• Journal of Science Education
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• v.46 no.1
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• pp.17-29
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• 2022

The purpose of this study is to look qualitatively into how efficiently and reasonably a computer can learn themes related to the Nature of Science (NOS). In this regard, a corpus has been constructed focusing on literature (920 abstracts) related to NOS, and factors of the optimized Word2Vec (CBOW, Skip-gram) were confirmed. According to the four dimensions (Inquiry, Thinking, Knowledge and STS) of NOS, the comparative evaluation on the word-level word embedding was conducted. As a result of the study, according to the previous studies and the pre-evaluation on performance, the CBOW model was determined to be 200 for the dimension, five for the number of threads, ten for the minimum frequency, 100 for the number of repetition and one for the context range. And the Skip-gram model was determined to be 200 for the number of dimension, five for the number of threads, ten for the minimum frequency, 200 for the number of repetition and three for the context range. The Skip-gram had better performance in the dimension of Inquiry in terms of types of words with high similarity by model, which was checked by applying it to the four dimensions of NOS. In the dimensions of Thinking and Knowledge, there was no difference in the embedding performance of both models, but in case of words with high similarity for each model, they are sharing the name of a reciprocal domain so it seems that it is required to apply other models additionally in order to learn properly. It was evaluated that the dimension of STS also had the embedding performance that was not sufficient to look into comprehensive STS elements, while listing words related to solution of problems excessively. It is expected that overall implications on models available for science education and utilization of artificial intelligence could be given by making a computer learn themes related to NOS through this study.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.125-134
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• 2022

The development of information communication and artificial intelligence technology requires the intelligent command and control (C2) system for Korean military, and various studies are attempted to achieve it. In particular, as a volume ofinformation in the C2 workflow increases exponentially, this study pays attention to the collaborative filtering (CF) and recommendation systems (RS) that can provide the essential information for the users of the C2 system has been developed. The RS performing information filtering in the C2 system should provide an explanatory recommendation and consider the context of the tasks and users. In this paper, we propose a contextual pre-filtering CARS framework that recommends information in the C2 workflow. The proposed framework consists of four components: 1) contextual pre-filtering that filters data in advance based on the context and relationship of the users, 2) feature selection to overcome the data sparseness that is a weak point for the CF, 3) the proposed CF with the features distances between the users used to calculate user similarity, and 4) rule-based post filtering to reflect user preferences. In order to evaluate the superiority of this study, various distance methods of the existing CF method were compared to the proposed framework with two experimental datasets in real-world. As a result of comparative experiments, it was shown that the proposed framework was superior in terms of MAE, MSE, and MSLE.

• Journal of Industrial Convergence
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• v.20 no.9
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• pp.99-107
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• 2022

This study was conducted to empirically analyze the effect of life behavior and socio-environmental satisfaction on life satisfaction of the elderly with or without hearing loss. To this end, 3,071 elderly people of 「the Survey on the Actual Condition of the Elderly」 in 2020 were set as the subjects. After controlling for their demographic variables, the multiple regression model showed that the elderly with hearing loss had different factors of life behavior and socio-environment satisfaction that affected their life satisfaction compared to those without. As a result, the sub-factors of life behavior in the elderly with hearing loss, such as economic activities, social group activities, the use of the center, and the sub-factors of social environment satisfaction, such as family satisfaction and environmental satisfaction, had a positive (+) effect on life satisfaction. Especially, the elderly with hearing loss had more limitations in both the area of living behavior and the area of social environment satisfaction than the elderly without hearing loss. Therefore, in order to improve the life satisfaction of the elderly with hearing loss, it is suggested that the government and local governments should simultaneously supplement welfare policies and facilities for the elderly with hearing loss.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.421-428
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• 2022

This study analyzes emerging earthquake and space use technologies from the latest Korean and Japanese scientific and technological foresights in 2022 and 2019, respectively. Unlike the earthquake prediction and early warning technologies presented in the 2017 study, the emerging earthquake technologies in 2022 in Korea was described as an earthquake/complex disaster information technology and public data platform. Many detailed future technologies were presented in Japan's 2019 survey, which includes largescale earthquake prediction, induced earthquake, national liquefaction risk, wide-scale stress measurement; and monitoring by Internet of Things (IoT) or artificial intelligence (AI) observation & analysis. The latest emerging space use technology in Korea and Japan were presented in more detail as robotic mining technology for water/ice, Helium-3, and rare earth metals, and manned station technology that utilizes local resources on the moon and Mars. The technological realization year forecasting in 2019 was delayed by 4-10 years from the prediction in 2015, which could be greater due to the Corona 19 epidemic, the declaration of carbon neutrality in Korea and Japan in 2020 and the Russo-Ukrainian War in 2022. However, it is required to more active research on earthquake and space technologies linked to information technology.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.271-282
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• 2022

