• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.559-560
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• 2018

순환 신경망(RNN) 기반의 Long Short-Term Memory(LSTM)는 자연어처리 분야에서 우수한 성능을 보이는 모델이다. 음성을 문자로 변환해주는 Speech to Text (STT)를 이용해 자막을 생성하고, 생성된 자막을 다른 언어로 동시에 번역을 해주는 서비스가 활발히 진행되고 있다. STT를 사용하여 자막을 추출하는 경우에는 마침표가 없이 전부 연결된 문장이 생성되기 때문에 정확한 번역이 불가능하다. 본 논문에서는 영어자막의 자동 번역 시, 정확도를 높이기 위해 텍스트를 문장으로 분할하여 마침표를 생성해주는 방법을 제안한다. 이 때, LSTM을 이용하여 데이터를 학습시킨 후 테스트한 결과 62.3%의 정확도로 마침표의 위치를 예측했다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.633-636
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• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• Annual Conference of KIPS
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• 2020.11a
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• pp.1025-1027
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• 2020

태권도 경기와 같이 동작의 정확한 기술을 판별하여 유효득점화하는 시스템에서는 점수 체계의 정확성과 전문성이 필요하다. 기존에 시행되었던 심판 판정은 객관성과 신뢰성의 결여 문제가 존재하여 이를 대체하기 위한 방법으로 전자호구가 도입되었다. 하지만 전자호구는 타격 강도에 따라 분류되는 문제로 인해 태권도 기술이 아닌 변칙 발차기 기술에서도 유효득점이 처리되는 문제가 발생하였다. 본 논문에서는 변칙 발차기와 일반 발차기를 분류하여 변칙 발차기에서의 유효득점을 무효 득점화 시키기 위한 분류 모델을 제안하였다. 순환 신경망 모델인 LSTM을 이용하여 변칙 발차기와 일반 발차기를 분류하였으며 94.90%의 정확도를 보였다.

• Korea Trade Review
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• v.48 no.2
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• pp.27-43
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• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.469-476
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• 2022

This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.297-299
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• 2021

Predicting future productivity of tight oil is an important task for analyzing residual oil recovery and reservoir behavior. In general, productivity prediction is made using the decline curve analysis(DCA). In this study, we intend to propose an effective model for predicting future production using deep learning-based recurrent neural networks(RNN), LSTM, and GRU algorithms. As input variables, the main parameters are oil, gas, water, which are calculated during the production of tight oil, and the type curve calculated through various cluster analyzes. the output variable is the monthly oil production. Existing empirical models, the DCA and RNN models, were compared, and an optimal model was derived through hyperparameter tuning to improve the predictive performance of the model.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.243-248
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• 2019

Multi-Task Learning(MTL) is a training method that trains a single neural network with multiple tasks influences each other. In this paper, we compare performance of MTL Named entity recognition(NER) model trained with Korean traditional culture corpus and other NER model. In training process, each Bi-LSTM layer of Part of speech tagging(POS-tagging) and NER are propagated from a Bi-LSTM layer to obtain the joint loss. As a result, the MTL based Bi-LSTM model shows 1.1%~4.6% performance improvement compared to single Bi-LSTM models.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.185-185
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• 2021

기후변화로 인한 돌발 강우 등 이상 기후 현상이 증가함에 따라 정확한 강우예측의 중요성은 더 증가하는 추세이다. 전통적인 강우예측의 경우 기상수치모델 또는 외삽법을 이용한 레이더 기반 강우예측 기법을 이용하며, 최근 머신러닝 기술의 발달에 따라 이를 활용한 레이더 자료기반 강우예측기법이 개발되고 있다. 기존 머신러닝을 이용한 강우예측 모델의 경우 주로 시계열 이미지 예측에 적합한 2차원 순환 신경망 기반 기법(Convolutional Long Short-Term Memory, ConvLSTM) 또는 합성곱 신경망 기반 기법(Convolutional Neural Network(CNN) Encoder-Decoder) 등을 이용한다. 본 연구에서는 생성적 적대 신경망 기반 기법(Generative Adversarial Network, GAN)을 이용해 미래 강우예측을 수행하도록 하였다. GAN 방법론은 이미지를 생성하는 생성자와 이를 실제 이미지와 구분하는 구별자가 경쟁하며 학습되어 현재 이미지 생성 분야에서 높은 성능을 보여주고 있다. 본 연구에서 개발한 GAN 기반 모델은 기상청에서 제공된 2016년~2019년까지의 레이더 이미지 자료를 이용하여 초단기, 단기 강우예측을 수행하도록 학습시키고, 2020년 레이더 이미지 자료를 이용해 단기강우예측을 모의하였다. 또한, 기존 머신러닝 기법을 기반으로 한 모델들의 강우예측결과와 GAN 기반 모델의 강우예측결과를 비교분석한 결과, 본 연구를 통해 개발한 강우예측모델이 단기강우예측에 뛰어난 성능을 보이는 것을 확인할 수 있었다.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.57-72
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• 2020

The price of iron ore has continued to fluctuate with high demand and supply from many countries and companies. In this business environment, forecasting the price of iron ore has become important. This study developed the machine learning model forecasting the price of iron ore a one month after the trading events. The forecasting model used distributed lag model and deep learning models such as MLP (Multi-layer perceptron), RNN (Recurrent neural network) and LSTM (Long short-term memory). According to the results of comparing individual models through metrics, LSTM showed the lowest predictive error. Also, as a result of comparing the models using the ensemble technique, the distributed lag and LSTM ensemble model showed the lowest prediction.