Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Reliable Filtering With Strict Dissipativity for T-S Fuzzy Time-Delay Systems

Abstract: In this paper, the problem of reliable filter design with strict dissipativity has been investigated for a class of discrete-time T-S fuzzy time-delay systems. Our attention is focused on the design of a reliable filter to ensure a strictly dissipative performance for the filtering error system. Based on the reciprocally convex approach, firstly, a sufficient condition of reliable dissipativity analysis is proposed for T-S fuzzy systems with time-varying delays and sensor failures. Then, a reliable filter with strict dissipativity is designed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, numerical examples are provided to illustrate the effectiveness of the developed techniques.

Review of nanocarbon-engineered multifunctional cementitious composites

Abstract: As structural materials, cementitious materials are quasi-brittle and susceptible to cracking, and have no functional properties. Nanotechnology is introduced into cementitious materials to address these issues. Nano materials, especially nano carbon materials (NCMs) were found to be able to improve/modify the mechanical property, durability and functional properties of cementitious materials due to their excellent intrinsic properties and composite effects. Here, this review focuses on the recent progress of fabrication, properties, and structural applications of high-performance and multifunctional cementitious composites with NCMs including carbon nanofibers, carbon nanotubes and nano graphite platelets. The improvement/modification mechanisms of these NCMs to composites are also discussed.

Computer vision and deep learning–based data anomaly detection method for structural health monitoring:

Abstract: The widespread application of sophisticated structural health monitoring systems in civil infrastructures produces a large volume of data. As a result, the analysis and mining of structural health monitoring data have become hot research topics in the field of civil engineering. However, the harsh environment of civil structures causes the data measured by structural health monitoring systems to be contaminated by multiple anomalies, which seriously affect the data analysis results. This is one of the main barriers to automatic real-time warning, because it is difficult to distinguish the anomalies caused by structural damage from those related to incorrect data. Existing methods for data cleansing mainly focus on noise filtering, whereas the detection of incorrect data requires expertise and is very time-consuming. Inspired by the real-world manual inspection process, this article proposes a computer vision and deep learning–based data anomaly detection method. In particular, the framework of the propose...