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.

Advances in designs and mechanisms of semiconducting metal oxide nanostructures for high-precision gas sensors operated at room temperature

Abstract: High-precision gas sensors operated at room temperature are attractive for various real-time gas monitoring applications, with advantages including low energy consumption, cost effectiveness and device miniaturization/flexibility. Studies on sensing materials, which play a key role in good gas sensing performance, are currently focused extensively on semiconducting metal oxide nanostructures (SMONs) used in the conventional resistance type gas sensors. This topical review highlights the designs and mechanisms of different SMONs with various patterns (e.g. nanoparticles, nanowires, nanosheets, nanorods, nanotubes, nanofilms, etc.) for gas sensors to detect various hazardous gases at room temperature. The key topics include (1) single phase SMONs including both n-type and p-type ones; (2) noble metal nanoparticle and metal ion modified SMONs; (3) composite oxides of SMONs; (4) composites of SMONs with carbon nanomaterials. Enhancement of the sensing performance of SMONs at room temperature can also be realized using a photo-activation effect such as ultraviolet light. SMON based mechanically flexible and wearable room temperature gas sensors are also discussed. Various mechanisms have been discussed for the enhanced sensing performance, which include redox reactions, heterojunction generation, formation of metal sulfides and the spillover effect. Finally, major challenges and prospects for the SMON based room temperature gas sensors are highlighted.

Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links

Abstract: This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperfect, where the phenomena of signal quantization and data packet dropouts occur simultaneously. The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links. Sufficient conditions for the solvability of such a problem are established. Based on the derived conditions, an explicit expression of the desired Markov switching estimator is presented. Finally, two illustrated examples are given to show the effectiveness of the proposed design method.

Biological nitrogen removal with nitrification and denitrification via nitrite pathway

Abstract: Presently, the wastewater treatment practices can be significantly improved through the introduction of new microbial treatment technologies. To meet increasingly stringent discharge standards, new applications and control strategies for the sustainable removal of ammonium from wastewater have to be implemented. Partial nitrification to nitrite was reported to be technically feasible and economically favorable, especially when wastewater with high ammonium concentrations or low C/N ratios is treated. For successful implementation of the technology, the critical point is how to maintain partial nitrification of ammonium to nitrite. Partial nitrification can be obtained by selectively inhibiting nitrite oxidizing bacteria through appropriate regulation of the system's DO concentration, microbial SRT, pH, temperature, substrate concentration and load, operational and aeration pattern, and inhibitor. The review addressed the microbiology, its consequences for their application, the current status regarding application, and the future developments.

New Delay-Dependent Exponential $H_{\infty}$ Synchronization for Uncertain Neural Networks With Mixed Time Delays

Abstract: This paper establishes an exponential H infin synchronization method for a class of uncertain master and slave neural networks (MSNNs) with mixed time delays, where the mixed delays comprise different neutral, discrete, and distributed time delays. The polytopic and the norm-bounded uncertainties are separately taken into consideration. An appropriate discretized Lyapunov-Krasovskii functional and some free-weighting matrices are utilized to establish some delay-dependent sufficient conditions for designing delayed state-feedback control as a synchronization law in terms of linear matrix inequalities under less restrictive conditions. The controller guarantees the exponential H infin synchronization of the two coupled MSNNs regardless of their initial states. Detailed comparisons with existing results are made, and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

Structural Health Monitoring in mainland China: Review and Future Trends

Abstract: Structural health monitoring (SHM) technology has been successfully applied to understand the loads, environment actions, and behaviors of a structure subjected to various actions through solving a...