PUBLICATIONS

2026

Ocean Eng.
Deep learning framework for regional maritime collision risk assessment using CNN and Grad-CAM

Suhyeon Jo, Gawon Lee, Sekil Park, and 4 more authors

Ocean Engineering. JCR Q1, Top 3.08%!!! , 2026

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Maritime transport handles over 80% of global trade but rising traffic density and congestion have increased collision risk. Traditional assessment methods, including the ship domain and closest point-of-approach approaches, are limited to pairwise interactions and cannot capture regional traffic dynamics. To overcome these shortcomings, we propose a regional collision risk assessment framework integrating AIS-derived relative traffic representations, convolutional neural networks, and explainable artificial intelligence. A gradient-weighted class activation mapping framework generated risk influence distribution maps and a centroid radial distribution, while a novel indicator, the risk influence radius (RIR), quantified radial influence patterns. Experiments with 3,660 low-risk and 366 high-risk cases from South Korean coastal waters revealed that the Visual Geometry Group (VGG) network backbone achieved the highest performance, with an average F1-score of 0.9324, outperforming ResNeXt, EfficientNet, ResNet, and MobileNetV3 models. Statistical tests revealed a significant difference in RIR (p < 0.05) between high- and low-risk collision areas at 10 and 20 km, but not at 30 km. These results confirm RIR as an interpretable indicator of collision risk at regional scales, while highlighting its limitations at broader ranges. The framework combines accuracy with interpretability and supports practical applications such as maritime monitoring, route planning, and early warning systems.applications such as maritime monitoring, route planning, and early warning systems.
@article{JO2026123452, title = {Deep learning framework for regional maritime collision risk assessment using CNN and Grad-CAM}, journal = {Ocean Engineering}, volume = {343}, pages = {123452}, year = {2026}, issn = {0029-8018}, doi = {https://doi.org/10.1016/j.oceaneng.2025.123452}, author = {Jo, Suhyeon and Lee, Gawon and Park, Sekil and Heo, Jaehyeon and Kim, Dohee and Sim, Sunghyun and Bae, Hyerim}, keywords = {Regional maritime collision risk assessment, Relative traffic representation, Risk influence distribution map, Centroid radial distribution, Deep learning, Explainable AI}, }

2025

IEEE Access
Continual Learning Approach with Adaptive Memory Mechanism for Predictive Process Monitoring in Dynamic Business Environments

Alif Nur Iman, Imam Mustafa Kamal, Dohee Kim, and 1 more author

IEEE Access, 2025

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Predictive process monitoring (PPM) faces challenges when process patterns evolve over time, as prediction models experience performance degradation due to concept drift while losing accuracy on previously learned process variants through catastrophic forgetting. This study addresses these challenges in outcome-oriented PPM by integrating continual learning principles with process mining domain knowledge. We propose an adaptive memory mechanism that extracts process structure information through activity relationship analysis, categorizing relationships between activities as "Always Follows," "Sometimes Follows," or "Never Follows." The mechanism computes relation entropy vectors to characterize process patterns and uses cosine similarity between current and historical process patterns to adaptively weight memory buffers during model training. This allows the model to weight historically relevant process knowledge based on pattern similarity. We evaluate the mechanism using stability, plasticity, and trade-off metrics on the Business Process Intelligence Challenge 2015 dataset across multiple temporal splits. Results show that the adaptive memory mechanism achieves stability of 0.87 ± 0.06, plasticity of 0.84 ± 0.11, and trade-off of 0.85 ± 0.08, achieving competitive performance compared to established baseline methods. The mechanism provides explainability through visualizations that demonstrate how the model weights different memory buffers during training. Validation using concept drift detection techniques confirms that the mechanism’s behavior aligns with observable process changes, indicating adaptation to evolving process patterns while reducing catastrophic forgetting.
@article{11264535, level = {SCI}, author = {Iman, Alif Nur and Kamal, Imam Mustafa and Kim, Dohee and Bae, Hyerim}, journal = {IEEE Access}, title = {Continual Learning Approach with Adaptive Memory Mechanism for Predictive Process Monitoring in Dynamic Business Environments}, year = {2025}, volume = {}, number = {}, pages = {1-1}, keywords = {Predictive process monitoring, Outcome-Oriented Prediction, Process mining, Continual learning, Concept drift, Deep learning}, doi = {10.1109/ACCESS.2025.3636194} }
BPM
Multi-task Trained Graph Neural Network for Business Process Anomaly Detection with a Limited Number of Labeled Anomalies

