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Institution

University of Electro-Communications

EducationTokyo, Japan
About: University of Electro-Communications is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Laser & Robot. The organization has 8041 authors who have published 16950 publications receiving 235832 citations. The organization is also known as: UEC & Denki-Tsūshin Daigaku.


Papers
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Journal ArticleDOI
TL;DR: The simulation of steady walking at 0.6 m/s of both the forelegs only and the hind legs only (with a supporting structure at the back and at the front respectively), achieved using the quadrupedal model is reported.

65 citations

Journal ArticleDOI
TL;DR: This paper proposes novel privacy models, namely, (l1, …, lq)-diversity and (t1,…, tq)-closeness, and a method that can treat sensitive QIDs, and is composed of two algorithms: An anonymization algorithm and a reconstruction algorithm.
Abstract: A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, …, lq)-diversity and (t1, …, tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: An anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer's objective. Our proposed method was experimentally evaluated using real data sets.

65 citations

Journal ArticleDOI
TL;DR: A model equation that describes the propagation of sound beams in a fluid is developed using the oblate spheroidal coordinate system and has a specific application to a theoretical prediction on focused, high-frequency beams from a circular aperture.
Abstract: A model equation that describes the propagation of sound beams in a fluid is developed using the oblate spheroidal coordinate system. This spheroidal beam equation (SBE) is a parabolic equation and has a specific application to a theoretical prediction on focused, high-frequency beams from a circular aperture. The aperture angle does not have to be small. The theoretical background is basically along the same analytical lines as the composite method (CM) reported previously [B. Ystad and J. Berntsen, Acustica 82, 698–706 (1996)]. Numerical examples are displayed for the amplitudes of sound pressure along and across the beam axis when sinusoidal waves are radiated from the source with uniform amplitude distribution. The primitive approach to linear field analysis is readily extended to the case where harmonic generation in finite-amplitude sound beams becomes significant due to the inherent nonlinearity of the medium. The theory provides the propagation and beam pattern profiles that differ from the CM sol...

65 citations

Book ChapterDOI
08 Dec 2011
TL;DR: This work pin-point Artificial Intelligence more specifically Machine Learning and Data Mining as a major driving force behind the Web 3.0 and therefore examines the influence that AI might exert on the development of e-Learning3.0.
Abstract: The concept of e-Learning 2.0 has become well established and widely accepted. Just like how e-Learning 2.0 replaced its predecessor, we are again on the verge of a transformation. Both previous generations of e-Learning 1.0 and 2.0 closely parody the prevalent technologies available in their kin Web versions 1.0 and 2.0, respectively. In order to acquire a better perspective to assess what technologies will be available in the Web 3.0 and therefore e-Learning 3.0, we take a historical glance at the previous generations of e-Learning and theWeb. We then survey some existing predictions for e-Learning 3.0 and finally provide our own. Previous surveys tend to identify educational needs for e-Learning, and then discuss what technologies are required to satisfy these needs. Educational needs are an important factor, but the required technologies may not reach fruition. Gauging past trends we take the reverse approach by first identifying technologies that are likely to be brought forth by the Web 3.0, and only then looking at how these technologies could be utilized in the learning domain. In particular, we pin-point Artificial Intelligence more specifically Machine Learning and Data Mining as a major driving force behind the Web 3.0. We therefore examine the influence that AI might exert on the development of e-Learning 3.0.

65 citations

Journal ArticleDOI
TL;DR: It is shown that the interface in a two-component Bose-Einstein condensate (BEC) with a dipole-dipole interaction spontaneously develops patterns similar to those formed in a ferrofluid.
Abstract: It is shown that the interface in a two-component Bose-Einstein condensate (BEC) with a dipole-dipole interaction spontaneously develops patterns similar to those formed in a ferrofluid. Hexagonal, labyrinthine, solitonlike structures, and hysteretic behavior are numerically demonstrated. Superflow is found to circulate around the hexagonal pattern at rest, offering evidence of supersolidity. The system sustains persistent current with a vortex line pinned by the hexagonal pattern. These phenomena may be realized using a 52Cr BEC.

65 citations


Authors

Showing all 8079 results

NameH-indexPapersCitations
Mildred S. Dresselhaus136762112525
Matthew Nguyen131129184346
Juan Bisquert10745046267
Dapeng Yu9474533613
Riichiro Saito9150248869
Shun-ichi Amari9049540383
Shigeru Nagase7661722099
Ingrid Verbauwhede7257521110
Satoshi Hasegawa6970822153
Yu Qiao6948429922
Yukio Tanaka6874419942
Zhijun Li6861414518
Iván Mora-Seró6723523229
Kazuo Tanaka6353527559
Da Xing6362414766
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202317
202258
2021644
2020815
2019908
2018837