• Communications of Mathematical Education
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• v.33 no.3
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• pp.335-354
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• 2019

The purpose of this study is to explore alternative ways of teaching derivatives in a way that emphasizes intuition. For this purpose, the contents related to derivatives in Korean curriculum and textbooks were analyzed by comparing with contents in Singapore Curriculum and textbooks. Singapore, where the curriculum deals with derivatives relatively earlier than Korea, introduces the concept of derivatives and differentiation as the slope of tangent instead of the rate of instantaneous change in textbook. Also, Singapore use technology and inductive extrapolation to emphasize intuition rather than form and logic. Further, from the results of the exploration of other foreign cases, we confirm that the UK and Australia also emphasized intuition in teaching derivatives and differentiation. Based on the results, we discuss the meaning and implication of introducing derivatives and teaching differentiation in a way that emphasizes intuition. Finally, we propose the implications for the alternative way of teaching differentiation.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.151-160
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• 2019

The study of spiders that function as predators in agroecosystem can broaden the understanding of agroecosystems. This study investigated the effect of heterogeneity at different spatial scales on richness and abundance of spiders in upland and paddy fields. We collected 48 samples using pitfall traps at upland and paddy fields, respectively. The total species richness of spiders estimated by sample- and coverage-based rarefaction and extrapolation curves. The total species richness was high in the upland fields at the total study sites, whereas the average species richness per study site was high in the paddy fields. We confirmed that the diversity enhancement of spiders was influenced by the structural complexity of habitat at field-scale, and crop diversity at broader scale.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.61-69
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• 2021

Image outpainting is a very interesting problem in that it can continuously fill the outside of a given image by considering the context of the image. There are two main challenges in this work. The first is to maintain the spatial consistency of the content of the generated area and the original input. The second is to generate high quality large image with a small amount of adjacent information. Existing image outpainting methods have difficulties such as generating inconsistent, blurry, and repetitive pixels. However, thanks to the recent development of deep learning technology, deep learning-based algorithms that show high performance compared to existing traditional techniques have been introduced. Deep learning-based image outpainting has been actively researched with various networks proposed until now. In this paper, we would like to introduce the latest technology and trends in the field of outpainting. This study compared recent techniques by analyzing representative networks among deep learning-based outpainting algorithms and showed experimental results through various data sets and comparison methods.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.33-40
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• 2021

The situation of high-altitude electromagnetic pulses (HEMP) arises from high-altitude nuclear explosions. The HEMP situation can be simulated through the threat level investigation (TLI). In this paper, the induced voltage according to the antenna type of the tactical mobile radio communication system was measured and analyzed by TLI. Under the influence of HEMP, electronic equipment can be paralyzed or damaged. HEMP protection filters are commercially available for power lines and signal lines. However, commercialization of HEMP filters for antennas is insufficient, and even some of them exist for lightning protection. In order to make an appropriate HEMP protection filter according to the frequency and type of the antenna, the induced voltage was measured and the maximum induced voltage was analyzed through extrapolation. It was found that the measured induced voltage decreased as the frequency increased, such as in the HF, VHF and UHF bands of the measurement results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1013-1018
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• 2021

Due to the recent accident caused by the automated vehicle, discussions on the ethical aspects of AI have been actively underway. This paper confirms that AI is inevitably connected to ethical components through the concepts and techniques related to robots-AI, and argues that ethical aspects are built-in, not post facto. Furthermore, this devises a solution to the trolley dilemma that can serve as a clue to ethical problems associated with automated vehicles. Preferentially, that process contains writing Bayesian networks. Next, only important and influential data are left after the pre-processing stage, and crowd-sourcing & extrapolation is used to calculate the exact figures of the networks. Through this process, this argues that humans' subjects are certainly included in implementing algorithms and models and discusses the necessity and direction of engineering liberal arts, especially education of ethics that distinguished from major education to prevent distortions and biases abouts AI systems.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.387-393
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• 2022

The article reflects short-term perspectives on the use of information and communication technologies in the training of teachers for higher education. Education is characterized by conservatism, so aspects of systematic development of the industry are relevant to this cluster of social activity. Therefore, forecasting the introduction of innovative elements of ICT training is in demand for the educational environment. Forecasting educational trends are most relevant exactly in the issues of training future teachers of higher education because these specialists are actually the first to implement the acquired professional skills in pedagogical activities. The article aims to consider the existing potential of ICT-based learning, its implementation in the coming years, and promising innovative educational elements that may become relevant for the educational space in the future. The tasks of scientific exploration are to show the optimal formats of synergy between traditional and innovative models of learning. Based on already existing experience, extrapolation of conditions of educational process organization with modeling realities of using information and communication technologies in various learning dimensions should be carried out. Educational trends for the next 3 years are a rather tentative forecast because, as demonstrated by the events associated with the COVID-19 pandemic, the socio-cultural space is very changeable. Consequently, the dynamism of the educational environment dictates the need for a value-based awareness of the information society and the practical use of technological advances. Thus, information and communication technologies are a manifestation of innovative educational strategies of today and become an important component along with traditional aspects of educational process organization. Future higher education teachers should develop a training strategy taking into account the expediency of the ICT component.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.300-304
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• 2022

The central feature of a digital society is the presence of a significant volume of digital services. The main research-analytical goal of the work is to identify the characteristic features of digital services, to classify and compare various types of digital services, to study the main levers for the development of digital services, the principal determinants of the observance and implementation of digital rights, to identify the dominant threats regarding the violation of digital rights, to analyze the features of legal relations that arise between the supplier and the consumer of digital technologies, consider the available taxation options for the digital economy. The work uses the following methods and research methods: hermeneutic, forecasting, in particular, extrapolation, analysis and synthesis, comparative. Research results: the definition of the concept of "digital service" is given, its main characteristics and types, according to the level of digitalization, the states-leaders are identified, slowing down, promising and problematic, the main triggers of slowing digitalization in some EU countries are investigated, by analyzing the regulatory legal acts of the European Commission on digitalization the strategy of the EU's actions to increase the degree of digitalization was determined, the positive and negative effects of digital services concerning the observance of human rights and freedoms were highlighted, the issue of levying taxes from digital companies was investigated.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.557-563
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• 2022

The rating curve is required to convert measured stage into a discharge and is developed using the measurement. In the development of the rating curve, the segmentation position is determined by considering the hydraulic characteristic and channel shape, and subjective judgment of the Hydrographer may intervene in this process. The segmentation position is so important that it determines the overall form of the rating curve, and the incorrect segmentation can cause errors in the rating curve, especially in extrapolation. In order to develop an accurate rating curve with a small number of measurements, the sections must be divided by considering hydraulic characteristic such as the cross-sectional shape. In this study, hydraulic examination methods such as stage-mean velocity, stage-area, stage-${\sqrt{Q}}$ investigated and supplemented to eliminate subjectivity in segmental positioning. Appropriateness for the segmentation position was verify in consideration of the physical meaning of the rating curve index (c).

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.135-142
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• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

• Computers and Concrete
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• v.28 no.6
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• pp.533-547
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• 2021

Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.