• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.201-211
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• 2018

The paper suggests making a policy and strategies for a way of exporting Korean ICT product effectively in the EU and Eastern area, and an effective preparation of 4^th industrial revolution through analysis of preparation status of 4^th industrial revolution of the Visegrad group. Analyze policy, status, what they want for 4^th preparation in the Visegrad group from comparing characteristics analysis of each country's official data, publication data, portal, paper, and etc. They have been preparing for 4^th industrial revolution long time ago as basic research and business before 4^th wave word. With these basic results, they are trying to apply such as, AI, S/W, security, ICT, etc. of 4^th wave core technology. For the development of new export market in EU, the Korean team should research with university and research center or venture company. Through these cooperation, they should understand their personal characteristic, lifestyle, and what consumers want to purchase in EU. And this results can be used in South Asia and India that give a big effect to all over the world ICT market. The external impact of the 4^th wave must have a long-term shift in manpower, and production policy is related to the EU's strategic role, or the preparation of the 4^th wave to the V4 country in the short term.

• Journal of Integrative Natural Science
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• v.14 no.1
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• pp.27-34
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• 2021

전 세계적으로 인공지능(AI)을 구현하려는 움직임이 많아지고 있다. AI구현에서는 많은 양의 데이터, 목적에 맞는 데이터의 분류 등 데이터의 중요성을 뺄 수 없다. 이러한 데이터를 생성하고 가공하는 기술에는 사물인터넷(IOT)과 빅데이터(Big-data) 분석이 있으며 4차 산업을 이끌어 가는 원동력이라 할 수 있다. 또한 이러한 기술은 국가와 개인 차원에서 많이 활용되고 있으며, 특히나 특정분야에 집결되는 데이터를 기준으로 빅데이터 분석에 활용함으로써 새로운 모델을 발견하고, 그 모델로 새로운 값을 추론하고 예측함으로써 미래비전을 제시하려는 시도가 많아지고 있는 추세이다. 데이터 분석을 통한 결론은 데이터가 가지고 있는 정보의 정확성에 따라 많은 변화를 가져올 수 있으며, 그 변화에 따라 잘못된 결과를 발생시킬 수도 있다. 이렇듯 데이터의 분석은 데이터가 가지는 정보 또는 분석 목적에 맞는 데이터 분류가 매우 중요하다는 것을 알 수 있다. 또한 빅데이터 분석결과 통계량의 신뢰성과 정교함을 얻기 위해서는 각 변수의 의미와 변수들 간의 상관관계, 다중공선성 등을 고려하여 분석해야 한다. 즉, 빅데이터 분석에 앞서 분석목적에 맞도록 데이터의 분류가 잘 이루어지도록 해야 한다. 이에 본 고찰에서는 AI기술을 구현하는 머신러닝(machine learning, ML) 기법에 속하는 분류분석(classification analysis, CA) 중 의사결정트리(decision tree, DT)기법, 랜덤포레스트(random forest, RF)기법, 선형분류분석(linear discriminant analysis, LDA), 이차선형분류분석(quadratic discriminant analysis, QDA)을 이용하여 데이터를 분류한 후 데이터의 분류정도를 평가함으로써 데이터의 분류 분석률 향상을 위한 방안을 모색하려 한다.

• Convergence Security Journal
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• v.22 no.5
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• pp.61-67
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• 2022

The purpose of this study is to analyze research trends in artificial intelligence. For a three-dimensional analysis, an attempt was made to objectively compare and present the difference between the research direction of artificial intelligence in social science and engineering. For the research method, topic modeling was used among the big data analysis methodologies, and 1000 English papers searched with the keyword artificial intelligence (AI) in the academic research information system were used for the analysis data. As a result of the analysis, in the field of social science, it was possible to identify groups formed around the keywords of 'human', 'impact', and 'future' for artificial intelligence, and in the field of engineering, 'artificial intelligence-based technology development', 'system', 'Groups such as 'Risk-Security' were formed.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.265-281
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• 2019

