• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5218-5228
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• 2012

The aim of this study is to classify the social network types among elderly people with dementia, and to examine the relationship of network type to quality of life. This is to identify influence of social network on quality of life of elderly people with dementia. And the study seeks to identify the differences of quality of life among types of social network. The data of 222 elderly people used in this study were collected from the health center and elderly people with dementia living in the community. The interview was conducted from July 17 to August 31 in 2012 using a structured questionnaire. Descriptive statistics, one way ANOVA, multiple regression analysis and also cluster analysis with the SPSS 18.0 program were used to analyze the data. As a results, three network types were classified.:(1) inactive isolated, (2) active independent (3) inactive dependent networks. Respondents in the different network type are found to have different degrees of quality of life. Respondents inactive independent network is reported to have the highest quality of life, while those with inactive isolated network are the lowest. The results of the study suggest that the classification of network types allows consideration of the interpersonal environments of elderly people with dementia. The relative effects on quality of life for elderly people with dementia, evident in the current analysis, are the case in point. Therefore Social service programs should focus on different groups based on social network type.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.383-394
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• 2018

This study analyzed the latent profile for identifying the difference in the dependence on smartphone use among middle school students in the 1st grade using the Korean Children and Youth Panel Survey (KCYPS). From the result of this study, first the latent class was separated according to the type of dependence on smartphone use. Class 1 included the students (from fifth grade in elementary school) whose level of reliance on smartphone use was low. Class 2 was selected as the group whose level of reliance on smartphone was high. Secondly, in comparing class 2 to class 1, it was found that the students who have a high probability of being in class 1 were those whose fathers are high achievers, have high early self-esteem and less age attachment. Thirdly, the students in class 1 had a higher sense of neglect than those in class 2. Furthermore, the self-determination of the students in class 2 mediated the effect of neglect on the adolescents' dependence on smartphone use both directly and indirectly.

• Journal of Wetlands Research
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• v.22 no.4
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• pp.245-256
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• 2020

The purpose of this study is to evaluate the technological competitiveness of the environmental industry with developed countries in order to establish an international market expansion strategy of the Korean environmental industry and technology. In order to evaluate the competitiveness of the environmental industry and technology, core technologies were classified by the environmental industry sectors based on the classification system of the domestic and international environmental industry and technology. After developing the evaluation index data, the Delphi analysis, journal and patent analysis, as well as the export and import analysis were carried out and the standardization analysis was performed on the index data. Moreover, the weights of each evaluation index were calculated using the AHP(Analytic Hierarchy Process) method and the evaluation results of competitiveness of the environmental industry and technology in Korea, the United States, the United Kingdom, Germany, and France were derived. As a result of the evaluation, the United States was rated with the highest technological competitiveness in all the environmental industry sectors, while Korea got the lowest technological competitiveness rating compared to the 4 developed countries. In particular, Korea got the lowest level of technological competitiveness in the sector of multi-media environmental management and development for a sustainable social system. Therefore, in order for the Korean environmental industry and technology to enter the global advanced market, it is necessary to strengthen the competitiveness through the development of the fourth environmental industry based on IoT(Internet of Things), cloud, big data, mobile, and AI(Artificial Intelligence), which are currently the country's domestic strengths.

• Journal of Korean Society of Archives and Records Management
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• v.18 no.3
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• pp.125-143
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• 2018

This study is a record of the process of jointly designing the records classification scheme and the retention schedule for archival appraisal, which are vital in records management. A total of 8 institutes participated voluntarily and redesigned the retention schedule, which consists of 16 common functions, 66 tasks, and 381 files for about 4 months. The process consists of reviewing the regulations related to the records management of the participating organizations, determining the hierarchy and scope of the retention schedule, deriving 16 common functions as well as the unit task for each function, and constructing the file of each task. In situations wherein it is difficult to expect the government-led policy and strategy specific to the research institute and the research records management, the retention schedule designed jointly by the records managers of the scientific and technological field serves as a useful records management tool. This collaborative effort between records managers of similar agency records will also be significant in jointly coping with the new policy and innovation plans in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.

