Beijing Wuzi University

About: Beijing Wuzi University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Supply chain & Computer science. The organization has 1210 authors who have published 1092 publications receiving 6370 citations.

New global stability conditions for genetic regulatory networks with time-varying delays

Abstract: The study of the global stability is essential for designing and controlling genetic regulatory networks. Most existing results on this issue are based on linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high dimensional LMIs. In our previous study, we present several stability conditions for genetic regulatory networks with time-varying delays, based on M-matrix theory and the non-smooth Lyapunov function. In this paper, we design a smooth Lyapunov function and employ M-matrix theory to derive new stability conditions for genetic regulatory networks with time-varying delays. Theoretically, these conditions are less conservative than existing ones in some cases. For genetic regulatory networks with n genes and n proteins, these conditions become to check if an n×n matrix is an M-matrix, which is much easier than existing results. To illustrate the effectiveness of our theoretical results, two genetic regulatory networks are analyzed.

Robust Unsupervised Lagrangian Support Vector Machines for Supply Chain Management

Abstract: Support Vector Machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems These years two-class unsupervised and semi-supervised classification algorithms based on Bounded C-SVMs, Bounded ν-SVMs, Lagrangian SVMs (LSVMs) and robust version to Bounded C − SVMs respectively, and which are relaxed to Semi-definite Programming (SDP), get good classification results The time consumed of method based on robust version to BC-SVMs is too long So it seems necessary to find a faster method, which has almost accurate results as above at least Therefore we proposed robust version to unsupervised and semi-supervised classification algorithms based on Lagrangian Support Vector Machines and its application on evaluation of supply chain management performance Numerical results confirm the robustness of the proposed method and show that our new unsupervised and semi-supervised classification algorithms based on LSVMs often obtain almost the same accurate results as other algorithms,while considerably faster than them

Framework Design of Financial Service Platform for Tobacco Supply Chain Based on Blockchain

Abstract: In the development of tobacco supply chain finance, the imperfection of the credit system and financial service platform has become a major issue. With this issue as the starting point, we elaborated how these issues affect the development of the tobacco supply chain, and introduced blockchain technology on the basis of this issue. We reviewed previous research results and theoretical basis and analyzed the development process of the tobacco supply chain and the characteristics of blockchain technology. Based on the above, a framework of tobacco supply chain financial service platform based on blockchain technology is constructed, and corresponding solutions are given.