Publications

2026

1. Adv.Eng.Info

   Time-series approach to vessel turnaround time forecasting using queuing-based operation indicators

   Daesan Park, Taeeon Noh, Yohan Koo, and 3 more authors

   Advanced Engineering Informatics. JCR Q1, Top 2.00%!!! , 2026

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   As global supply chains grow increasingly complex and cargo volumes continue to rise, the accurate prediction of vessel turnaround time (VTT) has become essential for enhancing port operational efficiency. Previous studies primarily utilized static features, such as vessel specifications and container volumes, often failing to capture the dynamic nature of real-world operations. To address this limitation, we propose a forecasting framework that incorporates dynamic operation indicators (OIs) from a two-stage queuing model into time-series models. Leveraging operational data from Busan Port, our approach achieves of up to 28% improvement in mean absolute percentage error compared with existing techniques. Explainability analysis using Shapley additive explanations (SHAP) further demonstrates that OIs rank among the most influential features. By providing more reliable VTT predictions, port operators can enhance berth planning, optimize resource allocation, minimize scheduling delays, and enhance overall port efficiency. These results highlight the practical value of the framework for data-driven decision-making in port operations.

2025

1. arXiv

   Reasoning-Aware GRPO using Process Mining

   Taekhyun Park, Yongjae Lee, and Hyerim Bae

   2025

   Abs DOI arXiv PDF

   Reinforcement learning (RL)-based post-training has been crucial for enabling multi-step reasoning in large reasoning models (LRMs), yet current reward schemes are typically outcome-centric. We propose PM4GRPO, a reasoning-aware Group Relative Policy Optimization (GRPO) that augments standard answer/format rewards with signals over the reasoning procedure. To this end, process mining techniques are utilized to compute a scalar conformance reward that measures how closely a policy model’s reasoning aligns with the pretrained teacher model. The empirical results on five benchmarks demonstrate that PM4GRPO significantly outperforms existing methodologies for GRPO-based post-training. These results highlight that leveraging process mining for reasoning-aware GRPO effectively enhances the reasoning capabilities of policy models.

2. Comput.Ind.Eng.

   Optimizing yard truck deployment at container terminals: a machine learning-enhanced non-dominated sorting genetic algorithm

   Kikun Park, Kihun Kim, Minseop Kim, and 1 more author

   Computers & Industrial Engineering. JCR Q1, Top 14.29%!!! , 2025

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   The continuous rise in global container traffic has necessitated more efficient operational strategies at container terminals (CTs). However, developing effective strategies requires mitigating delays in handling equipment operations. Yard trucks (YTs) are pivotal for transporting containers between quay cranes (QCs) and yard cranes (YCs). A YT shortage can cause operational delays for both QCs and YCs, extending the makespan of main-vessel operations at the berth. Conversely, deploying an excessive number of YTs eliminates these delays but incurs higher operational costs. Consequently, determining the optimal number of YTs constitutes a bi-objective optimization problem involving a tradeoff between minimizing the operational makespan and associated YT deployment costs. This study introduces a machine learning (ML)-enhanced non-dominated sorting genetic algorithm 2 (NSGA-2) approach designed to address this challenge by minimizing both the makespan and deployment costs. The proposed method uses ML models to predict fitness scores and efficiently guide the search toward optimal solutions, while also assisting in generating, selecting, and mutating promising solutions during the optimization process. The effectiveness of ML-enhanced NSGA-2 was validated using empirical data from Busan Port Terminal in South Korea. The results highlight the superior performance and practical applicability of the proposed approach, which achieved a significant reduction of 16% in deployed YTs compared to existing strategies.

3. arXiv

   IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting

   Gawon Lee, Hanbyeol Park, Minseop Kim, and 2 more authors

   arXiv preprint arXiv:2509.20783, 2025

   Abs arXiv

   Real-world time-series data often exhibit non-stationarity, including changing trends, irregular seasonality, and residuals. In terms of changing trends, recently proposed multi-layer perceptron (MLP)-based models have shown excellent performance owing to their computational efficiency and ability to capture long-term dependency. However, the linear nature of MLP architectures poses limitations when applied to channels with diverse distributions, resulting in local variations such as seasonal patterns and residual components being ignored. However, convolutional neural networks (CNNs) can effectively incorporate these variations. To resolve the limitations of MLP, we propose combining them with CNNs. The overall trend is modeled using an MLP to consider long-term dependencies. The CNN uses diverse kernels to model fine-grained local patterns in conjunction with MLP trend predictions. To focus on modeling local variation, we propose IConv, a novel convolutional architecture that processes the temporal dependency channel independently and considers the inter-channel relationship through distinct layers. Independent channel processing enables the modeling of diverse local temporal dependencies and the adoption of a large kernel size. Distinct inter-channel considerations reduce computational cost. The proposed model is evaluated through extensive experiments on time-series datasets. The results reveal the superiority of the proposed method for multivariate time-series forecasting.

