• 천문학회보
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• 제46권1호
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.

• Journal of Astronomy and Space Sciences
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• 제29권2호
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• pp.131-140
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• 2012

We presented fundamental stellar parameters and evolutionary statuses of six solar type detached eclipsing binaries whose masses are in the range of 0.97-1.43 $M_{\odot}$. EK Cep and FL Lyr belong to the zero age main sequence. HS Hya, IT Cas and CD Tau are on the main sequence. Their ages are 1.3, 1.9 and 2.2 Gyr, respectively. Both component stars of AI Phe evolved to sub giants and its age is 4.0 Gyr. Those ages of the detached binary systems show good agreement with the time scale for synchronization and circularization of the binary systems.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Advances in Computational Design
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• 제3권4호
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• pp.339-366
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• 2018

Parametric design systems serve as powerful assistive tools in the design process by providing a flexible approach for the generation of a vast number of design alternatives. However, contemporary parametric design systems focus primarily on low-level engineering and structural forms, without an explicit means to also take into account high-level, cognitively motivated people-centred design goals. We present a precedent-based parametric design method that integrates people-centred design "precedents" rooted in empirical evidence directly within state of the art parametric design systems. As a use-case, we illustrate the general method in the context of an empirical study focusing on the multi-modal analysis of wayfinding behaviour in two large-scale healthcare environments. With this use-case, we demonstrate the manner in which: (1). a range of empirically established design precedents -e.g., pertaining to visibility and navigation- may be articulated as design constraints to be embedded directly within state of the art parametric design tools (e.g., Grasshopper); and (2). embedded design precedents lead to the (parametric) generation of a number of morphologies that satisfy people-centred design criteria (in this case, pertaining to wayfinding). Our research presents an exemplar for the integration of cognitively motivated design goals with parametric design-space exploration methods. We posit that this opens-up a range of technological challenges for the engineering and development of next-generation computer aided architecture design systems.

• 디지털콘텐츠학회 논문지
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• 제12권2호
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• pp.217-223
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• 2011

인공지능(AI : Artificial Intelligence)은 지능을 만들 수 있는 방법론이나 실현 가능성 등을 연구하는 컴퓨터 공학 및 정보기술과학 분야이다. 오델로(Othello) 게임은 다른 게임에 비해 규칙이 간단하며 $8{\times}8$인 제한적인 공간에서 이루어지기 때문에 AI로 제작되는 사례가 많다. 기존의 알고리즘은 추후에 발생하는 모든 경우의 수를 탐색하거나 룰을 이용하여 처리하기 때문에 처리시간이 오래 걸리며 새로운 상황에 대처하는데 효율적이지 않다. 본 연구에서는 이런 단점을 보안하고자 오델로 게임에 AI의 한 분야인 사례기반추론(CBR : Case-based Reasoning)알고리즘을 도입한다. CBR알고리즘 이란 주어진 문제를 해결하기 위해 과거에 있었던 유사한 문제를 검색하여 상황에 맞는 해결방법을 제시하는 방식을 의미한다. 지금까지 오델로 게임에 여러가지 AI기술을 이용하였으나 CBR알고리즘을 적용한 사례가 없었다. 본 연구에서는 CBR알고리즘을 오델로 게임에 적용하여 보다 빠른 연산속도로 다음 작업을 처리할 수 있으며 기존의 사례가 충분할 때는 새로운 상황에 효율적으로 대처할 수 있을 뿐 아니라 사용자로 하여금 보다 어려운 오델로 게임을 만들 수 있는 시스템을 제안한다.

• 한국통신학회논문지
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• 제35권12A호
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• pp.1181-1187
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• 2010

본 논문은 게임에서 신경망기반으로 지능캐릭터에게 학습을 통한 상황판단을 하는 이동 인공지능을 제안하였다. 신경망은 게임 규칙과 문제해결 방법을 정의한 알고리즘을 통한 입출력 값을 이용하여 지도 학습된다. 지도 학습된 지능캐릭터는 변화하는 주변 환경을 인지하여, 적절한 행동을 하게 된다. 본 논문에서는 신경망을 이용한 이동 인공지능을 점진적으로 설계하였고, 성능 실험을 위하여 간단한 게임을 구현하였다. 이 게임은 일정한 2차원 공간에 목표, 캐릭터, 장애물이 존재하고 캐릭터는 목표 지점으로 장애물을 회피하며 이동해야한다. 이동 인공지능은 실험마다 정의한 알고리즘을 통해 규칙과 몇 가지 문제해결법을 학습하여 변화하는 환경에서 목표를 완수 할 수 있으며, 정의한 알고리즘과 신경망 구조를 동일하게 설계하였다. 실험 결과, 제안한 이동 인공지능은 주변 상황을 인지하여 이동을 수행하고 목표를 완수할 수 있음을 보였다. 이동 인공지능은 복잡한 구조의 게임도 학습 알고리즘을 정의하여 학습하면 신경망은 변화한 환경에서도 적절한 결과를 보여 줄 수 있을 것이다.

