• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.1-25
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• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.167-171
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• 2017

자연어 생성은 특정한 조건들을 만족하는 문장을 생성하는 연구로, 이러한 조건들은 주로 표와 같은 축약되고 구조화된 의미 표현으로 주어지며 사용자가 자연어로 생성된 문장을 받아야 하는 어떤 분야에서든 응용이 가능하다. 본 논문에서는 SC(Semantically Conditioned)-GRU기반 encoder-decoder모델을 이용한 자연어 생성 모델을 제안한다. 본 논문에서 제안한 모델이 SF Hotel 데이터에서는 0.8645 BLEU의 성능을, SF Restaurant 데이터에서는 0.7570 BLEU의 성능을 보였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.167-171
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• 2017

자연어 생성은 특정한 조건들을 만족하는 문장을 생성하는 연구로, 이러한 조건들은 주로 표와 같은 축약되고 구조화된 의미 표현으로 주어지며 사용자가 자연어로 생성된 문장을 받아야 하는 어떤 분야에서든 응용이 가능하다. 본 논문에서는 SC(Semantically Conditioned)-GRU기반 encoder-decoder모델을 이용한 자연어 생성 모델을 제안한다. 본 논문에서 제안한 모델이 SF Hotel 데이터에서는 0.8645 BLEU의 성능을, SF Restaurant 데이터에서는 0.7570 BLEU의 성능을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.850-858
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• 2022

To deploy Gate Recurrent Units (GRU) on resource-constrained embedded devices, this paper presents a reconfigurable FPGA-based GRU accelerator that enables structured compression. Firstly, a dense GRU model is significantly reduced in size by hybrid quantization and structured top-k pruning. Secondly, the energy consumption on external memory access is greatly reduced by the proposed reuse computing pattern. Finally, the accelerator can handle a structured sparse model that benefits from the algorithm-hardware co-design workflows. Moreover, inference tasks can be flexibly performed using all functional dimensions, sequence length, and number of layers. Implemented on the Intel DE1-SoC FPGA, the proposed accelerator achieves 45.01 GOPs in a structured sparse GRU network without batching. Compared to the implementation of CPU and GPU, low-cost FPGA accelerator achieves 57 and 30x improvements in latency, 300 and 23.44x improvements in energy efficiency, respectively. Thus, the proposed accelerator is utilized as an early study of real-time embedded applications, demonstrating the potential for further development in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.29-37
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• 2023

Due to climate change, interest in crop production and distribution is increasing, and attempts are being made to use bigdata and AI to predict production volume and control shipments and distribution stages. Prediction of agricultural product imports not only affects prices, but also controls shipments of farms and distributions of distribution companies, so it is important information for establishing marketing strategies. In this paper, we create an artificial intelligence prediction model that predicts the future import volume based on the wholesale market melon import volume data disclosed by the agricultural statistics information system and evaluate its accuracy. We create prediction models using three models: the Neural Prophet technique, the Ensembled Neural Prophet model, and the GRU model. As a result of evaluating the performance of the model by comparing two major indicators, MAE and RMSE, the Ensembled Neural Prophet model predicted the most accurately, and the GRU model also showed similar performance to the ensemble model. The model developed in this study is published on the web and used in the field for 1 year and 6 months, and is used to predict melon production in the near future and to establish marketing and distribution strategies.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.128-133
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• 2016

문장유사도 분석은 문서 평가 자동화에 활용될 수 있는 중요한 기술이다. 최근 순환신경망을 이용한 인코더-디코더 언어 모델이 기계학습 분야에서 괄목할만한 성과를 거두고 있다. 본 논문에서는 한국어 형태소임베딩 모델과 GRU(Gated Recurrent Unit)기반의 인코더를 제시하고, 이를 이용하여 언어모델을 한국어 위키피디아 말뭉치로부터 학습하고, 한국어 질의응답 시스템에서 질문에 대한 정답을 유추 할 수 있는 증거문장을 찾을 수 있도록 문장유사도를 측정하는 방법을 제시한다. 본 논문에 제시된 형태소임베딩 모델과 GRU 기반의 인코딩 모델을 이용하여 문장유사도 측정에 있어서, 기존 글자임베딩 방법에 비해 개선된 결과를 얻을 수 있었으며, 질의응답 시스템에서도 유용하게 활용될 수 있음을 알 수 있었다.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.128-133
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• 2016

문장유사도 분석은 문서 평가 자동화에 활용될 수 있는 중요한 기술이다. 최근 순환신경망을 이용한 인코더-디코더 언어 모델이 기계학습 분야에서 괄목할만한 성과를 거두고 있다. 본 논문에서는 한국어 형태 소임베딩 모델과 GRU(Gated Recurrent Unit)기반의 인코더를 제시하고, 이를 이용하여 언어모델을 한국어 위키피디아 말뭉치로부터 학습하고, 한국어 질의응답 시스템에서 질문에 대한 정답을 유추 할 수 있는 증거문장을 찾을 수 있도록 문장유사도를 측정하는 방법을 제시한다. 본 논문에 제시된 형태소임베딩 모델과 GRU 기반의 인코딩 모델을 이용하여 문장유사도 측정에 있어서, 기존 글자임베딩 방법에 비해 개선된 결과를 얻을 수 있었으며, 질의응답 시스템에서도 유용하게 활용될 수 있음을 알 수 있었다.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Smart Media Journal
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• v.12 no.11
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• pp.18-26
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• 2023

The building energy management system (BEMS), a system designed to efficiently manage energy production and consumption, aims to address the variable nature of power consumption within buildings due to their physical characteristics, necessitating stable power supply. In this context, accurate prediction of building energy consumption becomes crucial for ensuring reliable power delivery. Recent research has explored various approaches, including time series analysis, statistical analysis, and artificial intelligence, to predict power consumption. This paper analyzes the strengths and weaknesses of the Prophet model, choosing to utilize its advantages such as growth, seasonality, and holiday patterns, while also addressing its limitations related to data complexity and external variables like climatic data. To overcome these challenges, the paper proposes an algorithm that combines the Prophet model's strengths with the gated recurrent unit (GRU) to forecast short-term (2 days) and medium-term (7 days, 15 days, 30 days) building energy consumption. Experimental results demonstrate the superior performance of the proposed approach compared to conventional GRU and Prophet models.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.53-60
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• 2023

Recently, the demand and traffic volume for various multimedia contents are rapidly increasing through real-time streaming platforms. In this paper, we predict real-time streaming traffic to improve the quality of service (QoS). Statistical models have been used to predict network traffic. However, since real-time streaming traffic changes dynamically, we used recurrent neural network-based deep learning models rather than a statistical model. Therefore, after the collection and preprocessing for real-time streaming data, we exploit vanilla RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU models to predict real-time streaming traffic. In evaluation, the training time and accuracy of each model are measured and compared.