Yongjae Lee, Dohee Kim, Donghwan Kim, and 1 more author

In International Conference on Business Process Management, 2025

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Anomaly detection in business processes is crucial to prevent erroneous organizational decision-making. Existing anomaly detection methods often assume access to a clean event log without anomalies, a scenario that is impractical in the real world. To solve this problem, MuGAD is proposed, a novel framework that detects anomalies in scenarios where only a limited number of labeled anomalies are available. By converting traces into a newly designed graph structure, the framework captures the control flow, order of events, and attribute information, facilitating the effective use of graph neural networks. Furthermore, MuGAD uses a two-stage training procedure designed to (1) reduce the risks posed by unlabeled traces and (2) enable the detection of anomalous traces and events. Comparative experiments are conducted on five real-life event logs to validate our proposed framework. The experimental results indicate that MuGAD outperforms the existing methods and provides better detection accuracy along with better interpretability in terms of F1-score.
@inproceedings{lee2025multi, level = {International Conference}, doi = {https://doi.org/10.1007/978-3-032-02867-9_22}, keywords = {Anomaly detection, GNN, Multi-task learning, Process mining, Semi-supervised learning}, title = {Multi-task Trained Graph Neural Network for Business Process Anomaly Detection with a Limited Number of Labeled Anomalies}, author = {Lee, Yongjae and Kim, Dohee and Kim, Donghwan and Bae, Hyerim}, booktitle = {International Conference on Business Process Management}, pages = {361--378}, year = {2025} }
J. Forecasting
Long-term forecasting of maritime economics index using time-series decomposition and two-stage attention

Dohee Kim, Eunju Lee, Imam Mustafa Kamal, and 1 more author

Journal of Forecasting. JCR Q1, Top 16.17%!!! , 2025

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Forecasting the maritime economics index, including container volume and Baltic Panamax Index, is essential for long-term planning and decision-making in the shipping industry. However, studies on container volume prediction are not sufficient, and the bulk freight index has highly fluctuating characteristics, which pose a challenge in long-term prediction. This study proposes a new hybrid framework for the long-term prediction of the maritime economics index. The framework consists of time-series decomposition to break down a time-series into several components (trend, seasonality, and residual), a two-stage attention mechanism that prioritizes important variables to increase long-term prediction accuracy and a long short-term memory network that predicts and combines all components to derive the final predictive outcome. Extensive experiments are conducted using the container volume data, bulk freight index data, and various external variables. The proposed framework achieved a better predictive performance than existing time-series methods, including conventional machine learning and deep learning-based models, in the long-term prediction of container volume and the Baltic Panamax Index. Hence, the proposed method can help in decision-making through accurate long-term predictions of the maritime economics index.
@article{kim2025long, level = {SCI}, keywords = {Maritime economics forecasting, Time series decomposition, Attention mechanism, LSTM, Container volume prediction, Time Series Forecasting}, title = {Long-term forecasting of maritime economics index using time-series decomposition and two-stage attention}, author = {Kim, Dohee and Lee, Eunju and Kamal, Imam Mustafa and Bae, Hyerim}, journal = {Journal of Forecasting}, volume = {44}, number = {1}, pages = {153--172}, year = {2025}, publisher = {Wiley Online Library}, }
대한산업공학회지
센서 데이터의 시계열 특성을 고려한 선박 연료 소모량 예측 프레임워크

Hyunha Lee, Dohee Kim, Jungho Choo, and 2 more authors

대한산업공학회지, 2025

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The maritime industry plays a central role in global trade but also contributes significantly to environmental degradation due to high fuel consumption. Accordingly, the International Maritime Organization (IMO) has introduced environmental regulations, including the Carbon Intensity Indicator (CII). Complying with these regulations requires accurate fuel consumption forecasting to support optimal voyage planning. However, ship operation data frequently contain missing and anomalous values due to sensor failures, which reduce the accuracy of forecasts that rely on time-series characteristics. Although previous studies have applied machine learning and time-series models, they often fail to employ temporal dependencies or maintain data continuity. This study proposes a time-series forecasting framework that separates movement and anchorage states and performs imputation using environmental variables and similar trajectory group. The proposed method effectively leverages sensor data, despite frequent missing and anomalous values, and improves forecasting performance. The proposed framework facilitates voyage optimization by enabling accurate speed estimation and ensuring compliance with CII regulations.
@article{ART003254703, level = {KCI}, keywords = {Ship fuel consumption, Time Series Forecasting, Missing data imputation, CII regulations, Maritime sustainability}, author = {Lee, Hyunha and Kim, Dohee and Choo, Jungho and Jo, Sangmin and Bae, Hyerim}, title = {센서 데이터의 시계열 특성을 고려한 선박 연료 소모량 예측 프레임워크}, doi = {10.7232/JKIIE.2025.51.5.425}, journal = {대한산업공학회지}, issn = {1225-0988}, year = {2025}, volume = {51}, number = {5}, pages = {425-438} }

