• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.591-594
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• 2016

소비자의 선호도 및 여론을 정량적인 방법으로 분석하기 위해 비정형 데이터의 분석은 필수적인 요소가 되고 있다. 하지만 비정형 데이터는 언어의 구조 및 모호성 등으로 인해 분석하기 어려운 형태이다. 따라서 본 연구는 최근 각광받고 있는 인공신경망, 특히 그 중에서도 순환 신경망의 한 모델인 Deep LSTM을 이용하여 비정형 데이터를 분석하고 이를 활용하여 어순 및 어감 등의 언어의 구조적 문제에도 효과적인 정략적 모델을 설계하여 학습하고 이를 기존의 인공신경망 모델과 비교 분석하고자 한다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.8
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• pp.307-324
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• 2018

Recently, recurrent neural networks have been attracting attention in solving prediction problem of sequential data through structure considering time dependency. However, as the time step of sequential data increases, the problem of the gradient vanishing is occurred. Long short-term memory models have been proposed to solve this problem, but there is a limit to storing a lot of data and preserving it for a long time. Therefore, research on memory-augmented neural network (MANN), which is a learning model using recurrent neural networks and memory elements, has been actively conducted. In this paper, we describe the structure and characteristics of MANN models that emerged as a hot topic in deep learning field and present the latest techniques and future research that utilize MANN.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.15-21
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• 2018

The ocean drifter is a device for observing the ocean weather by floating off the sea surface. The data observed through the drifter is utilized in the ocean weather prediction and oil spill. Observed data may contain incorrect or missing data at the time of observation, and accuracy may be lowered when we use the data. In this paper, we propose a data correction model using recurrent neural networks. We corrected data collected from 7 drifters in 2015 and 8 drifters in 2016, and conducted experiments of drifter moving prediction to reflect the correction results. Experimental results showed that observed data are corrected by 13.9% and improved the performance of the prediction model by 1.4%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.473-474
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• 2023

본 논문에서는 딥러닝 기법 중 하나인 순환신경망(RNN)을 활용하여 자연어를 처리할 수 있는 모델 개발에 대하여 연구를 진행하였다. 다양한 주제에 대한 사용자들의 의견을 확보할 수 있는 유튜브 플랫픔을 활용하여 데이터를 확보하였으며, 감성 분류를 진행하는 만큼 학습 데이터셋으로는 네이버 영화 리뷰 데이터셋을 활용하였다. 사용자는 직접 데이터 파일을 삽입하거나 혹은 유튜브 댓글과 같이 데이터를 외부에서 확보하여 감성을 분석할 수 있으며, 자연어 속 등장하는 단어의 빈도수를 종합하여 해당 데이터들 속 키워드는 무엇인지를 분석할 수 있도록 하였다. 나아가 종합 데이터 분석 관리 플랫폼을 제작하기 위하여 해당 데이터를 데이터베이스에 저장하고GUI 프로그램을 통하여 접근 및 관리가 가능하도록 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.849-852
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• 2021

화석연료의 빈번한 사용으로 인한 지구온난화 문제가 심각해지면서 화석연료를 대체할 수 있는 신재생 에너지가 떠오르고 있다. 그중에서도 에너지원이 청정하고 무제한으로 사용할 수 있다는 장점을 가진 태양광 발전소가 주목을 받고 있다. 하지만 기후에 따라 영향을 많이 받는 특징 때문에 안정적인 전력 생산을 위해서는 태양광 발전량 예측이 매우 중요해지고 있다. 본 논문에서는 시계열 데이터에 특화된 순환신경망 기법인 RNN과 LSTM 모델을 이용하여 태양광 발전량을 예측하고 각 모델의 하이퍼 파라미터를 다르게 주어 비교 분석하였다. 그 결과 LSTM 모델이 RNN 모델보다 높은 예측력을 보였고, 손실 값이 0.1보다 낮은 높은 정확도를 보였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.401-410
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• 2021

Recently, as cyber attacks targeting industrial control systems increase, various studies are being conducted on the detection of abnormalities in industrial processes. Considering that the industrial process is deterministic and regular, It is appropriate to determine abnormality by comparing the predicted value of the detection model from which normal data is trained and the actual value. In this paper, HAI Datasets 20.07 and 21.03 are used. In addition, an ensemble model is created by combining models that have applied different time steps to Gated Recurrent Units. Then, the detection performance of the single model and the ensemble recurrent neural networks model were compared through various performance evaluation analysis, and It was confirmed that the proposed model is more suitable for abnormal detection in industrial control systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1784-1789
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• 2021

Korean sentiment classification task is used in real-world services such as chatbots and analysis of user's purchase reviews. And due to the development of deep learning technology, neural network models with high performance are being applied. However, the neural network model is not easy to interpret what the input sentences are predicting due to which words, and recently, model interpretation methods for interpreting these neural network models have been popularly proposed. In this paper, we used the LIME algorithm among the model interpretation methods to interpret which of the words in the input sentences of the models learned with the korean sentiment classification dataset. As a result, the interpretation of the Bi-LSTM model with 85.24% performance included 25,283 words, but 84.20% of the transformer model with relatively low performance showed that the transformer model was more reliable than the Bi-LSTM model because it contains 26,447 words.

• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.567-571
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• 2015

Transition-based dependency parsing requires much time and efforts to design and select features from a very large number of possible combinations. Recent studies have successfully applied Multi-Layer Perceptrons (MLP) to find solutions to this problem and to reduce the data sparseness. However, most of these methods have adopted greedy search and can only consider a limited amount of information from the context window. In this study, we use a Recurrent Neural Network to handle long dependencies between sub dependency trees of current state and current transition action. The results indicate that our method provided a higher accuracy (UAS) than an MLP based model.

• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.316-321
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• 2017

We analyze the online Hangul handwriting recognition problem (HHR) and present solutions based on recurrent neural networks. The solutions are organized according to the three kinds of sequence labeling problem - sequence classifications, segment classification, and temporal classification, with additional consideration of the structural constitution of Hangul characters. We present a stacked gated recurrent unit (GRU) based model as the natural HHR solution in the sequence classification level. The proposed model shows 86.2% accuracy for recognizing 2350 Hangul characters and 98.2% accuracy for recognizing the six types of Hangul characters. We show that the type recognizing model successfully follows the type change as strokes are sequentially written. These results show the potential for RNN models to learn high-level structural information from sequential data.