• Journal of Astronomy and Space Sciences
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• 제29권2호
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• pp.221-232
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• 2012

We analyzed the manufacturing procedure, specifications, repair history, and details of celestial movements of the water-hammering type $Honsang$ (celestial globe). Results from our study on the remaining $Honsangs$ in China and Japan and on the reconstruction models in Korea were applied to our conceptual design of the water-hammering type $Honsang$. A $Honui$ (armillary sphere) and $Honsang$ using the water-hammering method were manufactured in $Joseon$ in 1435 (the 17th year of King $Sejong$). $Jang$ $Yeong-Sil$ developed the $Honsang$ system based on the water-operation method of $Shui$ $y{\ddot{u}}n$ $i$ $hsiang$ $t'ai$ in China. Water-operation means driving water wheels using a water flow. The most important factor in this type of operation is the precision of the water clock and the control of the water wheel movement. The water-hammering type $Honsang$ in $Joseon$ probably adopted the $Cheonhyeong$ (天衡; oriental escapement device) system of $Shui$ $y{\ddot{u}}n$ $i$ $hsiang$ $t'ai$ in China and the overflow mechanism of $Jagyeongnu$ (striking clepsydra) in $Joseon$, etc. In addition to the $Cheonryun$ system, more gear instruments were needed to stage the rotation of the $Honsang$ globe and the sun's movement. In this study, the water-hammering mechanism is analyzed in the structure of a water clock, a water wheel, the $Cheonhyeong$ system, and the $Giryun$ system, as an organically working operation mechanism. We expect that this study will serve as an essential basis for studies on $Heumgyeonggaknu$, the water-operating astronomical clock, and other astronomical clocks in the middle and latter parts of the $Joseon$ dynasty.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.178-185
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• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• 한국산업융합학회 논문집
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• 제9권1호
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• pp.61-66
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• 2006

In perspective of saving natural resource and energy, today's automobiles are in process of regenerating by smaller and lighter. In order to achieve the sufficiency on the consumption of the fuel, new mechanism and new assembly are required. Therefore the expectations on the new materials are very high. Especially, AI materials are widely used to reduce the weight. AI that is used in automobiles is mostly casting material, and according to the innovation of technique is in rapid development. AI Die casting is an important field as today's trend of lightweight on automobiles. One of the parts in steering system, Valve Housing plays a role of reduce the operating effort of drivers. Unfortunately, the Valve Housing which is widely reliable to the most automobiles are not developed at this moment in our automobile industry. Therefore, they are produced by casting method which cost three times or even more expensive in production. If Valve Housing, which is a part of steering system is produced by Gravity Casting, the space that manufacturing equipment will be increased, and more time and workers would be brought into service. For such reason, Die Casting would replace Gravity Casting in order to minimize cost of time, manpower, and working space.

• 한국항공우주학회지
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• 제42권8호
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• pp.674-682
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• 2014

RFTS(Radio Frequency Test Set)은 저궤도 위성과 정지궤도 위성의 AI&T 기간 동안 TC&R RF 서브시스템을 검증하는 필수 장비이다. 본 논문에서는 새로운 RFTS의 상세 설계 내용을 제시하는데 기존의 RFTS와 달리 위성 프로젝트 별 시험 수행 시 수정 없이 바로 적용할 수 있도록 하였다. 하드웨어와 소프트웨어는 이를 만족하도록 설계 되었고 기능 별 모듈화와 상용품 사용의 확장으로 보다 손쉬운 제어와 운용이 될 수 있도록 하였으며 시스템의 신뢰성 향상을 가져오도록 하였다. 또한 보다 정확한 기준 클락을 채택하여 RF 측정에 대한 신뢰도를 높였다.