2024

CogMI

Neural Bezier Interpolation with Manifold Learning for Reliable Vessel Trajectory Prediction

Gawon Lee, Dohee Kim, Sekil Park, and 3 more authors

In 2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI), 2024

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@inproceedings{lee2024neural,
  level = {International Conference},
  title = {Neural Bezier Interpolation with Manifold Learning for Reliable Vessel Trajectory Prediction},
  author = {Lee, Gawon and Kim, Dohee and Park, Sekil and Sim, Sunghyun and Liu, Ling and Bae, Hyerim},
  keywords = {Vessel trajectory prediction, Bezier interpolation, Manifold learning, Deep learning, Maritime navigation},
  booktitle = {2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI)},
  pages = {189--196},
  year = {2024},
  organization = {IEEE}
}

ICIC-B
Predictive Process Monitoring for Remaining Time Prediction with Transfer Learning

Alif Nur Iman, Imam Mustafa Kamal, Muhammad Hanif Ramadhan, and 3 more authors

革新的コンピューティング・情報・制御に関する速報, 2024

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Recent advancements in predictive process monitoring (PPM) have led to an improved performance by incorporating deep learning methodologies. However, some of these approaches employ a massive parameter architecture that requires a large dataset for training, posing challenges in many business process management scenarios. This study presents a transformer-based model with transfer learning for estimating remaining time prediction. We aim to improve the efficiency of business process management by accurately predicting the remaining time of a process. Additionally, we assessed the feasibility and transferability of the transfer learning method to enhance the performance of predictive process monitoring. We conducted experiments on four publicly available event logs. Our proposed architecture outperforms prior work, with a significant 53% improvement over the best baseline in the Helpdesk dataset. Additionally, the use of transfer learning leads to both positive and negative performance outcomes, depending on characteristics of the source and target process model.
@article{nur2024predictive, level = {SCOPUS}, title = {Predictive Process Monitoring for Remaining Time Prediction with Transfer Learning}, author = {Iman, Alif Nur and Kamal, Imam Mustafa and Ramadhan, Muhammad Hanif and Kim, Dohee and Lee, Yongjae and Bae, Hyerim}, keywords = {Predictive process monitoring, Transfer learning, Remaining time prediction, Transformer, Process mining}, journal = {革新的コンピューティング・情報・制御に関する速報}, doi = {10.24507/icicel.18.08.851}, volume = {18}, number = {08}, pages = {851}, year = {2024}, publisher = {ICIC International 学会} }
ICIC-B
A Robust Explainable Feature Selection Method Based on Order Statistics

Dohee Kim, Sangjae Lee, Sunghyun Sim, and 1 more author

革新的コンピューティング・情報・制御に関する速報, 2024

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Recently, Feature Selection (FS) methods have been extensively researched using eXplainable Artificial Intelligence (XAI). Among these methods, SHapley Additive exPlanations (SHAP) is a representative approach. SHAP evaluates the impact of feature subset on predicted values based on game theory. Consequently, the feature importance can vary when iterated, and change depending on the prediction model used. In this paper, we propose a robust FS method based on order statistics. We determine feature ranking through two approaches: Feature Importance (FI) derived from the model-specific using different prediction models and model-agnostic using iterative experiments. Finally, we construct feature subsets based on the sum of these rankings and criteria. Through experiments, we validate the robustness and efficiency of our approach. By utilizing this approach, we can identify suitable feature subsets without the need to explore various FS methodologies, regardless of the prediction model and data dimension.
@article{dohee2024robust, level = {SCOPUS}, keywords = {Feature selection, Explainable AI, SHAP, Order statistics, Machine learning}, doi = {10.24507/icicel.18.03.255}, title = {A Robust Explainable Feature Selection Method Based on Order Statistics}, author = {Kim, Dohee and Lee, Sangjae and Sim, Sunghyun and Bae, Hyerim}, journal = {革新的コンピューティング・情報・制御に関する速報}, volume = {18}, number = {03}, pages = {255}, year = {2024}, publisher = {ICIC International 学会} }
한국빅데이터학회지
SCFI 지수 예측 모형의 성능 향상을 위한 텍스트 데이터 기반 문자열 가중치 추출 연구

