• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.337-351
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• 2018

This paper discusses 5G IOT, Augmented Reality, Cloud Computing, Big Data, Future Autonomous Driving Vehicle technology, and presents 5G utilization of Pyeongchang Winter Olympic Games and Jeju Smart City model. The reason is that 5G is the main artery of the 4th industry.5G is the fourth industrial aorta because 5G is the core infrastructure of the fourth industrial revolution. In order for the AI, autonomous vehicle, VR / AR, and Internet (IoT) era to take off, data must be transmitted several times faster and more securely than before. For example, if you send a stop signal to LTE, which is a communication technology, to a remote autonomous vehicle, it takes a hundredth of a second. It seems to be fairly fast, but if you run at 100km / h, you can not guarantee safety because the car moves 30cm until it stops. 5G is more than 20 gigabits per second (Gbps), about 40 times faster than current LTE. Theoretically, the vehicle can be set up within 1 cm. 5G not only connects 1 million Internet (IoT) devices within a radius of 1 kilometer, but also has a speed delay of less than 0.001 sec. Steve Mollenkov, chief executive officer of Qualcomm, the world's largest maker of smartphones, said, "5G is a key element and innovative technology that will connect the future." With 5G commercialization, there will be an economic effect of 12 trillion dollars in 2035 and 22 million new jobs We can expect to see the effect of creation.

• Journal of Information Technology Applications and Management
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• v.28 no.6
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• pp.69-85
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• 2021

In the era of the 4th Industrial Revolution, not only artificial intelligence, big data, robots, and biotechnology, but also cultural industries that require human creativity will lead. Among the cultural industries, the animation industry has high industrial utilization value due to its high connection with other industries. Among them, animation characters play the most important role as the subject leading the story of animation. In particular, the villain character not only serves as a medium for the main character to lead the story, but also captivates the audience with a different presence from the main character, adding to the fun and completeness of the animation. These characters consist of visual elements such as form and color, of which color is a tool that effectively conveys the character's personality and role to the audience, and is the first visual element to be considered in delicately describing the character's emotions and the relationship between characters. Therefore, this study attempts to analyze the color of the villain character. To this end, we will select eight Disney animations to derive the characteristics of the villain character's color by analyzing the color, value, chroma, and color association of the colors used in the Disney villain character. As a result of the analysis, the colors mainly used by Disney to convey the villain's image were red (R) and Orange (YR), and there was no difference depending on the times or animation production methods. Second, the brightness of Disney villain characters appeared to be the same medium/famous regardless of the times and production methods, and the frequency of use of high brightness was very low. In terms of saturation, the frequency of use of high and low saturation was high. Third, blackish (Bk), Strong (S), dull (Dl), and deep (Dp) tones were mainly used for tones. In particular, in recent 3D animations than previously produced 2D animations, the use of low chroma and the high black mixing rate increased. Fourth, it can be seen that Disney uses color as a visual method to more clearly express the psychology of the villain character using color association. In conclusion, the color selection of animation characters should be carefully considered as a tool to convey the character's personality, role, and emotion beyond simply using color, and the color selection of characters using color associations and symbols strengthens the narrative structure. It is hoped that this study will help analyze and select the character color of animation.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.75-81
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• 2021

In the new era of the 4th industrial revolution, new jobs using the latest technologies such as big data, cloud, IoT, and AI are increasing. In the field of various industries, talents who can converge IT and industrial fields are needed, but such convergence-type talents are insufficient. This study analyzed the effects of IT convergence startup education on the learning effect and educational performance of trainees preparing for re-employment. A survey was conducted with 160 trainees preparing for reemployment. Frequency analysis, reliability analysis, correlation analysis, and multiple regression analysis were performed using the analysis tool SPSS 22.0 program. As a result of the study, first, in the IT convergence startup education of trainees who are preparing for re-employment, it was found that the sub-factors such as education content, instructor, and member satisfaction had a positive effect on the learning effect. Second, in the IT convergence start-up education for employment trainees, it was found that the sub-factors such as education contents, instructors, and the satisfaction of learning members had a significant effect on educational performance. It is expected that this study will serve as a basic data for preparing a start-up support system to revitalize start-ups in the IT convergence field.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Information Systems Review
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• v.25 no.1
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• pp.129-143
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• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• 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 Korea Entertainment Industry Association
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• v.13 no.3
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• pp.193-202
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• 2019