• Korean Journal of Ecology and Environment
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• v.55 no.2
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• pp.156-166
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• 2022

Understanding the characteristics of reservoir water quality is fundamental in reservoir ecosystem management. The water quality of reservoirs is affected by various factors including hydro-morphology of reservoirs, land use/cover, and human activities in their catchments. In this study, we classified 83 major reservoirs in South Korea based on nine physicochemical factors (pH, dissolved oxygen, chemical oxygen demand, total suspended solid, total nitrogen, total phosphorus, total organic carbon, electric conductivity, and chlorophyll-a) measured for five years (2015~2019). Study reservoirs were classified into five main clusters through hierarchical cluster analysis. Each cluster reflected differences in the water quality of reservoirs as well as hydromorphological variables such as elevation, catchment area, full water level, and full storage. In particular, water quality condition was low at a low elevation with large reservoirs representing cluster I. In the comparison of eutrophication status in major reservoirs in South Korea using the Korean trophic state index, in some reservoirs including cluster IV composed of lagoons, the eutrophication was improved compared to 2004~2008. However, eutrophication status has been more impaired in most agricultural reservoirs in clusters I, III, and V than past. Therefore, more attention is needed to improve the water quality of these reservoirs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.41-49
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• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

• Archives of design research
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• v.20 no.3 s.71
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• pp.61-72
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• 2007

Having started with "Space War", the first game produced by MIT in the 1960's, the gaming industry expanded rapidly and grew to a large size over a short period of time: the brand new games being launched on the market are found to contain many different elements making up a single content in that it is often called the 'the most comprehensive ultimate fruits' of the design technologies. This also translates into a large increase in the number of things which need to be considered in developing games, complicating the plans on the financial budget, the work force, and the time to be committed. Therefore, an approach for analyzing the elements which make up a game, computing the importance of each of them, and assessing those games to be developed in the future, is the key to a successful development of games. Many decision-making activities are often required under such a planning process. The decision-making task involves many difficulties which are outlined as follows: the multi-factor problem; the uncertainty problem impeding the elements from being "quantified" the complex multi-purpose problem for which the outcome aims confusion among decision-makers and the problem with determining the priority order of multi-stages leading to the decision-making process. In this study we plan to suggest AHP (Analytic Hierarchy Process) so that these problems can be worked out comprehensively, and logical and rational alternative plan can be proposed through the quantification of the "uncertain" data. The analysis was conducted by taking FPS (First Person Shooting) which is currently dominating the gaming industry, as subjects for this study. The most important consideration in conducting AHP analysis is to accurately group the elements of the subjects to be analyzed objectively, and arrange them hierarchically, and to analyze the importance through pair-wise comparison between the elements. The study is composed of 2 parts of analyzing these elements and computing the importance between them, and choosing an alternative plan. Among these this paper is particularly focused on the Delphi technique-based objective element analyzing and hierarchy of the FPS games.

• Korea journal of population studies
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• v.31 no.3
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• pp.57-84
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• 2008

This study investigates the patterns of leisure in Korea and the effects of social class on the objective and subjective dimension of leisure activities and life satisfaction. A data set of 1376 Korean men and women over 18 years old is analyzed to yield five main results. First, Korean prefers domestic entertainment to outdoor activities as is exemplified by domestic audio-visual entertainment(TV/DVD/VCR) which ranks the highest in the favored leisure activity. Leisure activities are divided into four types; "activity-based", "relationship-based", "alcohol-based", "relaxation". Second, the function of leisure activity is to strengthen relationships. The main purpose of leisure activity is to relax and revitalize, while creating prospective social network ranks next to relax. But the effect of leisure time is often compromised by recurring thoughts related to work. Third, respondents with high educational and economic backgrounds are more likely to engage in "relationship-based," "activity-based", "alcohol-based" leisure type. However, such factors do not influence on "relaxation" type of leisure. While students and housewives rank highest in number of respondents, respondents with managerial/professional or white-collar/semi-professional occupations enjoy more diverse activities. Fourth, the effort to discern the significance of social class with respect to the leisure-activity-index revealed followings; the index scores elevate with higher education, younger age and higher income. Fifth, leisure-activity-index is the most important variable predicting leisure satisfaction. Leisure satisfaction is influenced by gender, age, income and occupation. The younger the age and higher the income, the higher it is the leisure satisfaction. Men are more satisfied with leisure activities than women. Students experience the highest satisfaction with leisure activities while service/sales workers, industrial/technical/blue-collar workers shows the least satisfaction. Also, the number of family members decreases significantly the leisure satisfaction. While "activity-based" leisure induces the highest satisfaction, "alcohol-based" leisure produces the least satisfaction. The frequency and diversity of leisure activities, and "activity-based" leisure incur the most positive effects on the life satisfaction.