Minseop Kim, Jaehyeon Heo, Dohee Kim, and 1 more author

한국빅데이터학회지, 2024

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다양한 매체를 통해 생성되는 비정형 데이터는 많은 정보를 내포하고 있으며, 이에 대한 활용의 필요성이 증가하고 있다. 하지만 텍스트 데이터를 효과적으로 정량화하여 예측 모델의 성능을 향상시키는 연구는 많이 이루어지고 있지 않다. 이러한 문제를 해결하기 위해 텍스트 데이터에 포함된 영향도를 정량화하여 외부 변수로 활용할 수 있는 Noun-Frequency Link Frequency 방법론을 제안한다. 제안하는 방법론은 텍스트 데이터를 활용해 주간 키워드와 키워드의 상대적 영향도를 정량화된 수치의 형태로 추출하며, 이러한 정보를 반영하여, 상하이 컨테이너 운임 지수(SCFI) 예측 모델의 성능을 향상시키는 효과적인 방법론이라는 것을 검증하였다. 본 연구를 통해, 사회적, 심리적 영향을 미치는 이벤트가 텍스트 데이터에 등장하였을 때, 이러한 정보를 포착하고 예측 성능을 향상시킴과 동시에 등장하는 키워드들을 분석하여 해석 가능성을 높일 수 있다.
@article{김민섭2024scfi, level = {KCI}, doi = {10.36498/kbigdt.2024.9.2.123}, keywords = {SCFI prediction, Text data, NF-LF methodology, Maritime freight index, Natural language processing}, title = {SCFI 지수 예측 모형의 성능 향상을 위한 텍스트 데이터 기반 문자열 가중치 추출 연구}, author = {Kim, Minseop and Heo, Jaehyeon and Kim, Dohee and Bae, Hyerim}, journal = {한국빅데이터학회지}, volume = {9}, number = {2}, pages = {123--134}, year = {2024}, publisher = {한국빅데이터학회} }
한국전자거래학회지
해양사고 데이터 기반 해역 그리드화 및 심각도 지표 도출 연구

Dohee Kim, Sangjae Lee, Suhyeon Jo, and 2 more authors

The Journal of Society for e-Business Studies, 2024

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우리나라는 해상 물동량 및 통항량이 증가하면서 해상교통 환경의 복잡도와 해양사고의 위험이 증가하고 있다. 해양사고는 다른 운송 수단에 비해 사고 비율은 상대적으로 낮지만 상당한 손실을 초래하는 심각한 사고로 이어지기도 한다. 해양사고 기록은 해양사고의 심판에 붙일 경우 해양안전심판원의 재결서를 통해 기록되고, 심판불필요처분에 대해서는 해양사고 통계에 기록되며 공공데이터로 공개되어 있다. 해양사고를 줄이기 위해 많은 연구들이 이루어졌다. 해양사고의 주요 원인을 규명하고, 심각도를 정의 및 도출하는 연구가 주로 이루어졌는데, 이들 연구는 주로 심판재결서와 해양사고통계 중 하나의 데이터만을 이용하였다. 본 연구에서는 해양사고의 원인을 도출하고, 심각도를 산출하는 데 있어 중요한 두 가지 데이터를 함께 고려할 수 있는 프레임워크를 제안한다. 해양사고에 대한 다각적 고려를 위해 심각도 기준을 재정의 하였으며, KeyBERT모델을 활용하여 사고의 주요 원인이 될 수 있는 키워드를 도출하였다. 또한 이를 해역별 그리드로 표현하여 사고의 심각도와 원인 키워드에 대해 시각화하여 정보를 제공한다. 추후 이를 해사 안전의 정량적 평가를 위한 안전지표 개발의 기초 자료로서 활용하고자 한다.
@article{김도희2024해양사고, level = {KCI}, keywords = {Maritime Accident, Grid-based analysis, Severity index, KeyBERT, Maritime safety}, doi = {10.7838/jsebs.2024.29.1.057}, title = {해양사고 데이터 기반 해역 그리드화 및 심각도 지표 도출 연구}, author = {Kim, Dohee and Lee, Sangjae and Jo, Suhyeon and Park, Sekil and Bae, Hyerim}, journal = {The Journal of Society for e-Business Studies}, volume = {29}, number = {1}, pages = {57--72}, year = {2024} }
한국전자거래학회지
상태기반 정비를 위한 초기 성능저하 시점 (First Prediction Time, FPT) 추정 연구

