Source Localization Method Research Articles

Improved Method for Acoustic Emission Source Location Based on DFB Laser Sensor and Particle Swarm Optimization Algorithm

Improved Method for Acoustic Emission Source Location Based on DFB Laser Sensor and Particle Swarm Optimization Algorithm

A novel method for acoustic emission source location in CFRP-concrete debonding using ΔT mapping and DBSCAN algorithm

Concrete-CFRP composite structures exhibit a stiff behavior, leading to sudden failure. Monitoring the brittle nature of this failure is crucial, and the acoustic emission (AE) technique provides a promising tool for locating and classifying damage. However, the conventional source location methodology assumes that the acoustic wave travels in a straight path with constant velocity. This assumption does not hold true for concrete-CFRP composite structures, where the presence of microcracks and CFRP layers induces anisotropy and defects, altering the wave speed and traveling direction. This, in turn, introduces errors in the source location. To address these challenges, this research presents a novel source location method using ΔT maps and achieving convergence through the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The methodology demonstrates significant improvement in AE source location when applied to concrete-CFRP composite specimens in the presence defects with varying magnitudes. Additionally, the proposed method shows effectiveness in monitoring damage progression when applied to single-lap shear tests performed on concrete specimens with attached CFRP laminate.

Improved acoustic source localization method for crack identification in structures

Acoustic Emission (AE) monitoring is considered one of the popular non-destructive testing (NDT) methodologies that have been used to predict and identify the location of damage in critical civil infrastructure. In this paper, an improved AE crack localization method is proposed by integrating the empirical mode decomposition (EMD)-based signal decomposition method with a source localization model. Unlike the conventional AE method, the proposed method is free of the use of bulk AE parameters such as counts, rise time, signal strength, and energy. First, EMD is used to minimize the presence of noise in the recorded AE waveforms and extract the key AE components. Then, key AE events are located using the source localization model to localize the crack in concrete structures. The performance of the proposed method is validated experimentally on small and large-scale concrete beams, where the damage is induced using progressive static load testing. In particular, the large-scale beams are designed for flexural and shear mode failure to evaluate the performance of the proposed method under various types of damage. Finally, the results of the proposed method are compared with the traditional method that uses raw AE waveforms and the method that uses bandpass-filtered AE waveforms. The results show higher crack location accuracy of the proposed method than the other methods, which makes it a suitable approach as a crack localization technology.

Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome.

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.

Religious Chanting and Self-Related Brain Regions: A Multi-Modal Neuroimaging Study.

This protocol presents a multi-modal neuroimaging approach to explore the potential brain activity associated with repetitive religious chanting, a widespread form of mind training in both Eastern and Western cultures. High-density electroencephalogram (EEG), with its superior temporal resolution, allows for capturing the dynamic changes in brain activity during religious chanting. Through source localization methods, these can be attributed to various alternative potential brain region sources. Twenty practitioners of religious chanting were measured with EEG. However, the spatial resolution of EEG is less precise, in comparison to functional magnetic resonance imaging (fMRI). Thus, one highly experienced practitioner underwent an fMRI scanning session to guide the source localization more precisely. The fMRI data helped guide the selection of EEG source localization, making the calculation of K-means of the EEG source localization in the group of 20 intermediate practitioners more precise and reliable. This method enhanced EEG's ability to identify the brain regions specifically engaged during religious chanting, particularly the cardinal role of the posterior cingulate cortex (PCC). The PCC is a brain area related to focus and self-referential processing. These multimodal neuroimaging and neurophysiological results reveal that repetitive religious chanting can induce lower centrality and higher delta-wave power compared to non-religious chanting and resting state conditions. The combination of fMRI and EEG source analysis provides a more detailed understanding of the brain's response to repetitive religious chanting. The protocol contributes significantly to the research on the neural mechanisms involved in religious and meditative practices, which is becoming more prominent nowadays. The results of this study could have significant implications for developing future neurofeedback techniques and psychological interventions.

A High-Resolution Time Reversal Method for Target Localization in Reverberant Environments

Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the time reversal (TR) method can overcome multipath effects and realize adaptive focusing, it is particularly suitable for SSL in a reverberant environment. On the basis of the significant advantages of the sparse Bayesian learning algorithm in the estimation of wave direction, a novel SSL is proposed in reverberant environments. First, the sound propagation model in a reverberant environment is studied and the TR focusing signal is obtained. We then use the sparse Bayesian framework to locate the broadband low-frequency sound source. To validate the effectiveness of the proposed method for broadband low-frequency targeting in a reverberant environment, simulations and real data experiments were performed. The localization performance under different bandwidths, different numbers of microphones, signal-to-noise ratios, reverberation times, and off-grid conditions was studied in the simulation experiments. The practical experiment was conducted in a reverberation chamber. Simulation and experimental results indicate that the proposed method can achieve satisfactory spatial resolution in reverberant environments and is robust.

