• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.17-24
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• 2019

Since the information of $4^{th}$ Industrial Revolution is introduced in WEF (World Economic Forum) in 2016, IoT, AI, Big Data, 5G, Cloud Computing, 3D/4DPrinting, Robotics, Nano Technology, and Bio Engineering have been rapidly developed as business applications as well as technologies themselves. Among the diverse business applications for IoT, wearable devices are recognized as the leading application devices for final customers. This longitudinal study is compared to the results of the 1st study conducted to identify customer needs of activity trackers, and links the identified users' needs with the well-known marketing frame of marketing mix. For this longitudinal study, a survey was applied to university students in June, 2018, and ANOVA were applied for major variables on usable features. Further, potential customer needs were identified and visualized by Word Cloud Technique. According to the analysis results, different from other high tech IT devices, activity trackers have diverse and unique potential needs. The results of this longitudinal study contribute primarily to understand usable features and their changes according to product maturity. It would provide some valuable implications in dynamic manner to activity tracker designers as well as researchers in this arena.

• Journal of the Korean Regional Science Association
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• v.35 no.3
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• pp.59-76
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• 2019

This study aims to develop urban scenarios for future cities and validate the future city scenarios using a Delphi method. The scenarios of future city was derived from urban structure, land use, transportation, and urban infrastructure and development using big data analysis, environmental scanning techniques, and literature review. The Delphi survey interviewed 24 erudite scholars and experts across 6 nations including Korea, USA, UK, Japan, China, Australia and India. The Delphi survey structure was designed to test future city scenarios, verified by the 5-point Likert scale. The survey also asked the timing of each scenario likely happens by the three terms of near-future, mid-future and far-future. Results of the Delphi survey reveal the following points. Firstly, for the future urban structure it is anticipated that urban concentration continues and higher density living in global mega cities near future. In the mid-future small and medium size cities may decrease. Secondly, the land use pattern in the near-future is expected of increasing space sharing and mixed or layered vertical land-use. In addition underground space is likely to be extended in the mid-future. Thirdly, in the near-future, transport and infrastructure was expected to show ICT embedded integration platform and public and private smart transport. Finally, the result of Delphi survey shows that TOD (Transit Oriented Development) becomes a development norm and more emphasis on energy and environment fields.

• 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 the Korea Institute of Building Construction
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• v.19 no.1
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• pp.95-103
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• 2019

Introduction and use of Building Information Modeling(BIM) in construction projects have increased steadily over the past few years. However, the level of domestic BIM utilization is still tenuous compared to the international scene. Therefore, this study aims to present the possible directions for BIM research through an analysis of research literatures in Korea as well as in foreign countries. Papers on BIM were collected for this study from Korea and foreign countries for the field of architecture, and analyses and comparisons were performed by year and field. Further, the research patterns were analyzed using WordCloud, which is one of the popular big data techniques. From the analysis, it is found that the design field still constitutes the largest component of research, but the construction field is actively developing as well. In addition, it is realized that domestic BIM research continues to grow on collaboration and environment-friendly methodologies since 2012; it is also demonstrated that foreign BIM research has undergone changes in research trends every year including recently, and is progressing actively. Therefore, this study concludes that it is necessary to actively conduct research in the field of Industry Foundation Class(IFC) in the future. The results of this study can further be used as reference data for conducting BIM studies in Korea in the future.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.382-391
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• 2019

This study was conducted to verify whether smartphone hiking apps, which generate social network data including location information, are useful tools for analyzing the use characteristics of a forest recreation area. For this purpose, the study identified the functions and service characteristics of smartphone hiking apps. Also, the use characteristics of the area of Daegwallyoung were analyzed, compared with the results of the field survey, and the applicability of hiking apps was reviewed. As a result, the service types of hiking apps were analyzed in terms of three categories: "information offering," "hiking record," and "information sharing." This study focused on an app that is one of the "hiking record" types with the greatest number of users. Analysis of the data from hiking apps and a field survey in the Daegwallyoung area showed that both hiking apps and the field survey can be used to identify the movement patterns, but hiking apps based on a global positioning system (GPS) are more efficient and objective tools for understanding the use patterns in a forest recreation area, as well as for extracting user-generated photos. Second, although it is advantageous to analyze the patterns objectively through the walking-speed data generated, field surveys and observation are needed as complements for understanding the types of activities in each space. The hiking apps are based on cellphone use and are specific to "hiking" use, so user bias can limit the usefulness of the data. It is significant that this research shows the applicability of hiking apps for analyzing the use patterns of forest recreation areas through the location-based social network data of app users who record their hiking information voluntarily.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.871-880
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• 2018

Recently, the technology of recommendation of POI (Point of Interest) related technology is getting attention with the increase of big data related to consumers. Previous studies on POI recommendation systems have been limited to specific data sets. The problem is that if the study is carried out with this particular dataset, it may be suitable for the particular dataset. Therefore, this study analyzes the similarity and correlation between stores using the user visit data obtained from the integrated sensor installed in Seoul and Songjeong roads. Based on the results of the analysis, we study the preference prediction system which recommends the stores that new users are interested in. As a result of the experiment, various similarity and correlation analysis were carried out to obtain a list of relevant stores and a list of stores with low relevance. In addition, we performed a comparative experiment on the preference prediction accuracy under various conditions. As a result, it was confirmed that the jacquard similarity based item collaboration filtering method has higher accuracy than other methods.

• Smart Media Journal
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• v.13 no.1
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• pp.36-44
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• 2024

Metaverse is attracting attention as the development of virtual environment technology and the emergence of untact culture due to the COVID-19 pandemic. In this study, by analyzing users' reviews on the "Zepeto" application, which has recently attracted attention as a metaverse service, we tried to confirm changes in the requirements for the metaverse after the COVID-19 pandemic. To this end, 109,662 reviews of "Zepeto" applications written on the Google Play Store from September 2018 to March 2023 were collected, topics were extracted using LDA topic modeling technique, and topics were analyzed using the Causal Impact technique to examine how topics changed before and after based on "March 11, 2020" when the COVID-19 pandemic was declared. As a result of the analysis, five topics were extracted: application functional problems (topic1), security problems (topic 2), complaints about cryptocurrency (Zem) in the application (topic 3), application performance (topic 4), and personal information-related problems (topic 5). Among them, it was confirmed that security problems (topic 2) were most affected by the COVID-19 pandemic.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.266-274
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• 2022

In this study, a smoothing method was applied to improve the accuracy of peak wave period estimation using the water surface elevation observed from the Oceanographic and Meteorological Observation Tower located on the west coast of the Korean Peninsula. Validation of the application of the smoothing method was per- formed using variance of the surface elevation and total amount wave energy, and then the effect on the application of smoothing was analyzed. As a result of the analysis, the correlation coefficient between variance of the surface elevation and total amount wave energy was 0.9994, confirming that there was no problem in applying the method. Thereafter, as a result of reviewing the effect of smoothing, it was found to be reduced by about 4 times compared to the confidence interval of the existing estimated spectrum, confirming that the accuracy of the estimated peak wave period was improved. It was found that there was a statistically significant difference in proba- bility density between 4 and 6 seconds due to the smoothing application. In addition, for optimal smoothing, the appropriate number of smoothings according to the significant wave height range was calculated using a statistical technique, and the number of smoothings was found to increase due to the unstable spectral shape as the significant wave height decreased.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.