This study focuses on what changes are required as to a curriculum of dance department in the university in the age of the 4th industrial revolution. By comparing and analyzing the curricula of dance department in the five universities in Seoul, five academic subjects as to curricula of dance department, which covers what to learn for dance education in the age of the 4th industrial revolution, are presented. First, dance integrative education, the integration of creativity and science education, can be referred to as a subject that stimulates ideas and creativity and raises artistic sensitivity based on STEAM. Second, the curriculum characterized by prediction of the future prospect through Big Data can be utilized well in dealing with dance performance, career path of dance-majoring people, and job creation by analyzing public opinion, evaluation, and feelings. Third, video education. Seeing the images as modern major media tends to occupy most of the expressive area of art, dance by dint of video enables existing dance work to be created as new form of art, expanding dance boundaries in academic and performing art viewpoint. Fourth, VR and AR are essential techniques in the era of smart media. Whether upcoming dance studies are in the form of performance or education or industry, for VR and AR to be digitally applied into every relevant field, keeping with the time, learning about VR and AR is indispensable. Last, the 4th industrial revolution and the curriculum of dance art are needed to foresee the changes in the 4th industrial revolution and to educate changes, development and seeking in dance curriculum.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.208-214
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• 2016

With the rising interest in intelligence information technology built on artificial intelligence and big data technologies, all countries in the world including advanced countries such as the United States, the United Kingdom, Japan and so on, have launched national investment programs in preparation for the fourth industrial revolution centered on the software industry. Our country belatedly recognized the importance of software and initiated the 2015 revised educational curriculum for elementary and secondary informatics subjects. This paper thoroughly analyzes the new educational curriculum for information science in high schools and, then, suggests improvements in the areas of information ethics and multimedia. The analysis of the information science curriculum is applied to over twenty science high schools and schools for gifted children, which are expected to play a leading role in scientific research in our country. In the future artificial intelligence era, in which our dependence on information technology will be further increased, information ethics education for talented students who will play the leading role in making and utilizing artificial intelligence systems should be strongly emphasized, and the focus of their education should be different from that of the existing system. Also, it is necessary that multimedia education centered on digital principles and compression techniques for images, sound, videos, etc., which are commonly used in real life, should be included in the 2015 revised educational curriculum. In this way, the goal of the 2015 revised educational curriculum can be achieved, which is to encourage innovation and the efficient resolution of problems in real life and diverse academic fields based on the fundamental concepts, principles and technology of computer science.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.1-14
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• 2017

As we enter the era of big data, the value of personal information is becoming ever more important. However, personal information protection laws in Korea have several issues. Furthermore, existing research are limited in their ability to facilitate a comprehensive understanding of measures to improve personal information protection laws. Accordingly, this study analyzes improvements to be made in the current personal information protection laws based on existing research. A total of 39 research articles discussing the problems of the personal information protection law were selected and analyzed by applying the meta - analysis technique. According to the results, the various issues such as the meaning and scope of personal information, the role and obligations of relevant parties, provision of personal information to third parties, and redundant and imbalanced regulations in special acts in each field. that exist in the current personal information protection laws were confirmed. This study contributes to the improvement of inconsistency between information protection laws and related special laws in each field in practice. Academically, it will contribute to understanding the problems of th law from the macro perspective and suggesting the integrated improvement ways of the law.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.9-21
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• 2020

The Gross Merchandise Volume for the China's Singles day event in 2019 is about $38.4 billion. More than 500 million customers placed about 1.3 billion orders a day, and the related delivery volume is 2.8 billion. The main technologies associated with the 4^th Industrial Revolution are bringing about a big change in the logistics industry. The purpose of this study is to present implications by reviewing the main technologies which are applied to China's Singles day event, the introduction of smart logistics in China, and analyzing the progress of Singles day, smart system of Alibaba, its significance. China still has poor infrastructure in non-capital areas. And many Chinese companies are actively introducing and developing smart logistics to cover the vast continental area of China. Singles Day is a representative case in point where the smart logistics and main technologies related to 4^th Industrial Revolution are applied. The data obtained through smart logistics would be reused for inventory management, production planning, and order processing, contributing to the optimization of the company's operations. In the era of the 4^th Industrial Revolution, domestic companies and governments need to make efforts to expand the introduction of smart logistics to secure competitiveness with global advanced companies.