4. Authorea

   Forecasting Hourly Container Throughput with EDI-Derived Factors and a Decomposition Pairwise Mixer

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

   Authorea Preprints, 2025

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   The continuous growth in global trade and rising uncertainty have driven extensive research on container throughput (CT) forecasting at monthly and annual scales. Recently, the demand for high-resolution CT forecasting has emerged to support real-time decision-making by practitioners. However, this presents challenges in effectively utilizing realtime port operation information and managing high-noise time series data. To address these limitations, this study introduces a preprocessing strategy and novel neural network architecture for forecasting hourly CT with a 72-hour horizon, an approach not attempted in previous studies. In the preprocessing strategy, we examined the electronic data interchange (EDI) system and extracted key factors affecting CT. Additionally, we present a novel neural network architecture—the decomposition pairwise mixer (DPM)—which is designed to effectively capture interactions among these factors. Extensive experiments were conducted with real operational data from the port of Busan in Korea, the world’s sixth-busiest port. The results reveal two key findings: derived key factors enhance the forecasting performance, and the proposed method outperforms state-ofthe-art time-series forecasting models. These findings suggest that the proposed method is not limited to long-term planning but can also substantially enhance short-term operational decision-making, thereby improving port operation efficiency.

5. IEEE Access

   Trend-fluctuation correlated attention unit for remaining useful life prediction

   Hanbyeol Park, Sunghyun Sim, Yunkyung Park, and 1 more author

   IEEE Access, 2025

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   Health indicators (HIs) are measures that consolidate complex machinery characteristics into a single dimension and have been extensively studied for predicting the remaining useful life (RUL). Recent research has proposed HI-prediction models based on recursive cell structures that demonstrate promising performance. However, recent cell structures have limitations, such as saturation in hidden states and difficulty learning interdependent patterns among various components. Accurate RUL predictions require minimizing error accumulation in the recursive HI-prediction process, which can be achieved using a cell structure capable of capturing interdependent patterns. This study proposes a new cell structure termed the trend-fluctuation correlated attention unit (TFCAU) that employs a gate mechanism to capture interactions between divided trends and fluctuations in HI and an attention mechanism for robust long-term predictions. The superiority of the TFCAU was validated using a bearing dataset from the PRONOSTIA platform and a real-world dataset from the Korean weapons system. Comparative experiments using six sub-datasets demonstrated that TFCAU outperformed the state-of-the-art model—the gated transient fluctuation dual-attention unit—achieving a 169.37% improvement in the score metric and a reduction of 49.93 units in the mean-absolute error. An extensive ablation study experimentally confirmed that the TFCAU effectively captured the interdependent characteristics between trends and fluctuations. TFCAU delivered excellent performance for both rotating and non-rotating systems, suggesting its potential applicability across multiple operational scenarios.

6. 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.

7. arXiv

   JustDense: Just using Dense instead of Sequence Mixer for Time Series analysis

   TaekHyun Park, Yongjae Lee, Daesan Park, and 2 more authors

   arXiv preprint, 2025

   Abs arXiv

   Sequence and channel mixers, the core mechanism in sequence models, have become the de facto standard in time series analysis (TSA). However, recent studies have questioned the necessity of complex sequence mixers, such as attention mechanisms, demonstrating that simpler architectures can achieve comparable or even superior performance. This suggests that the benefits attributed to complex sequencemixers might instead emerge from other architectural or optimization factors. Based on this observation, we pose a central question: Are common sequence mixers necessary for time-series analysis? Therefore, we propose JustDense, an empirical study that systematically replaces sequence mixers in various well-established TSA models with dense layers. Grounded in the MatrixMixer framework, JustDense treats any sequence mixer as a mixing matrix and replaces it with a dense layer. This substitution isolates the mixing operation, enabling a clear theoretical foundation for understanding its role. Therefore, we conducted extensive experiments on 29 benchmarks covering five representative TSA tasks using seven state-of-the-art TSA models to address our research question. The results show that replacing sequence mixers with dense layers yields comparable or even superior performance. In the cases where dedicated sequence mixers still offer benefits, JustDense challenges the assumption that "deeper and more complex architectures are inherently better" in TSA.

8. 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.

9. Appl. Intell.

   Angular triangle distance for ordinal metric learning

   Imam Mustafa Kamal, Hyerim Bae, and Ling Liu

   Applied Intelligence, 2025

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10. Manufacturing Systems

    Comprehensive issue identification for manufacturing data analytics implementation: Systematic literature review and case studies

    Sa-Eun Park, Sangjae Lee, Hyerim Bae, and 5 more authors

    Journal of Manufacturing Systems. JCR Q1, Top 0.94%!!! , 2025

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11. Eng. Optim.

