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.

State Estimation and Sliding-Mode Control of Markovian Jump Singular Systems

Abstract: This paper is concerned with the state estimation and sliding-mode control problems for continuous-time Markovian jump singular systems with unmeasured states. Firstly, a new necessary and sufficient condition is proposed in terms of strict linear matrix inequality (LMI), which guarantees the stochastic admissibility of the unforced Markovian jump singular system. Then, the sliding-mode control problem is considered by designing an integral sliding surface function. An observer is designed to estimate the system states, and a sliding-mode control scheme is synthesized for the reaching motion based on the state estimates. It is shown that the sliding mode in the estimation space can be attained in a finite time. Some conditions for the stochastic admissibility of the overall closed-loop system are derived. Finally, a numerical example is provided to illustrate the effectiveness of the proposed theory.

Rapid acceleration of ferrous iron/peroxymonosulfate oxidation of organic pollutants by promoting Fe(III)/Fe(II) cycle with hydroxylamine.

Abstract: The reaction between ferrous iron (Fe(II)) with peroxymonosulfate (PMS) generates reactive oxidants capable of degrading refractory organic contaminants. However, the slow transformation from ferric iron (Fe(III)) back to Fe(II) limits its widespread application. Here, we added hydroxylamine (HA), a common reducing agent, into Fe(II)/PMS process to accelerate the transformation from Fe(III) to Fe(II). With benzoic acid (BA) as probe compound, the addition of HA into Fe(II)/PMS process accelerated the degradation of BA rapidly in the pH range of 2.0-6.0 by accelerating the key reactions, including the redox cycle of Fe(III)/Fe(II) and the generation of reactive oxidants. Both sulfate radicals and hydroxyl radicals were considered as the primary reactive oxidants for the degradation of BA in HA/Fe(II)/PMS process with the experiments of electron spin resonance and alcohols quenching. Moreover, HA was gradually degraded to N2, N2O, NO2 (−), and NO3 (−), while the environmentally friendly gas of N2 was considered as its major end product in the process. The present study might provide a promising idea based on Fe(II)/PMS process for the rapid degradation of refractory organic contaminants in water treatment.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Abstract: A divide-and-conquer method classifying sentence types before sentiment analysis.Classifying sentence types by the number of opinion targets a sentence contain.A data-driven approach automatically extract features from input sentences. Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.