• 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.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.61-72
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• 2020

The fear of crime, discussed in the early 1960s in the United States, is a psychological response, such as anxiety or concern about crime, the potential victim of a crime. These anxiety factors lead to the burden of the individual in securing the psychological stability and indirect costs of the crime against the society. Fear of crime is not a good thing, and it is a part that needs to be adjusted so that it cannot be exaggerated and distorted by the policy together with the crime coping and resolution. This is because fear of crime has as much harm as damage caused by criminal act. Eric Pawson has argued that the popular impression of violent crime is not formed because of media reports, but by official statistics. Therefore, the police should watch and analyze news related to fear of crime to reduce the social cost of fear of crime and prepare a preemptive response policy before the people have 'fear of crime'. In this paper, we propose a deep - based news classification system that helps police cope with crimes related to crimes reported in the media efficiently and quickly and precisely. The goal is to establish a system that can quickly identify changes in security issues that are rapidly increasing by categorizing news related to crime among news articles. To construct the system, crime data was learned so that news could be classified according to the type of crime. Deep learning was applied by using Google tensor flow. In the future, it is necessary to continue research on the importance of keyword according to early detection of issues that are rapidly increasing by crime type and the power of the press, and it is also necessary to constantly supplement crime related corpus.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.425-431
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• 2019

Recently, people have been attracting attention to the good quality of water resources as well as water welfare. to improve the quality of life. This study is a papers on the prediction of benthic macroinvertebrate index (BMI), which is a aquatic ecological health, using the machine learning based CFS (Correlation-based Feature Selection) method and the random forest model to compare the measured and predicted values of the BMI. The data collected from the Han River's branch for 10 years are extracted and utilized in 1312 data. Through the utilized data, Pearson correlation analysis showed a lack of correlation between single factor and BMI. The CFS method for multiple regression analysis was introduced. This study calculated 10 factors(water temperature, DO, electrical conductivity, turbidity, BOD, $NH_3-N$, T-N, $PO_4-P$, T-P, Average flow rate) that are considered to be related to the BMI. The random forest model was used based on the ten factors. In order to prove the validity of the model, $R^2$, %Difference, NSE (Nash-Sutcliffe Efficiency) and RMSE (Root Mean Square Error) were used. Each factor was 0.9438, -0.997, and 0,992, and accuracy rate was 71.6% level. As a result, These results can suggest the future direction of water resource management and Pre-review function for water ecological prediction.

• Journal of The Korean Association For Science Education
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• v.32 no.4
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• pp.555-569
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• 2012

Many science education research and practices are recently emphasizing the importance of collaborative learning. This study also understands learning in aspects of socio-cultural context, and regarded the creation of meaning in a same-age group as an important learning process. This is most especially true in the premise that the formation of roles in a collaborative learning is important for successful interactive learning. This study aims to find out how roles form in a group. For this purpose, university students participating in a group discussion activity about energy flow and circulation of material were selected as research participants. Discussions among the nine students in one group consisted of cognitive conversations on the topic and operational conversations for preparing a presentation. Video-clips of the discussions were made and transcribed. For the analysis, we developed a framework that includes four interaction functions (cognitive, organizational, meta-cognitive, operational), four action elements (question, simple answer, providing opinion, response to opinion), and two to four intention elements by each action elements. As a result, a total of nine roles were revealed through the interaction function and element; cognitive questioner, operational questioner, simple answerer, operational suggester, organizational commander, operational commander, cognitive explainer, terminator, reflective thinker. These roles are re-classified into seven utterance patterns by the utterance order and object, and they were categorized into three role groups (facilitating interaction, sustaining interaction, finishing interaction). The result means that role formation and function can have influence on learning and interaction. This study is meaningful to the suggestion to collaborative learning including project-based learning, investigation, club activity, and for the re-illumination of the role in an aspect of the interaction.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of Science Education
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• v.47 no.2
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• pp.117-126
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• 2023

In an elementary school science teacher's guide, the guide in details that can help elementary school teachers with difficulties when preparing and conducting science classes are essential. To gain insights into the development of the 2022 revised curriculum elementary school science teacher's guide, it is necessary to investigate the perceptions and needs of elementary school teacher's guide in details. In this study, we developed a questionnaire by analyzing the components and design of an elementary school science teacher's guide in details to explore elementary school teachers' perceptions and needs. For this purpose, we first investigated elementary school teachers' perceptions of their needs, satisfaction, and the utilization of each component of the current guide in details. Next, we investigated teachers' needs regarding the specific components and design of a guide. The findings were as follows. First, elementary school teachers were delighted with the components that help them prepare and conduct lessons. Second, elementary school teachers wanted an easy-to-read design with a layout that allowed them to see the components they needed for their lessons at a glance. In conclusion, the elementary school science teacher's guide in details to be readable and organized to provide at-a-glance information on lesson preparation, lesson flow, and teaching and learning materials needed for science teaching-learning. Based on the results of this study, new approaches and attempts should be made to develop a textbook that elementary school teachers can utilize in the future.

• Journal of Information Management
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• v.42 no.2
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• pp.1-21
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• 2011

The copyright law is the greatest legal tool for promoting access to knowledge and information by striking a balance between owners rights and users rights. For libraries, copyright limitations and exceptions are critical to meeting our missions to support learning and research, promote the flow of information, provide equitable access to information to the public, preserve intellectual and cultural heritage. Based on these reasons, this study analysed the limitation of copyright law of Korea for library, suggested improvement of reproduction for the library preservation and distribution including rental and lending, printout and transmission of internet information resources, copy of library materials which are rarely available through normal trade channel and government publications, reproduction and electronic transmission for persons with disabilities, and proposed fair use model(limitations on exclusive rights) for libraries.

• Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.26-37
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• 2015

A lot of researchers have been investigating interactive portable projection systems such as a mini-projector. In addition, in exhibition halls and museums, there is a trend toward using interactive projection systems to make viewing more exciting and impressive. They can also be applied in the field of art, for example, in creating shadow plays. The key idea of the interactive portable projection systems is to recognize the user's gesture in real-time. In this paper, a vision-based shadow gesture recognition method is proposed for interactive projection systems. The gesture recognition method is based on the screen image obtained by a single web camera. The method separates only the shadow area by combining the binary image with an input image using a learning algorithm that isolates the background from the input image. The region of interest is recognized with labeling the shadow of separated regions, and then hand shadows are isolated using the defect, convex hull, and moment of each region. To distinguish hand gestures, Hu's invariant moment method is used. An optical flow algorithm is used for tracking the fingertip. Using this method, a few interactive applications are developed, which are presented in this paper.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.403-411
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• 2021

This study discusses the influence of economic factors on the clothing exports from China and 15 South and Southeast Asian countries to the United States. A basic gravity trade model with three predictors, including the GDP value produced by exporting and importing countries and their geographical distance was established to explain the bilateral trade patterns. The conventional approach of multiple regression and the novel approach of Artificial Neural Networks (ANNs) were developed based on the value of clothing exports from 2012 to 2018 and applied to the trade pattern prediction of 2019. The results showed that ANNs can achieve a more accurate prediction in bilateral trade patterns than the commonly-used econometric analysis of the basic gravity trade model. Future studies can examine the predictive power of ANNs on an extended gravity model of trade that includes explanatory variables in social and environmental areas, such as policy, initiative, agreement, and infrastructure for trade facilitation, which are crucial for policymaking and managerial consideration. More research should be conducted for the examination of the balance between developing countries' economic growth and their social and environmental sustainability and for the application of more advanced machine-learning algorithms of global trade flow examination.