Digital Library[ Search Result ]
A Diffusion-based Trajectory Prediction Model for Flight Vehicles Considering Pull-up Maneuvers
Seonggyun Lee, Joonseong Kang, Jeyoon Yeom, Dongwg Hong, Youngmin Kim, Kyungwoo Song
http://doi.org/10.5626/JOK.2025.52.3.241
This paper proposes a new model for processing multivariate time series data aimed at predicting nonlinear trajectories related to aircraft pull-up maneuvers. To achieve this, aircraft trajectories were predicted using CSDI (Conditional Score-based Diffusion Models for Imputation), a state-of-the-art generative AI model. Specifically, because the flight distance and shape of the aircraft vary significantly depending on the presence of pull-up maneuvers, the data were separated into subsets with and without these maneuvers to train and predict distinct models. Experimental results demonstrated that the model predicted trajectories very similar to actual trajectories and achieved superior performance in MAE, RMSE, and CRPS metrics compared to existing deep learning models. This study not only enhances the accuracy of aircraft trajectory prediction but also suggests the potential for more sophisticated predictions through future integration with Classifier Diffusion models.
Analysis of QoQ GDP Prediction Performance Using Deep Learning Time Series Model
Yeonhee Lee, Youngmin Kim, Taewan You
http://doi.org/10.5626/JOK.2022.49.10.873
In this paper, we proposed an algorithm for predicting GDP growth rate using a deep learning time series model spotlighted recently. The proposed algorithm adopts an ensemble deep learning method to ensure stable prediction performance using a large number of economic time series data with low frequency. It also uses a gradual learning method to ensure adaptive performance even in business fluctuations. By demonstrating that the performance could be improved by using economic sector information in learning, the necessity of convergence with domain knowledge was confirmed and the importance of AI operation technology to provide adaptive predictive power was emphasized. Through performance comparison with traditional machine learning models for the COVID-19 period, we proved that deep learning could be a relatively reasonable predictive tool under rapid economic fluctuations. The deep learning-based adaptive AI algorithm presented in this paper is expected to be developed into a deep learning-based autonomous adaptive economic prediction system through combination with AI operation technology.
KorQuAD 2.0: Korean QA Dataset for Web Document Machine Comprehension
Youngmin Kim, Seungyoung Lim, Hyunjeong Lee, Soyoon Park, Myungji Kim
http://doi.org/10.5626/JOK.2020.47.6.577
KorQuAD 2.0 is a Korean question and answering dataset consisting of a total of 100,000+ pairs. There are three major differences from KorQuAD 1.0, which is the standard Korean Q & A data. The first is that a given document is a whole Wikipedia page, not just one or two paragraphs. Second, because the document also contains tables and lists, it is necessary to understand the document structured with HTML tags. Finally, the answer can be a long text covering not only word or phrase units, but paragraphs, tables, and lists. As a baseline model, BERT Multilingual is used, released by Google as an open source. It shows 46.0% F1 score, a very low score compared to 85.7% of the human F1 score. It indicates that this data is a challenging task. Additionally, we increased the performance by no-answer data augmentation. Through the distribution of this data, we intend to extend the limit of MRC that was limited to plain text to real world tasks of various lengths and formats.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr