文献类型：期刊文献

英文题名：Discrete Slime Mold Algorithm used for Sparse TFM of Ultrasonic Array

作者：Zhu, Wenfa[1,2]; Liu, Sihao[1]; Xiang, Yanxun[2]; Fan, Guopeng[1]; Zhang, Haiyan[3]; Qi, Weiwei[1]; Zhang, Hui[1]

机构：[1] School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai, 201620, China; [2] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China; [3] School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230091499)

语种：英文

外文关键词：Acoustic fields - Computational efficiency - Matrix algebra - Molds - Simulated annealing - Ultrasonic testing

摘要：Large amounts of data must be acquired and processed for full matrix capture and total focus imaging (FMC-TFM), so its real-time performance cannot meet needs of the rapid and automatic detection of large-scale defects. In order to handle the issue of unsatisfactory real-time performance of FMC-TFM, the array element positions in an ultrasonic array are sparsely optimized, and a sparse matrix is used for full focus, which can improve computational efficiency. However, intelligent optimization methods such as genetic algorithms use binary coding to solve such a problem. The time used to calculate the array element distribution increases with the number of array elements, which isn’t appropriate for the sparse design of a large-scale ultrasonic array. This article researches a rail defect sparse array full focus imaging algorithm, and discrete slime mold algorithm (DSMA) proposes quickly deal with the sparse optimization issue of a discrete ultrasonic array. In slime mold algorithm (SMA), the slime mold position is coded in real numbers to overcome the disadvantages of binary coding. In the optimization process, the mapping model between the slime mold and ultrasonic array positions is established. A fitness function, which have a narrow main lobe and low side lobe, is constructed to obtain the sparse array position with the best performance to optimize the array performance. A simulation model of the rail finite element sound field is established and verified by experiments. The outcomes indicate that, compared with FMC-TFM of full array elements, the proposed rail sparse array full focus imaging algorithm can achieve data sparsity of 82% and imaging efficiency of 88% without affecting imaging quality. Compared with genetic algorithm and simulated annealing algorithm, the API performance index of the array designed by the proposed DSMA is improved by about 11%. As for the sparse optimization problem of large array ultrasound, under the same array performance index, the computing efficiency of DSMA is 63% higher than that of genetic algorithm when the sparsity rate is 0.5.We investigate numerically the effects of long-range temporal and spatial correlations based on probability distribution of interface width W(L,t) within the early growth regimes in the (1+1)-dimensional Kardar-Parisi-Zhang (KPZ) growth system. Through extensive numerical simulations, we find that longrange temporally correlated noise could not significantly impact the distribution form of interface width. Generally, W(L,t) obeys approximately TracyWidom Gaussian symplectic ensembles(TW-GSE) when the temporal correlation exponent θ ≥ 0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution form of W(L,t) approaches to TW-GSE, and when ρ > 0.20, the distribution becomes more asymmetric, leptokurtic, and fat-tailed, and tends to an unknown form, which approaches gradually to Gumbel distribution. ? 2023, The Authors. All rights reserved.