arXiv subjects

Sunghoon Park

Publications and source records attributed to Sunghoon Park.

3 recordsLinked to original sources

arXiv2024

CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model

The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language Models (MLLMs) has revolutionized information retrieval and expanded the practical applications of AI. However, current systems struggle in accurately interpreting user intent, employing diverse retrieval strategies, and effectively filtering unintended or inappropriate responses, limiting their effectiveness. This paper introduces Contextual Understanding and Enhanced Search with MLLM (CUE-M), a novel multimodal search framework that addresses these challenges through a multi-stage pipeline comprising image context enrichment, intent refinement, contextual query generation, external API integration, and relevance-based filtering. CUE-M incorporates a robust filtering pipeline combining image-based, text-based, and multimodal classifiers, dynamically adapting to instance- and category-specific concern defined by organizational policies. Extensive experiments on real-word datasets and public benchmarks on knowledge-based VQA and safety demonstrated that CUE-M outperforms baselines and establishes new state-of-the-art results, advancing the capabilities of multimodal retrieval systems.

cs.CL↗

arXiv2022

Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes

The braking performance of the brake system is a target performance that must be considered for vehicle development. Apparent piston travel (APT) and drag torque are the most representative factors for evaluating braking performance. In particular, as the two performance factors have a conflicting relationship with each other, a multidisciplinary design optimization (MDO) approach is required for brake design. However, the computational cost of MDO increases as the number of disciplines increases. Recent studies on inverse design that use deep learning (DL) have established the possibility of instantly generating an optimal design that can satisfy the target performance without implementing an iterative optimization process. This study proposes a DL-based multidisciplinary inverse design (MID) that simultaneously satisfies multiple targets, such as the APT and drag torque of the brake system. Results show that the proposed inverse design can find the optimal design more efficiently compared with the conventional optimization methods, such as backpropagation and sequential quadratic programming. The MID achieved a similar performance to the single-disciplinary inverse design in terms of accuracy and computational cost. A novel design was derived on the basis of results, and the same performance was satisfied as that of the existing design.

cs.LG↗

arXiv2016

Fe2O3/ Co3O4 composite nanoparticle ethanol sensor

In this study Fe2O3/Co3O4 nanocomposites were synthesized by using a simple hydrothermal route. The X-ray diffraction analysis results showed that synthesized powders were pure, and nanocrystalline in nature. Moreover, Scanning electron microscopy revealed that Fe2O3 nanoparticles had spherical shapes while Co3O4 particles had a rod-like morphology. Ethanol sensing properties of Fe2O3/Co3O4 nanocomposites were examined and compared with those of pristine Fe2O3 nanoparticles. It was shown that the gas sensing properties of Fe2O3/Co3O4 nanocomposites were superior to those of pristine Fe2O3 nanoparticles and for all concentrations of ethanol, the response of the nanocomposite sensor was higher than the pristine Fe2O3 nanoparticle sensor. In detail, the response of Fe2O3/Co3O4 nanocomposite sensor to 200 ppm of ethanol at 300°C was about 3 times higher than pristine one. Also in general, the response and recovery times of Fe2O3/Co3O4 nanocomposite sensor were shorter than those of the pristine one. The improved sensing characteristics of the Fe2O3/Co3O4 sensor were attributed to a combination of several effects: the formation of a potential barrier at the Fe2O3-Co3O4 interface, the enhanced modulation of the conduction channel width accompanying the adsorption and desorption of ethanol gas, the catalytic activity of Co3O4 for the oxidation of ethanol, the stronger oxygen adsorption of p-type Co3O4, and the formation of preferential adsorption sites.

physics.chem-ph↗