With the advent of the Internet-based economy, the dramatic changes in consumption patterns have been witnessed during the last decades. The seminal change has led by Data Technology, the integrated platform of mobile, online, offline and artificial intelligence, which remained unchallenged. In this paper, I use data analysis tool (TexTom) in order to articulate the definitfite notion of data technology from Internet sources. The data source is collected for last three years (November 2015 ~ November 2018) from Google and Naver. And I have derived several key keywords related to 'Data Technology'. As a result, it was found that the key keyword technologies of Big Data, O2O (Offline-to-Online), AI, IoT (Internet of things), and cloud computing are related to Data Technology. The results of this study can be used as useful information that can be referred to when the Data Technology age comes.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1091-1100
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• 2022

As deep learning technology advances and various high-resolution remote sensing images are available, interest in using deep learning technology and remote sensing big data to detect buildings and change in urban areas is increasing significantly. In this paper, for semantic building segmentation of high-resolution remote sensing images, we propose a new building segmentation model, Convolutional Block Attention Module (CBAM)-DRUNet that uses the DeepResUNet model, which has excellent performance in building segmentation, as the basic structure, improves the residual learning unit and combines a CBAM with the basic structure. In the performance evaluation using WHU dataset and INRIA dataset, the proposed building segmentation model showed excellent performance in terms of F1 score, accuracy and recall compared to ResUNet and DeepResUNet including UNet.

• Convergence Security Journal
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• v.22 no.5
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• pp.135-143
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• 2022

The defense environment is rapidly changing, such as nuclear and missile threats of North Korea, changes in war patterns, and a decrease in military service resources due to low birth rate. In order to actively respond to these changes, the Korean military is promoting Defense Innovation 4.0 and is trying to foster an army armed with high technology such as artificial intelligence (AI), big data analysis, etc. In this regard, we analyze the effectiveness of the radar-based AI scientific guard system applied by high technology for guard operations using Analytic Hierarchy Process (AHP). We first select evaluation factors that can assess the effectiveness of the scientific guard system, and analyze its relative importance. Each evaluation factor was selected by deriving a significant concept from operating principle and how they work, and by consulting experts on the correlation between each factor and effectiveness of the scientific guard system. We examine the relative effects of the radar-based AI scientific guard system and existing scientific guard system based on the importance of the evaluation factors.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.17-25
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• 2023

In this paper, we propose a model that can perform human pose estimation through a MobileViT-based model with fewer parameters and faster estimation. The based model demonstrates lightweight performance through a structure that combines features of convolutional neural networks with features of Vision Transformer. Transformer, which is a major mechanism in this study, has become more influential as its based models perform better than convolutional neural network-based models in the field of computer vision. Similarly, in the field of human pose estimation, Vision Transformer-based ViTPose maintains the best performance in all human pose estimation benchmarks such as COCO, OCHuman, and MPII. However, because Vision Transformer has a heavy model structure with a large number of parameters and requires a relatively large amount of computation, it costs users a lot to train the model. Accordingly, the based model overcame the insufficient Inductive Bias calculation problem, which requires a large amount of computation by Vision Transformer, with Local Representation through a convolutional neural network structure. Finally, the proposed model obtained a mean average precision of 0.694 on the MS COCO benchmark with 3.28 GFLOPs and 9.72 million parameters, which are 1/5 and 1/9 the number compared to ViTPose, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.205-210
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• 2018

This paper examines the wearable technology in two aspects. First, it is the desire that permeates the clothes that smart technology is embroidered. Second, it is a reflection on the gaze that looks at the user wearing clothes. This article is a study on clothes that will be newly appeared with wearable technology. But this is not just a technical issue. Rather, it is a system design that takes human instinct into clothes. Therefore, this study encompasses social scientific boundaries. This article does not refer to data collected from wearables as simple sensing based data. Rather, wearable technology reveals human life activities and emotions. This paper is an attempt to combine or combine humanities and technology.