    Contrastive loss with dynamic margin for rubber-recipe physical-property prediction

    Hanbyeol Park, Kihun Kim, Kikun Park, and 3 more authors

    Engineering Optimization, 2025

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    Predicting the physical properties of rubber presents difficulties because of the intricate and nonlinear relationships between its constituent elements. The laborious nature of rubber manufacture results in restricted data sets, hence exacerbating this difficulty. To achieve successful training of a neural network with a smaller amount of data, it is crucial to use an appropriate weight initialization approach and carefully construct the network architecture to avoid overfitting. This study presents a novel approach to representation learning that uses a contrastive loss with dynamic margin loss function. The approach is specifically designed to capture semantic similarities in rubber material quantities and physical-property recipes. In addition, a novel neural network architecture is proposed that uses latent vectors for assigning initial weights. The proposed approach, validated using data from a rubber manufacturing company, showed a significant improvement of up to 30% in predictive accuracy for physical properties with a restricted amount of data.

12. 대한산업공학회지

    항만 혼잡 정보를 반영한 2-Stage 선박 도착 시간 예측 방법론 연구

    Seonghan Kim, Mingi Han, Minseop Kim, and 1 more author

    대한산업공학회지, 2025

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13. 인공위성 이미지 기반 미세먼지 예측 및 고해상도 공간 해석 프레임워크

    Eunhee Park, Minseop Kim, Hanbyeol Park, and 1 more author

    대한산업공학회지, 2025

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14. 대한산업공학회

    무기체계 상태기반정비를 위한 이미지 인코딩 기반 시계열 데이터 이상탐지

    Minseop Lee, Gi-gun Park, Hanbyeol Park, and 2 more authors

    대한산업공학회지, 2025

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    The radar detection weapon system is a surveillance and reconnaissance system designed to precisely detect long-range targets and determine their position and status. A critical component of the radar detection weapon system is the wave guide, which serves as a path for high-frequency signals. Maintaining appropriate temperature and humidity levels is essential to ensure seamless signal transmission, and this is regulated using an air compressor. However, if compressed air leakage occurs, temperature and humidity may rise, leading to performance degradation and potential component failure, necessitating early fault detection. The sensor data collected form actual operations exhibits high-dimensional and nonlinear characteristics, with abnormal patterns appearing irregularly, making numerical approaches challenging. Furthermore, such methods face limitations in effectively learning overall patterns and dependencies over time. In this paper, we propose an anomaly detection model that combines time-domain feature with image encoding to effectively capture nonlinear signal patterns. Experimental result using real-world operational data demonstrate that the proposed method outperforms existing approaches. The proposed approach is expected to be useful for applying Condition-Based Maintenance(CBM) to various equipment that utilizes time-series data.

15. 대한산업공학회

    LLM 기반의 시계열 예측 모형을 활용한 컨테이너 물동량 예측 방법론 연구

    Minseop Kim, Jaeeon Kown, Taekhyun Park, and 2 more authors

    대한산업공학회 춘계공동학술대회 논문집, 2025

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16. 대한산업공학회

    현실적 QC 작업 계획 수립을 위한 TSP 기반 프레임워크

    Minhee Kim, Kikun Park, Kihun Kim, and 1 more author

    대한산업공학회 춘계공동학술대회 논문집, 2025

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17. 한국전자거래학회지

    랜덤 포레스트와 전자 데이터 교환 정보를 활용한 수입 컨테이너 체류 시간 예측 연구

    Hanbyeol Park, Kikun Park, Jaehyeon Heo, and 3 more authors

    한국전자거래학회지, 2025

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    연간 수백만 Twenty-foot Equivalent Unit (TEU) 컨테이너를 하역하는 항만에서, 제한된 야드 공간 내에서 효율적인 컨테이너 관리는 중요한 문제로 여겨진다. 하지만, 야드 운용을 효율적으로 관리하기에 많은 난항을 겪고 있는데, 그 원인 중 하나는 Import Container Dwell Time (ICDT)이 서로 다른 원인에서 기인한다. ICDT는 선박을 통해 양하 된 컨테이너를 운송사가 육지로 반출할 때까지 소요되는 시간으로 정의되는데, 컨테이너의 속성과 운송사의 배차계획 등 여러 복잡한 요인들에 영향을 받기 때문에, 정확히 예측하기 어렵다. 이러한 어려움에도 불구하고, 정확한 ICDT 예측은 효율적인 야드 운영에 기여하므로 매우 중요하다. 본 연구에서는 ICDT의 예측 성능을 향상시키기 위해 Electric Data Interchange (EDI) 데이터를 활용할 것을 제안한다. EDI는 컨테이너의 반입부터 반출까지의 프로세스 내 세관 신고, 컨테이너 반출 마감 기한과 같은 시간 정보를 포함하고 있다. 이러한 시간 정보들을 사용하여 ICDT 예측에 사용할 수 있는 외부 변수들을 생성할 수 있으며, 이는 ICDT 예측에 기여할 수 있다. 제안하는 접근법의 효과성을 검증하기 위해 실제 현장에서 수집된 데이터를 사용하였다. 실험 결과, 다양한 예측 모델 중 Random Forest (RF)가 가장 우수한 성능을 보여주었으며, EDI 데이터를 통합함으로써 예측 성능이 크게 개선됨을 확인하였다. 특히, EDI 데이터에서 파생된 변수들이 높은 변수 중요도를 보여주었다. 이러한 결과는 ICDT의 정확한 예측을 위해 EDI 정보의 필요성을 시사한다.

