• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1308-1310
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• 2022

소리 데이터 분류는 단순 소리를 통한 분류, 감정 인식등 다양한 연구가 진행중이다. 심층 신경망에서 데이터의 부족과 과적합 문제를 개선하는 방법으로 데이터 증강은 중요하다. 본 논문에서는 3가지의 소리데이터(UrbanSound8K, RAVDESS, IRMAS)를 사용하였으며, 소리데이터는 멜 스펙트로그램을 통한 변환과정을 거쳐 네트워크 망에 입력된다. 입력된 신호는 다양한 네크워크 신경망(Bidirection LSTM, Bidirection LSTM Attention, Multi-Head Attention, CNN)을 통해 학습되어지며, 각각의 네트워크 신경망에서 데이터 증강 전후의 분류 정확도를 확인 하였다. 다양한 데이터셋과 다양한 네트워크 망에서의 데이터 증강 방법의 결과 비교를 통한 통찰을 얻을수 있을 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.559-560
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• 2020

우리는 패션 의류 이미지의 빠르고 정확한 분류를 달성하기 위해 최적화 된 동적 감쇠 학습률과 개선 된 모델 구조를 갖춘 딥 러닝 모델을 기반으로 하는 새로운 방법을 제안했습니다. 우리는 Fashion-MNIST 데이터 셋에 대해 제안 된 모델을 사용하여 실험을 수행하고 이를 CNN, LeNet, LSTM 및 BiLSTM의 방법과 비교했습니다.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.105-127
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• 2024

We propose recurrent neural network (RNN) methods for forecasting realized volatility (RV). The data are RVs of ten major stock price indices, four from the US, and six from the EU. Forecasts are made for relative ratio of adjacent RVs instead of the RV itself in order to avoid the out-of-scale issue. Forecasts of RV ratios distribution are first constructed from which those of RVs are computed which are shown to be better than forecasts constructed directly from RV. The apparent asymmetry of RV ratio is addressed by the Piecewise Min-max (PM) normalization. The serial dependence of the ratio data renders us to consider two architectures, long short-term memory (LSTM) and gated recurrent unit (GRU). The hyperparameters of LSTM and GRU are tuned by the nested cross validation. The RNN forecast with the PM normalization and ratio transformation is shown to outperform other forecasts by other RNN models and by benchmarking models of the AR model, the support vector machine (SVM), the deep neural network (DNN), and the convolutional neural network (CNN).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.342-344
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• 2018

This paper shows the possibility to substitute statistical methods such as mean imputation, correlation coefficient analysis, graph correlation analysis for the proposed algorithm, and replace statistician for processing various abnormal data measured in the water treatment process with it. In addition, this study aims to model a data-filtering system based on a recent fractile pattern and a deep learning-based LSTM algorithm in order to improve the reliability and validation of the algorithm, using the open-sourced libraries such as KERAS, THEANO, TENSORFLOW, etc.

• 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.

• Broadcasting and Media Magazine
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• v.22 no.1
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• pp.35-46
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• 2017

글로벌 라이프로그 미디어 클라우드 서비스를 위하여 네트워크 기술, 클라우드 기술 멀티미디어 App 기술 및 하이라이팅 엔진 기술이 요구된다. 본 논문에서는 미디어 클라우드 서비스를 위한 개발 기술 및 서비스 기술 개발 결과를 보였다. 하이라이팅 엔진은 표정인식기술, 이미지 분류기술, 주목도 지도 생성기술, 모션 분석기술, 동영상 분석 기술, 얼굴 인식 기술 및 오디오 분석기술 등을 포함하고 있다. 표정인식 기술로는 Alexnet을 최적화하여 Alexnet 대비 1.82% 우수한 인식 성능을 보였으며 처리속도면에서 28배 빠른 결과를 보였다. 행동 인식 기술에 있어서는 기존 2D CNN 및 LSTM에 기반한 인식 방법에 비하여 제안하는 3D CNN 기법이 0.8% 향상된 결과를 보였다. (주)판도라티비는 클라우드 기반 라이프로그 동영상 생성 서비스를 개발하여 현재 테스트 서비스를 진행하고 있다.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.388-391
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• 2019

과편향 뉴스 판별(hyperpartisan news detection)은 뉴스 기사가 특정 인물 또는 정당에 편향되었는지 판단하는 task이다. 이를 위해 feature-based ELMo + CNN 모델이 제안되었으나, 이는 문서 임베딩이 아닌 단어 임베딩의 평균을 사용한다는 한계가 존재한다. 따라서 본 논문에서는 feature-based 접근법을 따르며 Sentence-BERT(SentBERT)의 문서 임베딩을 이용한 feature-based SentBERT 기반의 과편향 뉴스 판별 모델을 제안한다. 제안 모델의 효과를 입증하기 위해 ELMO, BERT, SBERT와 CNN, BiLSTM을 적용한 비교 실험을 진행하였고, 기존 state-of-the-art 모델보다 f1-score 기준 1.3%p 높은 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.176-178
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• 2018

트위터는 최대 140자의 단문을 주고받는 소셜 네트워크 서비스이다. 트위터의 해시 태그는 주로 문장의 핵심 단어나 주요 토픽 등을 링크하게 되는데 본 논문에서는 이러한 정보를 이용하여 키워드 추출에 활용한다. 문장을 Character CNN, Bi-LSTM을 통해 문장 표현을 얻어내고 각 Span에서 이러한 문장 표현을 활용하여 Span 표현을 생성한다. Span 표현을 이용하여 각 Span에 대한 Score를 얻고 높은 점수의 Span을 이용하여 키워드를 추출한다.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.55-62
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• 2017

The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.