Highly efficient synthesis of LTA-type aluminophosphate molecular sieve by improved ionothermal method

This work was supported by the National Natural Science Foundation of China (Grant No. 21306072, 21203081) and Development Program of Lanzhou University of Technology for excellent teachers (Grant No. Q201113). WZ thanks EPSRC for financial support to upgrade the SEM facilities (No. EP/F019580/1). We cordially thank the Reviewers and Editors for providing us with valuable comments and suggestions.LTA-type aluminophosphate molecular sieve has been successfully synthesized by improvedionothermal method from a gel containing low-dosage ionic liquids. The optimum syntheticconditions of this material are refined. The resultant LTA molecular sieves were characterized byXRD, SEM, TG-DTA, CHN elemental analysis, solution 13C NMR, EDX, TEM and N2physisorption. The composition of the resulting LTA-type molecular sieves is determined to be(Al12P12O48)(C4H9NO)2(C8H15N2+)2(F-)2, for which morpholine together with1-butyl-3-methylimidazolium cations act as the structure-directing agent. The high zeolite yield, as well as the high specific surface area and micropore volume for the calcined LTA-type materials imply that these zeolitic materials have a high potential in applications.PostprintPeer reviewe

A Two-Stage Structural Damage Detection Method Based on 1D-CNN and SVM

Deep learning has been applied to structural damage detection and achieved great success in recent years, such as the popular structural damage detection methods based on structural vibration response and convolutional neural networks (CNN). However, due to the limited number of vibration response samples that can be acquired in practice for damage detection, the CNN-based models may not be fully trained; thus, their performance for identifying different damage severity as well as the damage locations may be reduced. To solve this issue, in this paper, we follow the strategy of "divide-and-conquer" and propose a two-stage structural damage detection method. Specifically, in the first stage, a 1D-CNN model is constructed to extract the damage features automatically and identify the damage locations. In the second stage, a support vector machine (SVM) model and wavelet packet decomposition technique are combined to further quantify the damage. Experiments are conducted on an eight-level steel frame structure, and the accuracy of the experimental results is greater than 99%, which demonstrates the superiority of the proposed method compared to the state-of-the-art approaches

A Two-Stage Structural Damage Detection Method Based on 1D-CNN and SVM

Deep learning has been applied to structural damage detection and achieved great success in recent years, such as the popular structural damage detection methods based on structural vibration response and convolutional neural networks (CNN). However, due to the limited number of vibration response samples that can be acquired in practice for damage detection, the CNN-based models may not be fully trained; thus, their performance for identifying different damage severity as well as the damage locations may be reduced. To solve this issue, in this paper, we follow the strategy of "divide-and-conquer" and propose a two-stage structural damage detection method. Specifically, in the first stage, a 1D-CNN model is constructed to extract the damage features automatically and identify the damage locations. In the second stage, a support vector machine (SVM) model and wavelet packet decomposition technique are combined to further quantify the damage. Experiments are conducted on an eight-level steel frame structure, and the accuracy of the experimental results is greater than 99%, which demonstrates the superiority of the proposed method compared to the state-of-the-art approaches

Classification and Identification Method of Radio Fuze Target and Sweep Jamming Signals Based on Third-Order Spectrum Features

To overcome the problem of insufficiency of linear frequency modulation (LFM) radio fuzes against sweep-type jamming, a method is proposed to classify and identify radio fuze targets and interfering signals based on third-order spectrum features. Using the measured data of an LFM radio fuze, the third-order spectral transform is applied to the output signals of the detector end under the action of the target and several amplitude modulated sweeping interfering signals, and the amplitude mean value, third-order spectral amplitude entropy, and third-order spectral singular value entropy based on the third-order spectrum are extracted as three-dimensional features. The experimental results show that the classification and identification of targets and AM sweep-type interference using the third-order spectral features of the signal at the detector end has a high success rate, with a comprehensive identification accuracy of 98.33%