• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.264-269
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• 2021

상호참조해결은 주어진 문서에서 상호참조해결의 대상이 될 수 있는 멘션을 추출하고, 같은 개체를 의미하는 멘션 쌍 또는 집합을 찾는 자연어처리 작업이다. 하나의 멘션 내에 멘션이 될 수 있는 다른 단어를 포함하는 중첩 멘션은 순차적 레이블링으로 해결할 수 없는 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위해 멘션의 시작 단어의 위치를 여는 괄호('('), 마지막 위치를 닫는 괄호(')')로 태깅하고 이 괄호들을 예측하는 멘션 탐지 모델과 멘션 탐지 모델에서 예측된 멘션을 바탕으로 포인터 네트워크를 이용하여 같은 개체를 나타내는 멘션을 군집화하는 상호참조해결 모델을 제안한다. 실험 결과, 4개의 영어 대화 데이터셋에서 멘션 탐지 모델은 F1-score (Light) 94.17%, (AMI) 90.86%, (Persuasion) 92.93%, (Switchboard) 91.04%의 성능을 보이고, 상호참조해결 모델에서는 CoNLL F1 (Light) 69.1%, (AMI) 57.6%, (Persuasion) 71.0%, (Switchboard) 65.7%의 성능을 보인다.

• Review of KIISC
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• v.32 no.3
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• pp.57-64
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• 2022

우리 사회에서 AI 활용이 더욱 보편화 되어가고 있는 가운데 AI 신뢰에 대한 사회적 요구도 증가했다. 특히 최근 대화형 인공지능'이루다'사건으로 AI 윤리에 대한 논의가 뜨거워졌다. 금융 분야에서도 로보어드바이저, 보험 심사 등 AI가 다양하게 활용되고 있지만, AI 윤리 문제가 AI 활성화에 큰 걸림돌이 되고 있다. 본 논문에서는 인공지능으로 발생할 수 있는 윤리적 문제를 활용 도메인과 데이터 분석 파이프라인에 따라 나눈다. 금융 AI 기술 분야에 따른 윤리 문제를 분류했으며 각 분야별 윤리사례를 제시했고 윤리 문제 분류에 따른 대응 방안과 해외에서의 대응방식과 우리나라의 대응방식을 소개하며 해결방안을 제시했다. 본 연구를 통해 금융 AI 기술 발전에 더불어 윤리 문제에 대한 경각심을 고취시킬 수 있을 것으로 기대한다. 금융 AI 기술 발전이 AI 윤리와 조화를 이루며 성장하길 바라며, 금융 AI 정책 수립 시에도 AI 윤리적 문제를 염두해 두어 차별, 개인정보유출 등과 같은 AI 윤리 규범 미준수로 파생되는 문제점을 줄이며 금융분야 AI 활용이 더욱 활성화되길 기대한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.766-768
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• 2004

자연영상에서 획득된 문자를 인식하는 연구는 대부분 디지털 카메라나 캠코더를 이용하여 획득된 고해상도 영상을 입력영상으로 사용하고 있다. 본 논문에서는 휴대폰 카메라로 획득된 저해상도 영상을 입력영상으로 사용하였다. 저해상도의 영상은 적은 수의 픽셀로 정보를 표현하고 있기 때문에 기존에 제시되었던 다양한 이진화 방법으로는 문자와 배경을 깨끗하게 분리해 낼 수 없다. 본 논문은 입력영상의 이진화를 위친 K-Means 알고리즘을 이용하여 칼라 클러스터링을 하였으며, 이진화 성능을 향상시키기 위해 지능형 주파수 필터를 사용하였다. 이진화된 영상을 파이프라인 구조의 인식 시스템에 인식시킴으로써 기존의 제안 방법들에 비하여 인식 성능을 향상시킬 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.839-842
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• 2004

