• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.764-770
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• 2019

In this research, text mining techniques were used to analyze the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers and use topic modeling techniques based on LDA algorithm implemented in R language for data analysis. As a result of perplexity analysis on the collected papers, nine topics were determined optimally and nine representative topics of the collected papers were extracted using the Gibbs sampling method. As a result, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been conducted on the changes due to the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.632-637
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• 2019

Recent researches on autonomous driving of vehicles are becoming very active, and it is a trend to assist safe driving and improve driver's convenience. Autonomous vehicles are required to combine artificial intelligence, image recognition capability, and Internet communication between objects. Because mobile telecommunication networks have limitations in their processing, they can be easily implemented and scale using an easily expandable Wi-Fi network. We propose a wireless design method to construct such a vehicle control network. We propose the arrangement of AP and the software configuration method to minimize loss of data transmission / reception of mobile terminal. Through the design of the proposed network system, the communication performance of the moving vehicle can be dramatically increased. We also verify the packet structure of GPS, video, voice, and data communication that can be used for the vehicle through experiments on the movement of various terminal devices. This wireless design technology can be extended to various general purpose wireless networks such as 2.4GHz, 5GHz and 10GHz Wi-Fi. It is also possible to link wireless intelligent road network with autonomous driving.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.411-418
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• 2018

With the proliferation of artificial intelligence technology, it is becoming important to obtain, store, and utilize scientific data in research and science sectors. A number of methods for extracting meaningful objects such as graphs and tables from research articles have been proposed to eventually obtain scientific data. Existing extraction methods using heuristic approaches are hardly applicable to electronic documents having heterogeneous manuscript formats because they are designed to work properly for some targeted manuscripts. This paper proposes a prototype of an object extraction system which exploits a recent deep-learning technology so as to overcome the inflexibility of the heuristic approaches. We implemented our trained model, based on the Faster R-CNN algorithm, using the Google TensorFlow Object Detection API and also composed an annotated data set from 100 research articles for training and evaluation. Finally, a performance evaluation shows that the proposed system outperforms a comparator adopting heuristic approaches by 5.2%.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.137-147
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• 2018

To prepare for and implement policies for the Fourth Industrial Revolution, which is characterized by convergence, super-connectivity, and AI, this study summarized the effects and characteristics of individual technologies on our society and discussed the issues with humanity and social science perspectives. As a result, in terms of AI technology, the issues of job losses, project-type works, basic income and robot taxes, accountability of AI, and algorithm inequality were dealt with. Security, cyber hacking and privacy infringement issues were highlighted in big-data technology. In the part of block-chain and bioengineering, the society of decentralization, the concentration, digital divide, and ethical issues were discussed. On-demand economic aspects highlighted the problems of civil ethics and human commercialization. Lastly, the development of VR is discussed including side effects such as cyber-syndrom, avoidance of reality, and so on.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.393-399
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• 2018

The development of unmanned AI continues, and the development of AI unmanned is aimed at more efficiently, accurately, and speedily the work that has been resolved by manpower such as industry, welfare, and manpower. AI unmanned technology is evolving in various places, and it is time to switch to unmanned systems from many industries and factories. We take this into consideration, and use the Deep Learning technology, which is one of the core technologies of artificial intelligence (AI), not the manpower but the fruits that pour the rails at once in a large orchard. We want to study the unmanned fruit sorting machine that can be operated under manager's supervision without dividing the fruit by type and grade and dividing by country of origin and grade. This unmanned automated classification system aims to reduce the labor cost by minimizing the manpower and to improve the

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.77-86
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• 2019

This paper proposes using machine-learning technology to analyze and classify historical collected documents based on them. Data is collected based on keywords associated with a specific domain and the non-conceptuals such as special characters are removed. Then, tag each word of the document collected using a Korean-language morpheme analyzer with its nouns, verbs, and sentences. Embedded documents using Doc2Vec model that converts documents into vectors. Measure the similarity between documents through the embedded model and learn the document classifier using the machine running algorithm. The highest performance support vector machine measured 0.83 of F1-score as a result of comparing the classification model learned.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.77-82
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• 2019

AI is applied increasingly to overall industries such as living, medical, financial service, autonomous car, etc. thanks to rapid technology development. AI-leading countries are strengthening their competency to secure competitiveness since AI is positioned as the core technology in $4^{th}$ Industrial Revolution. Although Korea has the competitive IT infra and human resources, it lags behind traditional AI-leaders like United States, Canada, Japan and, even China which devotes all its might to develop intelligent technology-intentive industry. AI is the critical technology influencing on the national industry in the near future according to advancement of intelligent information society so that concentration of capability is required with national interest. Also, joint development with global AI-leading companies as well as development of own technology are crucial to prevent technology subordination. Additionally, regulatory reform and preparation of related law are very urgent.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.381-387
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• 2019

The limitations of applying a variety of artificial intelligence to the medical community are, first, subjective views, extensive interpreters and physical fatigue in interpreting the image of an interpreter's illness. And there are questions about how long it takes to collect annotated data sets for each illness and whether to get sufficient training data without compromising the performance of the developed deep learning algorithm. In this paper, when collecting basic images based on acne data sets, the selection criteria and collection procedures are described, and a model is proposed to classify data into small loss rates (5.46%) and high accuracy (96.26%) in the sequential structure. The performance of the proposed model is compared and verified through a comparative experiment with the model provided by Keras. Similar phenomena are expected to be applied to the field of medical and skin care by applying them to the acne classification model proposed in this paper in the future.

• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.70-85
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• 2021

Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.