• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.501-503
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• 2017

In the water sector, various data such as flow rate, pressure, water quality and water level are collected during the whole process of water purification plant and piping system. The collected data is stored in each water treatment plant's DB, and the collected data are combined in the regional DB and finally stored in the database server of the head office of the Korea Water Resources Corporation. Various abnormal data can be generated when a measuring instrument measures data or data is communicated over various processes, and it can be classified into missing data and wrong data. The cause of each abnormal data is different. Therefore, there is a difference in the method of detecting the wrong side and the missing side data, but the method of cleansing the data is the same. In this study, a program that can automatically refine missing or wrong data by applying deep learning LSTM (Long Short Term Memory) algorithm will be studied.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.21-28
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• 2021

In this study, we propose a deep learning-based NILM technique using actual measured power data for 5 kinds of home appliances and verify its effectiveness. For about 3 weeks, the active power of the central power measuring device and five kinds of home appliances (refrigerator, induction, TV, washing machine, air cleaner) was individually measured. The preprocessing method of the measured data was introduced, and characteristics of each household appliance were analyzed through spectogram analysis. The characteristics of each household appliance are organized into a learning data set. All the power data measured by the central power measuring device and 5 kinds of home appliances were time-series mapping, and training was performed using a LSTM neural network, which is excellent for time series data prediction. An algorithm that can disaggregate five types of energies using only the power data of the main central power measuring device is proposed.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.7-13
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• 2021

This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.124-130
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• 2017

Character encoding is the method used to represent characters or symbols on a computer, and there are many encoding detection software tools. For the widely used encoding detection software"uchardet", the accuracy of encoding detection of unmodified normal text document is 91.39%, but the accuracy of language detection is only 32.09%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 3.55% and the accuracy of language detection is 0.06%. Therefore, in this paper, we propose encoding and language detection of text document using the deep learning algorithm called LSTM(Long Short-Term Memory). The results of LSTM are better than encoding detection software"uchardet". The accuracy of encoding detection of normal text document using the LSTM is 99.89% and the accuracy of language detection is 99.92%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 99.26%, the accuracy of language detection is 99.77%.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.71-75
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• 2017

외래어란 외국어로부터 들어와 한국어에 동화되고 한국어로서 사용되는 언어이다. 나날이 우리의 언어사용 문화에서 외래어의 사용 비율은 높아져가는 추세로, 전문분야에서는 특히 두드러진다. 그러므로 더 효율적이고 효과적인 자연언어처리를 위해서 문서 내 외래어 인식은 중요한 전처리 과정이다. 따라서 본 논문에서는 bidirectional LSTM(이하 bi-LSTM)-CRF 모형의 심층학습을 이용한 음절태깅 기반의 외래어 인식 시스템을 제안한다. 제안하는 시스템의 외래어 인식 학습 과정은 다음과 같다. 첫째, 학습용 말뭉치 자료의 한글 음절들과 공백, 마침표(.)를 토대로 word2vec을 통해 학습용 피쳐(feature) 자료를 생성한다. 둘째, 학습용 말뭉치 자료와 학습용 피쳐 자료를 결합하여 bi-LSTM 모형 학습 자료를 구축한다. 셋째, bi-LSTM 모형을 거쳐 학습된 결과물을 CRF 모형에서 로그 가능도(log likelyhood)와 비터비(Viterbi) 알고리즘을 통해 학습 결과물을 내놓는다. 넷째, 학습용 말뭉치 자료의 정답과 비교한 뒤 모형 내부의 수치들을 조정한다. 다섯째, 학습을 마칠 때까지 반복한다. 본 논문에서 제안하는 시스템을 이용하여 자체적인 뉴스 수집 자료에 대해서 높은 정확도와 재현율을 기록하였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.71-75
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• 2017

외래어란 외국어로부터 들어와 한국어에 동화되고 한국어로서 사용되는 언어이다. 나날이 우리의 언어사용 문화에서 외래어의 사용 비율은 높아져가는 추세로, 전문분야에서는 특히 두드러진다. 그러므로 더 효율적이고 효과적인 자연언어처리를 위해서 문서 내 외래어 인식은 중요한 전처리 과정이다. 따라서 본 논문에서는 bidirectional LSTM(이하 bi-LSTM)-CRF 모형의 심층학습을 이용한 음절태깅 기반의 외래어 인식 시스템을 제안한다. 제안하는 시스템의 외래어 인식 학습 과정은 다음과 같다. 첫째, 학습용 말뭉치 자료의 한글 음절들과 공백, 마침표(.)를 토대로 word2vec을 통해 학습용 피쳐(feature) 자료를 생성한다. 둘째, 학습용 말뭉치 자료와 학습용 피쳐 자료를 결합하여 bi-LSTM 모형 학습 자료를 구축한다. 셋째, bi-LSTM 모형을 거쳐 학습된 결과물을 CRF 모형에서 로그 가능도(log likelyhood)와 비터비(Viterbi) 알고리즘을 통해 학습 결과물을 내놓는다. 넷째, 학습용 말뭉치 자료의 정답과 비교한 뒤 모형 내부의 수치들을 조정한다. 다섯째, 학습을 마칠 때까지 반복한다. 본 논문에서 제안하는 시스템을 이용하여 자체적인 뉴스 수집 자료에 대해서 높은 정확도와 재현율을 기록하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.319-319
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• 2019

기후변화로 인한 극한 기후 상황의 증가로 홍수기 홍수피해와 갈수기 가뭄피해가 심화되고 있으며, 수자원 관리에 대한 어려움이 발생하고 있다. 효율적인 수자원 관리를 위해 국내에는 약 1,8000여개의 댐을 운영하고 있으며, 댐의 유입량과 저수량을 감안하여 물을 적절하게 방류하는 것을 목적으로 한다. 그러기 위해서는 유입량이 우선적으로 확보되어야 하며, 더 나아가 유입량을 미리 예측할 수 있다면 더욱 효율적인 댐 운영이 가능할 것이다. 기존에는 수위나 유량을 예측하기 위해서는 주로 물리적 모형이 사용되어 왔으며, 물리적 모형은 매개변수 결정을 위한 많은 자료를 필요로 하고 그 과정에서 많은 불확실성을 포함하고 있기 때문에 계산 과정을 거치는 동안 다양한 오차가 반복 누적되는 단점이 있다. 반면에 시계열 데이터 예측을 위한 알고리즘 LSTM(Long Short-Term Memory)은 입력된 데이터와 출력된 데이터를 동시에 이용하여 보다 정확한 예측 값을 얻을 수 있다. 따라서 본 연구는 다목적댐의 유입유량 예측을 위해 구글에서 제공하는 딥러닝 오픈소스 라이브러리를 활용하여 LSTM모형을 구축하고 댐 유입유량을 예측하였다. 분석 자료로는 wamis에서 제공하는 용담댐의 2006년부터 2018년까지의 시간당 유입량 자료를 사용하였으며, 입력 데이터로 모형을 학습한 후 2018년의 유입량을 예측하였다. 예측 값의 정확도를 판단하기 위해 2018년의 실제 유입량 자료와 비교하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.