A5006229695- Ultrasonics and Acoustic Wave Propagation
- Structural Health Monitoring Techniques
- Seismic Waves and Analysis
- Non-Destructive Testing Techniques
- Speech Recognition and Synthesis
- Geophysical Methods and Applications
- Domain Adaptation and Few-Shot Learning
- Advanced Optical Sensing Technologies
- Integrated Circuits and Semiconductor Failure Analysis
- Model Reduction and Neural Networks
- Image and Signal Denoising Methods
- Random lasers and scattering media
- Matrix Theory and Algorithms
- Machine Learning and ELM
- COVID-19 diagnosis using AI
- Seismic Imaging and Inversion Techniques
- Electrical Fault Detection and Protection
- Photoacoustic and Ultrasonic Imaging
- Advanced Fiber Laser Technologies
University of Florida
2019-2025
This article explains the use of supervised and unsupervised dictionary learning approaches on spread spectrum time domain (SSTDR) data to detect locate disconnections in a PV array consisting five panels. The aim is decompose an SSTDR reflection signature into different components where each component has physical interpretation, such as noise, environmental effects, faults. In approach, decomposed are inspected localize maximum difference between actual predicted location fault 0.44 m...
With the recent success of representation learning methods, which includes deep as a special case, there has been considerable interest in developing techniques that incorporate known physical constraints into learned representation. As one example, many applications involve signal propagating through media (e.g., optics, acoustics, fluid dynamics, etc), it is dynamics must satisfy imposed by wave equation. Here we propose matrix factorization technique decomposes such signals sum...
We analyze the generalization ability of joint-training meta learning algorithms via Gibbs algorithm. Our exact characterization expected error for algorithm is based on symmetrized KL information, which measures dependence between all meta-training datasets and output parameters, including task-specific parameters. Additionally, we derive an super-task algorithm, in terms conditional information within super-sample framework introduced [1] [2], respectively. results also enable us to...
Detecting and locating damage information from waves reflected off is a common practice in non-destructive structural health monitoring systems. Yet, the transmitted ultrasonic guided are affected by physical material properties of structure often complicated to model mathematically. This calls for data-driven approaches behaviour waves, where patterns wave data due can be learned distinguished non-damage data. Recent works have used popular dictionary learning algorithm, K-SVD, learn an...
Guided wave dispersion curves characterize materials and the waves that propagate in them. While there have been many efforts to learn from data, multipath reflections cause most methods either fail or require a significant quantity of data. This paper uses compressive sensing sparse recovery algorithms known multi-path characteristics. We assume reflection signals originate virtual sources, according method mirrors. demonstrate our methodology with guided simulation data 1.125m by 1.00 m...
With the recent success of representation learning methods, which includes deep as a special case, there has been considerable interest in developing techniques that can incorporate known physical constraints into learned representation. As one example, many applications involve signal propagating through media (e.g., optics, acoustics, fluid dynamics, etc), it is dynamics must satisfy imposed by wave equation. Here we propose matrix factorization technique decomposes such signals sum...
We analyze the generalization ability of joint-training meta learning algorithms via Gibbs algorithm. Our exact characterization expected error for algorithm is based on symmetrized KL information, which measures dependence between all meta-training datasets and output parameters, including task-specific parameters. Additionally, we derive an super-task algorithm, in terms conditional information within super-sample framework introduced Steinke Zakynthinou (2020) Hellstrom Durisi (2022)...
Abstract Recent advancements in physics-informed machine learning have contributed to solving partial differential equations through means of a neural network. Following this, several network works followed solve inverse problems arising structural health monitoring. Other involving networks the wave equation with data and modeling wavefield generator for efficient sound generation. While lot work has been done show that can be solved identified using network, little same more basic (ML)...
Ultrasonic wavefields are widely employed in nondestructive testing and structural health monitoring to detect evaluate damage. However, measuring continuously throughout space poses challenges can be costly. To address this, we propose a novel approach that combines the wave equation with computer vision algorithms visualize wavefields. Our algorithm incorporates equation, which encapsulates our knowledge of propagation, infer regions where direct measurement is not feasible. Specifically,...
With the recent success of representation learning methods, which includes deep as a special case, there has been considerable interest in developing techniques that incorporate known physical constraints into learned representation. As one example, many applications involve signal propagating through media (e.g., optics, acoustics, fluid dynamics, etc), it is dynamics must satisfy imposed by wave equation. Here we propose matrix factorization technique decomposes such signals sum...
Modern machine learning has been on the rise in many scientific domains, such as acoustics. Many problems face challenges with limited data, which prevent use of powerful strategies. In response, physics wave-propagation can be exploited to reduce amount data necessary and improve performance techniques. Based this need, we present a physics-informed framework, known wave-informed regression, extract dispersion curves from guided wave wavefield non-homogeneous media. Wave-informed regression...