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: Microstructure & Control theory. 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).

Small in-fiber Fabry-Perot low-frequency acoustic pressure sensor with PDMS diaphragm embedded in hollow-core fiber

Abstract: A small and low cost extrinsic Fabry-Perot (FP) fiber low-frequency acoustic pressure sensor (EFPAS) based on polydimethylsiloxane (PDMS) diaphragm is proposed. The EFPAS is fabricated by fusion splicing a single mode fiber (SMF) with a hollow-core fiber (HCF), which embed a PDMS diaphragm by the capillary effect, to form an air micro-cavity. The structural strength of the whole thin diaphragm based sensing probe is improved by the ingenious design, and a high precision optical fiber cutting system can be used to control the length of air micro-cavity. Simulation proves that the PDMS diaphragm with large radius and small thickness will help to improve the sensitivity, but there is a tradeoff between sensitivity and the mechanical strength. Experimental results show that the sensor has the acoustic pressure sensitivity of 0.427 mV/mPa with a high linear pressure response in the range of 5 mPa–720 mPa. A stable signal-to-noise ratio (SNR) about 80 dB with a noise floor at 0 dB can be realized at 30 Hz and a 0.5 Hz resolution can be observed ranging from 10 to 50 Hz of low-frequency acoustic wave (The relative error of measured frequency is lower than 1%). Its high precision of low-frequency acoustic measurement and simple fabrication process make it an attractive tool for acoustic sensing and photo-acoustic spectroscopy.

An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification

Abstract: Diabetic retinopathy (DR) is one kind of eye disease that is caused by overtime diabetes. Lots of patients around the world suffered from DR which may bring about blindness. Early detection of DR is a rigid quest which can remind the DR patients to seek corresponding treatments in time. This paper presents an automatic image-level DR detection system using multiple well-trained deep learning models. Besides, several deep learning models are integrated using the Adaboost algorithm in order to reduce the bias of each single model. To explain the results of DR detection, this paper provides weighted class activation maps (CAMs) that can illustrate the suspected position of lesions. In the pre-processing stage, eight image transformation ways are also introduced to help augment the diversity of fundus images. Experiments demonstrate that the method proposed by this paper has stronger robustness and acquires more excellent performance than that of individual deep learning model.

Kinetics of CO2/Coal Gasification in Molten Blast Furnace Slag

Abstract: The coal/CO2 gasification reactions in molten BF (blast furnace) slag were studied kinetically by temperature-programmed thermogravimetry using a thermal analyzer. The effect of heating rates and molten BF slag on coal gasification were studied, and the activation energies, frequency factors, and most possibility mechanism functions were calculated. The results show that the order of reactivity sequence at these temperatures was DT (Datong) coal > FX (Fuxin) coal > coke. With the increase in heating rate, the carbon conversion, and the peak value of reaction rate increased at the same reaction time, the carbon conversion curve shifts to a higher temperature and the reaction rate curve shifts rightward systematically, both of the time required for the carbon conversion to reach nearly unity and the time necessary for reaction rate to reach its maximum decreased. The carbon conversion and reaction rates were sensitive to BF slag; at the same time, the carbon conversion and reaction rates of coal gasificatio...

Multiblock Concurrent PLS for Decentralized Monitoring of Continuous Annealing Processes

Abstract: In this paper, a data-driven multiblock concurrent projection to latent structures (CPLS) method is proposed for monitoring large-scale manufacturing lines, particularly for cold rolling continuous annealing processes (CAPs) fault diagnosis. The proposed method provides decentralized process monitoring and helps localize faults in both input variables and output variables concurrently. First, the CPLS-based process monitoring method is briefly reviewed. Second, a multiblock CPLS algorithm, which incorporates process block partition, is proposed to diagnose faults relevant to process inputs or outputs with a decentralized structure. For the CAP line application, tension-specific variations, roll-specific variations, and tension–roll covariations are analyzed in each partitioned block. Furthermore, within the roll-specific subspace of an abnormal block, a delay-alignment scheme based on strip transportation delay is proposed to diagnose defective processing materials.