• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.133-136
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• 2017

공간 정보 추출은 대량의 텍스트 문서에서 자연어로 표현된 공간 관련 개체 및 관계를 추출하는 것으로 질의응답 시스템, 챗봇 시스템, 네비게이션 시스템 등에서 활용될 수 있다. 본 연구는 한국어에 나타나 있는 공간 개체들을 효과적으로 추출하기 위한 앙상블 기법이 적용된 Bidirectional LSTM-CRF 모델을 소개한다. 한국어 공간 정보 말뭉치를 이용하여 실험한 결과, 기존 모델보다 매크로 평균이 향상되어 전반적인 공간 관계 추출에 유용할 것으로 기대한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.301-302
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• 2022

본 논문에서는 사용자 정보를 바탕으로, 사용자에게 적합한 다이빙 플랜을 자동으로 매칭하고, 해양생태정보를 수집하는 시스템을 제안한다. 이 시스템은 사용자의 정보를 바탕으로 사용자에게 적합한 다이빙 플랜이 자동으로 매칭되므로, 최적 조건의 다이빙 플랜이 사용자에게 제공될 수 있다. 또한, 해양 생태 정보를 수집하여 데이터화함으로써 해양 생태 변화에 대한 자료가 사용자에게 제공될 수 있다.

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2022.03a
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• pp.55-56
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• 2022

• Annual Conference of KIPS
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• 2017.11a
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• pp.971-973
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• 2017

국제화 시대를 맞이하여 세계 공용어인 영어의 중요성이 부각되고 있다. 특히, 영어 교육의 학습 연령대는 점점 낮아지고 있는 추세이며, 이에 동반하여 저 연령층 영어 교육 콘텐츠가 출시되고 있다. 하지만 현재 저 연령층을 대상으로 출시되는 콘텐츠들은 연령에 맞지 않는 교육 자료를 제시하거나 언어 학습에 필요한 상황적 다양성이 부족한 것이 현실이다. 본 논문에서는 딥러닝을 적용하여 사용자가 원하는 상황을 촬영한 영상에서 대상 연령에 적합한 영어 문장을 생성하고 읽어주는 학습 시스템을 제안한다. 본 시스템을 통하여 저 연령층에 적합한 영어 교육 환경을 제공하고, 저 연령층에게 나타나는 영어 교육의 불균형을 해소하고자 한다.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1209-1216
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• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1255-1262
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• 2021

The proportion of students dropping out of universities is increasing year by year, and they are trying to identify risk factors and eliminate them in advance to prevent dropouts. However, there is a problem in the management of students at risk of dropping out and the forecast is inaccurate because crisis students are managed through the univariable analysis of specific risk factors. In this paper, we identify risk factors for university dropout and analyze multivariables through machine learning method to predict university dropout. In addition, we derive the optimization method by evaluation performance for various prediction methods and evaluate the correlation and contribution between risk factors that cause university dropout.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.807-812
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• 2020

In order to prevent the national wide spread of the COVID-19 virus, the government enforces to use an electronic access registration system for public facilities to effectively track and manage the spread. Initially, there was a lot of hassle to write a directory, but recently a system for creating an electronic access list using QR codes, what is called KI-Pass, is mainly used. However, the procedure for generating a QR code is somewhat cumbersome. In this paper, we propose a new electronic access registration system that does not require QR code. This system effectively controls the suspicious visitor by using a mask wearing discriminator which has been implemented using deep learning technology, and a non-contact thermometer package. In addition, by linking the beacon, a short-range wireless communication technology, and the visitor's smartphone application, basic information of the facility visitor is automatically registered to KDCA through the server. On the other hand, the user access information registered in the server is encrypted and stored, and is automatically destroyed after up to 4 weeks. This system is expected to be very effective in preventing the spread of other new infectious diseases as well as responding to the coronavirus which is recording a high spread worldwide.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.389-393
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• 2018

딥러닝의 발달로 기계번역, 대화 시스템 등의 자연언어처리 분야가 크게 발전하였다. 딥러닝 모델의 성능을 향상시키기 위해서는 많은 데이터가 필요하다. 그러나 많은 데이터를 수집하기 위해서는 많은 시간과 노력이 소요된다. 본 연구에서는 이미지 생성 모델로 좋은 성능을 보이고 있는 생성적 적대 네트워크(Generative adverasarial network)를 문장 생성에 적용해본다. 본 연구에서는 긍/부정 조건에 따른 문장을 자동 생성하기 위해 SeqGAN 모델을 수정하여 사용한다. 그리고 분류기를 포함한 SeqGAN이 긍/부정 감성인식 학습데이터를 자동 생성할 수 있는지 실험한다. 실험을 수행한 결과, 분류기를 포함한 SeqGAN 모델이 생성한 문장과 학습데이터를 혼용하여 학습할 경우 실제 학습데이터만 학습 시킨 경우보다 좋은 정확도를 보였다.