• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.661-662
• /
• 2021

본 논문에서는 인공신경망 기반의 슈퍼 해상도(Super-resolution, SR) 기법을 이용하여 저해상도(Low-resolution, LR) 헤어 시뮬레이션을 고해상도(High-resolution, HR)로 노이즈 없이 표현할 수 있는 기법을 제안한다. LR과 HR 머리카락 간의 쌍은 헤어 시뮬레이션을 통해 얻을 수 있으며, 이렇게 얻어진 데이터를 이용하여 HR-LR 데이터 쌍을 설정한다. 학습할 때 사용되는 데이터는 머리카락의 위치를 지오메트리 이미지로 변환하여 사용한다. 우리가 제안하는 헤어 네트워크는 LR 이미지를 HR 이미지로 업스케일링 시키는 이미지 합성기를 위해 사용된다. 테스트 결과로 얻어진 HR 이미지가 HR 머리카락으로 다시 변환되면, 하나의 매핑 함수로 표현하기 어려운 머리카락의 찰랑거리는(Elastic) 움직임을 잘 표현할 수 있다. 합성 결과에 대한 성능으로는 전통적인 물리 기반 시뮬레이션보다 빠른 성능을 보였으며, 복잡한 수치해석을 몰라도 쉽게 실행이 가능하다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.05a
• /
• pp.238-241
• /
• 2021

가상현실은 시·공간을 초월하는 경험이 가능하며 아이들의 몰입감과 학습효과를 높일 수 있어 교육 도구로서의 가치를 인정받고 있다. 정부는 VR 기기를 전국의 초등학교에 보급하고 있지만, 양질의 교육용 실감형 콘텐츠는 부족한 상황이다. 따라서, 본 논문에서는 가상현실 기술을 활용한 국악기 연주 체험 프로그램을 제안하였다. Unity의 물리 엔진과 Oculus의 햅틱 기능을 이용하여 전통 국악기 중 편경을 실감 나게 연주할 수 있도록 가상환경을 구현하였다. 리듬 게임 형식을 적용하여 아이들의 흥미 또한 유발하였다. 이 프로그램을 통해 국악기를 직접 연주하며 해당 국악기에 대한 지식을 자연스럽게 습득할 수 있을 것이라 기대한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.1035-1036
• /
• 2023

본 논문은 저시력자를 위한 체외진단 의료기기 결과 판독 시스템을 제안한다. 이 시스템은 YOLOv8n 객체 탐지 모델을 기반으로 하며, 라즈베리파이4B+에서 홈 디바이스 형태로 구현하였다. 사용자는 음성 및 물리 버튼을 통해 명령을 입력하고, 동작 감지를 통해 자동으로 체외진단 의료기기를 촬영하여 학습된 모델로 결과를 판독하고 해당 결과를 사용자에게 출력한다. 또한, 판독 결과물과 함께 검사 일시 및 의료기기 종류를 데이터베이스에 저장하여 사용자에게 보다 높은 편의성을 제공한다.

• Geophysics and Geophysical Exploration
• /
• v.23 no.4
• /
• pp.230-242
• /
• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.

• Geophysics and Geophysical Exploration
• /
• v.23 no.3
• /
• pp.192-207
• /
• 2020

We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.

• Journal of The Korean Association For Science Education
• /
• v.24 no.5
• /
• pp.1008-1017
• /
• 2004

In physics education, language is an fundamental learning tool as in other subjects. In writing activity, students can get fair opportunities to express their own ideas during the class. Even though there are various styles of writing, students are usually supposed to make a report in their science classes. But there have been few studies in science education on the tasks and features of student's science writing. In this research, different styles of writing tasks were designed for science classes, and students' writing was analysed in terms of conceptual and emotional aspects. Also the usefulness of each task type was discussed relating to school physics education. Four types of writing, i.e. , , , and writing were developed, and 'The reflection of light' was selected as the theme and given to students. Four types of writing were analysed in this paper. In each type of writing, students showed different features in their conception. They also showed emotional expressions in imaginative writing types, that is, and types. Based on these results, it is recommended that in physics teaching various types of writing need to be designed, developed and applied according to the aim of a particular lesson.