Mingyu Park, Hanbyeol Park, Minseop Kim, and 3 more authors

한국전자거래학회지, 2024

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장비의 고장 및 성능의 저하를 방지하기 위해서는 시스템의 상태를 모니터링 하고 장비의 고장을 예측하는 것이 필요하다. 이는 상태기반정비(Condition Based Maintenance +, CBM+)라고 불리며, 이를 위해서는 기계 혹은 장비의 상태를 실시간으로 진단하고, 초기 고장 감지 및 열화 상태를 예측하는 예측유지관리(Prognostics and Health Management, PHM)가 필수적이다. 본 논문에서는 무기체계의 냉매 누출의 문제에서 CBM+를 구현하기 위한 초기 냉매 누출시점(First Prediction Time, FPT) 추정방법론을 제시한다. 이는 냉매 누출 상태인 건강상태지표(Health Indicator, HI)를 보다 효과적으로 표현하여, 잔존수명(Remaining Useful Lifetime, RUL) 예측 성능에 큰 기여를 할 수 있다. 제안하는 방법론의 효율성은 현재 운용중인 무기체계에서 수집된 데이터를 통해 검증되었으며, FPT을 반영한 RUL 예측 성능이 FPT를 반영하지 않은 경우에 비해 우수한 성능을 보였다. 본 연구 방법론은 유사한 특성을 가진 하위부품에 대한 CBM+ 적용에 유용한 가이드라인으로 활용될 수 있을 것으로 보인다.
@article{박민규2024상태기반, level = {KCI}, doi = {10.7838/jsebs.2024.29.1.093}, title = {상태기반 정비를 위한 초기 성능저하 시점 (First Prediction Time, FPT) 추정 연구}, author = {Park, Mingyu and Park, Hanbyeol and Kim, Minseop and Kim, Dohee and Park, Yeonkyung and Bae, Hyerim}, journal = {한국전자거래학회지}, volume = {29}, number = {1}, pages = {93--114}, year = {2024} }

2023

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

Sunghyun Sim, Dohee Kim, and Hyerim Bae

IEEE Transactions on Pattern Analysis and Machine Intelligence. JCR Q1, Top 0.85%!!!! , 2023

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Time-series forecasting (TSF) is a traditional problem in the field of artificial intelligence, and models such as recurrent neural network, long short-term memory, and gate recurrent units have contributed to improving its predictive accuracy. Furthermore, model structures have been proposed to combine time-series decomposition methods such as seasonal-trend decomposition using LOESS. However, this approach is learned in an independent model for each component, and therefore, it cannot learn the relationships between the time-series components. In this study, we propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time-series decomposition within a neural cell and learn correlations (autocorrelation and correlation) between each decomposition component. The proposed neural architecture was evaluated through comparative experiments with previous studies using four univariate and four multivariate time-series datasets. The results showed that long- and short-term predictive performance was improved by more than 10%. The experimental results indicate that the proposed CRU is an excellent method for TSF problems compared to other neural architectures.
@article{sim2023correlation, level = {SCI}, keywords = {Time Series Forecasting, Correlation recurrent unit, Time series decomposition, Neural Network, Autocorrelation, Deep learning}, title = {Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data}, author = {Sim, Sunghyun and Kim, Dohee and Bae, Hyerim}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {45}, number = {12}, pages = {14266--14283}, google_sholar_id = {roLk4NBRz8UC}, year = {2023}, publisher = {IEEE}, doi = {10.1109/TPAMI.2023.3319557}, }

Mar Sci&Eng

Solving the inter-terminal truck routing problem for delay minimization using simulated annealing with normalized exploration rate

Muhammad Hanif Ramadhan, Imam Mustafa Kamal, Dohee Kim, and 1 more author

Journal of Marine Science and Engineering, 2023

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@article{ramadhan2023solving,
  level = {SCI},
  title = {Solving the inter-terminal truck routing problem for delay minimization using simulated annealing with normalized exploration rate},
  author = {Ramadhan, Muhammad Hanif and Kamal, Imam Mustafa and Kim, Dohee and Bae, Hyerim},
  keywords = {Inter-terminal truck routing, Simulated annealing, Delay minimization, Container terminal operations, Optimization},
  journal = {Journal of Marine Science and Engineering},
  volume = {11},
  number = {11},
  pages = {2103},
  year = {2023},
  publisher = {MDPI}
}

ICIC-B

Ensemble Method for Feature Selection in Time-Series Prediction Based on Image Similarity

Hyunjae Lee, Dohee Kim, Sangjae Lee, and 3 more authors

革新的コンピューティング・情報・制御に関する速報-B: 応用, 2023

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@article{hyunjae2023ensemble,
  level = {SCOPUS},
  title = {Ensemble Method for Feature Selection in Time-Series Prediction Based on Image Similarity},
  author = {Lee, Hyunjae and Kim, Dohee and Lee, Sangjae and Park, Hanbyeol and Bae, Hyerim and Choi, Keonwoo},
  keywords = {Ensemble method, Feature selection, Time Series Forecasting, Image similarity, Deep learning},
  journal = {革新的コンピューティング・情報・制御に関する速報-B: 応用},
  volume = {14},
  number = {07},
  pages = {709},
  year = {2023},
  publisher = {ICIC International 学会}
}

