Donghua University

About: Donghua University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Fiber & Nanofiber. The organization has 21155 authors who have published 21841 publications receiving 393091 citations. The organization is also known as: Dōnghuá Dàxué & China Textile University.

Inception-v3 for flower classification

Abstract: The study of flower classification system is a very important subject in the field of Botany. A classifier of flowers with high accuracy will also bring a lot of fun to people's lives. However, because of the complex background of flowers, the similarity between the different species of flowers, and the differences among the same species of flowers, there are still some challenges in the recognition of flower images. The traditional flower classification is mainly based on the three features: color, shape and texture, this classification requires people to select features for classification, and the accuracy is not very high. In this paper, based on Inception-v3 model of TensorFlow platform, we use the transfer learning technology to retrain the flower category datasets, which can greatly improve the accuracy of flower classification.

Exponential Stabilization of a Class of Stochastic System With Markovian Jump Parameters and Mode-Dependent Mixed Time-Delays

Abstract: In this technical note, the globally exponential stabilization problem is investigated for a general class of stochastic systems with both Markovian jumping parameters and mixed time-delays. The mixed mode-dependent time-delays consist of both discrete and distributed delays. We aim to design a memoryless state feedback controller such that the closed-loop system is stochastically exponentially stable in the mean square sense. First, by introducing a new Lyapunov-Krasovskii functional that accounts for the mode-dependent mixed delays, stochastic analysis is conducted in order to derive a criterion for the exponential stabilizability problem. Then, a variation of such a criterion is developed to facilitate the controller design by using the linear matrix inequality (LMI) approach. Finally, it is shown that the desired state feedback controller can be characterized explicitly in terms of the solution to a set of LMIs. Numerical simulation is carried out to demonstrate the effectiveness of the proposed methods.