Simulation studies of multiple dipole neuromagnetic source localization: model order and limits of source resolution

1993 ◽  
Vol 40 (6) ◽  
pp. 529-540 ◽  
Author(s):  
S. Supek ◽  
C.J. Aine
Keyword(s):  
Source Localization ◽  
Simulation Studies ◽  
Model Order ◽  
Localization Model ◽  
Source Localization Model

Acoustic source localization model using in-skull reverberation and time reversal

2007 ◽  
Vol 90 (6) ◽  
pp. 063902 ◽  
Author(s):  
Stefan Catheline ◽  
Mathias Fink ◽  
Nicolas Quieffin ◽  
Ros Kiri Ing
Keyword(s):  
Source Localization ◽  
Time Reversal ◽  
Acoustic Source ◽  
Acoustic Source Localization ◽  
Localization Model ◽  
Source Localization Model

Deep learning-enhanced single point sound source localization for spherical microphone array

2021 ◽  
Vol 263 (4) ◽  
pp. 2279-2283
Author(s):  
Soo Young Lee ◽  
Jiho Chang ◽  
Seungchul Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Source Localization ◽  
Sound Source ◽  
Single Point ◽  
Data Driven ◽  
Sound Source Localization ◽  
Localization Model ◽  
Source Localization Model

In this contribution, we present a high-resolution and accurate sound source localization via a deep learning framework. While the spherical microphone arrays can be utilized to produce omnidirectional beams, it is widely known that the conventional spherical harmonics beamforming (SHB) has a limit in terms of its spatial resolution. To accomplish the sound source localization with high resolution and preciseness, we propose a convolutional neural network (CNN)-based source localization model as a way of a data-driven approach. We first present a novel way to define the source distribution map that can spatially represent the single point source's position and strength. By utilizing paired dataset with spherical harmonics beamforming maps and our proposed high-resolution maps, we develop a fully convolutional neural network based on the encoder-decoder structure for establishing the image-to-image transformation model. Both quantitative and qualitative results are demonstrated to evaluate the powerfulness of the proposed data-driven source localization model.

scholarly journals Passive Radar Source Localization Based on Cuckoo Search NVTDOA

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Gang Niu ◽  
Jie Gao ◽  
Tai-hang Du
Keyword(s):  
Source Localization ◽  
Search Algorithm ◽  
Sufficient Conditions ◽  
Pulse Signal ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Passive Radar ◽  
Electronic Warfare ◽  
Periodic Rotation ◽  
Localization Model

In order to meet the requirement of passive radar source localization in electronic warfare, the concept of the virtual time differences of arrival (VTDOA) is proposed by taking advantage of the characteristics of the same UAV in different positions at different times and the periodic rotation of radar pulse signal. The VTDOAs are the passive localization information defined as the time differences of the radar pulse transmission from the radar position to different virtual receivers. Firstly, a nonlinear VTDOA (NVTDOA) localization model is constructed. Moreover, sufficient conditions for accurately calculating the periodic integers in the model are analyzed, and the observability conditions of the localization model determined are deduced. Secondly, the convergence solution of the NVTDOA localization equation is obtained by Cuckoo search algorithm; thus, passive radar source localization is realized. Finally, the performance of the proposed method is verified by comparing with the existing methods.

Nonlinear Modeling of Mechanical Gas Face Seal Systems Using Proper Orthogonal Decomposition

2005 ◽  
Author(s):  
Haojiong Zhang ◽  
Brad A. Miller ◽  
Robert G. Landers
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Initial Conditions ◽  
Nonlinear Modeling ◽  
Orthogonal Decomposition ◽  
Simulation Studies ◽  
Face Seal ◽  
Model Order ◽  
Reduced Order ◽  
Gas Face Seal ◽  
Proper Orthogonal

A nonlinear reduced-order modeling approach based on Proper Orthogonal Decomposition (POD) is utilized to develop an efficient low order model, based on ordinary differential equations, for mechanical gas face seal systems. An example of a coned mechanical gas face seal in a flexibly mounted stator configuration is presented. The axial mode is modeled, and simulation studies are conducted using different initial conditions and forcing inputs. The results agree well with a fully meshed finite difference model, while the resulting model order is significantly decreased.

scholarly journals Efficient Technique for Model Order Reduction Retaining Non-Minimum Phase Characteristics using Clustering Dominant Pole-Zero Algorithm

2020 ◽  
Vol 7 (2) ◽  
pp. 26-31
Author(s):  
Abraham Anuj ◽  
N. Pappa ◽  
Daniel Honc
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Least Square Method ◽  
Least Square ◽  
Phase Behaviour ◽  
Cluster Center ◽  
Minimum Phase ◽  
Simulation Studies ◽  
Model Order ◽  
Dominant Pole

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">Model Order Reduction (MOR) challenges a high dimensional problem and plays a key role in areas where dynamic simulation studies are necessary for modern simulation strategy. Many conventional reduction methods namely, reduced order models based on Least Square Method (LSM), Balanced Truncation, Hankel Norm reduction, Dominant Pole Algorithm (DPA) and CDPA method have been developed in the field of control theory. Among these, recently proposed Clustering Dominant Pole Algorithm (CDPA) is able to compute the full set of dominant poles and their cluster center efficiently. In this paper, a hybrid algorithm for model order reduction known as Clustering Dominant Pole-Zero Algorithm (CDPZA) is proposed to identify and preserve the dominant zeros of the processes exhibiting non-minimum phase behaviour. The CDPZA method combines the features of clustering method and DPA. Further, the cluster centers of the dominant zeros in the numerator polynomial are determined using factor division algorithm. The Benchmark HiMAT system of 6<sup>th</sup> order is considered for testing and validation of the proposed algorithm. The simulation studies are carried out to show the efficacy of the proposed algorithm over conventional MOR algorithms.</span>

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