arXiv subjects

Sang Min Park

Publications and source records attributed to Sang Min Park.

5 recordsLinked to original sources

arXiv2026

High Spectral Energy Density All-Fiber Nanosecond Pulsed 1.7 $μ$m Light Source for Photoacoustic Microscopy

We present a high spectral energy density all-fiber nanosecond pulsed 1.7 $μ$m light source specifically designed for photoacoustic microscopy (PAM). The system targets the first overtone absorption of C-H bonds near 1720 nm within the near-infrared-III (NIR-III) window, where lipids exhibit strong optical absorption and tissues benefit from reduced scattering and high permissible fluence. To achieve narrow linewidth, high pulse energy, and high pulse repetition rate (PRR), we developed a master oscillator fiber amplifier architecture based on stimulated Raman scattering. A 1589.80 nm Raman pump and a custom-built narrow-linewidth Raman seed laser were employed to generate spectrally pure 1719.44 nm pulses with an approximately 0.10 nm linewidth. The proposed light source delivers nanosecond pulses of approximately 5 ns with high pulse energy of at least 2.2 $μ$J and tunable PRRs up to 300 kHz, resulting in a spectral energy density of approximately 22 $μ$J/nm, which is significantly higher than that of conventional 1.7 $μ$m light sources. The performance of the NIR-PAM system was validated through resolution testing with a 1951 USAF target, demonstrating a spatial resolution of approximately 4.14 $μ$m and an axial resolution of approximately 85.5 $μ$m. Phantom imaging of CH$_2$-rich polymer films and ex vivo lipid-rich biological tissues confirmed the system's high spatial fidelity and strong contrast for lipid-specific structures. This compact, stable, and spectrally refined light source with high spectral energy density can offer an effective solution for high-resolution, label-free molecular imaging and represents a promising platform for clinical photoacoustic imaging applications involving lipid detection and metabolic disease diagnostics.

physics.optics↗

arXiv2026

Real-time quasi-distributed fiber optic sensor based on resonance frequency mapping

Distributed optical fiber sensors (DOFS) based on Raman, Brillouin, and Rayleigh scattering have recently attracted considerable attention for various sensing applications, especially large-scale monitoring, owing to their capacity for measuring strain or temperature distributions. However, ultraweak backscatter signals within optical fibers constitute an inevitable problem for DOFS, thereby increasing the burden on the entire system in terms of limited spatial resolution, low measurement speed, high system complexity, or high cost. We propose a novel resonance frequency mapping for a real-time quasi-distributed fiber optic sensor based on identical weak fiber Bragg gratings (FBG), which has stronger reflection signals and high sensitivity to multiple sensing parameters. The resonance configuration, which amplifies optical signals during multiple round-trip propagations, can simply and efficiently address the intrinsic problems in conventional single round-trip measurements for identical weak FBG sensors, such as crosstalk and optical power depletion. Moreover, it is technically feasible to perform individual measurements for a large number of quasi-distributed identical weak FBGs with relatively high signal-to-noise ratio (SNR), low crosstalk, and low optical power depletion. By mapping the resonance frequency spectrum, the dynamic response of each identical weak FBG is rapidly acquired in the order of kilohertz, and direct interrogation in real time is possible without time-consuming computation, such as fast Fourier transformation (FFT). This resonance frequency spectrum is obtained on the basis of an all-fiber electro-optic configuration that allows simultaneous measurement of quasi-distributed strain responses with high speed (>5 kHz), high stability (approximately 2.4 microstrain), and high linearity (R^2 = 0.9999).

physics.optics↗

arXiv2024

Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space

Ventilated acoustic resonator(VAR), a type of acoustic metamaterial, emerge as an alternative for sound attenuation in environments that require ventilation, owing to its excellent low-frequency attenuation performance and flexible shape adaptability. However, due to the non-linear acoustic responses of VARs, the VAR designs are generally obtained within a limited parametrized design space, and the design relies on the iteration of the numerical simulation which consumes a considerable amount of computational time and resources. This paper proposes an acoustic response-encoded variational autoencoder (AR-VAE), a novel variational autoencoder-based generative design model for the efficient and accurate inverse design of VAR even with non-parametrized designs. The AR-VAE matches the high-dimensional acoustic response with the VAR cross-section image in the dimension-reduced latent space, which enables the AR-VAE to generate various non-parametrized VAR cross-section images with the target acoustic response. AR-VAE generates non-parameterized VARs from target acoustic responses, which show a 25-fold reduction in mean squared error compared to conventional deep learning-based parameter searching methods while exhibiting lower average mean squared error and peak frequency variance. By combining the inverse-designed VARs by AR-VAE, multi-cavity VAR was devised for broadband and multitarget peak frequency attenuation. The proposed design method presents a new approach for structural inverse-design with a high-dimensional non-linear physical response.

cs.CE↗

arXiv2023

D-Score: A Synapse-Inspired Approach for Filter Pruning

This paper introduces a new aspect for determining the rank of the unimportant filters for filter pruning on convolutional neural networks (CNNs). In the human synaptic system, there are two important channels known as excitatory and inhibitory neurotransmitters that transmit a signal from a neuron to a cell. Adopting the neuroscientific perspective, we propose a synapse-inspired filter pruning method, namely Dynamic Score (D-Score). D-Score analyzes the independent importance of positive and negative weights in the filters and ranks the independent importance by assigning scores. Filters having low overall scores, and thus low impact on the accuracy of neural networks are pruned. The experimental results on CIFAR-10 and ImageNet datasets demonstrate the effectiveness of our proposed method by reducing notable amounts of FLOPs and Params without significant Acc. Drop.

cs.NE↗

arXiv2023

Generalized Supervised Contrastive Learning

With the recent promising results of contrastive learning in the self-supervised learning paradigm, supervised contrastive learning has successfully extended these contrastive approaches to supervised contexts, outperforming cross-entropy on various datasets. However, supervised contrastive learning inherently employs label information in a binary form--either positive or negative--using a one-hot target vector. This structure struggles to adapt to methods that exploit label information as a probability distribution, such as CutMix and knowledge distillation. In this paper, we introduce a generalized supervised contrastive loss, which measures cross-entropy between label similarity and latent similarity. This concept enhances the capabilities of supervised contrastive loss by fully utilizing the label distribution and enabling the adaptation of various existing techniques for training modern neural networks. Leveraging this generalized supervised contrastive loss, we construct a tailored framework: the Generalized Supervised Contrastive Learning (GenSCL). Compared to existing contrastive learning frameworks, GenSCL incorporates additional enhancements, including advanced image-based regularization techniques and an arbitrary teacher classifier. When applied to ResNet50 with the Momentum Contrast technique, GenSCL achieves a top-1 accuracy of 77.3% on ImageNet, a 4.1% relative improvement over traditional supervised contrastive learning. Moreover, our method establishes new state-of-the-art accuracies of 98.2% and 87.0% on CIFAR10 and CIFAR100 respectively when applied to ResNet50, marking the highest reported figures for this architecture.

cs.CV↗