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Wyświetlanie 1-2 z 2
Tytuł:
Robust estimation in interlaboratory measurements with small number of measurements
Autorzy:
Volodarsky, E. T.
Warsza, Z. L.
Powiązania:
https://bibliotekanauki.pl/articles/114483.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
interlaboratory comparisons
proficiency testing of laboratory outliers
robust statistics
precision
uncertainty of measurements
Opis:
In this paper two robust methods of assessing the value and the uncertainty of the measurand from the samples of small number of experimental data are presented and compared. Those methods can be used when some measurements results contain outliers, i.e. when the values of certain measurement results significantly differ from the others. They allow to set a credible statistical parameters of the measurements with the use of all experimental data. The following considerations are illustrated by the numerical examples of multi-laboratory measurement data key comparison. Compared are the results obtained by a classical method with rejection of outliers with two robust methods: a rescaled median absolute deviation MADS and an iterative two-criteria method. The paper also presents the advantages of the robust iterative statistical method in estimating the accuracy of the tested laboratory measurement results during its accreditation on the sample of four elements with outlier. A comparison with the estimates obtained by the standard procedure for evaluating performance accuracy is also provided.
Źródło:
Measurement Automation Monitoring; 2015, 61, 4; 104-110
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wild Image Retrieval with HAAR Features and Hybrid DBSCAN Clustering For 3D Cultural Artefact Landmarks Reconstruction
Autorzy:
Pitchandi, Perumal
Powiązania:
https://bibliotekanauki.pl/articles/2201730.pdf
Data publikacji:
2022
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
outliers removal
culturalartefact objects
3D reconstruction
particle swarm optimization
PSO
spatial clustering
density based spatial clustering
noise clustering algorithm
Opis:
In this digital age large amounts of information, images and videos can be found in the web repositories which accumulate this information. These repositories include personal, historic, cultural, and business event images. Image mining is a limited field in research where most techniques look at processing images instead of mining. Very limited tools are found for mining these images, specifically 3D (Three Dimensional) images. Open source image datasets are not structured making it difficult for query based retrievals. Techniques extracting visual features from these datasets result in low precision values as images lack proper descriptions or numerous samples exist for the same image or images are in 3D. This work proposes an extraction scheme for retrieving cultural artefact based on voxel descriptors. Image anomalies are eliminated with a new clustering technique and the 3D images are used for reconstructing cultural artefact objects. Corresponding cultural 3D images are grouped for a 3D reconstruction engine’s optimized performance. Spatial clustering techniques based on density like PVDBSCAN (Particle Varied Density Based Spatial Clustering of Applications with Noise) eliminate image outliers. Hence, PVDBSCAN is selected in this work for its capability to handle a variety of outliers. Clustering based on Information theory is also used in this work to identify cultural object’s image views which are then reconstructed using 3D motions. The proposed scheme is benchmarked with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to prove the proposed scheme’s efficiency. Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques.
Źródło:
Advances in Science and Technology. Research Journal; 2022, 16, 3; 269--281
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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