지능정보연구
화물의 특성 및 적재 공간을 고려한 LCL 화물 적재 알고리즘

Daesan Park, Sangmin Jo, Dongyoon Park, and 3 more authors

지능정보연구, 2023

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다품종 소량 생산 품목의 수요 증가와 전자상거래 시장의 발달로 인하여 소량화물(Less than Container Load, LCL)의수출입 비중이 확대되고 있다. 이에 따라 국제물류주선업에서 소량화물을 다루는 업체의 비중이 늘어나고 있다. 화물 크기의 다양성과 이해당사자의 이익 구조가 맞물려, 컨테이너의 공간 효율성을 최대화할 수 있는 화물 적재 방법론이 중요 해지고 있다. 그러나, 화물 적재 계획을 미리 수립하여 컨테이너 조작장(Container Freight Station, CFS)에 전달하는 현 상황의 특성상, 산업현장에서 확인 가능한 변수들을 적재 계획에 반영할 수 없다는 한계가 있다. 따라서, 본 연구에서는 산업현장에서 적재 계획의 수정이 용이한 화물 적재 방법론을 제시한다. 화물의 특성에 따른 불용 공간과 컨테이너 내부 현황을 고려할 수 있도록 하여 산업 현장의 요구사항을 반영하고, 3차원 공간을 2차원 평면 레이어로 조작하여 적재 계획을 수립하여 시간 복잡도를 줄임으로써 산업현장의 활용성을 높였다. 본 연구에서 제시하는 방법론을 통해 화물 적재 계획 품질의 일관성을 높이고, 적재 계획 수립 자동화에 기여를 할 수 있다.
@article{박대산2023화물의, level = {KCI}, title = {화물의 특성 및 적재 공간을 고려한 LCL 화물 적재 알고리즘}, keywords = {LCL cargo loading, Container loading algorithm, Space optimization, Logistics, 3D bin packing}, author = {Park, Daesan and Jo, Sangmin and Park, Dongyoon and Lee, Yongjae and Kim, Dohee and Bae, Hyerim}, journal = {지능정보연구}, volume = {29}, number = {4}, pages = {375--393}, year = {2023} }
해운물류연구
설명가능한 인공지능을 활용한 컨테이너 운임 예측 모형 연구

Dohee Kim, Hyerim Bae, and Sangjae Lee

해운물류연구, 2023

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컨테이너 운임은 대외 수출 비중이 높은 우리나라 산업의 물류비용을 결정하는 중요한 요인 중 하나이며, 산업 및 물가에 직접적인 영향을 미치는 중요한 경제 지표 중 하나이다. COVID-19 발생 이후 강한 충격에 급격히 상승한 운임에 대해 관련 영향 요인을 파악하고, 이를 정확히 예측하는 모델의 필요성이 대두되었다. 본 연구에서는 컨테이너 운임 지수 중 하나인 SCFI에 대한 정확한 예측을 목표로 하는 딥러닝 모델 기반의 프레임워크를 제안하고, 설명할 수 있는 인공지능 방법론 중 하나인 SHAP을 이용하여 사후 분석을 수행하였다. GRU, LSTM, RNN 모형 중 GRU 모형이 가장 좋은 예측 성능을 보였으며, 1주, 4주 예측 시 90%이상의 높은 예측 정확도를 보였으나, 12주 예측 시 80%이상의 예측 정확도를 보였다. 또한 SHAP 분석을 통해 변수 별 중요도를 바탕으로 구성한 변수 조합으로 실험 시 예측 성능이 개선되는 것을 확인하였다. 이를 통해 딥러닝 방법론이 가지고 있던 고질적인 한계점인 설명력 문제를 일부 해소하고, 예측력 개선에서도 활용할 수 있을 것이라 기대된다.
@article{김도희2023설명가능한, level = {KCI}, title = {설명가능한 인공지능을 활용한 컨테이너 운임 예측 모형 연구}, keywords = {Container freight rate prediction, Explainable AI, SHAP, Deep learning, SCFI, Maritime economics}, author = {Kim, Dohee and Bae, Hyerim and Lee, Sangjae}, journal = {해운물류연구}, volume = {39}, number = {2}, pages = {197--215}, year = {2023} }

대한산업공학회

외부 충격도 반영을 위한 텍스트 데이터 기반 NF-LF 방법론 개발

Jaehyeon Heo, Minseop Kim, Dohee Kim, and 2 more authors

대한산업공학회 춘계공동학술대회 논문집, 2023

Bib

@article{허재현2023외부,
  level = {Domestic Conference},
  title = {외부 충격도 반영을 위한 텍스트 데이터 기반 NF-LF 방법론 개발},
  author = {Heo, Jaehyeon and Kim, Minseop and Kim, Dohee and Park, Daesan and Bae, Hyerim},
  journal = {대한산업공학회 춘계공동학술대회 논문집},
  pages = {4213--4223},
  year = {2023},
  keywords = {External shock, Text data, NF-LF methodology, Time Series Forecasting, Natural language processing}
}

IEEE BigData

A novel training mechanism for health indicator construction and remaining useful lifetime (RUL) prediction

