JOURNAL ARTICLE

Using self-organizing map for road network extraction from ikonos imagery

Lili YunKeiichi Uchimura

Year: 2007 Journal:   International journal of innovative computing, information & control Vol: 3 (3)Pages: 641-656   Publisher: Kyushu Tokai University

Abstract

High methane dissolution capacity in a liquid is important for methane storage and transformation. In this work, methane solubility in different ionic liquids (ILs) was studied and was found associated with IL's structural and physical properties. In imidazolium-based ILs, ILs containing C-F and long alkyl chain showed high methane solubility mainly due to lower surface tension and molar density. Reducing the surface tension of solvent by adding 0.16 mol of trimethyl-1-propanaminium iodide (FC-134) with respect to [Bmim][NTf2] increased methane solubility by 39.3%. In situ high-pressure attenuated total reflection Fourier transform infrared spectroscopic results indicated a reversible process of methane dissolution in the ILs. The antisymmetric C-H stretching band of dissolved methane in ILs showed highly prominent rotational-vibrational bands with high intensity and narrow half-peak width compared to gaseous methane. Induced interaction between methane and IL resulted in increased dipole variation strength and reduced methane molecular symmetry. The constant antisymmetric C-H stretching peak at 3016.85 cm-1 revealed an unconstrained methane rotation in the stable physical and chemical environment of IL. Methane insertion into the IL's intranetwork space needs activation energy to overcome the interaction of cation-anion network. Kinetic analysis of methane in [Bmim][NTf2] and [Bmim][HSO4] at different temperatures indicated that methane dissolution in these two ILs was a reversible first-order and very weak endothermic process and that methane dissolution required high activation energy in ILs with stronger cation-anion interaction.

Keywords:
Computer science Artificial intelligence Extraction (chemistry) Self-organizing map Computer vision Cluster analysis

Metrics

9
Cited By
1.47
FWCI (Field Weighted Citation Impact)
12
Refs
0.82
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Automated Road and Building Extraction
Physical Sciences →  Engineering →  Ocean Engineering
Remote Sensing and LiDAR Applications
Physical Sciences →  Environmental Science →  Environmental Engineering
Remote-Sensing Image Classification
Physical Sciences →  Engineering →  Media Technology

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