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Hyeong-Tae Jou

Publications and source records attributed to Hyeong-Tae Jou.

2 recordsLinked to original sources

Lattice parameter determination of mullite by energy-filtered needle-texture electron diffraction pattern.

The thermal transformations of pyrophyllite to mullite by heating were re-examined using mainly energy-filtering transmission electron microscopy and, for the first time, the texture electron diffraction pattern of the mullite was completely interpreted. Through a temperature range in which pyrophyllite dehydroxylate maintained a long-range order with a fluctuation of approximately 1% in d-spacings of (100) and (010) planes at 1000 degrees C, without prominent exothermic feature, pyrophyllite dehydroxylate was gradually decomposed and transformed into mullite through topotaxy. Pyrophyllite dehydroxylate did not collapse completely until 1100 degrees C, which promoted the rapid growth of mullite in random orientation at 1200 degrees C and the crystallization of amorphous silica to cristobalite at 1300 degrees C. The mullite needles, having their c-axis (texture axis) parallel to the elongation direction, lined up along the b(*)-axis of the pyrophyllite dehydroxylate in the needle-texture electron diffraction patterns. The mullite needles had monoclinic symmetry with lattice parameters of 7.27 A (a), 7.75 A (b), 2.90 A (c), 90 degrees (alpha), 90 degrees (beta) and 88.41 degrees (gamma), which, because of the structural affiliation to the parent pyrophyllite dehydroxylate, differ to the orthorhombic 3/2-mullite.

Journal Article↗

Seabed classification from acoustic profiling data using the similarity index.

We introduce the similarity index (SI) for the classification of the sea floor from acoustic profiling data. The essential part of our approach is the singular value decomposition of the data to extract a signal coherent trace-to-trace using the Karhunen-Loeve transform. SI is defined as the percentage of the energy of the coherent part contained in the bottom return signals. Important aspects of SI are that it is easily computed and that it represents the textural roughness of the sea floor as a function of grain size, hardness, and a degree of sediment sorting. In a real data example, we classified a section of the sea floor off Cheju Island south of the Korean Peninsula and compared the result with the sedimentology defined from direct sediment sampling and side scan sonar records. The comparison shows that SI can efficiently discriminate the bottom properties by delineating sediment-type boundaries and transition zones in more detail. Therefore, we propose that SI is an effective parameter for geoacoustic modeling.

Acoustics↗