Wyniki wyszukiwania - BazTech

1

SEWiK & GIS, czyli czarne mapy dróg i niepokojące wnioski

Maciąg Michał, Maciąg Klaudia, Słowik Agnieszka, Leń Przemysław

Przegląd Geodezyjny

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2023

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R. 95, nr 12

23--26

PL

Statystyki dotyczące liczby wypadków drogowych oraz rozmiaru ich skutków stanowią nieodłączny element medialnych przekazów, podsumowujących rok kalendarzowy, sezon urlopowy lub okres świąteczny. Wieloletnie obserwacje specyfiki zdarzeń drogowych mogą być przedmiotem szczegółowych analiz organów administracji publicznej, prowadzonych celem opracowania kierunków działań na rzecz poprawy bezpieczeństwa komunikacji. Szczególnie istotnych wniosków mogą dostarczyć analizy przestrzenne. Niniejsza publikacja przedstawia autorskie rozwiązanie, zapewniające możliwość sprawnego mapowania zdarzeń drogowych wraz z delimitacją szczególnie niebezpiecznych dróg oraz automatyczną lokalizacją tzw. „czarnych punktów”, charakteryzujących się wysoką liczbą wypadków na stosunkowo krótkim odcinku trasy. W analizie wykorzystano udostępnione nieodpłatnie dane Systemu Ewidencji Wypadków i Kolizji. W rezultacie podjętych działań utworzono pilotażowe opracowanie, opisujące rozkład przestrzenny wypadków drogowych ze skutkiem śmiertelnym dla obszaru miasta Lublin.

EN

Statistics concerning the number of the road accidents and the extent of their consequences are the integral element of the media reports, summarizing e.g. a year or a holiday season. The long-term observation of the road accidents characteristics may be the subject of the detailed analyses, carried out by the local authorities to develop the methods of improving the transport safety. The particularly useful conclusions may be obtained using the spatial analyses. This paper describes an authors’ own solution, providing the possibility of an efficient mapping of road accidents with a delimitation of the particularly dangerous roads and an automatic identification of the “black spots”, characterized by a high number of the accidents on a relatively short distance. The analysis used free data from the System of Accidents and Collisions Register. As a result of the research carried out, a pilot study of the fatal road accidents on the area of Lublin city has been developed.

2

GrDBSCAN: A granular density-based clustering algorithm

Suchy Dawid, Siminski Krzysztof

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 2

297--312

EN

Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback-its worst-case computational complexity is O(n2) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity. The proposed GrDBSCAN first granulates data into fuzzy granules and then runs density-based clustering on the resulting granules. The complexity of GrDBSCAN is linear with regard to the input data size and higher only for the number of granules. That number is, however, a parameter of the GrDBSCAN algorithm and is (significantly) lower than that of input data items. This results in shorter clustering time than in the case of DBSCAN. The paper is accompanied by numerical experiments. The implementation of GrDBSCAN is freely available from a public repository.

3

Segmentation of the melanoma lesion and its border

Surówka Grzegorz, Ogorzałek Maciej

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 4

683--699

EN

Segmentation of the border of the human pigmented lesions has a direct impact on the diagnosis of malignant melanoma. In this work, we examine performance of (i) morphological segmentation of a pigmented lesion by region growing with the adaptive threshold and density-based DBSCAN clustering algorithm, and (ii) morphological segmentation of the pigmented lesion border by region growing of the lesion and the background skin. Research tasks (i) and (ii) are evaluated by a human expert and tested on two data sets, A and B, of different origins, resolution, and image quality. The preprocessing step consists of removing the black frame around the lesion and reducing noise and artifacts. The halo is removed by cutting out the dark circular region and filling it with an average skin color. Noise is reduced by a family of Gaussian filters 3×3−7×7 to improve the contrast and smooth out possible distortions. Some other filters are also tested. Artifacts like dark thick hair or ruler/ink markers are removed from the images by using the DullRazor closing images for all RGB colors for a hair brightness threshold below a value of 25 or, alternatively, by the BTH transform. For the segmentation, JFIF luminance representation is used. In the analysis (i), out of each dermoscopy image, a lesion segmentation mask is produced. For the region growing we get a sensitivity of 0.92/0.85, a precision of 0.98/0.91, and a border error of 0.08/0.15 for data sets A/B, respectively. For the density-based DBSCAN algorithm, we get a sensitivity of 0.91/0.89, a precision of 0.95/0.93, and a border error of 0.09/0.12 for data sets A/B, respectively. In the analysis (ii), out of each dermoscopy image, a series of lesion, background, and border segmentation images are derived. We get a sensitivity of about 0.89, a specificity of 0.94 and an accuracy of 0.91 for data set A, and a sensitivity of about 0.85, specificity of 0.91 and an accuracy of 0.89 for data set B. Our analyses show that the improved methods of region growing and density-based clustering performed after proper preprocessing may be good tools for the computer-aided melanoma diagnosis.

