Unsupervised classification techniques for determination of storm region correspondences

1999 ◽  
Author(s):  
Jo Ann Parikh ◽  
John S. DaPonte ◽  
Joseph N. Vitale
Keyword(s):  
Unsupervised Classification ◽  
Classification Techniques

scholarly journals Determination of Transformers’ Insulating Paper State Based on Classification Techniques

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Sherif S. M. Ghoneim
Keyword(s):  
Prediction Model ◽  
Power Flow ◽  
Degree Of Polymerization ◽  
Classification Techniques ◽  
Aging Rate ◽  
Insulating Oil ◽  
Insulating Paper ◽  
Key Indicator ◽  
Carbon Dioxide Co2

The continuity of transformer operation is very necessary for utilities to maintain a continuity of power flow in networks and achieve a desired revenue. Most failures in a transformer are due to the degradation of the insulating system, which consists of insulating oil and paper. The degree of polymerization (DP) is a key detector of insulating paper state. Most research in the literature has computed the DP as a function of furan compounds, especially 2-furfuraldehyde (2-FAL). In this research, a prediction model was constructed based on some of most periodical tests that were conducted on transformer insulating oil, which were used as predictors of the insulating paper state. The tests evaluated carbon monoxide (CO), carbon dioxide (CO2), breakdown voltage (VBD), interfacial tension (IF), acidity (ACY), moisture (M), oil color (OC), and 2-furfuraldehyde (2-FAL). The DP, which was used as the key indicator for the paper state, was categorized into five classes labeled 1, 2, 3, 4, and 5 to express the insulating paper normal aging rate, accelerating aging rate, excessive aging danger zone, high risk of failure, and the end of expected life, respectively. The classification techniques were applied to the collected data samples to construct a prediction model for the insulating paper state, and the results revealed that the fine tree was the best classifier of the data samples, with a 96.2% prediction accuracy.

NIR hyperspectral imaging (NIR-HI) and μXRD for determination of the transition between juvenile and mature wood of Pinus sylvestris L.

Holzforschung ◽  
2019 ◽  
Vol 73 (7) ◽  
pp. 621-627 ◽  
Author(s):  
Antonio Ruano ◽  
Andreas Zitek ◽  
Barbara Hinterstoisser ◽  
Eva Hermoso
Keyword(s):  
Pinus Sylvestris ◽  
Hyperspectral Imaging ◽  
Transition Point ◽  
Near Infrared ◽  
Juvenile Wood ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Mature Wood ◽  
X Ray

AbstractThe ratio of juvenile wood (jW) to mature wood (mW) is relevant for structural wood applications because of their different properties. Near infrared-hyperspectral imaging (NIR-HI) indicates after calibration, the spatial distribution of jW and mW, and this approach is less time consuming than the established micro X-ray densitometry (μXRD). In the present study, a comparative detection of the jW and mW ofPinus sylvestrisL. was performed by NIR-HI and μXRD and the NIR-HI results were evaluated in combination with three chemometric approaches, namely, the principal component analysis (PCA), partitional k-means unsupervised classification (p-k-mUC) and partial least squares discriminant analysis (PLS-DA) in the range of 900–1700 nm. The best NIR-HI results can be obtained when the transition point of earlywood (EW) and latewood (LW) are assessed separately by PLS-DA. The presented results are useful for an automating data evaluation and simplified data collection.

scholarly journals Use of unsupervised classification for the determination of prevailing land use topology

2017 ◽  
Vol 61 (04) ◽  
pp. 541-581
Author(s):  
Miha Konjar ◽  
Alma Zavodnik Lamovšek ◽  
Dejan Grigillo
Keyword(s):  
Land Use ◽  
Unsupervised Classification

Identification of selected CITES-protected Araucariaceae using DART TOFMS

IAWA Journal ◽  
2017 ◽  
Vol 38 (2) ◽  
pp. 266-S3 ◽  
Author(s):  
Philip D. Evans ◽  
Ignacio A. Mundo ◽  
Michael C. Wiemann ◽  
Gabriela D. Chavarria ◽  
Pamela J. McClure ◽  
...  
Keyword(s):  
Wood Anatomy ◽  
Direct Analysis ◽  
Unsupervised Classification ◽  
Species Determination ◽  
Macroscopic Appearance ◽  
Flight Mass Spectrometry ◽  
Araucaria Heterophylla ◽  
Araucaria Araucana ◽  
The Look

Determining the species source of logs and planks suspected of being Araucaria araucana (Molina) K.Koch (CITES Appendix I) using traditional wood anatomy has been difficult, because its anatomical features are not diagnostic. Additionally, anatomical studies of Araucaria angustifolia (Bertol.) Kuntze, Araucaria heterophylla (Salisb.) Franco, Agathis australis (D.Don) Lindl., and Wollemia nobilis W.G.Jones, K.D.Hill & J.M.Allen have reported that these taxa have similar and indistinguishable anatomical characters from A. araucana. Transnational shipments of illegal timber obscure their geographic provenance, and therefore identification using wood anatomy alone is insufficient in a criminal proceeding. In this study we examine the macroscopic appearance of selected members of the Araucariaceae and investigate whether analysis of heartwood chemotypes using Direct Analysis in Real Time (DART) Time-of-Flight Mass Spectrometry (TOFMS) is useful for making species determinations. DART TOFMS data were collected from 5 species (n =75 spectra). The spectra were analyzsed statistically using supervised and unsupervised classification algorithms. Results indicate that A. araucana can be distinguished from the look-alike taxa. Another statistical inference of the data suggests that Wollemia nobilis is more similar and within the same clade as Agathis australis. We conclude that DART TOFMS spectra can help in making species determination of the Araucariaceae even when the geographic provenance is unknown.

Introduction to the special issue on supervised and unsupervised classification techniques and their applications

2014 ◽  
Vol 41 ◽  
pp. 1-2
Author(s):  
Kim L. Boyer ◽  
José Francisco Martínez-Trinidad ◽  
Jesús Ariel Carrasco-Ochoa
Keyword(s):  
Unsupervised Classification ◽  
Special Issue ◽  
Classification Techniques
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