Efficient SO3H-functionalized metal organic framework, UiO-66-NHSO3H, has been successfully prepared through a post-synthetic modification strategy of UiO-66-NH2 with Chlorosulfonic acid reagent. The UiO-66-NH2 has be...
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Efficient SO3H-functionalized metal organic framework, UiO-66-NHSO3H, has been successfully prepared through a post-synthetic modification strategy of UiO-66-NH2 with Chlorosulfonic acid reagent. The UiO-66-NH2 has been synthesized by an ultrasound-assisted (US) method and compared with the conventional-heating method. The UiO-66-NHSO3H was characterized by elemental analysis, SEM, PXRD and FT-IR to serve as an efficient heterogeneous catalyst for the benzimidazole formation and reductive amination reaction. Furthermore, the UiO-66-NHSO3H catalyst exhibits good stability, general applicability and excellent recycling performance. (C) 2019 Published by Elsevier Ltd.
The problem of air-fuel ratio (AFR) stabilization in spark ignition engines is addressed in this paper. The proposed strategy consists of proper switching among two control laws to improve the quality of the closed-lo...
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The paper addresses the problem of air-fuel ratio (AFR) stabilization applying learning techniques. The proposed strategy consists in iterative redesign of control coefficients based on the control obtained in a previ...
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The problem of air-fuel ratio (AFR) stabilization in spark ignition engines is addressed in this paper. The proposed strategy consists of proper switching among two control laws to improve the quality of the closed-lo...
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ISBN:
(纸本)9781424474264
The problem of air-fuel ratio (AFR) stabilization in spark ignition engines is addressed in this paper. The proposed strategy consists of proper switching among two control laws to improve the quality of the closed-loop system. The first control law is based on the a priori off-line identified engine model and ensures robust and reliable stabilization of the system at large, while the second control law is adaptive, provides on-line adaptive adjustment to the current fluctuations and improves the accuracy of the closed-loop system. The supervisor realizes the switching rule between these control laws providing better performance for AFR regulation. Results of application are reported and discussed.
The paper addresses the problem of air-fuel ratio (AFR) stabilization applying learning techniques. The proposed strategy consists in iterative redesign of control coefficients based on the control obtained in a previ...
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ISBN:
(纸本)9781424474264
The paper addresses the problem of air-fuel ratio (AFR) stabilization applying learning techniques. The proposed strategy consists in iterative redesign of control coefficients based on the control obtained in a previous step in conjunction with an adaptive scheme. The interesting feature of the proposed solution is that the adaptive control attempts to match not the AFR dynamics but rather the error dynamics originated by the substitution of the feedforward control in the real system. Results of application are reported and discussed.
We present a new method to detect random noise in seismic data using fuzzy Gustafson-Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The ne...
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We present a new method to detect random noise in seismic data using fuzzy Gustafson-Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections;we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f-k domain;finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise.
ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.
ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.
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