University of Electro-Communications

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.

Guaranteed Cost Control of Polynomial Fuzzy Systems via a Sum of Squares Approach

Abstract: This paper presents the guaranteed cost control of polynomial fuzzy systems via a sum of squares (SOS) approach. First, we present a polynomial fuzzy model and controller that are more general representations of the well-known Takagi-Sugeno (T-S) fuzzy model and controller, respectively. Second, we derive a guaranteed cost control design condition based on polynomial Lyapunov functions. Hence, the design approach discussed in this paper is more general than the existing LMI approaches (to T-S fuzzy control system designs) based on quadratic Lyapunov functions. The design condition realizes a guaranteed cost control by minimizing the upper bound of a given performance function. In addition, the design condition in the proposed approach can be represented in terms of SOS and is numerically (partially symbolically) solved via the recent developed SOSTOOLS. To illustrate the validity of the design approach, two design examples are provided. The first example deals with a complicated nonlinear system. The second example presents micro helicopter control. Both the examples show that our approach provides more extensive design results for the existing LMI approach.

Food image recognition with deep convolutional features

Abstract: In this paper, we report the feature obtained from the Deep Convolutional Neural Network boosts food recognition accuracy greatly by integrating it with conventional hand-crafted image features, Fisher Vectors with HoG and Color patches. In the experiments, we have achieved 72.26% as the top-1 accuracy and 92.00% as the top-5 accuracy for the 100-class food dataset, UEC-FOOD100, which outperforms the best classification accuracy of this dataset reported so far, 59.6%, greatly.

Controlling phase separation of binary Bose-Einstein condensates via mixed-spin-channel Feshbach resonance

Abstract: We investigate controlled phase separation of a binary Bose-Einstein condensate in the proximity of a mixed-spin-channel Feshbach resonance in the $|F=1,{m}_{F}=+1\ensuremath{\rangle}$ and $|F=2,{m}_{F}=\ensuremath{-}1\ensuremath{\rangle}$ states of $^{87}\mathrm{Rb}$ at a magnetic field of 9.10 G. Phase separation occurs on the lower-magnetic-field side of the Feshbach resonance while the two components overlap on the higher-magnetic-field side. The Feshbach resonance curve of the scattering length is obtained from the shape of the atomic cloud by comparison with the numerical analysis of coupled Gross-Pitaevskii equations.