• Informatization Policy
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• v.29 no.3
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• pp.61-81
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• 2022

This paper explores how the provided information from Enterprise Architecture (EA) affects the activities to for performing IT projects. The IT projects analyzed in this paper are projects to for developing and maintaining Korea Customs' UNI-PASS. This research was conducted based on surveys to demonstrate the effects of the information from EA on activities for IT projects. Information from EA is categorized into propriety, sufficiency and consistency. Activities for IT projects are defined as management, participation, communication, requirement management and human resource. Correlational analysis is used to measure the effects of the inf ormation on the defined activities. The analysis, which verifies the provided information by EA, does not have meaningful correlation with project management nor human resource. For public officials in charge, Sufficiency of the information produces a positive effect on decision making. For operation company, consistency of the information encourages utilization of the resources required for the project. This research suggests that strategies for performing IT projects with EA information that can support the verification of characteristic environments of each project and performance of vital activities required by the participants' roles will ensure the success of government IT projects.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.21-28
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• 2022

The increase in the number of large cities and the size of their population sharpens attention to the new role of cities as entities to ensure a high-quality, safe and modern life of citizens, which has become significantly more active in recent years. The rapid spread of smart cities in the modern world has actualized the issue of analyzing their success and assessing the role of various factors in this. Every success of a smart city is always the result of a unique combination of the most modern technologies, environmental and social initiatives, skillful and consistent management, as well as available human potential. The purpose of the article is to analyze the success factors of smart cities based on the generalization of the results of the most famous ratings. In order to identify the impact of various factors, primarily intellectual, on the success and leadership positions of smart cities, the following ratings were consistently analyzed: Smart City Index (SCI), City in Motion Index (CIMI), Global Power City Index (GPCI), Global Cities Index (GCI), Global Cities Outlook (GCO). They have a different list of indicators and main pillars (dimensions), but all ratings take into account aspects such as: governance, ICT, mobility, functionality, human capital, etc. The highest correlation coefficient, that is, the strongest linear relationship of the CIMI index was found with such factors as: Human capital, Economy, Governance and Technologies. Summarizing the results of the TOP 20 smart cities according to different ratings allowed us to confirm that the list of leaders is very similar in all ratings. Among those cities that are in the TOP-20 in all five indexes are: London, Sydney and Singapore. There are four indices: New York, Paris, Tokyo, Copenhagen, Berlin, Amsterdam, Melbourne. Achieving leadership positions in smart city rankings is always the result of a combination and synergy of certain factors, and first of all, it is the quality of human capital. The intensity and success of the use of information and communication technologies in locality management processes, city planning and improvement of the city's living conditions depend on it.

• Journal of the Korean earth science society
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• v.44 no.4
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• pp.331-341
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• 2023

The goal of this study was to examine the PCK required for science teachers and PCK required for university teacher educators in terms of school science knowledge, science teaching and learning, and the role of science educators, which are the main axes of science education in future schools, and to explore the relationship between them. This study is a follow-up to a previous stage of research that explored the prospects for changes in schools in the future (2040-2050) in terms of school knowledge, educational methods, and teacher roles. Based on in-depth interviews, qualitative and semantic network analyses were conducted to derive and compare the characteristics of PCK and PCK. As for the main research results, science teacher PCK in future schools should include expertise in organizing science classes centered on convergence topics, expertise in digital platforms and ICT use, and expertise in building a network of learning communities and resources, as part of the expertise of human teachers differentiated from AI. Teacher educators' PCK includes expertise in the research and development of T-L methods using AI, expertise in the knowledge construction process and practice, and expertise in developing preservice teachers' research competencies. Discussed in the conclusion is the change in teacher PCK and teacher educator PCK with changes in science knowledge, such as convergence-type knowledge and cognition-value integrated knowledge; and the need to emphasize values, attitudes, and ethical judgments for the coexistence of humans and non-humans as school science knowledge in the post-humanism future society.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.48-51
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• 2022

정보통신기술의 (ICT) 발전에 따라 기업은 사용자에게 다양한 서비스를 제공하기 위해 클라우드 서비스 공급자의 클라우드를 활용하고 있다. 클라우드 서비스 공급자는 기업에게 전문적이고 효율적인 클라우드 서비스를 제공하고, 기업은 특별한 클라우드 설치 없이 제공받은 서비스를 활용하여 다양한 서비스를 제공할 수 있다. 최근 클라우드 서비스를 이용하는 기업이 세계적으로 증가함에 따라 클라우드 공급자는 효과적인 서비스를 제공하기 위해 다양한 클라우드 컴퓨팅 기술을 적용하고 있다. 클라우드를 사용하는 기업이 증가함에 따라 클라우드 서버의 수가 증가하고 있으며, 그 중에 해킹을 당하기 때문에 악의적인 노드가 되거나 어떤 불가피한 원인으로 저성능 노드가 되는 경우의 수도 증가하고 있다. 악의적인 노드는 사용자의 보안을 위협하며, 저성능 노드는 효과적인 서비스를 사용자에게 제공할 수 없다. 따라서 서비스 사용자는 다양한 클라우드 서버 중 사용자 수요에 최적화 된 클라우드 노드를 선택하는 기술이 필요하다. 본 논문에서는 효율적인 클라우드 컴퓨팅을 위한 스마트 계약 기반의 노드 선택 기법을 제안한다. 제안하는 노드 선택 기법은 스마트 계약에 따라 사용자가 원하는 서비스에 적합한 클라우드 서버를 선택할 수 있도록 하여 최적화된 클라우드 서비스를 제공한다. 동시에 클라우드 서버들에게 명예 등급에 부여하고 명예 등급이 높을수록 선택되는 확률이 증가한다. 결과적으로 사용자는 효과적인 클라우드 서비스를 선택할 수 있어 효과적인 클라우드 서비스 활용이 가능하다.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.302-313
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• 2023

