scholarly journals A boundary element based method for accurate prediction of the surface pressure cross-spectral density matrix

2017 ◽  
Vol 141 (5) ◽  
pp. 3697-3697
Author(s):  
Jerry W. Rouse
Keyword(s):  
Density Matrix ◽  
Spectral Density ◽  
Boundary Element ◽  
Surface Pressure ◽  
Accurate Prediction ◽  
Spectral Density Matrix ◽  
Cross Spectral Density

scholarly journals Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3492 ◽  
Author(s):  
Jongkwon Choi ◽  
Youngmin Choo ◽  
Keunhwa Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Density Matrix ◽  
Spectral Density ◽  
Input Data ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Matrix Elements ◽  
Spectral Density Matrix ◽  
Cross Spectral Density

Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean.

scholarly journals Matrix Methods for Estimating the Coherence Functions from Estimates of the Cross-Spectral Density Matrix

1996 ◽  
Vol 3 (4) ◽  
pp. 237-246 ◽  
Author(s):  
D.O. Smallwood
Keyword(s):  
Density Matrix ◽  
Spectral Density ◽  
Multiple Input Multiple Output ◽  
Spectral Density Matrix ◽  
Multiple Input ◽  
Cross Spectral Density ◽  
Input Multiple Output ◽  
The Cross ◽  
Value Decomposition ◽  
Physical Order

It is shown that the usual method for estimating the coherence functions (ordinary, partial, and multiple) for a general multiple-input! multiple-output problem can be expressed as a modified form of Cholesky decomposition of the cross-spectral density matrix of the input and output records. The results can be equivalently obtained using singular value decomposition (SVD) of the cross-spectral density matrix. Using SVD suggests a new form of fractional coherence. The formulation as a SVD problem also suggests a way to order the inputs when a natural physical order of the inputs is absent.

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