• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.233-237
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• 2021

With the advent of the 4th industrial revolution, interest in artificial intelligence education is increasing in elementary schools. In order to nurture future talents with artificial intelligence capabilities, AI education should be actively conducted at school sites. Although basic software education is provided in the 2015 revised curriculum, there is a tendency to view the programming process that creates artificial intelligence only as a problem-solving process. However, when creating an artificial intelligence, the value of the developer who creates artificial intelligence is projected. Therefore, it is necessary to deal with the contents of artificial intelligence value judgment during SW education. This study has limitations due to the fact that Delphi research was conducted with a group of experts. In the future, it is judged that quantitative research should be conducted to supplement these limitations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.186-188
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• 2021

The architecture named IDPS is a design concept of web-based integrated platform for heterogeneous naval combat system, which accomplishes efficiency(decreasing complexity) of diagnosis process and reduces time to diagnose system. Each type of surface vessel has its own diagnostic processes and applications, and that means it also requires its own diagnostic engineer(inefficiency in human resource management). In addition, man-based diagnostic causes quality issues such as difference approach of log analysis in accordance with engineer skills. Thus In this paper, we designed integrated diagnostic platform named IDPS with simplified common process regardless of type of surface vessel and we reinforced IDPS with status decision algorithm(SDA) that judges current software status of vessel based on gathered lots of logs. It will enable engineers to diagnose system more efficiently and to use more resources in utilizing SDA-analyzed diagnostic results.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.72-77
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• 2024

Recently, the necessity for sound source localization has grown significantly across various industrial sectors. Among the sound source localization methods, sound intensimetry has the advantage of having high accuracy even with a small microphone array. However, the increase in localization error at high Helmholtz numbers have been pointed out as a limitation of this method. The study proposes a method to compensate for the bias error of the measured sound intensity vector according to the Helmholtz numbers by applying deep learning. The method makes it possible to estimate the accurate direction of arrival of the source by applying a dense layer-based deep learning model that derives compensated sound intensity vectors when inputting the sound intensity vectors measured by a tetrahedral microphone array for the Helmholtz numbers. The model is verified based on simulation data for all sound source directions with 0.1 < kd < 3.0. One can find that the deep learning-based approach expands the measurement frequency range when implementing the sound intensimetry-based sound source localization method, also one can make it applicable to various microphone array sizes.

• Management & Information Systems Review
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• v.34 no.3
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• pp.161-177
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• 2015

Currently, the influence of the Internet portal sites that can make it quick and easy to contact the vast amount of information is increasing. Users can connect the Internet through a portal to obtain information, such as communication between Internet users, which can be used to meet a variety of purposes. People are exposed to a variety of information from other users in the search for a movie and get information. The impact on the reviews and ratings with the limited number of characters of the film allows users to form a relationship to the movie, decide whether you want to see the movie or find another movie. but, the user can not read the whole movie review. When user see the overall evaluation, the user can receive the correct information. This research conducted a study on the prediction of the rating by the use of review data. Information of reviews, is divided into two main areas: the"fact" and "opinion". "Fact" is to convey the dispassionate information and "Opinion" is, to represent the user's feelings. In this study, we built sentiment dictionary based on the assessment and evaluation of the online review and applied to evaluate other movies. In the comparative study with a simple emotion evaluation technique, we found the suggested algorithm got the more accurate results.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.233-243
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• 2021

