• Journal of Korea Technology Innovation Society
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• v.22 no.3
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• pp.446-474
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• 2019

In conjunction with securing R&D competitiveness through technology convergence, there is a growing interest in the strategy to derive innovation based on the measurement of convergence. In this paper, we present a data-based model that suggests convergence strategies for the research projects of government research institutes (GRIs) in Korea. The convergence characteristics of the research projects are represented by inherent attributes of technology and behavioral attributes of actors. The measuring process of the proxy variables (Rao-Stirling and Herfindahl-Hirschman Indices) for each attributes are proposed. These two indices are used to construct the convergence diagram, through which the research projects of GRIs are distributed into four areas: AllianceIntra, Alliance-Inter, Competition, Mission-Oriented. Based on the convergence diagram proposed in this study the convergence index is measured to derive 30 convergence research areas of GRIs. As a result, we expect to provide appropriate guidelines for setting the policy direction of the convergence research projects.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.440-442
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• 2019

본 논문은 딥 러닝 기반의 독해 기술이 풀지 못하는 문제를 분류해내는 기술에 관한 것이다. 해당 연구에서는 독해 데이터 및 시스템 결과 분석을 통해 시스템이 풀지 못하는 문제들의 특징을 도출해내고, 이에 알맞은 전략들을 시도해 보았다. 분석 결과에 따른 시도들은 각 목적에 부합하는 결과를 나타냈으며, 특히 독해 기술의 특징에 기반한 방법론이 효과적이었다. 본 논문에서 제안하는 방법은 본문과 질의 간 유사도 행렬을 활용하는 것으로, 기존의 독해 기술이 본문과 질의의 유사도를 활용하여 정답을 내는 것에 영감을 얻었다.

• 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 the Korean Society of Systems Engineering
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• v.20 no.spc1
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• pp.12-29
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• 2024

The purpose of this study is to identify the social issues discussed in Korea regarding Maritime Autonomous Surface Ships (MASS), the most advanced ICT field in the shipbuilding industry, and to suggest policy implications. In recent years, it has become important to reflect social issues of public interest in the policymaking process. For this reason, an increasing number of studies use media data and social media to identify public opinion. In this study, we collected 2,843 domestic media articles related to MASS from 2017 to 2022, when MASS was officially discussed at the International Maritime Organization, and analyzed them using text mining techniques. Through term frequency-inverse document frequency (TF-IDF) analysis, major keywords such as 'shipbuilding,' 'shipping,' 'US,' and 'HD Hyundai' were derived. For LDA topic modeling, we selected eight topics with the highest coherence score (-2.2) and analyzed the main news for each topic. According to the combined analysis of five years, the topics '1. Technology integration of the shipbuilding industry' and '3. Shipping industry in the post-COVID-19 era' received the most media attention, each accounting for 16%. Conversely, the topic '5. MASS pilotage areas' received the least media attention, accounting for 8 percent. Based on the results of the study, the implications for policy, society, and international security are as follows. First, from a policy perspective, the government should consider the current situation of each industry sector and introduce MASS in stages and carefully, as they will affect the shipbuilding, port, and shipping industries, and a radical introduction may cause various adverse effects. Second, from a social perspective, while the positive aspects of MASS are often reported, there are also negative issues such as cybersecurity issues and the loss of seafarer jobs, which require institutional development and strategic commercialization timing. Third, from a security perspective, MASS are expected to change the paradigm of future maritime warfare, and South Korea is promoting the construction of a maritime unmanned system-based power, but it emphasizes the need for a clear plan and military leadership to secure and develop the technology. This study has academic and policy implications by shedding light on the multidimensional political and social issues of MASS through news data analysis, and suggesting implications from national, regional, strategic, and security perspectives beyond legal and institutional discussions.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.189-198
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• 2024

AI is accepted not only in the private sector but also in the defense sector as a cutting-edge technology that must be introduced for the development of national defense. In particular, artificial intelligence has been selected as a key task in defense science and technology innovation, and the importance of data is increasing. As the national defense department shifts from a closed data policy to data sharing and activation, efforts are being made to secure high-quality data necessary for the development of national defense. In particular, we are promoting a review of the business budget system to secure data so that related procedures can be improved to reflect the unique characteristics of AI and big data, and research and development can begin with sufficient large quantities and high-quality data. However, there is a need to establish standardization and quality standards for structured data and unstructured data at the national defense level, but the defense department is still proposing standardization and quality standards for structured data, so this needs to be supplemented. In this paper, we propose an unstructured data set standard format for defense unstructured data sets, which are most needed in defense artificial intelligence, and based on this, we propose a standardization method for defense unstructured data sets.

• Industry Promotion Research
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• v.2 no.1
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• pp.1-7
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• 2017

In the case of manufacturing companies, various types of data are collected. Many of these data can be used as useful information for product reliability evaluation. In this study, we first look at data that can be collected by a manufacturing company and related to products, technology, finance, and customers. Next, we will look at the company's business management system, scientific journals, test and marketing survey data, etc., as sources of data. Next, look at what kind of data is collected over the product life cycle to evaluate the reliability of the product. In the development stage of the product, reliability test is performed for each component, and reliability data is collected by performing reliability test at the subsystem and system level. On the other hand, at the manufacturing stage, data on the functional test and the design change test of the product are collected, and at the field stage, the problem of the product is detected in the field and collected in the form of data. Finally, let's look at what you need to do to make a reasonable analysis later in your data collection.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.1
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• pp.10-20
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• 2024

RNA-sequencing (RNA-seq) is a technique used for providing global patterns of transcriptomes in samples. However, it can only provide the average gene expression across cells and does not address the heterogeneity within the samples. The advances in single-cell RNA sequencing (scRNA-seq) technology have revolutionized our understanding of heterogeneity and the dynamics of gene expression at the single-cell level. For example, scRNA-seq allows us to identify the cell types in complex tissues, which can provide information regarding the alteration of the cell population by perturbations, such as genetic modification. Since its initial introduction, scRNA-seq has rapidly become popular, leading to the development of a huge number of bioinformatic tools. However, the analysis of the big dataset generated from scRNA-seq requires a general understanding of the preprocessing of the dataset and a variety of analytical techniques. Here, we present an overview of the workflow involved in analyzing the scRNA-seq dataset. First, we describe the preprocessing of the dataset, including quality control, normalization, and dimensionality reduction. Then, we introduce the downstream analysis provided with the most commonly used computational packages. This review aims to provide a workflow guideline for new researchers interested in this field.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.159-164
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• 2024

The domestic petroleum market is highly sensitive to changes in international oil prices. So, it is important to identify and respond to those changes. In particular, it is necessary to clearly understand the factors causing the price fluctuations of gasoline, which exhibits high consumption. International gasoline prices are influenced by global factors such as gasoline supplies, geopolitical events, and fluctuations in the U.S. dollar. However, previous studies have only focused on gasoline supplies. In this study, we explore the causal relationship between economic indicators and international gasoline prices using various machine learning-based regression models. First, we collect data on various global economic indicators. Second, we perform data preprocessing. Third, we model using Multiple linear regression, Ridge regression, and Lasso(Least Absolute Shrinkage and Selection Operator) regression. The multiple linear regression model showed the highest accuracy at 96.73% in test sets. As a result, Our Multiple linear regression model showed the highest accuracy at 96.73% in test sets. We will expect that our proposed model will be helpful for domestic economic stability and energy policy decisions.