2024

1. arXiv

   A Surrogate Model for Quay Crane Scheduling Problem

   Kikun Park and Hyerim Bae

   arXiv, 2024

   Abs arXiv

   In ports, a variety of tasks are carried out, and scheduling these tasks is crucial due to its significant impact on productivity, making the generation of precise plans essential. This study proposes a method to solve the Quay Crane Scheduling Problem (QCSP), a representative task scheduling problem in ports known to be NP-Hard, more quickly and accurately. First, the study suggests a method to create more accurate work plans for Quay Cranes (QCs) by learning from actual port data to accurately predict the working speed of QCs. Next, a Surrogate Model is proposed by combining a Machine Learning (ML) model with a Genetic Algorithm (GA), which is widely used to solve complex optimization problems, enabling faster and more precise exploration of solutions. Unlike methods that use fixed-dimensional chromosome encoding, the proposed methodology can provide solutions for encodings of various dimensions. To validate the performance of the newly proposed methodology, comparative experiments were conducted, demonstrating faster search speeds and improved fitness scores. The method proposed in this study can be applied not only to QCSP but also to various NP-Hard problems, and it opens up possibilities for the further development of advanced search algorithms by combining heuristic algorithms with ML models.

2. MARTRA

   Identifying key factors influencing import container dwell time using eXplainable Artificial Intelligence

   Yongjae Lee, Kikun Park, Hyunjae Lee, and 3 more authors

   Maritime Transport Research, 2024

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   In a container terminal, the length of time that containers remain in the yard, known as Container Dwell Time (CDT), is considered one of the significant operational indicators due to its direct correlation with terminal productivity and efficiency. However, due to complex processing procedure and the involvement of various logistics stakeholders, CDT is subject to high uncertainty, making it more difficult for the terminal to manage. To address this issue, this paper presents a comprehensive framework to identify the Key Factors (KFs) influencing prolongation of CDT for import containers. In order to elucidate abnormal cases from dataset which contains yard loading information, the Process Mining (PM) method is utilized. Subsequently, XAI has been utilized to identify the KFs of import CDT. To reflect reality as closely as possible, we collected event data from a container terminal in Busan, Korea. Based on experiments, the KFs thus identified were: 1) Temperature, 2) Weight of container, 3) Voyage number of container 4) Block, 5) Shipping company, and 6) Month of discharging. To conclude, we formulated domain knowledge-based interpretations of the six most influential KFs.

3. CogMI

   Neural Bezier Interpolation with Manifold Learning for Reliable Vessel Trajectory Prediction

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

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

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4. ICIC-B

   SIMULATION LEARNING FOR PREDICTING THE MAKESPAN FOR SCHEDULING PROBLEM

   Gawon Lee, Kikun Park, Muhammad Hanif Ramadhan, and 2 more authors

   ICIC Express Letters Part B : Applications, 2024

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   Simulation models offer the advantage of unrestricted exploration of various solutions, including those for complex or difficult-to-formulate problems. In this context, simulation has been widely employed to address scheduling problems, which often demand extensive trial-and-error approaches. However, the computational time required to identify optimal solutions increases exponentially with problem size, rendering exhaustive evaluation of all possible scenarios impractical. Moreover, the prolonged search time makes the simulation method susceptible to disruptions such as unexpected machine failures or additional orders. To enhance the computational speed, we propose the application of Machine Learning (ML) models as an alternative to simulation. By leveraging ML models, we aim to preserve the expressive power of complex simulations while significantly reducing the required trial time. We evaluate the performance of various ML models and compare their prediction accuracy and computational efficiency. The results demonstrate promising prediction accuracy alongside a substantial reduction in computational time

5. 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.

6. IEEE

   A predictive discrete event simulation for predicting operation times in container terminal

