University of Tokyo

About: University of Tokyo is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Population & Gene. The organization has 134564 authors who have published 337567 publications receiving 10178620 citations. The organization is also known as: Todai & Universitas Tociensis.

Severe Plastic Deformation (SPD) Processes for Metals

Abstract: Processes of severe plastic deformation (SPD) are defined as metal forming processes in which a very large plastic strain is imposed on a bulk process in order to make an ultra-fine grained metal The objective of the SPD processes for creating ultra-fine grained metal is to produce lightweight parts by using high strength metal for the safety and reliability of micro-parts and for environmental harmony In this keynote paper, the fabrication process of equal channel angular pressing (ECAP), accumulative roll-bonding (ARB), high pressure torsion (HPT), and others are introduced, and the properties of metals processed by the SPD processes are shown Moreover, the combined processes developed recently are also explained Finally, the applications of the ultra-fine grained (UFG) metals are discussed

Automatic recognition of multi-word terms:. the C-value/NC-value method

Abstract: Technical terms (henceforth called terms ), are important elements for digital libraries. In this paper we present a domain-independent method for the automatic extraction of multi-word terms, from machine-readable special language corpora. The method, (C-value/NC-value ), combines linguistic and statistical information. The first part, C-value, enhances the common statistical measure of frequency of occurrence for term extraction, making it sensitive to a particular type of multi-word terms, the nested terms. The second part, NC-value, gives: 1) a method for the extraction of term context words (words that tend to appear with terms); 2) the incorporation of information from term context words to the extraction of terms.

Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion

Abstract: Coupled nonnegative matrix factorization (CNMF) unmixing is proposed for the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution multispectral data to produce fused data with high spatial and spectral resolutions. Both hyperspectral and multispectral data are alternately unmixed into end member and abundance matrices by the CNMF algorithm based on a linear spectral mixture model. Sensor observation models that relate the two data are built into the initialization matrix of each NMF unmixing procedure. This algorithm is physically straightforward and easy to implement owing to its simple update rules. Simulations with various image data sets demonstrate that the CNMF algorithm can produce high-quality fused data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution.