Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2009. / Härtill 4 uppsatser.

Tesis (licenciatura en derecho)--Universidad de Chile. / Bibliography: p. [91]-100.

This research proposes a method to detect and classify the smoke particles of common household fires by analysing the image histogram features of smoke particles generated by Rayleigh scattered light. This research was motivated by the failure of commercially available photoelectric smoke detectors to detect smoke particles less than 100 nm in diameter, such as those in polyurethane (in furniture) fires, and the occurrence of false positives such as those caused by steam. Seven different types of particles (pinewood smoke, polyurethane smoke, steam, kerosene smoke, cotton wool smoke, cooking oil smoke and a test Smoke) were selected and exposed to a continuous spectrum of light in a closed particle chamber. A significant improvement over the common photoelectric smoke detectors was demonstrated by successfully detecting and classifying all test particles using colour histograms. As Rayleigh theory suggested, comparing the intensities of scattered light of different wavelengths is the best method to classify different sized particles. Existing histogram comparison methods based on histogram bin values failed to evaluate a relationship between the scattered intensities of individual red, green and blue laser beams with different sized particles due to the uneven particles movements inside the chamber. The current study proposes a new method to classify these nano-scale particles using the particle density independent intensity histograms feature; Maximum Value Index. When a Rayleigh scatter (particles that have the diameter which is less than one tenth of the incident wavelength) is exposed to a light with different wavelengths, the intensities of scattered light of each wavelength is unique according to the particle size and hence, a single unique maximum value index in the image intensity histogram can be detected. Each captured image in the video frame sequence was divided into its red, green and blue planes (single R, G, B channel arrays) and the particles were isolated using a modified frame difference method. Mean and the standard deviation of the Maximum Value Index of intensity histograms over predefined number of frames (N) were used to differentiate different types of particles. The proposed classification algorithm successfully classified all the monotype particles with 100% accuracy when N ≥ 100. As expected, the classifier failed to distinguish wood smoke from other monotype particles due to the rapid variation of the maximum value index of the intensity histograms of the consecutive images of the image sequence since wood smoke is itself a complex composition of many monotype particles such as water vapour and resin smoke. The results suggest that the proposed algorithm may enable a smoke detector to be safer by detecting a wider range of fires and reduce false alarms such as those caused by steam.

published_or_final_version / abstract / toc / Ecology and Biodiversity / Doctoral / Doctor of Philosophy

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This study contributes a methodology and associated toolkit developed to allow users to experiment with the use of combined features in classification problems. Methods are provided for efficiently generating combined features from an original feature set, for efficiently selecting the most discriminating of these generated combined features, and for efficiently performing a preliminary comparison of the classification results when using the original features exclusively against the results when using the selected combined features. The potential benefit of considering combined features in classification problems is demonstrated by applying the developed methodology and toolkit to three sample data sets where the discovery of combined features containing new discriminating information led to improved classification results.

7th Information Architecture Summit, Vancouver, BC, March 23-27 / This paper examines the results of a study of the three groups involved in creating index keywords or tags: users, authors and intermediaries. Keywords from each of the three groups were compared to determine similarities and differences in term use. Comparisons suggested that there were important differences in the contexts of the three groups that should be taken into account when assigning keywords or designing systems for the organisation of information.

The article has been reviewed and accepted for publication in Knowledge Organization 33 (2006) / This is a preprint of a paper to be published in Knowledge Organization. The paper gives a short overview of the history of use of UDC in Internet subject gateways (SGs) with an English interface, from 1993 to 2006. There were in total, nine quality controlled SGs that were functional for shorter or longer periods of time. Their typology and functionality is described. Quality SGs have evolved and the role of classification has changed accordingly from supporting subject organization on the interface and automatic categorization of resources, towards supporting a semantic linking, control and vocabulary mapping between different indexing systems in subject hubs and federated SGs. In this period, many SGs ceased to exist and little information remains available regarding their status. SGs currently using UDC, for some part of their resource organization, do not use a UDC subject hierarchy at the interface and its role in resource indexing has become more difficult to observe. Since 2000, UDC has become more prevalent in East European SGs, portals and hubs, which are outside the scope of this research. This paper is an attempt to provide a record on this particular application of UDC and to offer some consideration of the changes in requirements when it comes to the use of library classification in resource discovery.