• The Journal of Sustainable Design and Educational Environment Research
• /
• v.15 no.1
• /
• pp.1-10
• /
• 2016

School facilities are one of the important educational requirements, and it is necessary to maintain safe and sustainable for the ongoing educational environment. For this reason, educational government department have an effort to produce school facilities that have become safe, comfort, convenient and high-quality. There are many methods to improve existing buildings and build new schools by energy efficient technology. Even though educational environment of school facilities are improved by the efforts, the energy consumption has a huge increase. Energy is a major budget item for schools. The accurate estimation of energy cost is critical for effective budgeting and financing for the school facility maintenance. Accurate energy cost estimation also allow for a anticipatable LCC (Life Cycle Cost) of new school facilities. In this study, in order to produce a basic information about the present energy usage status in domestic school facilities, energy usage quantity is regionally analysed according to physical characteristics of elementary, middle, and high school facilities. Those results will give a chance to see deeply the pros and cons of energy usage each regional school facilities.

• Economic and Environmental Geology
• /
• v.54 no.3
• /
• pp.353-364
• /
• 2021

Since the mid-twentieth century, geology has gradually evolved as an interdisciplinary context in South Korea. The journal of Economic and Environmental Geology (EEG) has a long history of over 52 years and published interdisciplinary articles based on geology. In this study, we performed a literature review using topic modeling based on Latent Dirichlet Allocation (LDA), an unsupervised machine learning model, to identify geological topics, historical trends (classic topics and emerging topics), and association by analyzing titles, keywords, and abstracts of 2,571 publications in EEG during 1968-2020. The results showed that 8 topics ('petrology and geochemistry', 'hydrology and hydrogeology', 'economic geology', 'volcanology', 'soil contaminant and remediation', 'general and structural geology', 'geophysics and geophysical exploration', and 'clay mineral') were identified in the EEG. Before 1994, classic topics ('economic geology', 'volcanology', and 'general and structure geology') were dominant research trends. After 1994, emerging topics ('hydrology and hydrogeology', 'soil contaminant and remediation', 'clay mineral') have arisen, and its portion has gradually increased. The result of association analysis showed that EEG tends to be more comprehensive based on 'economic geology'. Our results provide understanding of how geological research topics branch out and merge with other fields using a useful literature review tool for geological research in South Korea.

• Geophysics and Geophysical Exploration
• /
• v.25 no.3
• /
• pp.99-108
• /
• 2022

Among induced polarization (IP) methods, spectral IP (SIP) uses alternating current as a transmission source to measure amplitudes and phase of complex electrical resistivity at each source frequency, which disperse with respect to source frequencies. The frequency dependence, which can be explained by a relaxation model such as Cole-Cole model or equivalent models, is analyzed to estimate SIP parameters from dispersion curves of complex resistivity employing multi-objective optimization (MOO). The estimation uses a generic algorithm to optimize two objective functions minimizing data misfits of amplitude and phase based on Cole-Cole model, which is most widely used to explain IP relaxation effects. The MOO-based estimation properly recovered Cole-Cole model parameters for synthetic examples but hardly fitted for the real laboratory measures ones, which have relatively smaller values of phases (less than about 10 mrad). Discrepancies between scales for data misfits of amplitude and phase, used as parameters of MOO method, and it is in necessity to employ other methods such as machine learning, which can deal with the discrepancies, to estimate SIP parameters from dispersion curves of complex resistivity.

• Geophysics and Geophysical Exploration
• /
• v.26 no.3
• /
• pp.126-137
• /
• 2023

In this study, a recurrent neural network (RNN) was employed as a methodological approach to classify dolphin click signals derived from ocean monitoring data. To improve the accuracy of click signal classification, the single time series data were transformed into fractional domains using fractional Fourier transform to expand its features. Transformed data were used as input for three RNN models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM), which were compared to determine the optimal network for the classification of signals. Because the fractional Fourier transform displayed different characteristics depending on the chosen angle parameter, the optimal angle range for each RNN was first determined. To evaluate network performance, metrics such as accuracy, precision, recall, and F1-score were employed. Numerical experiments demonstrated that all three networks performed well, however, the BiLSTM network outperformed LSTM and GRU in terms of learning results. Furthermore, the BiLSTM network provided lower misclassification than the other networks and was deemed the most practically appliable to field data.