English
norskBlar i forfatter "Choi, Changkyu"
Viser treff 1-8 av 8
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Advancing Deep Learning for Marine Environment Monitoring
(Doctoral thesis; Doktorgradsavhandling, 2023-06-09)Marine environment monitoring has become increasingly significant due to the excessive exploitation of oceans, which detrimentally impacts ecosystems. Deep learning provides an effective monitoring approach by automating the analysis of vast amounts of observed image data, enabling stakeholders to make informed decisions regarding fishing quotas or conservation efforts. The success of deep learning ... -
Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-02-01)Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. Semantic segmentation, a deep learning based analysis method predicting the class attribute of each acoustic intensity, has recently been in the spotlight of the fisheries and aquatic ... -
Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics
(Research report; Forskningsrapport, 2021-06-15)This report documents a workshop organised by the COGMAR and CRIMAC projects. The objective of the workshop was twofold. The first objective was to give an overview of ongoing work using machine learning for Acoustic Target Classification (ATC). Machine learning methods, and in particular deep learning models, are currently being used across a range of different fields, including ATC. The objective ... -
Semi-supervised target classification in multi-frequency echosounder data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-08-12)Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem of current methods is the heavy dependence on the manual categorization of data samples. As a solution, we propose a novel semi-supervised deep learning method leveraging a few ... -
Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks
(Conference object; Konferansebidrag, 2020-02-06)Forecasting the dynamics of time-varying systems is essential to maintaining the sustainability of the systems. Recent studies have discovered that Recurrent Neural Networks(RNN) applied in the forecasting tasks outperform conventional models that include AutoRegressive Integrated Moving Average(ARIMA). However, due to the structural limitation of vanilla RNN which holds unit-length internal ... -
Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-02-06)Forecasting the dynamics of time-varying systems is essential to maintaining the sustainability of the systems. Recent studies have discovered that Recurrent Neural Networks(RNN) applied in the forecasting tasks outperform conventional models that include AutoRegressive Integrated Moving Average(ARIMA). However, due to the structural limitation of vanilla RNN which holds unit-length internal ... -
Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks
(Conference object; Konferansebidrag, 2020)Data without annotation are easy to obtain in the real-world, however, established supervised learning methods are not applicable to analyze them. Several learning approaches have been proposed in recent years to exploit the underlying structure of the data without requiring annotations. Semi-supervised learning aims to improve the predictive performance of these unsupervised approaches, by exploiting ... -
Time Series Forecasting with Recurrent Neural Networks in Presence of Missing Data
(Master thesis; Mastergradsoppgave, 2018-11-24)In many applications, time series forecasting plays an irreplaceable role in time-varying systems such as energy markets, financial markets, and so on. Predicting the dynamic of time-varying systems is essential but is a difficult task because it depends on not only the nature of the system but also on external influences, such as environmental conditions and social and economic status. Recurrent ...