Noise source localization using deep learning

Summary Ambient noise source localization is of great significance for estimating seismic noise source distribution, understanding source mechanisms and imaging subsurface structures. The commonly used methods for source localization, such as the matched field processing and the full-waveform inversion, are time-consuming and not applicable for time-lapse monitoring of the noise source distribution. We propose an efficient alternative of using deep learning for noise source localization. In the neural network, the input data are noise cross-correlation functions and the output are matrices containing the information of noise source distribution. It is assumed that the subsurface structure is a horizontally layered earth model and the model parameters are known. A wavefield superposition method is employed to efficiently simulate ambient noise data with quantities of local noise sources labelled as training datasets. We use a weighted binary cross-entropy loss function to address the prediction inaccuracy caused by a sparse label matrix during training. The proposed deep learning framework is validated by synthetic tests and two field data examples. The successful applications to locate an anthropogenic noise source and a carbon dioxide (CO2) degassing area demonstrate the accuracy and efficiency of the proposed deep learning method for noise source localization, which has great potential for monitoring the changes of the noise source distribution in a survey area.

Post-processing of a distributed source method for the localization of somatosensory cortex in a cohort of epilepsy patients

Localizing eloquent cortices is crucial for many neurosurgical applications, such as epilepsy and tumor resections. Clinicians may use non-invasive methods such as magnetoencephalography (MEG) to localize these cortical regions using equivalent current dipoles (ECDs). While dipoles are clinically validated, they provide the estimated strength, location, and orientation of only one or a few sources that best describe the recorded neuromagnetic data, requiring clinicians to make subjective decisions on the spatial extent of the underlying cortical area. More accurate delineation of eloquent cortical areas using distributed source localization methods would provide additional pre-surgical information on these regions’ location and spatial distribution, which could lead to reduced post-surgical complications associated with damage to or removal of eloquent cortices. Our objective in this paper was to present a method to post-process the distributed source localization results to yield a directly interpretable, distributed region of activation. As a test case, we selected somatosensory stimulation in a retrospective cohort of focal and multi-focal epilepsy patients. Our algorithm performs source localization using a distributed method (sLORETA), followed by post-processing and blind source separation to identify the area and boundary of the cortical tissue that primarily activates in response to somatosensory stimulation. We calculated the statistical significance of localization by comparing the identified region to an anatomical atlas and random chance. While examining patients who received left (upper left, UL) and right (upper right, UR) sided median nerve stimulation, the cortical areas identified by the algorithm were in anatomically appropriate areas with a median overlap of 97.6% and 94.7%, respectively. We observe that our algorithm localized somatosensory responses better than random chance in 57/58 (98%) patients who performed the UL task (p < 10 × 10−10, binomial test) and 49/50 (98%) patients who performed the UR task (p < 10 × 10−10, binomial test). We compared the localization of our algorithm to current clinical methods and found that our algorithm is not inferior to dipole localization. The algorithm can successfully localize somatosensory responses on the cortical surface in anatomically appropriate regions while providing the spatial extent of cortical activation, reducing subjectivity associated with dipole localization.

Multi unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish's field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

Sound Source Localization Method Based on Time Reversal Operator Decomposition in Reverberant Environments

Predicting sound sources in reverberant environments is a challenging task because reverberation causes reflection and scattering of sound waves, making it difficult to accurately determine the position of the sound source. Due to the characteristics of overcoming multipath effects and adaptive focusing of the time reversal technology, this paper focuses on the application of the time reversal operator decomposition method for sound source localization in reverberant environments and proposes the image-source time reversal multiple signals classification (ISTR-MUSIC) method. Firstly, the time reversal operator is derived, followed by the proposal of a subspace method to achieve sound source localization. Meanwhile, the use of the image-source method is proposed to calculate and construct the transfer matrix. To validate the effectiveness of the proposed method, simulations and real-data experiments were performed. In the simulation experiments, the performance of the proposed method under different array element numbers, signal-to-noise ratios, reverberation times, frequencies, and numbers of sound sources were studied and analyzed. A comparison was also made with the traditional time reversal method and the MUSIC algorithm. The experiment was conducted in a reverberation chamber. Simulation and experimental results show that the proposed method has good localization performance and robustness in reverberant environments.

Determine the location of acoustic emission source on wood surface by using a wavelet-energy attenuation method

ABSTRACT To determine the location of wood damage and fracture, an acoustic emission source localization method based on wavelet-energy attenuation law was proposed. High-speed acquisition equipment and LabVIEW software were used to design a three-channel AE signal acquisition system. The wavelet transform is used to decompose the original signals into five frequency levels, and the second, third, and fourth level were selected for reconstruction of the acoustic emission signal. Finally, the reconstructed signals was used to determine the location of the acoustic emission source in accordance with the energy attenuation law. The results indicated that when the distance between the three sensors were 100 and 200 mm, the average location error obtained using the original signals in accordance with the energy attenuation law was 12.6 and 18.4 mm, respectively. However, the average location error obtained using the reconstructed signals in accordance with the energy attenuation law was 2.2 and 6.9 mm, respectively. These results reveal that the proposed method can achieve high-precision localization of acoustic emission source. This research enrich and expand the content of AE technology and wooden building research, provide new ideas and methods for non-destructive testing of similar structures, and promote research progress in related fields..