   Kikun Park, Minseop Kim, and Hyerim Bae

   IEEE Access, 2024

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   Container terminals (CTs) play a crucial role within the global supply chain in the context of the global container transportation business. The primary obligation associated with these terminals involves ensuring the punctual execution of primary vessel operations, while strictly adhering to the Estimated Time of Departure (ETD) for vessels. To accomplish this goal, it is crucial to develop rigorous and exact operating strategies. Nonetheless, the accurate prediction of operation times in CT presents a significant challenge due to the concurrent involvement of various Container Handling Equipment (CHE) and the occurrence of unforeseen situations. To address this issue, this study proposes a novel approach called Predictive Discrete Event Simulation (PDES) that utilizes data collected from CT to predict the operation times. The PDES is an advanced approach that builds upon the widely used Discrete Event Simulation (DES) technique in the field of simulations. It is specifically designed to provide precise predictions of the operation times in CTs, where multiple events take place concurrently. The PDES in this paper aims to overcome the shortcomings of current simulation-based approaches for prediction in CT. These approaches often rely on predefined task sequences and assumed time for job handling time of CHE in their scenarios, which can result in reduced accuracy when predicting operation times. Through the resolution of these issues, the proposed PDES exhibits the capacity to improve predictive performance. To enhance the predictive performance of operation times in CTs through PDES, two approaches are introduced. The first approach entails the application of Support Vector Machine (SVM) algorithms for the purpose of predicting operation times. This approach is further augmented by integrating it with DES to improve the accuracy of predictive performance. The second approach involves predicting by simulating real-world operational scenarios in CTs using algorithms for CHE assignment. The predictive performance of the proposed PDES is assessed through the utilization of data gathered from Busan Port Terminal (BPT) in South Korea, demonstrating superior performance compared to alternative prediction approaches.

7. Adv.Eng.Info

   Semi-supervised binary classification with latent distance learning

   Imam Mustafa Kamal and Hyerim Bae

   Advanced Engineering Informatics. JCR Q1, Top 1.9%!!! , 2024

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   Binary classification (BC) is a practical task that is ubiquitous in real-world problems, such as distinguishing healthy and unhealthy objects in biomedical diagnostics and defective and non-defective products in manufacturing inspections. Nonetheless, fully annotated data are commonly required to effectively solve this problem, and their collection by domain experts is a tedious and expensive procedure. In contrast to BC, several significant semi-supervised learning techniques that heavily rely on stochastic data augmentation techniques have been devised for solving multi-class classification. In this study, we demonstrate that the stochastic data augmentation technique is less suitable for solving typical BC problems because it can omit crucial features that strictly distinguish between positive and negative samples. To address this issue, we propose a new learning representation to solve the BC problem using a few labels with a random pair cross-distance learning mechanism. First, by harnessing a few labeled samples, the encoder network learns the projection of positive and negative samples in angular spaces to maximize and minimize their inter-class and intra-class distances, respectively. Second, the classifier learns to discriminate between positive and negative samples using on-the-fly labels generated based on the angular space and labeled samples to solve BC tasks. Extensive experiments were conducted using four real-world publicly available BC datasets. With few labels and without any data augmentation techniques, the proposed method outperformed state-of-the-art semi-supervised and self-supervised learning methods. Moreover, with 10% labeling, our semi-supervised classifier could obtain competitive accuracy compared with a fully supervised setting.

8. 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.

9. 대한산업공학회

   딥러닝 기반 직접 발화 음성과 딥보이스 판별 시스템 구축

   Kyunghoon Lee, Sujin Cho, Kikun Park, and 1 more author

   대한산업공학회 추계학술대회 논문집, 2024
10. 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

    Abs DOI HTML

    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.

11. 대한산업공학회

    유전 알고리즘 기반의 지능형 네트워크 구조 탐색을 활용한 Automatic Identification System 데이터 복원 연구

    Jaehyeon Heo, Kikun Park, Hanbyeol Park, and 3 more authors

    대한산업공학회 추계학술대회 논문집, 2024
12. 한국전자거래학회지

    해양사고 데이터 기반 해역 그리드화 및 심각도 지표 도출 연구

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

    The Journal of Society for e-Business Studies, 2024

    Abs DOI HTML

    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.

13. 대한산업공학회

    시공간정보를 활용한 선박 연료 소모량 예측 프레임워크

    Hyunha Lee, Jeongho Choo, Sangmin Jo, and 4 more authors

    대한산업공학회 추계학술대회 논문집, 2024
14. 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

    Abs DOI

    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.

2023

1. 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

   Abs DOI arXiv HTML PDF

   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.

2. 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

   PDF
3. 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

   PDF
4. 지능정보연구

   화물의 특성 및 적재 공간을 고려한 LCL 화물 적재 알고리즘

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

   지능정보연구, 2023

   Abs HTML

   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.

5. 한국빅데이터논문지

   대리운전 시장의 지역별 수요 예측 모형의성능 향상을 위한 방법론 연구

   Minseop Kim, Kikun Park, Jaehyeon Heo, and 2 more authors

   한국빅데이터논문지 제, 2023
6. 해운물류연구

   설명가능한 인공지능을 활용한 컨테이너 운임 예측 모형 연구

   Dohee Kim, Hyerim Bae, and Sangjae Lee

   해운물류연구, 2023

   Abs HTML

   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.