4

Research on ship trajectory extraction based on multiattribute DBSCAN optimisation algorithm

Xu Xiaofeng, Cui Deqaing, Li Yun, Xiao Yingjie

Polish Maritime Research

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2021

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nr 1

136--148

EN

With the vigorous development of maritime traffic, the importance of maritime navigation safety is increasing day by day. Ship trajectory extraction and analysis play an important role in ensuring navigation safety. At present, the DBSCAN (density-based spatial clustering of applications with noise) algorithm is the most common method in the research of ship trajectory extraction, but it has shortcomings such as missing ship trajectories in the process of trajectory division. The improved multi-attribute DBSCAN algorithm avoids trajectory division and greatly reduces the probability of missing sub-trajectories. By introducing the position, speed and heading of the ship track point, dividing the complex water area and vectorising the ship track, the function of guaranteeing the track integrity can be achieved and the ship clustering effect can be better realised. The result shows that the cluster fitting effect reaches up to 99.83%, which proves that the multi-attribute DBSCAN algorithm and cluster analysis algorithm have higher reliability and provide better theoretical guidance for the analysis of ship abnormal behaviour.

5

A new method for automatic determining of the DBSCAN parameters

Starczewski Artur, Goetzen Piotr, Er Meng Joo

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 3

209--221

EN

Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

6

Wydobywanie wiedzy z danych złożonych

Nowak-Brzezińska A., Xięski T.

Studia Informatica

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2014

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Vol. 35, nr 2

175--186

PL

Artykuł przedstawia uniwersalną metodę wydobywania wiedzy z danych złożonych, uwzględniającą wykorzystanie technik opisu danych, algorytmów analizy skupień oraz efektywnych środków wizualizacji wydobytej wiedzy. Charakterystyczną cechą opisywanej metody jest zastosowanie dwuetapowego grupowania danych.

EN

This work presents a universal knowledge discovery method from complex data, which takes into account the usage of data description techniques, cluster analysis algorithms and effective means of visualization of the discovered knowledge. A characteristic feature of this method is the usage of a two-stage clustering process.

7

Gęstościowa metoda grupowania i wizualizacji danych złożonych

Nowak-Brzezińska A., Xięski T.

Studia Informatica

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2012

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Vol. 33, nr 2A

453-464

PL

Artykuł dokonuje przeglądu dotychczas stosowanych rozwiązań implementacyjnych w zakresie grupowania dużych wolumenów danych oraz opisuje problematykę doboru parametrów startowych dla algorytmu gęstościowego DBSCAN. Ponadto stanowi on wprowadzenie w tematykę wizualizacji struktury złożonych skupień, wykorzystując w tym celu algorytm oparty na idei gęstości – OPTICS.

EN

This work reviews currently used implementation solutions for clustering large volumes of data, and describes the problem of choosing proper initial values for the density-based DBSCAN algorithm. Furthermore it should be also treated as an introduction to the topic of visualization of complex clusters using another density-based algorithm - OPTICS.

8

Grupowanie danych złożonych

Nowak-Brzezińska A., Xięski T.

Studia Informatica

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2011

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Vol. 32, nr 2A

391-402

PL

Artykuł stanowi wprowadzenie do tematyki grupowania danych złożonych i przeszukiwania takiej struktury. Przedstawia problemy z tym związane, skupiając się przede wszystkim na aspekcie tworzenia reprezentantów skupień. Przeprowadzone eksperymenty opierające się na wykorzystaniu algorytmu DBSCAN, pozwalają na porównanie efektywności wyszukiwania, relewantnych do zadanego pytania skupień, w zależności od sposobu tworzenia reprezentantów grup.

EN

This work provides an introduction to the matter of clustering complex data and searching through such a structure. It presents related problems, focusing primarily on the aspect of creating cluster representatives. Carried out experiments based on using the DBSCAN algorithm allow to compare the efficiency of finding relevant to the given question clusters, depending on the way of cluster representatives were created.

9

Application of Density Based Clustering to Microarray Data Analysis

Raczynski L., Wozniak K., Rubel T., Zaremba K.

International Journal of Electronics and Telecommunications

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2010

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Vol. 56, No. 3

281-286

EN

In just a few years, gene expression microarrays have rapidly become a standard experimental tool in the biological and medical research. Microarray experiments are being increasingly carried out to address the wide range of problems, including the cluster analysis. The estimation of the number of clusters in datasets is one of the main problems of clustering microarrays. As a supplement to the existing methods we suggest the use of a density based clustering technique DBSCAN that automatically defines the number of clusters. The DBSCAN and other existing methods were compared using the microarray data from two datasets used for diagnosis of leukemia and lung cancer.

10

A New Density-Based Scheme for Clustering Based on Genetic Algorithm

Lin C.-Y., Chang C.-C., Lin C.-C.

Fundamenta Informaticae

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2005

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Vol. 68, nr 4

315--331

EN

Density-based clustering can identify arbitrary data shapes and noises. Achieving good clustering performance necessitates regulating the appropriate parameters in the density-based clustering. To select suitable parameters successfully, this study proposes an interactive idea called GADAC to choose suitable parameters and accept the diverse radii for clustering. Adopting the diverse radii is the original idea employed to the density-based clustering, where the radii can be adjusted by the genetic algorithm to cover the clusters more accurately. Experimental results demonstrate that the noise and all clusters in any data shapes can be identified precisely in the proposed scheme. Additionally, the shape covering in the proposed scheme is more accurate than that in DBSCAN.