Application of fuzzy c-means clustering technique in vehicular pollution

Das, Samarjit; Baruah, K Hemanta

doi:10.5937/jpmnt1303094d

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…The fuzzy c-means clustering algorithm is an iterative optimization algorithm which allows the determination of the optimal cluster centers without requiring a priori information [22]. In literature, the fuzzy c-means clustering has been found to be very popular within the research community, being used for a large variety of applications, such as risk and claim classification or vehicular pollution estimation [23,24].…”

Section: The Fuzzy C-means Clustering Optimizationmentioning

confidence: 99%

Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data

et al. 2017

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This paper presents for the first time a detailed study on information content of X-band single-pass interferometric spaceborne SAR data with respect to snow facies characterization. An approach for classifying different snow facies of the Greenland Ice Sheet by exploiting X-band TanDEM-X interferometric synthetic aperture radar acquisitions is firstly detailed. Large-scale mosaics of radar backscatter and volume correlation factor, derived from quicklook images of the interferometric coherence, represent the starting point for applying an unsupervised classification method based on the c-means fuzzy clustering algorithm. The data was acquired during winter 2010/2011. A partition of four different snow facies was chosen and interpreted using reference melt data, snow density, and in situ measurements. The variations in the stratification and micro-structure of firn, such as the variations of density with depth and the presence of percolation features, are identified as relevant parameters for explaining the significant differences in the observed interferometric signatures among different snow facies. Moreover, a statistical analysis of backscatter and volume correlation factor provided useful parameters for characterizing the snow facies behavior and analyzing their dependency on the acquisition geometry. Finally, knowing the location and characterization of the different snow facies, the two-way X-band penetration depth over the whole Ice Sheet was estimated. The obtained mean values vary from 2.3 m for the outer snow facies up to 4.18 m for the inner one. The presented approach represents a starting point for a long-term monitoring of ice sheet dynamics, by acquiring time-series, and is of high relevance for the design of future SAR missions as well.

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Section: The Fuzzy C-means Clustering Optimizationmentioning

confidence: 99%

Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data

et al. 2017

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“…The fuzzy c-means clustering algorithm is an iterative optimization algorithm which allows the determination of the optimal cluster centers without requiring a priori information [128]. In literature, the fuzzy c-means clustering has been found to be very popular within the research community, being used for a large variety of applications, such as risk and claim classification or vehicular pollution estimation [129,130].…”

Section: The C-means Fuzzy Clustering Optimizationmentioning

confidence: 99%

Radar Backscatter Modeling Based on Global TanDEM-X Mission Data

Rizzoli

Bräutigam

2014

IEEE Trans. Geosci. Remote Sensing

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nunterschied zwischen dem globalen TanDEM-X-DEM und ICESat-Messungen verglichen. Abschließend wird ein neu entwickelter Algorithmus zur Generierung von Rückstreukarten großer Gebiete vorgestellt. Dieser erlaubt unter Verwendung von Rückstreumodellen das Angleichen der erstellten Karten anhand eines Referenzeinfallswinkels, was dann das Füllen verbleibender Lücken ermöglicht, die aufgrund fehlender Eingangsdaten vorhanden sind.

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“…A weighted local fuzzy regression model showed a better efficiency than the least squares regression for nonlinear and high-dimensional pattern recognition of transport system in China [7]. The new kernelized fuzzy C-means clustering algorithm [8] uses a kernel-induced distance function as a similarity measure showed improved performance in identifying the patterns when compared to the conventional fuzzy C-means technique. In many research findings, it is observed that the fuzzy-based learning approach was used in a wide variety of ways to achieve better results in extracting non-linear and overlapping patterns.…”

Section: Introductionmentioning

confidence: 96%

Fuzzy Perceptron Learning for Non-Linearly Separable Patterns

Duggirala¹

2022

Data Clustering

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Perceptron learning has its wide applications in identifying interesting patterns in the large data repositories. While iterating through their learning process perceptrons update the weights, which are associated with the input data objects or data vectors. Though perceptrons exhibit their robustness in learning about interesting patterns, they perform well in identifying the linearly separable patterns only. In the real world, however, we can find overlapping patterns, where objects may associate with multiple patterns. In such situations, a clear-cut identification of patterns is not possible in a linearly separable manner. On the other hand, fuzzy-based learning has its wide applications in identifying non-linearly separable patterns. The present work attempts to experiment with the algorithms for fuzzy perceptron learning, where perceptron learning and fuzzy-based learning techniques are implemented in an interfusion manner.

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Application of fuzzy c-means clustering technique in vehicular pollution

Cited by 3 publications

References 5 publications

Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data

Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data

Radar Backscatter Modeling Based on Global TanDEM-X Mission Data

Fuzzy Perceptron Learning for Non-Linearly Separable Patterns

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