure the total amount of line symmetry of a particular point in a class, a new definition of line symmetry based distance is also provided. Proposed LSC preserves the advantages of PSC. The performance of PSC and LSC are demonstrated in classifying fourteen artificial and real-life data sets of varying complexities. For the purpose of comparison, k-NN classifier and the well-known support vector machine (SVM) based classifiers are executed on the data sets used here for the experiments. Statistical analysis, ANOVA, is also performed to compare the performance of these classification techniques.

4

Semi-GAPS: A Semi-supervised Clustering Method Using Point Symmetry

Saha S., Bandyopadhyay S.

Fundamenta Informaticae

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2009

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Vol. 96, nr 1/2

195-209

EN

In this paper, an evolutionary technique for the semi-supervised clustering is proposed. The proposed technique uses a point symmetry based distance measure. Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. In this paper the existing point symmetry based genetic clustering technique, GAPS-clustering, is extended in two different ways to handle the semi-supervised classification problem. The proposed semi-GAPS clustering algorithmis able to detect any type of clusters irrespective of shape, size and convexity as long as they possess the point symmetry property. Kd-tree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. Experimental results demonstrate practical performance benefits of the methodology in detecting classes having symmetrical shapes in case of semi-supervised clustering.

5

Nearest neighbor search by using Partial KD-tree method

Kraus P., Dzwinel W.

Theoretical and Applied Informatics

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2008

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Vol. 20, No. 3

149-165

EN

We present a new nearest neighbor (NN) search algorithm, the Partial KD-Tree Search (PKD), which couples the Friedman’s algorithm and the Partial Distance Search (PDS ) technique. Its efficiency was tested using a wide spectrum of input datasets of various sizes and dimensions. The test datasets were both generated artificially and selected from the UCI repository. It appears that our hybrid algorithm is very efficient in comparison to its components and to other popular NN search technique – the Slicing Search algorithm. The results of tests show that PKD outperforms up to 100 times the brute force method and is substantially faster than other techniques. We can conclude that the Partial KD-Tree is a universal and effcient nearest neighbor search scheme.

PL

W pracy prezentujemy rezultaty poprawy efektywności poszukiwania najbliższego sąsiada poprzez hybrydyzację dwóch najczęściej używanych algorytmów: algorytmu Friedman’a opartego o tzw. K-D drzewa oraz prostej techniki liczenia odległości fragmentami (Partial Distance Search (PDS)). Efektywność powstałego algorytmu przetestowano na danych wygenerowanych w sposób sztuczny oraz na popularnym repozytorium danych UCI. Badano efektywność w zależności od wymiaru przestrzeni oraz strukturalnej złożoności testowanych danych. Okazuje sią że nasz hybrydowy algorytm jest wyraźnie efektywniejszy niż jego części składowe oraz inny popularny algorytmy znajdowania najbliższego sąsiada jak poszukiwanie plasterkowe (Slicing Search (SS)). Rezultaty testów pokazują, że na wybranych danych algorytm jest nawet parą rzędów szybszy niż metoda typu Exhaustive Search i kilka razy szybsza niż inne konkurencyjne wyspecjalizowane algorytmy.