Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization

Abstract: Many complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. A fundamental question is how to extract communities in multi-layer networks. The current algorithms either collapses multi-layer networks into a single-layer network or extends the algorithms for single-layer networks by using consensus clustering. However, these approaches have been criticized for ignoring the connection among various layers, thereby resulting in low accuracy. To attack this problem, a quantitative function (multi-layer modularity density) is proposed for community detection in multi-layer networks. Afterward, we prove that the trace optimization of multi-layer modularity density is equivalent to the objective functions of algorithms, such as kernel $K$ -means, nonnegative matrix factorization (NMF), spectral clustering and multi-view clustering, for multi-layer networks, which serves as the theoretical foundation for designing algorithms for community detection. Furthermore, a S emi- S upervised j oint N onnegative M atrix F actorization algorithm ( S2-jNMF ) is developed by simultaneously factorizing matrices that are associated with multi-layer networks. Unlike the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the S2-jNMF algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method outperforms the state-of-the-art approaches for community detection in multi-layer networks.

Global Asymptotic Saturated PID Control for Robot Manipulators

Abstract: This paper addresses the global asymptotic regulation of robot manipulators under input constraints, both with and without velocity measurements. It is proven that robot systems subject to bounded inputs can be globally asymptotically stabilized via a saturated proportional-integral-derivative (PID) control in agreement with Lyapunov's direct method and LaSalle's invariance principle. Advantages of the proposed controller include an absence of modeling parameters in the control law formulation and an ability to ensure actuator constraints are not breached. This is accomplished by selecting control gains a priori, removing the possibility of actuator failure due to excessive torque input levels. The effectiveness of the proposed approach is illustrated via simulations.

On Siphon Computation for Deadlock Control in a Class of Petri Nets

Abstract: As a structural object, siphons are well recognized in the analysis and control of deadlocks in resource allocation systems modeled with Petri nets. Many deadlock prevention policies characterize the deadlock behavior of the systems in terms of siphons and utilize this characterization to avoid deadlocks. This paper develops a novel methodology to find interesting siphons for deadlock control purposes in a class of Petri nets, i.e., a system of simple sequential processes with resources . Resource circuits in an are first detected, from which, in general, a small portion of emptiable minimal siphons can be derived. The remaining emptiable ones can be found by their composition. A polynomial-time algorithm for finding the set of elementary siphons is proposed, which avoids complete siphon enumeration. It is shown that a dependent siphon can always be controlled by properly supervising its elementary siphons. A computationally efficient deadlock control policy is accordingly developed. Experimental study shows the efficiency of the proposed siphon computation approach.

Semi-supervised clustering algorithm for community structure detection in complex networks

Abstract: Discovering a community structure is fundamental for uncovering the links between structure and function in complex networks. In this paper, we discuss an equivalence of the objective functions of the symmetric nonnegative matrix factorization (SNMF) and the maximum optimization of modularity density. Based on this equivalence, we develop a new algorithm, named the so-called SNMF-SS, by combining SNMF and a semi-supervised clustering approach. Previous NMF-based algorithms often suffer from the restriction of measuring network topology from only one perspective, but our algorithm uses a semi-supervised mechanism to get rid of the restriction. The algorithm is illustrated and compared with spectral clustering and NMF by using artificial examples and other classic real world networks. Experimental results show the significance of the proposed approach, particularly, in the cases when community structure is obscure.