• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.69-78
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• 1999

In this paper, a neural network approach to forecast Korean regional precipitation is presented. We first analyze the characteristics of the conventional models for time series prediction, and then propose a new model and its learning method for the precipitation forecast. The proposed model is a layered network in which the outputs of a layer are buffered within a given period time and then fed fully connected to the upper layer. This study adopted the dual connections between two layers for the model. The network behavior and learning algorithm for the model are also described. The dual connection structure plays the role of the bias of the ordinary Multi-Layer Perceptron(MLP), and reflects the relationships among the features effectively. From these advantageous features, the model provides the learning efficiency in comparison with the FIR network, which is the most popular model for time series prediction. We have applied the model to the monthly and seasonal forecast of precipitation. The precipitation data and SST(Sea Surface Temperature) data for several decades are used as the learning pattern for the neural network predictor. The experimental results have shown the validity of the proposed model.

• Journal of The Korean Association For Science Education
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• v.18 no.3
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• pp.273-282
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• 1998

The purpose of this study is to compare the effect of concept formation between conflicted case and non-conflicted case as a method of learning science concepts. This study consists of 8 classes in 5th and 6th grade of elementary school children's in Kyoung-Buk, which were divided into conflicted group and non-conflicted group. The research procedure is as follows : first, two groups were asked the introducing problems-one was asked the conflicting problem, the other was asked the non-conflicting problems. Futhermore, the incorrect-answered students of conflicting problems were classified into conflict group am the correct-answered students of non-conflicting problems were classified into non-conflict group. Secondly, the demonstration and picture presentation about the introducing problems were carried out. Thirdly, the researcher introduced scientific concepts to the students. Afterwards, posttests, made up of the same items, were presented to the students-three times-posttest, delayed posttest(one week), second delayed posttest(one month). Finally, the degree of concept formation between the two groups was compared and analyzed by these results.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.723-734
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• 2021

Wind energy is one of the rapidly developing renewable energies which is being developed and invested in response to climate change. As renewable energy policies and power plant installations are promoted, the supply of wind power in Korea is gradually expanding and attempts to accurately predict demand are expanding. In this paper, the ARIMA and ARIMAX models which are Time series techniques and the SVR, Random Forest and XGBoost models which are machine learning models were compared and analyzed to predict wind power generation in the Jeonnam and Gyeongbuk regions. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as indicators to compare the predicted results of the model. After subtracting the hourly raw data from January 1, 2018 to October 24, 2020, the model was trained to predict wind power generation for 168 hours from October 25, 2020 to October 31, 2020. As a result of comparing the predictive power of the models, the Random Forest and XGBoost models showed the best performance in the order of Jeonnam and Gyeongbuk. In future research, we will try not only machine learning models but also forecasting wind power generation based on data mining techniques that have been actively researched recently.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.872-884
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• 2022

With the development of the IoT industry, different types of time series data are being generated in various industries, and it is evolving into research that reproduces and utilizes it through re-integration. In addition, due to data processing speed and issues of the utilization system in the actual industry, there is a growing tendency to compress the size of data when using time series data and integrate it. However, since the guidelines for integrating time series data are not clear and each characteristic such as data description time interval and time section is different, it is difficult to use it after batch integration. In this paper, two integration methods are proposed based on the integration criteria setting method and the problems that arise during integration of time series data. Based on this, integration framework of a heterogeneous time series data was constructed that is considered the characteristics of time series data, and it was confirmed that different heterogeneous time series data compressed can be used for integration and various machine learning.

• The Journal of Korean Association of Computer Education
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• v.3 no.2
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• pp.11-20
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• 2000

This study is to develop the web-based English learning courseware and improve the motivational attributes, with systematic methods called the ARCS model of Keller. There are four categories to the ARCS motivation model : Attention, Relevance, Confidence, Satisfaction. Applying these four components to web-based courseware strategically will enable learners to be taught English conversation more effectively and more systematically. In order to implement this study, the main web pages are made of ASP(Active Server Page). Personal Web Server is used as the web sever and Authorware is employed as a courseware authoring tool.

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.153-158
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• 2011

The idea that the mobile phone could be used in the education field recently comes from it's strengths. They have mobility, on-the-spot-study, portablity, immediacy and they are easy to connect to educational information. The application which I developed in this study is using Google-Map API and is based on GPS. It can share the information about the area where user is located like text data and pictures of geography, culture, and historical remains. This application makes the best use of the mobile phone's strengths. It's more effective and we can expect that users could manage resources of learning and do self-directed study in spite of being outside of the classroom or after regular classtime.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.335-341
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• 2022

In this paper, we adopt a transformer that shows remarkable performance in natural language processing as an acoustic model of hybrid speech recognition. The transformer acoustic model uses attention structures to process sequential data and shows high performance with low computational cost. This paper proposes a method to improve the performance of transformer AM by applying each of the four algorithms of sequence discriminative training, a weighted finite-state transducer (wFST)-based learning used in the existing DNN-HMM model. In addition, compared to the Cross Entropy (CE) learning method, sequence discriminative method shows 5 % of the relative Word Error Rate (WER).

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.93-93
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• 2023

최근 범지구적인 기후변화로 인해 도시유역의 홍수 발생 빈도가 빈번하게 발생하고 있다. 이로 인해 불투수성이 큰 도시지역의 침수 등의 자연재해 증가로 인명 및 재산피해가 발생하고 있다. 이에 따라 하수도의 제 기능을 수행하고 있다면 문제가 없지만 이상기후로 인한 기록적인 폭우에 의해 침수가 발생하고 있다. 홍수 및 집중호우와 같은 극치사상의 발생빈도가 증가됨에 따라 강우 사상의 변동에 따른 하수관로의 수위를 예측하고 침수에 대해 대처하기 위해 과거 수위에 따른 수위 예측은 중요할 것으로 판단된다. 본 연구에서는 수위 예측 연구에 많이 활용되고 있는 시계열 학습에 탁월한 LSTM 알고리즘을 활용한 하수관로 수위 예측을 진행하였다. 데이터의 학습과 검증을 수행하기 위해 실제 하수관로 수위 데이터를 수집하여 연구를 수행하였으며, 대상자료는 서울특별시 강동구에 위치한 하수관로 수위 자료를 활용하였다. 하수관로 수위 예측에는 딥러닝 알고리즘 RNN-LSTM 알고리즘을 활용하였으며, RNN-LSTM 알고리즘은 하천의 수위 예측에 우수한 성능을 보여준 바 있다. 1분 뒤 하수관로 수위 예측보다 5분, 10분 뒤 또는 1시간 3시간 등 다양한 분석을 실시하였다. 데이터 분석을 위해 하수관로 수위값 변동이 심한 1주일을 선정하여 분석을 실시하였다. 연구에는 Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하였으며, 하수관로 수위 고유번호 25-0001을 대상으로 예측을 하였다. 학습에는 2012년 ~ 2018년의 하수관로 수위 자료를 활용하였으며, 모형의 검증을 위해 결정계수(R square)를 이용하여 통계분석을 실시하였다.