Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Synchronization of Dynamical Networks With Nonidentical Nodes: Criteria and Control

Abstract: This paper presents a framework for global synchronization of dynamical networks with nonidentical nodes. Several criteria for synchronization are given using free matrices for both cases of synchronizing to a common equilibrium solution of all isolated nodes and synchronizing to the average state trajectory. These criteria can be viewed as generalizations of the master stability function method for local synchronization of networks with identical nodes to the case of nonidentical nodes. The controlled synchronization problem is also studied. The control action, which is subject to certain constraints, is viewed as reorganization of the connection topology of the network. Synchronizability conditions via control are put forward. The synchronizing controllers can be obtained by solving an optimization problem.

A Review on Grain Refinement of Aluminum Alloys: Progresses, Challenges and Prospects

Abstract: Aluminum becomes the most popular nonferrous metal and is widely used in many fields such as packaging, building transportation and electrical materials due to its rich resource, light weight, good mechanical properties, suitable corrosion resistance and excellent electrical conductivity. Grain refinement, which is obtained by changing the size of grain structure by different techniques, is a preferred method to improve simultaneously the strength and plasticity of metallic materials. Therefore, grain refining of aluminum is regarded as a key technique in aluminum processing industry. Up to now, there have been a number of techniques for aluminum grain refining. All the techniques can be classified as four categories as follows: grain refining by vibration and stirring during solidification, rapid solidification, the addition of grain refiner and severe plastic deformation. Each of them has its own merits and demerits as well as applicable conditions, and there are still some arguments in the understanding of the mechanisms of these techniques. In this article, the research progresses and challenges encountered in the present techniques and the future research issues and directions are summarized.

Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

Abstract: In the recent years, data-driven machinery fault diagnostic methods have been successfully developed, and the tasks where the training and testing data are from the same distribution have been well addressed. However, due to sensor malfunctions, the training and testing data can be collected at different places of machines, resulting in the feature space with significant distribution discrepancy. This challenging issue has received less attention in the current literature, and the existing approaches generally fail in such scenarios. This article proposes a domain adaptation method for machinery fault diagnostics based on deep learning. Adversarial training is introduced for marginal domain fusion, and unsupervised parallel data are explored to achieve conditional distribution alignments with respect to different machine health conditions. Experiments on two rotating machinery datasets are carried out for validations. The results suggest the proposed method is promising to address the fault diagnostic tasks with data from different places of machines, further enhancing applicability of data-driven methods in real industries.

Cyanobacterium metallothionein decorated graphene oxide nanosheets for highly selective adsorption of ultra-trace cadmium

Abstract: Graphene oxide (GO) nanosheets were decorated with a cysteine-rich metal-binding protein, cyanobacterium metallothionein (SmtA). The SmtA–GO composites were characterized by means of FT-IR, AFM and TGA, giving rise to a SmtA binding amount of 867 mg g−1. The SmtA–GO composites exhibit ultra-high selectivity toward the adsorption of cadmium, i.e., the tolerant concentrations for the coexisting metal and anionic species were 1–800 000 fold improved after SmtA decoration with respect to bare GO. The SmtA–GO composites were then assembled onto the surface of cytopore microbeads and used for highly selective adsorption and preconcentration of ultra-trace cadmium. In comparison with bare GO (carboxyl-rich GO) loaded cytopore (GO@cytopore), SmtA–GO loaded cytopore (SmtA–GO@cytopore) shows a 3.3-fold improvement over the binding capacity of cadmium, i.e. 7.70 mg g−1 for SmtA–GO@cytopore compared to 2.34 mg g−1 for that by GO@cytopore. A novel procedure for selective cadmium preconcentration was developed using SmtA–GO@cytopore beads as a renewable sorption medium incorporated into a sequential injection lab-on-valve system, with detection by graphite furnace atomic absorption spectrometry (GFAAS). The cadmium retained on the SmtA–GO surface was eluted with a small amount of nitric acid. An enrichment factor of 14.6 and a detection limit of 1.2 ng L−1 were achieved within a linear range of 5–100 ng L−1 by using a sample volume of 1 mL. The procedure was validated by analyzing cadmium in certified reference materials and a series of environmental water samples.