Hanbyeol Park, Dohee Kim, Minseop Kim, and 3 more authors

In 2023 IEEE International Conference on Big Data and Smart Computing (BigComp), 2023

Bib PDF

@inproceedings{park2023novel,
  level = {International Conference},
  title = {A novel training mechanism for health indicator construction and remaining useful lifetime (RUL) prediction},
  author = {Park, Hanbyeol and Kim, Dohee and Kim, Minseop and Park, Myunggwan and Bae, Hyerim and Sim, Sunghyun},
  keywords = {Health indicator, Remaining Useful Life Prediction, Deep learning, Predictive maintenance, Training mechanism},
  booktitle = {2023 IEEE International Conference on Big Data and Smart Computing (BigComp)},
  pages = {379--382},
  year = {2023},
  organization = {IEEE}
}

IEEE CogMI

Improving time-series classification accuracy based on temporal feature representation learning using CRU-LSTM autoencoder

Dohee Kim, Hanbyeol Park, Sunghyun Sim, and 3 more authors

In 2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI), 2023

Bib PDF

@inproceedings{kim2023cruauto,
  level = {International Conference},
  title = {Improving time-series classification accuracy based on temporal feature representation learning using CRU-LSTM autoencoder},
  author = {Kim, Dohee and Park, Hanbyeol and Sim, Sunghyun and Yoon, Bori and Liu, Ling and Bae, Hyerim},
  keywords = {Time series classification, CRU-LSTM, Autoencoder, Feature representation, Deep learning},
  booktitle = {2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI)},
  pages = {98--105},
  year = {2023},
  organization = {IEEE}
}

2022

한국빅데이터논문지

LSTM-AutoEncoder 를 활용한선박 메인엔진의 이상 탐지 및 라벨링

Dohee Kim, Yeongjae Han, Hyemee Kim, and 3 more authors

한국빅데이터논문지 제, 2022

Bib

@article{김도희2022lstm,
  level = {KCI},
  title = {LSTM-AutoEncoder 를 활용한선박 메인엔진의 이상 탐지 및 라벨링},
  author = {Kim, Dohee and Han, Yeongjae and Kim, Hyemee and Kang, Seongpil and Kim, Kihun and Bae, Hyerim},
  keywords = {LSTM, Ship main engine, Anomaly detection, Labeling, Predictive maintenance},
  journal = {한국빅데이터논문지 제},
  volume = {7},
  number = {1},
  year = {2022}
}

IEEE Big Data

TDTA: A new hybrid framework for long-term forecasting of container volume

Dohee Kim and Hyerim Bae

In 2022 IEEE International Conference on Big Data (Big Data), 2022

Bib PDF

@inproceedings{kim2022tdta,
  level = {International Conference},
  title = {TDTA: A new hybrid framework for long-term forecasting of container volume},
  author = {Kim, Dohee and Bae, Hyerim},
  keywords = {Container volume forecasting, Hybrid framework, Time Series Forecasting, Deep learning, Maritime logistics},
  booktitle = {2022 IEEE International Conference on Big Data (Big Data)},
  pages = {6718--6720},
  year = {2022},
  organization = {IEEE}
}

ICIC-B

Colored Petri Nets simulation on the cloud with python: A framework overview

Muhammad Hanif Ramadhan, Imam Mustafa Kamal, Alif Nur Iman, and 3 more authors

ICIC Express Letters, Part B: Applications, 2022

Bib PDF

@article{ramadhan2022cpn,
  level = {SCOPUS},
  title = {Colored Petri Nets simulation on the cloud with python: A framework overview},
  author = {Ramadhan, Muhammad Hanif and Kamal, Imam Mustafa and Iman, Alif Nur and Kim, Dohee and Bae, Hyerim and Park, You-Jin},
  keywords = {Colored Petri Nets, Cloud computing, Python, Simulation framework, Process modeling},
  journal = {ICIC Express Letters, Part B: Applications},
  volume = {13},
  number = {7},
  pages = {729--736},
  year = {2022}
}

2021

Appl. Sci.