This paper aims to develop a framework that can fully automate the quality management of training data used in large-scale Artificial Intelligence (AI) models built by the Ministry of Science and ICT (MSIT) in the 'AI Hub Data Dam' project, which has invested more than 1 trillion won since 2017. Autonomous driving technology using AI has achieved excellent performance through many studies, but it requires a large amount of high-quality data to train the model. Moreover, it is still difficult for humans to directly inspect the processed data and prove it is valid, and a model trained with erroneous data can cause fatal problems in real life. This paper presents a dataset reconstruction framework that removes abnormal data from the constructed dataset and introduces strategies to improve the performance of AI models by reconstructing them into a reliable dataset to increase the efficiency of model training. The framework's validity was verified through an experiment on the autonomous driving dataset published through the AI Hub of the National Information Society Agency (NIA). As a result, it was confirmed that it could be rebuilt as a reliable dataset from which abnormal data has been removed.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.209-213
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• 2023

Currently, 2.4 GHz and 5 GHz bands are used as frequencies for drone operation. In December 2019, the Ministry of Science and ICT newly allocated the 433 MHz band for the invisible long-distance operation of drones. However, since the 433 MHz band is the same as the previously allocated frequency band for amateur radio communication, interference cannot be avoided. Therefore, as a prerequisite for the development of a drone operation system based on the 433 MHz band, interference avoidance technology for this frequency band must be developed and applied. In this paper, we report the results of measurement and analysis of 433 MHz band signals necessary for the development of interference avoidance and reduction technologies for 433 MHz signals. The measurement and analysis of the 433 MHz band signal are performed through the spectrum measured at 5-minute intervals at three locations. Since the measurements and analyzes performed in this study considered spatial characteristics, temporal characteristics, and traffic characteristics, it is considered to be the basic data necessary for the development of interference avoidance technology in the 433 MHz band.

• Journal of Engineering Education Research
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• v.25 no.6
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• pp.93-102
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• 2022

Due to COVID-19, all activities in society are emphasized non-face-to-face, and the educational environment is changing without exception. Looking at the results of the survey after conducting non-face-to-face education, there was a lot of rejection of non-face-to-face practical education. The biggest reason was that instructors were not familiar with the non-face-to-face education method, and feedback was not smooth during or after education. In particular, software practice education was not easy to share the software development environment, but communication and feedback on class contents and tasks were important. In particular, if face-to-face and non-face-to-face are alternately variable, it is not easy for practical education to be consistently connected. Even if non-face-to-face hands-on education is changed to face-to-face hands-on education, we will present a plan to use a data sharing system such as question-and-answer, assignment, practice content, and board content so that it can proceed smoothly. This study presents an efficient software education process that can provide learners with a software integrated practice environment based on a shared server, question-and-answer between instructors and learners, and share feedback on tasks. For the verification of the presented process, the effectiveness was confirmed through the survey results by applying the face-to-face/non-face-to-face education process to 220 trainees for 30 months in software education classes such as A university hands-on education, B company new employees, and ICT education courses.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.214-220
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• 2023

In this paper, we propose a method to improve texture recognition performance from haptic acceleration signals representing the texture characteristics of object surface materials by using a Conformer model that combines the advantages of a convolutional neural network and a transformer. In the proposed method, three-axis acceleration signals generated by impact sound and vibration are combined into one-dimensional acceleration data while a person contacts the surface of the object materials using a tool such as a stylus , and the logarithmic Mel-spectrogram is extracted from the haptic acceleration signal similar to the audio signal. Then, Conformer is applied to the extracted the logarithmic Mel-spectrogram to learn main local and global frequency features in recognizing the texture of various object materials. Experiments on the Lehrstuhl für Medientechnik (LMT) haptic texture dataset consisting of 60 materials to evaluate the performance of the proposed model showed that the proposed method can effectively recognize the texture of the object surface material better than the existing methods.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.181-189
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• 2022

The purpose of this study is to investigate the innovation of business model and the effectiveness of the data-driven model. the main concepts and policies related to the data economy are reviewed, and implications are drawn through the analysis of data-based convergence service creation cases. This study identified the existing data-driven business model of the creation of MyData service industry in the financial industry and concept of the data economy. According to the empirical analysis result, this study confirmed that t considering the mobile environment and consumer acceptance of data portability, the ripple effect of the implementation of My Data on the financial industry is expected to be significant.

• The Journal of Bigdata
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• v.7 no.2
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• pp.11-29
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• 2022

Pre-trained language models are the most important and widely used tools in natural language processing tasks. Since those have been pre-trained for a large amount of corpus, high performance can be expected even with fine-tuning learning using a small number of data. Since the elements necessary for implementation, such as a pre-trained tokenizer and a deep learning model including pre-trained weights, are distributed together, the cost and period of natural language processing has been greatly reduced. Transformer variants are the most representative pre-trained language models that provide these advantages. Those are being actively used in other fields such as computer vision and audio applications. In order to make it easier for researchers to understand the pre-trained language model and apply it to natural language processing tasks, this paper describes the definition of the language model and the pre-learning language model, and discusses the development process of the pre-trained language model and especially representative Transformer variants.