• Journal of the Korean School Mathematics Society
• /
• v.18 no.3
• /
• pp.281-309
• /
• 2015

The purpose of this study is provides an implication of further teaching learning process by analyze the common and difference and characteristic of mathematical self-efficiency between three peer tutoring groups discourse in the mathematical teaching leaning process that use peer tutoring. To achieve this goal, three groups formed that consist of one peer tutor who received a first grade of mathematic achievement and one peer student. Peer student of each group is divided into high grade, middle grade, low grade of mathematic achievement. Then analyze the discourse in the exponential function problem solving process. Based on the results of study, this paper provides a concrete example of merit of peer tutoring on the peer tutor. Result of study also provides a practical help to make a peer tutoring group by considering a difference of grades between peer tutor and peer student. Because there is a possibility of mutual discourse on the tutoring group that consist of similar grades.

• The Journal of the Korea Contents Association
• /
• v.11 no.3
• /
• pp.128-136
• /
• 2011

Although learners learn with the same educational games, they show differences in successes. This is because they do not share the same individual characteristics. Currently, there are various studies conducted on effective education according to gender among other characteristics of learners off-line. When this effective educational method according to gender is applied to educational games, it will create more effective outcomes. In addition, this study observed the effective educational method according to gender and learning styles. Through the observation, the considering factors for educational game designs according to the learners' gender was defined. These considering factors were categorized into cognitive and perceptive factors, with each definitions reflected on the characteristics of each gender. This study has its meanings in that it has conducted theoretical results of the considering factors for educational game designs based on reference studies. This study will be utilized as a reference study when designing various games.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.1
• /
• pp.123-130
• /
• 2021

With the recent developments in computer technology, there has been an increasing interest in the field of machine learning. This also has led to a significant increase in real business cases of machine learning theory in various sectors. In finance, it has been a major challenge to predict the future value of financial products. Since the 1980s, the finance industry has relied on technical and fundamental analysis for this prediction. For future value prediction models using machine learning, model design is of paramount importance to respond to market variables. Therefore, this paper quantitatively predicts the stock price movements of individual stocks listed on the KOSPI market using machine learning techniques; specifically, the reinforcement learning model. The DQN and A2C algorithms proposed by Google Deep Mind in 2013 are used for the reinforcement learning and they are applied to the stock trading strategies. In addition, through experiments, an input value to increase the cumulative profit is selected and its superiority is verified by comparison with comparative algorithms.

• Journal of the Korea Convergence Society
• /
• v.9 no.6
• /
• pp.39-44
• /
• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.12
• /
• pp.663-670
• /
• 2019

Corrective thermal performance analysis is required for power plants' turbine cycles to determine the performance status of the cycle and improve the economic operation of the power plant. We developed a sectional classification method for the main feed-water flow to make precise corrections for the performance analysis based on the Performance Test Code (PTC) of the American Society of Mechanical Engineers (ASME). The method was developed for the estimation of the turbine cycle performance in a classified section. The classification is based on feature identification of the correlation status of the main feed-water flow measurements. We also developed predictive algorithms for the corrected main feed-water through a Kernel Regression (KR) model for each classified feature area. The method was compared with estimation using an Artificial Neural Network (ANN). The feature classification and predictive model provided more practical and reliable methods for the corrective thermal performance analysis of a turbine cycle.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.5
• /
• pp.607-613
• /
• 2019

This paper proposes LF-MMI (Lattice Free Maximum Mutual Information)-based acoustic modeling using external knowledge for speech recognition. Note that an external knowledge refers to text data other than training data used in acoustic model. LF-MMI, objective function for optimization of training DNN (Deep Neural Network), has high performances in discriminative training. In LF-MMI, a phoneme probability as prior probability is used for predicting posterior probability of the DNN-based acoustic model. We propose using external knowledges for training the prior probability model to improve acoustic model based on DNN. It is measured to relative improvement 14 % as compared with the conventional LF-MMI-based model.

• Journal of the Korean Institute of Gas
• /
• v.25 no.5
• /
• pp.63-69
• /
• 2021

In this study, we established an algorithm to determine whether the work in the gas facility is a normal work or an abnormal work using the threshold of the reconstruction error of the autoencoder. This algorithm do deep learning the autoencoder only with time-series data of a normal work, and derives the optimized threshold of the reconstruction error of the normal work. We applied this algorithm to the time series data of the new work to get the reconstruction error, and then compare it with the reconstruction error threshold of the normal work to determine whether the work is normal work or abnormal work. In order to train and validate this algorithm, we defined the work in a virtual gas facility, and constructed the training data set consisting only of normal work data and the validation data set including both normal work and abnormal work data.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.4
• /
• pp.57-62
• /
• 2021

As the parallel computing capability for artificial intelligence improves, the field of artificial intelligence technology is expanding in various industries. In particular, artificial intelligence is being introduced to process data generated from IoT sensors that have enoumous data. However, the limitation exists when applying the AI techniques on IoT network because IoT has time series data, where the importance of data changes over time. In this paper, we propose time-weighted and user-state based artificial intelligence processing techniques to effectively process IoT sensor data. This technique aims to effectively classify IoT sensor data through a data pre-processing process that personalizes time series data and places a weight on the time series data before artificial intelligence learning and use status of personal data. Based on the research, it is possible to propose a method of applying artificial intelligence learning in various fields.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.2
• /
• pp.148-154
• /
• 2021

Text-Independent speaker verification needs to extract text-independent speaker embedding to improve generalization performance. However, deep neural networks that depend on training data have the potential to overfit text information instead of learning the speaker information when repeatedly learning from the identical time series. In this paper, to prevent the overfitting, we propose a segment unit shuffling layer that divides and rearranges the input layer or a hidden layer along the time axis, thus mixes the time series information. Since the segment unit shuffling layer can be applied not only to the input layer but also to the hidden layers, it can be used as generalization technique in the hidden layer, which is known to be effective compared to the generalization technique in the input layer, and can be applied simultaneously with data augmentation. In addition, the degree of distortion can be adjusted by adjusting the unit size of the segment. We observe that the performance of text-independent speaker verification is improved compared to the baseline when the proposed segment unit shuffling layer is applied.

• Journal of the Korean Geosynthetics Society
• /
• v.21 no.2
• /
• pp.11-19
• /
• 2022

In this paper, we propose a GoogleNet transfer learning and CNN-LSTM combination method to improve the time-series prediction performance for crack detection using crack data captured inside the sewer pipes. LSTM can solve the long-term dependency problem of CNN, so spatial and temporal characteristics can be considered at the same time. The predictive performance of the proposed method is excellent in all test variables as a result of comparing the RMSE(Root Mean Square Error) for time series sections using the crack data inside the sewer pipe. In addition, as a result of examining the prediction performance at the time of data generation, the proposed method was verified that it is effective in predicting crack detection by comparing with the existing CNN-only model. If the proposed method and experimental results obtained through this study are utilized, it can be applied in various fields such as the environment and humanities where time series data occurs frequently as well as crack data of concrete structures.