자연영상에서 문자를 인식하는 연구는 활발히 진행되고 있지만 대부분 디지털 카메라나 캠코더 등으로 획득한 고해상도의 영상에서의 연구에 국한되어 있다. 휴대폰 카메라로 획득된 저해상도의 영상은 아주 적은 수의 픽셀로 정보를 표현하기 때문에 기존의 이진화 알고리즘으로는 문자와 배경을 깨끗하게 분리해 낼 수 없다. 본 논문은 영상의 칼라정보를 K-Means 클러스터링을 이용하여 전경과 배경으로 이진화 하였으며, 이진화 성능을 향상시키기 위해 지능형 주파수 필터와 비등방성 확산 필터를 사용하였다. 또한 입력영상을 파이프라인 구조의 이진화 및 인식 시스템에 인식시킴으로써 인식성능을 향상시켰다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.204-205
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• 2023

This study studied a prediction model for sending collision alarms for small fishing boats based on semi-supervised learning(SSL). The supervised learning (SL) method requires a large number of labeled data, but the labeling process takes a lot of resources and time. This study used service data collected through a data pipeline linked to 'intelligent maritime traffic information service' and data collected from real-sea experiment. The model accuracy was improved as a result of learning not only real-sea experiment data with labeling determined based on actual user satisfaction but also service data without label determined together.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.308-317
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• 2022

Researches are underway to efficiently reduce the size of video data transmitted and stored in the image analysis process using deep learning-based machine vision technology. MPEG (Moving Picture Expert Group) has newly established a standardization project called VCM (Video Coding for Machine) and is conducting research on video encoding for machines rather than video encoding for humans. We are researching a multitask that performs various tasks with one image input. The proposed pipeline does not perform all object detection of each task that should precede object detection, but precedes it only once and uses the result as an input for each task. In this paper, we propose a pipeline for efficient multitasking and perform comparative experiments on compression efficiency, execution time, and result accuracy of the input image to check the efficiency. As a result of the experiment, the capacity of the input image decreased by more than 97.5%, while the accuracy of the result decreased slightly, confirming the possibility of efficient multitasking.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.267-273
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• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.654-664
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• 2022

Immersive content overcomes spatial limitations through convergence with extended reality, artificial intelligence, and photogrammetry technology along with interest due to the COVID-19 pandemic, presenting a new paradigm in the content market such as entertainment, media, performances, and exhibitions. However, it can be seen that in order for realistic content to have sustained public interest, it is necessary to study storytelling method that can increase immersion in content rather than technological freshness. Therefore, in this study, we propose a immersive content storytelling method using artificial intelligence and photogrammetry technology. The proposed storytelling method is to create a content story through interaction between interactive virtual beings and participants. In this way, participation can increase content immersion. This study is expected to help content creators in the accelerating immersive content market with a storytelling methodology through virtual existence that utilizes artificial intelligence technology proposed to content creators to help in efficient content creation. In addition, I think that it will contribute to the establishment of a immersive content production pipeline using artificial intelligence and photogrammetry technology in content production.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1019-1034
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• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.29-43
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• 2014

The development of NGS technologies, such as scientific workflows, has reduced the time required for decoding DNA sequences. Although the automated technologies change the genome sequence analysis environment, limited computing resources still pose problems for the analysis. Most scientific workflow systems are pre-built platforms and are highly complex because a lot of the functions are implemented into one system platform. It is also difficult to apply components of pre-built systems to a new system in the cloud environment. Cloud computing technologies can be applied to the systems to reduce analysis time and enable simultaneous analysis of massive DNA sequence data. Web service techniques are also introduced for improving the interoperability between DNA sequence analysis systems. The workflow-based middleware, which supports Web services, DBMS, and cloud computing, is proposed in this paper for expecting to reduceanalysis time and aiding lightweight virtual instances. It uses DBMS for managing the pipeline status and supporting the creation of lightweight virtual instances in the cloud environment. Also, the RESTful Web services with simple URI and XML contents are applied for improving the interoperability. The performance test of the system needs to be conducted by comparing results other developed DNA analysis services at the stabilization stage.