• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.527-529
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• 2023

Generative Adversarial Networks (GANs) models have developed rapidly due to the emergence of various variation models and their wide applications. Despite many recent developments in GANs, mode collapse, and instability are still unresolved issues. To address these problems, we focused on the fact that a single GANs model itself cannot realize local failure during the training phase without external standards. This paper introduces a novel training process involving multiple GANs, inspired by auction mechanisms. During the training, auxiliary performance metrics for each GANs are determined by the others through the process of various auction methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.713-714
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• 2023

본 논문에서는 사전학습모델을 활용한 수학학습 도구 자동 생성 시스템을 제안한다. 본 시스템은 사전학습모델을 활용하여 수학학습 도구를 교과과정 및 단원, 유형별로 다각화하여 자동 생성하고 사전학습모델을 자체 구축한 Dataset을 이용해 Fine-tuning하여 학생들에게 적절한 학습 도구와 적절치 않은 학습 도구를 분류하여 학습 도구의 품질을 높이었다. 본 시스템을 활용하여 학생들에게 양질의 수학학습 도구를 많은 양으로 제공해 줄 수 있는 초석을 다지었으며, 추후 AI 교과서와의 융합연구의 가능성도 열게 되었다.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.641-651
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• 2023

Malicious hate speech and gender bias comments are common in online communities, causing social problems in our society. Gender bias and hate speech detection has been investigated. However, it is difficult because there are diverse ways to express them in words. To solve this problem, we attempted to detect malicious comments in a Korean hate speech dataset constructed in 2020. We explored bidirectional encoder representations from transformers (BERT)-based deep learning models utilizing hyperparameter tuning, data sampling, and logits ensembles with a label distribution. We evaluated our model in Kaggle competitions for gender bias, general bias, and hate speech detection. For gender bias detection, an F1-score of 0.7711 was achieved using an ensemble of the Soongsil-BERT and KcELECTRA models. The general bias task included the gender bias task, and the ensemble model achieved the best F1-score of 0.7166.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.757-759
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• 2024

Software vulnerabilities represent security weaknesses in software systems that attackers exploit for malicious purposes, resulting in potential system compromise and data breaches. Despite the increasing prevalence of these vulnerabilities, manual repair efforts by security analysts remain time-consuming. The emergence of deep learning technologies has provided promising opportunities for automating software vulnerability repairs, but existing AIbased approaches still face challenges in effectively handling complex vulnerabilities. This paper explores the potential of large language models (LLMs) in addressing these limitations, examining their performance in code vulnerability repair tasks. It introduces the latest research on utilizing LLMs to enhance the efficiency and accuracy of fixing security bugs.

• Journal of the Korea Computer Graphics Society
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• v.24 no.3
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• pp.61-71
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• 2018

When triangular meshes are generated from the point clouds in global space reconstructed through camera pose estimation against captured RGB-D streams, the quality of the resulting meshes improves as more triangles are hired. However, for 3D reconstructed models beyond some size threshold, they become to suffer from the ugly-looking artefacts due to the insufficient precision of RGB-D sensors as well as significant burdens in memory requirement and rendering cost. In this paper, for the generation of 3D models appropriate for real-time applications, we propose an effective technique that extracts high-quality textures for moderate-sized meshes from the captured colors associated with the reconstructed point sets. In particular, we show that via a simple method based on the mapping between the 3D global space resulting from the camera pose estimation and the 2D texture space, textures can be generated effectively for the 3D models reconstructed from captured RGB-D image streams.

• Journal of Information Technology and Architecture
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• v.9 no.3
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• pp.243-251
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• 2012

This paper presents the Conceptual Models of the Mission Space-Korea (CMMS-K), which is an ontology-based conceptual modeling framework of the mission space. Through modeling and simulating military trainings, we can reduce the cost of actual military trainings in terms of time, space, and supplies. CMMS-K is being developed to improve the interoperability and reusability of defense models and simulations. CMMS-K reflects the needs and characteristics of Korean military while referring to existing military conceptual modeling frameworks. The main components of CMMS-K contain domain ontologies, a mission space model description language, a mission space modeling tool, and a CMMS-K management system. CMMS-K domain ontologies consist of entity and task ontologies. In this paper, the CMMS-K domain ontologies are described in detail and the feasibility of the proposed method is discussed with a case study.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.489-509
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• 2024

Slope stability analysis and prediction are of critical importance to geotechnical engineers, given the severe consequences associated with slope failure. This research endeavors to forecast the factor of safety (FOS) for slopes through the implementation of six distinct ML techniques, including back propagation neural networks (BPNN), feed-forward neural networks (FFNN), Takagi-Sugeno fuzzy system (TSF), gene expression programming (GEP), and least-square support vector machine (Ls-SVM). 344 slope cases were analyzed, incorporating a variety of geometric and shear strength parameters measured through the PLAXIS software alongside several loss functions to assess the models' performance. The findings demonstrated that all models produced satisfactory results, with BPNN and GEP models proving to be the most precise, achieving an R² of 0.86 each and MAE and MAPE rates of 0.00012 and 0.00002 and 0.005 and 0.004, respectively. A Pearson correlation and residuals statistical analysis were carried out to examine the importance of each factor in the prediction, revealing that all considered geomechanical features are significantly relevant to slope stability. However, the parameters of friction angle and slope height were found to be the most and least significant, respectively. In addition, to aid in the FOS computation for engineering challenges, a graphical user interface (GUI) for the ML-based techniques was created.

• ETRI Journal
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• v.42 no.1
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• pp.90-100
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• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.23-29
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• 2013

This paper presents inventory models for fresh agriculture products with time-varying deterioration rate. Due to the particularity of fresh agriculture products, the demand rate is a function that depends on sale price and freshness. The deterioration rate increases with time and is assumed to be a time-varying function. In the models, the inventory cycle may be constant or variable. The optimal solutions of models are discussed for different freshness and the deterioration rate. The results of experiments show that the profit depends on the freshness and deterioration rate of products. With the increasing inventory cycle, the sale price and profit increase at first and then start decreasing. Furthermore, when the inventory cycle is variable, the total profit is a binary function of the sale price and inventory cycle. There exist unique sale price and inventory cycle such that the profit is optimal. The results also show that the optimal sale price and inventory cycle depend on the freshness and the deterioration rate of fresh agriculture products.

• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.491-500
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• 2003

The need for network security is being increasing due to the development of information communication and internet technology, In this paper, firewall models, operating system models and other network component models are constructed. Each model Is defined by basic or compound model using MODSIM III. In this simulation environment with representative attacks, the following attacks are generated, SYN flooding and Smurf attack as an attack type of denial of service. The simulation is performed with the models that exploited various security policies against these attacks. In addition, the results of the simulation show that the analysis of security performance according to various security policies, and the analysis of correlation between availability and confidentiality according to security empowerment.