Photovoltaic panels are hazardous electronic waste that has heavy metal as one of the hazardous components. Each year, hazardous electronic waste is increasing worldwide and every heavy rainfall exposes the photovoltaic panel to become the source of heavy metal soil contamination. the development needs a monitoring technology for this hazardous exposure. this research use relationships between SAR temporal baseline and coherence of Sentinel-1 satellite to detected photovoltaic panel. Also, the photovoltaic plant detection tested using the difference between that photovoltaic panel and the other difference surface of coherence. The author tested the photovoltaic panel and its environment to calculate differences in coherence relationships. As a result of the experiment, the coherence of the photovoltaic panel, which is assumed to be a permanent scatterer, shows a bias that is biased toward a median value of 0.53 with a distribution of 0.50 to 0.65. Therefore, further research is needed to improve errors that may occur during processing. Additionally, the author found that the change detection using a temporal baseline is possible as the rate of reduction of coherence of photovoltaic panels differs from those of artificial objects such as buildings. This result could be an efficient way to continuously monitor regardless of weather conditions, which was a limitation of the existing optical satellite image-based photovoltaic panel detection research and to understand the spatial distribution in situations such as photovoltaic panel loss.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• The Journal of Bigdata
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• v.5 no.2
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• pp.201-213
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• 2020

Despite empirical research that the response to human risk is significantly influenced affective factors, the role of affective factors has been unexplored in information privacy research. This study aims to explore the privacy behaviors of location-based service (LBS) users from an affective point of view. Specifically, the study explored the relationship between three types of privacy threats (collection, hacking, secondary use), two affects (worry, anger), and a coping behavior (continuous use intentions). The structured survey was conducted with 552 users. In order to analyze the effect of the combination of perception of particular privacy threats and particular affects on the intention of continuous use, association rules, one of the data mining techniques, was employed. As a result, there was a difference in the intention to use according to the combination of the perception of risk and affect responses, and the most significant influence on the intention is when the second use of personal information was combined with anger. This study has significant theoretical contribution in that it includes affective factors in the research of information privacy users, complementing the biases of existing cognition-oriented approaches and providing a comprehensive understanding of privacy response behavior.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.365-392
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• 2021

In order to assess the state of library and information science (LIS) research in Korea, the study analyzed bibliometric data of papers published in past 18 years in Korea Citation Index (KCI) and Social Science Citation Index (SSCI) journals. The analysis of study data, which consisted of 6,301 KCI journal papers with 26,474 citations and 86,727 SSCI journal papers with 1,196,961 citations from 2002 to 2020, involved comparison of research productivity and impact, collaboration trends, and key areas of research between domestic and international LIS scholars with normalizations by units of analysis for size differences. Even with size normalization, the study found a marked difference in citation patterns between domestic and international LIS research. Korean LIS authors were twice as productive as international LIS authors but a little over a half as impactful. The results also showed a much higher level of skewness in international research, where a fraction of top authors, institutions, and journals received a lion's share of citations. The trend of increasing co-authorship was much more pronounced among international publication, where the recent popularity of larger collaboration groups suggests multi-disciplinary and increasingly complex nature of modern LIS research in the world stage. The keyword analysis revealed a much more diverse subject area in international than domestic LIS research with a recent shift towards technology, such as big data, blockchain, and altmetrics. Keywords in SSCI journals also exhibited a less connection between popularity and impact than KCI keywords, where popular keywords did not necessarily correspond to impactful keywords.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.317-340
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• 2023

In a business context, ESG is defined as the use of environmental, social, and governance factors to assess a firm's progress in terms of sustainability. Social media has enabled the public to actively share firms' good and/or bad deeds, increasing public interest in ESG management. Therefore, this study aimed to investigate the association of firm performances with the respective sentiments towards each of environmental, social, and governance activities, as well as comprehensive ESG sentiments, which encompass all environmental, social, and governance sentiments. This study used panel regression models to examine the relationship between social media ESG sentiment and the Return on Assets (ROA) and Return on Equity (ROE) of 143 companies listed on the KOSPI 200. We collected data from 2018 to 2021, including sentiment data from a variety of social media channels, such as online communities, Instagram, blogs, Twitter, and other news. The results indicated that firm performance is significantly related to respective ESG and comprehensive ESG sentiments. This study has several implications. By using data from various social media channels, it presents an unbiased view of public ESG sentiment, rather than relying on ESG ratings, which may be influenced by rating agencies. Furthermore, the findings can be used to help firms determine the direction of their ESG management. Therefore, this study provides theoretical and practical insights for researchers and firms interested in ESG management.