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Weili Shao,Wanli Yue,Gaihuan Ren,Chen Cui,Junpeng Xiong,Ling Wang,Tong Lu,Wanjun Bu,Fan Liu,Jianxin He 한국섬유공학회 2022 Fibers and polymers Vol.23 No.5
Electrospun nanofibers are widely used in air-filtration materials because of their fine fiber diameter, small poresize, and high porosity. However, nanofiber membranes exhibit a dense structure, such that they present a large resistance toany air flow. In this study, we set out to design and develop composite nanofiber materials with fluffy structures, as well asblended structures of coarse and fine fibers, through electrospinning technology. These materials could be used in airfiltration applications, given that they offer high efficiency and low resistance. The results show that, compared with purePAN nanofibers, the diameter of PAN nanofibers doped with CNT decreased from 192.36 to 124.37 nm; when the spinningratio of PS coarse fiber (1053 nm) and PAN/CNT fine fiber is 3:1, the resulting nanofiber membrane materials has an obviousthree-dimensional structure, with a specific surface area of 103.16 m2/g, a pore size of 2.25 μm, and a quality factor of0.0947 Pa-1. Under test conditions featuring an air flow of 32 L/min, and 0.3 μm NaCl aerosol particles, the filtrationefficiency was 99.37 % and the resistance was 35 Pa. Furthermore, the dust-holding capacity of the nanofiber air-filter paperwas found to be almost the same as that of melt-blown air-filter papers. Even after being water-soaked 50 times, the filteringefficiency of the nanofiber air-filter paper was still higher. Interestingly, the nanofiber membrane materials doped with CNTalso exhibited excellent sound-absorption abilities. Thus, the composite nanofiber material could potentially be applied toareas with serious air pollution and high noise pollution.
Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data
Ling Xu,Weili Xiong,Ahmed Alsaedi,Tasawar Hayat 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.4
This paper studies the problem of parameter estimation for frequency response signals. For a linear system, the frequency response is a sine signal with the same frequency as the input sine signal. When a multifrequency sine signal is applied to a system, the system response also is a multi-frequency sine signal. The signal modeling for multi-frequency sine signals is very difficult due to the highly nonlinear relations between the characteristic parameters and the model output. In order to obtain the parameter estimates of the multi-frequency sine signal, the signal modeling methods based on statistical identification are proposed by means of the dynamical window discrete measured data. By constructing a criterion function with respect to the model parameters to be estimated, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition. Moreover, the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm. The simulation results show the effectiveness of the proposed methods.
Robust Gradient Estimation Algorithm for a Stochastic System with Colored Noise
Wentao Liu,Weili Xiong 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.2
This paper studies the parameter estimation algorithms of a finite impulse response system with colored noise. To suppress the negative effects of the colored noises, a novel gradient-based algorithm is developed by means of the cost function of the continuous mixed p-norm (CMPN). It combines the p-norms for 1 6 p 6 2, which control the proportions of the error norms and generate an adjustable gain to adapt the data quality. Moreover, to improve the convergence rate, a CMPN multi-innovation gradient recursive algorithm is derived through expanding the innovation scalar to the innovation vector. Finally, two examples are given to demonstrate the validity of the proposed algorithms.
Wentao Liu,Weili Xiong 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.1
This paper discusses the parameter estimation problems for the output-error moving average (OEMA) systems under stochastic environments. The estimation problems with unknown inner variables and unmeasurable noise terms existed in the information vector are solved by the auxiliary model framework. Meanwhile, the algorithms utilize the continues mixed p-norm (CMPN) method to control the proportions of the error norms, which take into account each p-norm of errors for 1 6 p 6 2. To improve the identification accuracy further, a multiinnovation CMPN optimization algorithm is developed by expanding the scalar innovation to the innovation vector. The proposed optimal algorithms offer faster tracking speed and can obtain higher parameter estimation accuracy for both stochastic white noise and α-stable noise. Two examples of identification of OEMA systems are given to validate the theoretical analysis.
Xinyue Wang,Junxia Ma,Weili Xiong 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.3
In this paper, the parameter estimation of bilinear state-space systems with missing outputs is studied. The bilinear model is transformed into a linear time-varying state-space model, and Kalman smoother with a timevarying gain is adopted to estimate missing outputs and unmeasurable states. Under the expectation-maximization (EM) algorithm scheme, an iterative estimation algorithm based on Kalman smoother is derived, in which the unknown parameters, missing outputs, and unmeasurable states can be estimated simultaneously. Two simulation examples, including a numerical example and a three-tank system experiment, are adopted to verify the effectiveness of the proposed algorithm.
Iterative Identification Algorithms for Input Nonlinear Output Error Autoregressive Systems
Feng Ding,Junxia Ma,Weili Xiong 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.1
This paper focuses on the parameter estimation problems of input nonlinear output error autoregressivesystems. Based on the key variables separation technique and the auxiliary model identification idea, the output ofthe system is expressed as a linear combination of all the system parameters, the unknown inner variables in theinformation vector are replaced with the outputs of the auxiliary model and a gradient based and a least squaresbased iterative identification algorithms are derived. Simulation example is provided to illustrate the effectivenessof the proposed algorithms.