7. 한국신뢰성학회

   무기체계 상태 기반 정비를 위한 인공신경망 기반 상태진단모델 개발

   Mingyu Park, Hanbyeol Park, Minseop Kim, and 4 more authors

   한국신뢰성학회 학술대회논문집, 2023
8. 대한산업공학회

   GPS 데이터 기반 내부트럭 경로 예측 모델 개발

   Sangjae Lee, Kikun Park, Alif Nur Iman, and 2 more authors

   대한산업공학회 춘계공동학술대회 논문집, 2023
9. 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
10. 대한산업공학회

    항만 정시성 예측을 위한 선박 ETA/RTA 예측 알고리즘

    Suhyeon Jo, Kikun Park, Gawon Lee, and 2 more authors

    대한산업공학회 춘계공동학술대회 논문집, 2023
11. 대한산업공학회

    고무 물성 예측을 위한 클러스터링 기반 프레임워크 개발

    Jaeeun Kwon, Hanbyeol Park, Mingyu Park, and 2 more authors

    대한산업공학회 춘계공동학술대회 논문집, 2023
12. 대한산업공학회

    독립변수들 간의 상호관계 학습을 위한 CoRepNet

    Sunghyun Sim, Dohee Kim, and Hyerim Bae

    대한산업공학회 춘계공동학술대회 논문집, 2023
13. Trans. Serv. Comput.

    Metric learning as a service with covariance embedding

    Imam Mustafa Kamal, Hyerim Bae, and Ling Liu

    IEEE Transactions on Services Computing, 2023
14. 해양환경안전학회지

    Maritime Safety Tribunal Ruling Analysis using SentenceBERT

    Bori Yoon, SeKil Park, Hyerim Bae, and 1 more author

    Journal of the Korean Society of Marine Environment & Safety, 2023

2022

1. IEEE

   A data-driven recipe simulation for synthetic rubber production

   Kikun Park, Hanbyeol Park, and Hyerim Bae

   IEEE Access, 2022

   PDF
2. TRD

   Deep collaborative learning model for port-air pollutants prediction using automatic identification system

   Sunghyun Sim, Jin-Hyoung Park, and Hyerim Bae

   Transportation Research Part D: Transport and Environment. JCR Q1, Top 8.62%!!! , 2022

   Abs HTML

   Air pollution in port cities is aggravated by ship pollutant emissions. A deep collaborative learning (DCL)-based prediction model using automatic identification system (AIS) is proposed in this study to predict this type of pollution. In the model training process, a novel data preprocessing method was devised to efficiently handle heterogeneous data: air pollution, weather conditions, and AIS data. To combine these data together, a pretraining step is introduced using an autoencoder-based model, which is a customized convolutional long short-term memory network model, followed by a DCL method for the prediction of highly accurate air pollution values (for both short- and long-term predictions). Compared with other approaches, this method showed on an average, a performance improvement of nearly 10% in terms of the root mean squared error. Experiments to test and validate the model were conducted near the North/Old Busan Port, Republic of Korea.

3. PRL

   Cooperative auto-classifier networks for boosting discriminant capacity

   Imam Mustafa Kamal and Hyerim Bae

   Pattern Recognition Letters, 2022

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4. ICIC-B

   Improving the Performance of Mixing-Process Machine Classification Based on Feature Engineering Techniques: A Case Study in Rubber-Belt Industry

   Alif Nur Iman, Jinuk Kim, Jeong Su Son, and 2 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, 2022

   PDF
5. Appl. Sci.

   Big data and AI for process innovation in the Industry 4.0 era

   Jaehun Park and Hyerim Bae

   Applied Sciences, 2022

   PDF
6. ICIC-B

   Obtaining lightweight human activity recognition model through knowledge distillation of deep neural network

   Muhammad Hanif Ramadhan, Taufik Nur Adi, Hyerim Bae, and 1 more author

   ICIC Express Letters, Part B: Applications, 2022

   PDF
7. PR

   Super-encoder with cooperative autoencoder networks

   Imam Mustafa Kamal and Hyerim Bae

   Pattern Recognition. JCR Q1, Top 9.02%!!! , 2022

   Abs DOI HTML

   Dimensionality reduction plays a crucial role in classification, object detection, and pattern recognition tasks. Its main objective is to decrease the dimension of the original data while retaining the most distinctive information. With the emergence of deep learning, an autoencoder has become a state-of-the-art non-linear dimensionality-reduction method. Nonetheless, as the existing autoencoder models are devised to follow the data distribution and employ similarity techniques, preserving distinctive information can be problematic. To tackle this issue, we propose super-encoder (SE) networks trained in a supervised and cooperative manner. The SE consists of an encoder, separator, and decoder networks. The encoder combined with separator networks are dedicated to generating separable latent representation based on the label, and the decoder network should be able to reconstruct it to the original data simultaneously. Herein, we introduce a novel cooperative learning mechanism with a new loss function; therefore, the encoder, separator, and decoder networks can cooperate to achieve these objectives. Extensive experiments using benchmark datasets were conducted. The results indicated that the SE is more effective in extracting separable latent code than the existing supervised and unsupervised dimensionality-reduction models. Furthermore, as a generator, it can obtain highly competitive realistic images.