Research on a Sound Source Localization Method for UAV Detection Based on Improved Empirical Mode Decomposition.

To address the challenge of accurately locating unmanned aerial vehicles (UAVs) in situations where radar tracking is not feasible and visual observation is difficult, this paper proposes an innovative acoustic source localization method based on improved Empirical Mode Decomposition (EMD) within an adaptive frequency window. In this study, the collected flight signals of UAVs undergo smoothing filtering. Additionally, Robust Empirical Mode Decomposition (REMD) is applied to decompose the signals into Intrinsic Mode Function (IMF) components for spectrum analysis. We introduce a sliding frequency window with adjustable bandwidth, which is automatically determined using a Grey Wolf Optimizer (GWO) with a sliding index. This window is used to lock and extract specific frequencies from the IMFs. Based on predefined criteria, the extracted IMF components are reconstructed, and trigger signal times are analyzed and recorded from these reconstructed IMFs. The time differences between sensor receptions are then calculated. Furthermore, this study introduces the Chan-Taylor localization algorithm based on weighted least squares. This advanced algorithm takes sensor time delay parameters as input and solves a set of nonlinear equations to determine the target's location. Simulations and real-world signal tests are used to validate the robustness and performance of the proposed method. The results indicate that the localization error remains below 5% within a 15 m × 15 m measurement area. This provides an efficient and real-time method for detecting the location of small UAVs.

Three-dimensional acoustic emission source localization method for layered rock considering anisotropic P-wave velocity

Three-dimensional acoustic emission source localization method for layered rock considering anisotropic P-wave velocity

Proposal of Practical Sound Source Localization Method Using Histogram and Frequency Information of Spatial Spectrum for Drone Audition

Proposal of Practical Sound Source Localization Method Using Histogram and Frequency Information of Spatial Spectrum for Drone Audition

Tetris-inspired detector with neural network for radiation mapping

Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation

EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.

Broadband Source Localization Using Asynchronous Distributed Hydrophones Based on Frequency Invariability of Acoustic Field in Shallow Water

This paper introduces a model-independent passive source localization method, employing asynchronous distributed hydrophones in shallow water. Based on the frequency invariability of the acoustic field, assuming the correct source range information, the warped spectra of received signals at distributed hydrophones exhibit identical shapes. Subsequently, a cost function is formulated to mutually align the warped spectra, with its maximum point indicating the source location. The proposed method can locate the source in two-dimensional horizontal space without requiring either angle- or time-synchronization information. Numerical simulations are conducted to demonstrate the performance of the proposed method.

Multiple methods for acoustic emission source location in metal waveguides

Research on sound source location, which is widely used in engineering, is an important topic in acoustic emission (AE) technology. Traditional double-ended location methods have certain limitations, such as sound sources being located between two sensors, a single application scenario and possible time delays. In this study, three theoretically feasible location methods were proposed and experimentally verified based on AE propagation theory to test the accuracy of the AE source location and explore single/double-sensor line location in metal waveguides. The experiments measured the propagation speed of longitudinal and transverse waves in the waveguide and verified the feasibilities of the three acoustic source location methods based on waveform analysis, with waveform matching using the wave range and wave speed differences. The experimental results show a location error of less than 3% for the double-ended and double wave-speed methods and less than 6% for the double-waveguide and reflective-surface types.

A planning method for chemical plume tracking and source localization with autonomous underwater vehicle

Underwater target detection technology is an important mean for the development and utilization of marine environment, which has important value in commercial and military fields, and is valued by more and more experts, scholars, and production technicians. A set of behavior-based autonomous underwater vehicle online task planning algorithm is proposed in this article, including the searching behavior based on changing Z-shape, tracking behavior based on an improved artificial potential field theory with likelihood map, rediscovering behavior, and source declare behavior. The proposed algorithm can improve the accuracy and success rate of autonomous underwater vehicle to the source positionficient. The physical experiment results verify the effectiveness of the proposed method.

A new seperation method for rotationg and static sound source localization

Traditional sound source localization methods encounter significant challenges in simultaneously locating rotating and static sources. These challenges arise from the differing motion patterns of these two types of sound sources, and they are typically not situated on the same plane. To address this issue, a method based on Modal Composition Beamforming (MCB) and the equivalent source method is proposed for separating rotating and static sound sources that can fully utilize priori knowledge of the spatiotemporal properties of the sources. The proposed approach involves establishing a Rotating-Static Sources Power Propagation (R-S2P) model, utilizing the relationship between the beamforming output of equivalent sources and the actual beamforming output. Solving this model allows for matching the contributions of rotating and static equivalent sources. Simulations for three cases with different source strengths are presented, and the R-S2P model is solved using the Least Absolute Shrinkage and Selection Operator (LASSO). This method enables accurate separation and localization of rotating and static sources on different planes with varying relative intensities, even if the background noise is strong