• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.4
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• pp.42-55
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• 2014

The purpose of this research was to identify expertise in ait traffic control by using cognitive skill analysis for novices and experts in routine and non-routine situations. The result of study was to understand expertise in air traffic control tasks in terms of what cognitive processes are responsible for the expert's high performance levels. The problem solving task was difficult for novices, but performed relatively automatically by experts in a routine situation. The difficulty could indicate the presence of controlled processing. Rather than rules and strategies, novices focused more on environmental factors, which merely increase cognitive load. In a non-routine situation, novices showed that they did not categorize the information consistently and alternative resources were not available for them. Experts, however, performed automatically a task by arranging and organizing information related to problem solving components in contexts without regard to a routine and non-routine situation. Especially experts developed a stable representation and directed alternative resources for air traffic flow and efficiency. Based on the results, cognitive processes of experts could be useful to understand expert performance and analyze the learning process, which imply the necessity of developing expertise systematically.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.405-408
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• 2010

Cyber education community in the digital age is going on across all areas of knowledge accumulation rate of acceleration of the demand for retraining and lifelong learning can effectively accommodate. Therefore, new teaching methods in the future as consumer-driven 21st century is expected to settle into the main education system. However, to properly interact with people due to lack of feedback delay, reading and writing intensive courses is detrimental to the environment caused by a particular student, and Computer skills are based on computer-related technology to disadvantage poor students, and many have problems. In this paper, recent university education and cyber education requires a paradigm shift is required in an environment that factors affecting students' satisfaction were evaluated, and Improvement of cyber education will be studied.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.193-196
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• 2019

Digital Game design with educational purposes and User Experience measurement via game analytics has been extensively covered in literature, however non-digital games such as tabletops in education and its corresponding educational impact have limited research. In this paper, we propose a guideline to create non-digital educational games from scratch and evaluate them based on the know-how of developers and the investigation of scholars who have studied the engagement factors related to the digital games and applied their findings to non-digital games. Along with the guideline we provide an actual implementation, a game called HXGN_766, meant to serve as scaffolding of computational thinking and rudimentary Python programing concepts. We believe both, guideline and game, can be a useful reference for those interested on game design, educational content design, game quality control check, and unplugged computer science activities. This is the first in a series of papers where the game design concept, the evaluation methodology and the game itself will be presented with more detail.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.245-251
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• 2021

This work aims to focus on the current features and characteristics of Human Element and Artificial intelligence (AI), ask some questions about future information security, and whether we can avoid human errors by improving machine learning and AI or invest in human knowledge more and work them both together in the best way possible? This work represents several related research results on human behavior towards information security, specified with elements and factors like knowledge and attitude, and how much are they invested for ISA (information security awareness), then presenting some of the latest studies on AI and their contributions to further improvements, making the field more securely advanced, we aim to open a new type of thinking in the cybersecurity field and we wish our suggestions of utilizing each point of strengths in both human attributions in software security and the existence of a well-built AI are going to make better future software security.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.22 no.1
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• pp.15-20
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• 2022

The most common urea cycle disorder is ornithine transcarbamylase deficiency. More than 80 percent of patients with symptomatic ornithine transcarbamylase deficiency are late-onset, which can present various phenotypes from infancy to adulthood. With no regards to the severity of the disease, characteristic fluctuating courses due to hyperammonemia may develop unexpectedly, and can be precipitated by various metabolic stressors. Late-onset ornithine transcarbamylase deficiency is not merely related to a type of genetic variation, but also to the complex relationship between genetic and environmental factors that result in hyperammonemia; therefore, it is difficult to predict the prevalence of neurological symptoms in late-onset ornithine transcarbamylase deficiency. Most common acute neurological manifestations include psychological changes, seizures, cerebral edema, and death; subacute neurological manifestations include developmental delays, learning disabilities, intellectual disabilities, attention-deficit/hyperactivity disorder, executive function deficits, and emotional and behavioral problems. This review aims to increase awareness of late-onset ornithine transcarbamylase deficiency, allowing for an efficient use of biochemical and genetic tests available for diagnosis, ultimately leading to earlier treatment of patients.

• Science of Emotion and Sensibility
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• v.25 no.3
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• pp.107-116
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• 2022

Purpose: Researchers have shown that aesthetic judgments of artworks depend on contexts, such as the authenticity of an artwork (Newman & Bloom, 2011) and an artwork's location of display (Kirk et al., 2009; Silveira et al., 2015). The present study aims to examine whether contextual information related to the creator, such as whether an artwork was created by a human or artificial intelligence (AI), influences viewers' preference judgments of an artwork. Methods: Images of Impressionist landscape paintings were selected as human-made artworks. AI-made artwork stimuli were created using Google's Deep Dream Generator by mimicking the Impressionist style via deep learning algorithms. Participants performed a preference rating task on each of the 108 artwork stimuli accompanied by one of the two creator labels. After this task, an art experience questionnaire (AEQ) was given to participants to examine whether individual differences in art experience influence their preference judgments. Results: Setting AEQ scores as a covariate in a two-way ANCOVA analysis, the stimuli with the human-made context were preferred over the stimuli with the AI-made context. Regarding the types of stimuli, the viewers preferred AI-made stimuli to human-made stimuli. There was no interaction effect between the two factors. Conclusion: These results suggest that preferences for visual artworks are influenced by the contextual information of the creator when the individual differences in art experience are controlled.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.11-19
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• 2023

In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.649-651
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• 2021

In Korea, there are around 30,000 cardiac arrest patients annually. Gradually the number is increasing. Against this background, CPR education and publicity programs were expanded nationwide, but the rate of witness CPR by the general public was 4.4%, which is significantly lower than the 20%~70% rate in other countries. Therefore, in this paper, we analyzed the factors affecting the performance of CPR by witnesses who discovered cardiac arrest patients. Based on the results, an application planning and development study was conducted to provide users with correct cardiorespiratory response tips and step-by-step CPR guidelines to help users effectively assist in increasing the rate of CPR by general eyewitnesses.