8. IEEE

   Automatic conversion of event data to event logs using CNN and event density embedding

   Sunghyun Sim, Riska Asriana Sutrisnowati, Seokrae Won, and 2 more authors

   IEEE Access, 2022

   PDF
9. 한국산업정보학회논문지

   벌크항만의 하역 최적화를 위한 정수계획모형

   Taeseon Yu, Yusin Lee, Hyun-gon Park, and 2 more authors

   한국산업정보학회논문지, 2022
10. 한국빅데이터논문지

    LSTM-AutoEncoder 를 활용한선박 메인엔진의 이상 탐지 및 라벨링

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

    한국빅데이터논문지 제, 2022
11. 한국빅데이터학회

    밀도기반 선박 도메인을 이용한 안전 성능 지표 활용성 연구

    Youngjae Han, Sunghyun Sim, and Hyerim Bae

    한국빅데이터학회지, 2022
12. 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

    PDF

2021

1. MARTRA

   Vessel estimated time of arrival prediction system based on a path-finding algorithm

   Kikun Park, Sunghyun Sim, and Hyerim Bae

   Maritime Transport Research, 2021

   Abs DOI HTML PDF

   

   Port operation efficiency has grown in importance as container volumes and vessel sizes have increased. For improved port operations efficiency, the estimated time of arrival (ETA) of sea-going vessels must be accurately predicted. In this paper, an AIS data-driven method- ology is proposed for the estimation of vessel ETA at ports. For ETA prediction, we first introduce how to find possible vessel trajectories using AIS data mining methods and reinforcement learning (RL); next, we introduce the Markov Chain property and Bayesian Sampling to estimate the speed over ground (SOG) of a vessel. Experimentation comparing the proposed methodology with an existing one was performed to verify the former’s performance. We expect the proposed ETA prediction methodology to predict ETA to help build an intelligent port system.

2. Appl. Sci.

   Parallel-structure deep learning for prediction of remaining time of process instances

   Nur Ahmad Wahid, Hyerim Bae, Taufik Nur Adi, and 2 more authors

   Applied Sciences, 2021

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3. 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

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4. IEEE TSC

   Bagging recurrent event imputation for repair of imperfect event log with missing categorical events

   Sunghyun Sim, Hyerim Bae, and Ling Liu

   IEEE Transactions on Services Computing. JCR Q1, Top 7.5%!!! , 2021

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5. Processes

   Interterminal truck routing optimization using cooperative multiagent deep reinforcement learning

   Taufik Nur Adi, Hyerim Bae, and Yelita Anggiane Iskandar

   Processes, 2021

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6. Processes

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

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

   Processes, 2021

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7. ICIC-EL

   Adversarial multivariate regression and auto-regression for predicting completion time of container terminals operation

   Imam Mustafa Kamal and Hyerim Bae

   ICIC Express Letters, 2021

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8. 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

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9. 한국빅데이터학회

   CNN 기반의 준지도학습을 활용한 GPR 이미지 분류

   Hyemee Kim and Hyerim Bae

   The Journal of Bigdata, 2021

2020

1. IEEE

   Trajectory linkage and spreader centrality for social epidemic networks

   Imam Mustafa Kamal, Hyerim Bae, and Ki Haing Cho

   IEEE Access, 2020

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2. Appl. Sci.

   Mining shift work operation from event logs

   Nur Ichsan Utama, Riska Asriana Sutrisnowati, Imam Mustafa Kamal, and 2 more authors

   Applied Sciences, 2020

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3. ICIC-B

   Q-learning-based technique for reduction of number of empty-truck trips in inter-terminal transportation

   Taufik Nur Adi, Yelita Anggiane Iskandar, and Hyerim Bae

   ICIC Express Letters, Part B: Applications, 2020

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4. Appl. Sci.

   DERN: Deep ensemble learning model for short-and long-term prediction of baltic dry index

   Imam Mustafa Kamal, Hyerim Bae, Sunghyun Sim, and 1 more author

   Applied Sciences, 2020

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5. Neurocomputing

   Data pixelization for predicting completion time of events

   Imam Mustafa Kamal, Hyerim Bae, Nur Ichsan Utama, and 1 more author

   Neurocomputing. JCR Q1, Top 18.14%!!! , 2020

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6. 한국빅데이터논문지

   글로벌 팬데믹 상황에서의 긴급지원금 예산 배분 정책에 대한 연구

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

   한국빅데이터논문지, 2020
7. 한국빅데이터논문지

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

   Hyunho Kwon, Dohee Kim, Chan-ho Park, and 3 more authors

   한국빅데이터논문지, 2020

2019

1. Procedia Comput.

   Predictive business process monitoring–remaining time prediction using deep neural network with entity embedding

   Nur Ahmad Wahid, Taufik Nur Adi, Hyerim Bae, and 1 more author

   Procedia Computer Science, 2019
2. IJGI

   Simplification and detection of outlying trajectories from batch and streaming data recorded in harsh environments

