Výsledky vyhledávání - "Sim, Sunghyun"

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Artificial Intelligence-based Smart Port Logistics Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case Study of Busan Port

Autor: Sim, Sunghyun, Kim, Dohee, Park, Kikun, Bae, Hyerim

The increase in global trade, the impact of COVID-19, and the tightening of environmental and safety regulations have brought significant changes to the maritime transportation market. To address these challenges, the port logistics sector is rapidly

Externí odkaz: http://arxiv.org/abs/2409.10519

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Legacy Learning Using Few-Shot Font Generation Models for Automatic Text Design in Metaverse Content: Cases Studies in Korean and Chinese

Autor: Kim, Younghwi, Jeong, Seok Chan, Sim, Sunghyun

Generally, the components constituting a metaverse are classified into hardware, software, and content categories. As a content component, text design is known to positively affect user immersion and usability. Unlike English, where designing texts i

Externí odkaz: http://arxiv.org/abs/2408.16900

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DLFormer: Enhancing Explainability in Multivariate Time Series Forecasting using Distributed Lag Embedding

Autor: Kim, Younghwi, Kim, Dohee, Sim, Sunghyun

Most real-world variables are multivariate time series influenced by past values and explanatory factors. Consequently, predicting these time series data using artificial intelligence is ongoing. In particular, in fields such as healthcare and financ

Externí odkaz: http://arxiv.org/abs/2408.16896

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Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning

Autor: Sim, Sunghyun, Liu, Ling, Bae, Hyerim

Process mining is a methodology for the derivation and analysis of process models based on the event log. When process mining is employed to analyze business processes, the process discovery step, the conformance checking step, and the enhancements s

Externí odkaz: http://arxiv.org/abs/2211.16687

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Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data

Autor: Sim, Sunghyun, Kim, Dohee, Bae, Hyerim

The time-series forecasting (TSF) problem is a traditional problem in the field of artificial intelligence. Models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and GRU (Gate Recurrent Units) have contributed to improving the

Externí odkaz: http://arxiv.org/abs/2211.16653

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