Container volume prediction using time-series decomposition with a long short-term memory models

Eunju Lee, Dohee Kim, and Hyerim Bae

Applied Sciences, 2021

Bib PDF

@article{lee2021container,
  level = {SCOPUS},
  title = {Container volume prediction using time-series decomposition with a long short-term memory models},
  author = {Lee, Eunju and Kim, Dohee and Bae, Hyerim},
  keywords = {Container volume prediction, Time series decomposition, LSTM, Maritime logistics, Time Series Forecasting},
  journal = {Applied Sciences},
  volume = {11},
  number = {19},
  pages = {8995},
  year = {2021},
  publisher = {MDPI}
}

Processes

Residual life prediction for induction furnace by sequential encoder with s-convolutional LSTM

Yulim Choi, Hyeunho Kwun, Dohee Kim, and 2 more authors

Processes, 2021

Bib PDF

@article{choi2021residual,
  level = {SCOPUS},
  title = {Residual life prediction for induction furnace by sequential encoder with s-convolutional LSTM},
  author = {Choi, Yulim and Kwun, Hyeunho and Kim, Dohee and Lee, Eunju and Bae, Hyerim},
  keywords = {Remaining Useful Life Prediction, Induction furnace, Sequential encoder, LSTM, Predictive maintenance},
  journal = {Processes},
  volume = {9},
  number = {7},
  pages = {1121},
  year = {2021},
  publisher = {MDPI}
}

ICIC-B

Prediction of the quay crane’s handling time with external handling factors

Eunju Lee, Kikun Park, Dohee Kim, and 2 more authors

ICIC Exp. Lett., B, Appl., 2021

Bib PDF

@article{lee2021prediction,
  level = {SCOPUS},
  title = {Prediction of the quay crane's handling time with external handling factors},
  author = {Lee, Eunju and Park, Kikun and Kim, Dohee and Bae, Hyerim and Hong, Changwoo},
  keywords = {Quay crane, Handling time prediction, External factors, Container terminal operations, Machine learning},
  journal = {ICIC Exp. Lett., B, Appl.},
  volume = {12},
  pages = {351--358},
  year = {2021}
}

2020

한국빅데이터논문지

글로벌 팬데믹 상황에서의 긴급지원금 예산 배분 정책에 대한 연구

Kikun Park, Dohee Kim, Seulgi Kim, and 2 more authors

한국빅데이터논문지, 2020

Bib

@article{박기군2020글로벌,
  level = {KCI},
  title = {글로벌 팬데믹 상황에서의 긴급지원금 예산 배분 정책에 대한 연구},
  author = {Park, Kikun and Kim, Dohee and Kim, Seulgi and Choi, Jiwon and Bae, Hyerim},
  journal = {한국빅데이터논문지},
  year = {2020},
  volume = {5},
  number = {2},
  keywords = {Global pandemic, Emergency relief fund, Budget allocation, Policy analysis, COVID-19}
}

한국빅데이터논문지

시계열 군집을 활용한 부산시 감염병 지원 정책방향: COVID-19 사례를 중심으로

Hyeunho Kwun, Dohee Kim, Chan-ho Park, and 3 more authors

한국빅데이터논문지, 2020

Bib

@article{권현호2020시계열,
  level = {KCI},
  title = {시계열 군집을 활용한 부산시 감염병 지원 정책방향: COVID-19 사례를 중심으로},
  author = {Kwun, Hyeunho and Kim, Dohee and Park, Chan-ho and Lee, Eun-joo and Cho, Gi-hyung and Bae, Hyerim},
  journal = {한국빅데이터논문지},
  volume = {5},
  number = {1},
  year = {2020},
  keywords = {Time Series Forecasting, COVID-19, Infectious disease policy, Busan, Public health}
}

2019

ICIC-B

Forecasting high-dimensional multivariate regression of Baltic dry index (BDI) using deep neural networks (DNN)

Imam Mustafa Kamal, Hyerim Bae, Sunghyun Sim, and 4 more authors

ICIC Express Letters, 2019

Bib PDF

@article{kamal2019forecasting,
  level = {SCOPUS},
  title = {Forecasting high-dimensional multivariate regression of Baltic dry index (BDI) using deep neural networks (DNN)},
  author = {Kamal, Imam Mustafa and Bae, Hyerim and Sim, Sunghyun and Kim, Hyemee and Kim, Dohee and Choi, Yulim and Yun, Heesung},
  keywords = {Baltic Dry Index, Multivariate regression, Deep Neural Network, Time Series Forecasting, Maritime economics},
  journal = {ICIC Express Letters},
  volume = {13},
  number = {5},
  pages = {427--434},
  year = {2019}
}

대한산업공학회지

딥러닝을 활용한 건화물선 운임 지수 예측

Dohee Kim, Hyemee Kim, Sunghyun Sim, and 3 more authors

대한산업공학회지, 2019

Bib

@article{김도희2019딥러닝을,
  level = {KCI},
  title = {딥러닝을 활용한 건화물선 운임 지수 예측},
  author = {Kim, Dohee and Kim, Hyemee and Sim, Sunghyun and Choi, Yulim and Bae, Hyerim and Yoon, Heeseong},
  journal = {대한산업공학회지},
  volume = {45},
  number = {2},
  pages = {111--116},
  year = {2019},
  keywords = {Deep learning, Baltic Dry Index, Freight rate prediction, Maritime economics, Time Series Forecasting}
}