   Iq Reviessay Pulshashi, Hyerim Bae, Hyunsuk Choi, and 2 more authors

   ISPRS International Journal of Geo-Information, 2019

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3. 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

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4. 한국빅데이터학회지

   컨테이너 터미널 성능평가를 위한 대용량 이벤트 로그 정제 방안 연구

   Eunjeong Park and Hyerim Bae

   한국빅데이터학회지, 2019
5. 한국컴퓨터정보학회논문지

   Automatic false-alarm labeling for sensor data

   Taufik Nur Adi, Hyerim Bae, and Nur Ahmad Wahid

   Journal of The Korea Society of Computer and Information, 2019
6. 한국컴퓨터정보학회논문지

   Hyper parameter tuning method based on sampling for optimal LSTM model

   Hyemee Kim, Ryeji Jeong, and Hyerim Bae

   한국컴퓨터정보학회논문지, 2019
7. 대한산업공학회지

   딥러닝을 활용한 건화물선 운임 지수 예측

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

   대한산업공학회지, 2019

2018

1. ICIC-B

   Gear classification for defect detection in vision inspection system using deep convolutional neural networks

   Imam Mustafa Kamal, Riska Asriana Sutrisnowati, Hyerim Bae, and 1 more author

   ICIC Express Letters, 2018

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2. ICIC-B

   Cloud-Based Predictive Maintenance Framework for Sensor Data Analytics

   Taufik Nur Adi, Nur Ahmad Wahid, Riska Asriana Sutrisnowati, and 5 more authors

   ICIC Express Letters, Part B: Applications, 2018

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3. ICIC-B

   Petri Nets simulation for operational analytics based on process data: An overview

   Natanael Yabes Wirawan, Riska Asriana Sutrisnowati, Sunghyun Sim, and 7 more authors

   ICIC Experss Letters, Part B: Applications, 2018

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4. ICIC-B

   Production scheduling in steel manufacturing with cutting and parallel operations

   Sujin Won, Hyerim Bae, and Riska Asriana Sutrisnowati

   ICIC Express Letters, Part B: Applications, 2018

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5. INFOR

   A study on the quality-embedded efficiency measurement in DEA

   Jaehun Park, Si-Il Sung, Yong-Jeong Kim, and 2 more authors

   INFOR: Information Systems and Operational Research, 2018

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6. IJCIS

   Statistical verification of process model conformance to execution log considering model abstraction

   Sunghyun Sim, Hyerim Bae, Yulim Choi, and 1 more author

   International Journal of Cooperative Information Systems, 2018

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7. INFOR

   A study on the quality-embedded efficiency measurement in DEA

   Jaehun Park, Si-Il Sung, Yong-Jeong Kim, and 2 more authors

   INFOR: Information Systems and Operational Research, 2018

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8. 대한산업공학회지

   Utilizing Big Data and Fuzzy Inference for Evaluation of Port Operations

   Wahyu Andy Prastyabudi, Taufik Nur Adi, and Hyerim Bae

   대한산업공학회지, 2018
9. 한국컴퓨터정보학회논문지

   The study of a full cycle semi-automated business process re-engineering: A comprehensive framework

   Sanghwa Lee, Riska A Sutrisnowati, Seokrae Won, and 2 more authors

   Journal of The Korea Society of Computer and Information, 2018
10. LSTM 을 활용한 BDI (Bulk Dry Index) 예측

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

    대한산업공학회 춘계공동학술대회 논문집, 2018

2016

1. CIE

   Domain-driven actionable process model discovery

   Bernardo Nugroho Yahya, Minseok Song, Hyerim Bae, and 2 more authors

   Computers & Industrial Engineering. JCR Q1, Top 14.29%!!! , 2016

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2012

1. IJICIC

   Generating valid reference business process model using genetic algorithm

   Bernardo Nugroho Yahya, Hyerim Bae, Joonsoo Bae, and 1 more author

   International Journal of Innovative Computing, Information and Control, 2012
2. IJPR

   Flexible job-shop scheduling problems with ‘AND’/‘OR’precedence constraints

   Sanghyup Lee, Ilkyeong Moon, Hyerim Bae, and 1 more author

   International Journal of Production Research, 2012

2011

1. ESA

   A study on the selection of benchmarking paths in DEA

   Sungmook Lim, Hyerim Bae, and Loo Hay Lee

   Expert Systems with Applications, 2011

2010

1. ISF

   Increasing the efficiency of business processes using a theory of constraints

   Seung-Hyun Rhee, Nam Wook Cho, and Hyerim Bae

   Information Systems Frontiers, 2010

2008

1. AMC

   Analytic hierarchy process to assess and optimize distribution network

   Mithun J Sharma, Ilkyeong Moon, and Hyerim Bae

   Applied mathematics and computation, 2008

2004

1. IEEE TKDE

   Automatic control of workflow processes using ECA rules

   Joonsoo Bae, Hyerim Bae, Suk-Ho Kang, and 1 more author

   IEEE transactions on knowledge and data engineering, 2004

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