Уважаемые коллеги, доброго времени суток! Представляем вам британское научное издание Applied Acoustics. Журнал имеет первый квартиль, издаётся в Elsevier Ltd., его SJR за 2022 г. равен 0,864, импакт-фактор 3,4, печатный ISSN - 0003-682X, электронный - 1872-910X, предметная область Акустика и ультразвук. Вот так выглядит обложка:
Редактором является Шиу Кеунг Танг, контактные данные - S.Tang@hull.ac.uk.
С момента своего основания в 1968 году журнал Applied Acoustics публикует высококачественные исследовательские статьи, обеспечивающие современное освещение результатов исследований для инженеров и ученых, занимающихся применением акустики в самом широком смысле этого слова. Прикладная акустика рассматривает не только последние достижения в понимании акустики, но и способы использования этого понимания. Журнал стремится поощрять обмен практическим опытом посредством публикаций и тем самым создает фонд технологической информации, которая может быть использована для решения смежных проблем. Особенно приветствуется представление информации в графической или табличной форме. Если отчет о математической разработке является необходимой частью статьи, важно убедиться, что он присутствует только как неотъемлемая часть практического решения проблемы и подкреплен данными. Прикладная акустика поощряет обмен практическим опытом следующими способами:
• Полные документы;
• Краткие технические заметки;
• Обзорные статьи.
Таким образом, предоставляется обширная технологическая информация, которая может быть использована для решения связанных с этим проблем. Приветствуются рукописи, касающиеся всех областей применения акустики, начиная от медицины и неразрушающего контроля и заканчивая окружающей средой и зданиями.
Адрес издания - https://www.sciencedirect.com/journal/applied-acoustics
Пример статьи, название - Underwater spectral line enhancement and transient interference suppression based on constrained non-negative matrix factorization. Заголовок (Abstract) - Spectral line enhancement and transient interference suppression of underwater objects are critical issues for passive sonar systems. Conventional approaches for processing spectral lines have focused on either time-domain or frequency-domain methods. In this study, the constrained non-negative matrix factorization is proposed to process the underwater spectral lines in the joint time-frequency domain. Based on the sparsity of spectral lines in the frequency domain, the sparseness criterion is utilized to constrain the basis matrix that represents the frequency-mode of the signal. The correlation between sparsity and frequency estimation accuracy is examined with weight coefficients, and an effective weight coefficient interval for the sparseness term is determined to optimize the detection of the spectral line. To address the issue of abrupt changes in signal energy caused by transient interference, the temporal continuity criterion is applied to constrain the coefficient matrix representing the temporal gain mode of the signal. An analysis is conducted to determine the impact of weight coefficient on the continuity of the coefficient matrix, and the optimal weight coefficient of the temporal continuity term is established to suppress local transient strong interference. Experimental results demonstrate that the algorithm significantly enhances the capacity for the detection and extraction of spectral lines.
Section snippets
Signal model
In passive sonar object detection, the object-radiated noise can be modeled asx(t)=s(t)+n(t)+i(t),t∈[0,T] where T is the signal duration, s(t) is the spectral line component in the object-radiated noise and can be expressed ass(t)=Acos(2πf0t+φ) where, A is the signal amplitude, f0 is the frequency of the spectral line, and φ is the random phase evenly distributed inside [0,2π]. n(t) is the ambient background noise, distributed throughout the entire duration of the observed signal. i(t) is a
Algorithm principle
NMF is a matrix factorization that imposes non-negativity constraints on all elements in the input matrix. The objective of NMF is to factorize the input matrix into the product of two factor matrices with non-negative elements. X is the input matrix with dimension M×N. W is the first-factor matrix, also known as the basis matrix, and the dimension is M×K. While the second-factor matrix, H, referred to as the coefficient matrix, has dimension K×N. Notably, K≤min(M,N), implying that NMF can
Reconstruction error
The reconstruction error is a fundamental objective function term of NMF, which quantifies the difference between the input matrix X and the output product matrix WH. There are several objective functions commonly used to measure the reconstruction error, including Euclidean distance, Kullback-Leibler (KL) divergence, and Itakura-Saito (IS) divergence. The divergence-based objective function is more sensitive to low-energy signal components compared to the other two functions. Yang [32]
Evaluation criteria
Sparsity and root mean square error (RMSE) are adopted as evaluation criteria to evaluate the performance of spectral line enhancement based on the sparseness criterion. The influence of temporal continuity criterion is not being considered, and therefore β is set to 0.
Evaluation criteria
STD is utilized to evaluate the continuity of coefficient matrix H. STD can reflect the dispersion degree of data and indirectly indicate the suppression performance of temporal continuity criterion on transient interference. STD is expressed as followsσ=∑iN(xi−x¯)2N where, N is the dimension of input x, and x is the coefficient matrix H in this section. A smaller STD signifies a stronger continuity of H and a better suppression effect on transient interference.
Simulation analysis
In this section, we focus on
Experimental verification
The performance of constrained NMF was verified by lake experimental data. The schematic diagram of lake experiment is depicted in Fig. 8. The experiment was conducted to simulate the passive detection process. The surveying vessels were fixed at the set position. Various equipment, including a signal generator, power amplifier, preamplifier, signal conditioner, and signal acquisition display, were connected to the transducer and placed on the surveying vessel. The acoustic source and receiving
Conclusion
This study explores the use of the constrained NMF for processing underwater spectral lines, with the goal of enhancing the spectral line and suppressing transient interference in the joint time-frequency domain. The basis matrix and coefficient matrix of NMF are constrained by sparseness and temporal continuity criteria, respectively. The results indicate that the performance of sparseness criterion goes through three stages, namely unconstrained, effective-constrained, and over-constrained