In this paper, we propose a digital human content creation system. The digital human content creation system works with 3D AI modeling through whole-body scanning, and is produced with 3D modeling post-processing, texturing, rigging. By combining this with virtual reality(VR) content information, natural motion of the virtual model can be achieved in virtual reality, and digital human content can be efficiently created in one system. Therefore, there is an effect of enabling the creation of virtual reality-based digital human content that minimizes resources. In addition, it is intended to provide an automated pre-processing process that does not require a pre-processing process for 3D modeling and texturing by humans, and to provide a technology for efficiently managing various digital human contents. In particular, since the pre-processing process such as 3D modeling and texturing to construct a virtual model are automatically performed by artificial intelligence, so it has the advantage that rapid and efficient virtual model configuration can be achieved. In addition, it has the advantage of being able to easily organize and manage digital human contents through signature motion.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.251-260
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• 2022

Although the number of trees affected by pine wilt disease is decreasing, the affected area is expanding across the country. Recently, with the development of deep learning technology, it is being rapidly applied to the detection study of pine wilt nematodes and dead trees. The purpose of this study is to efficiently acquire deep learning training data and acquire accurate true values to further improve the detection ability of U-Net models through learning. To achieve this purpose, by using a filtering method applying a step-by-step deep learning algorithm the ambiguous analysis basis of the deep learning model is minimized, enabling efficient analysis and judgment. As a result of the analysis the U-Net model using the true values analyzed by period in the detection and performance improvement of dead pine trees of wilt nematode using the U-Net algorithm had a recall rate of -0.5%p than the U-Net model using the previously provided true values, precision was 7.6%p and F-1 score was 4.1%p. In the future, it is judged that there is a possibility to increase the precision of wilt detection by applying various filtering techniques, and it is judged that the drone surveillance method using drone orthographic images and artificial intelligence can be used in the pine wilt nematode disaster prevention project.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.541-546
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• 2021

Recently, there is a trend of developing various identification and prediction models for hypertension using clinical information based on artificial intelligence and machine learning around the world. However, most previous studies on identification or prediction models of hypertension lack the consideration of the ideas of non-invasive and cost-effective variables, race, region, and countries. Therefore, the objective of this study is to present hypertension prediction model that is easily understood using only general and simple sociodemographic variables. Data used in this study was based on the Korea National Health and Nutrition Examination Survey (2018). In men, the model using the naive Bayes with the wrapper-based feature subset selection method showed the highest predictive performance (ROC = 0.790, kappa = 0.396). In women, the model using the naive Bayes with correlation-based feature subset selection method showed the strongest predictive performance (ROC = 0.850, kappa = 0.495). We found that the predictive performance of hypertension based on only sociodemographic variables was higher in women than in men. We think that our models based on machine leaning may be readily used in the field of public health and epidemiology in the future because of the use of simple sociodemographic characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1627-1634
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• 2021

This paper introduces a low-power compact ADC circuit for analog Convolutional filter for low-power neural network accelerator SOC. While convolutional neural network accelerators can speed up the learning and inference process, they have drawback of consuming excessive power and occupying large chip area due to large number of multiply-and-accumulate operators when implemented in complex digital circuits. To overcome these drawbacks, we implemented an analog convolutional filter that consists of an analog multiply-and-accumulate arithmetic circuit along with an ADC. This paper is focused on the design optimization of a low-power 8bit SAR ADC for the analog convolutional filter accelerator We demonstrate how to minimize the capacitor-array DAC, an important component of SAR ADC, which is three times smaller than the conventional circuit. The proposed ADC has been fabricated in CMOS 65nm process. It achieves an overall size of 1355.7㎛², power consumption of 2.6㎼ at a frequency of 100MHz, SNDR of 44.19 dB, and ENOB of 7.04bit.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.45-55
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• 2022

BIM models allow building spaces to be instantiated and recognized as unique objects independently of model elements. These instantiated spaces provide the required semantics that can be leveraged for building code checking, energy analysis, and evacuation route analysis. However, theses spaces or rooms need to be designated manually, which in practice, lead to errors and omissions. Thus, most BIM models today does not guarantee the semantic integrity of space designations, limiting their potential applicability. Recent studies have explored ways to automate space allocation in BIM models using artificial intelligence algorithms, but they are limited in their scope and relatively low classification accuracy. This study explored the use of Graph Convolutional Networks, an algorithm exclusively tailored for graph data structures. The goal was to utilize not only geometry information but also the semantic relational data between spaces and elements in the BIM model. Results of the study confirmed that the accuracy was improved by about 8% compared to algorithms that only used geometric distinctions of the individual spaces.