Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Distributed Primal–Dual Subgradient Method for Multiagent Optimization via Consensus Algorithms

Abstract: This paper studies the problem of optimizing the sum of multiple agents' local convex objective functions, subject to global convex inequality constraints and a convex state constraint set over a network. Through characterizing the primal and dual optimal solutions as the saddle points of the Lagrangian function associated with the problem, we propose a distributed algorithm, named the distributed primal-dual subgradient method, to provide approximate saddle points of the Lagrangian function, based on the distributed average consensus algorithms. Under Slater's condition, we obtain bounds on the convergence properties of the proposed method for a constant step size. Simulation examples are provided to demonstrate the effectiveness of the proposed method.

Highly Thermoconductive, Thermostable, and Super-Flexible Film by Engineering 1D Rigid Rod-Like Aramid Nanofiber/2D Boron Nitride Nanosheets.

Abstract: Polymer-based thermal management materials have many irreplaceable advantages not found in metals or ceramics, such as easy processing, low density, and excellent flexibility. However, their limited thermal conductivity and unsatisfactory resistance to elevated temperatures ( 100 MPa, 450 °C) enable effective thermal management for microelectrodes operating at temperatures beyond 200 °C.

Robust Structured Nonnegative Matrix Factorization for Image Representation

Abstract: Dimensionality reduction has attracted increasing attention, because high-dimensional data have arisen naturally in numerous domains in recent years. As one popular dimensionality reduction method, nonnegative matrix factorization (NMF), whose goal is to learn parts-based representations, has been widely studied and applied to various applications. In contrast to the previous approaches, this paper proposes a novel semisupervised NMF learning framework, called robust structured NMF, that learns a robust discriminative representation by leveraging the block-diagonal structure and the $\ell _{2,p}$ -norm (especially when $0 ) loss function. Specifically, the problems of noise and outliers are well addressed by the $\ell _{2,p}$ -norm ( $0 ) loss function, while the discriminative representations of both the labeled and unlabeled data are simultaneously learned by explicitly exploring the block-diagonal structure. The proposed problem is formulated as an optimization problem with a well-defined objective function solved by the proposed iterative algorithm. The convergence of the proposed optimization algorithm is analyzed both theoretically and empirically. In addition, we also discuss the relationships between the proposed method and some previous methods. Extensive experiments on both the synthetic and real-world data sets are conducted, and the experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods.