• Journal of Astronomy and Space Sciences
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• 제19권4호
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• pp.367-376
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• 2002

As of September 2002, there are more than 60 operational permanent Global Positioning System (GPS) stations in South Korea. Their data are being used for a variety of purposes: geodynamics, geodesy, real-time navigation, atmospheric science, and geography. Especially, many of the sites are reference stations for DGPS (Differential GPS). However, there has been no comprehensive and qualitative analysis published to evaluate the data quality. In this study, we present preliminary results of our assessment of the permanent GPS sites in South Korea. We have analyzed the multi-path characteristics of each station using a quality-checking software package called TEQC. Another multipath analysis tool based on post-fit phase residuals was used to check the repeating patterns and the amount of the multipath at each site. The long-term stability of each station was analyzed using the root-mean-square (RMS) error of the estimated site positions for one year, which enabled us to evaluate the mount stability. In addition, the number of cycle slips at each site was derived by TEQC. Based on these series of tests, we compared the stability and data quality of permanent GPS stations in South Korea.

• 천문학회지
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• 제51권5호
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• pp.129-142
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• 2018

The DEEP-South (the Deep Ecliptic Patrol of the Southern Sky) photometric census of small Solar System bodies produces massive time-series data of variable, transient or moving objects as a by-product. To fully investigate unexplored variable phenomena, we present an application of multi-aperture photometry and FastBit indexing techniques for faster access to a portion of the DEEP-South year-one data. Our new pipeline is designed to perform automated point source detection, robust high-precision photometry and calibration of non-crowded fields which have overlap with previously surveyed areas. In this paper, we show some examples of catalog-based variability searches to find new variable stars and to recover targeted asteroids. We discover 21 new periodic variables with period ranging between 0.1 and 31 days, including four eclipsing binary systems (detached, over-contact, and ellipsoidal variables), one white dwarf/M dwarf pair candidate, and rotating variable stars. We also recover astrometry (< ${\pm}1-2$ arcsec level accuracy) and photometry of two targeted near-earth asteroids, 2006 DZ169 and 1996 SK, along with the small- (~0.12 mag) and relatively large-amplitude (~0.5 mag) variations of their dominant rotational signals in R-band.

• 천문학회보
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• 제41권1호
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• pp.54.2-54.2
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• 2016

Korea Microlensing Telescope Network (KMTNet) is the first optical survey system of its kind in a way that three KMTNet observatories are longitudinally well-separated, and thus have the benefit of 24-hour continuous monitoring of the southern sky. The wide-field and round-the-clock operation capabilities of this network facility are ideal for survey and the physical characterization of small Solar System bodies. We obtain their orbits, absolute magnitudes (H), three dimensional shape models, spin periods and spin states, activity levels based on the time-series broadband photometry. Their approximate surface mineralogy is also identified using colors and band slopes. The automated observation scheduler, the data pipeline, the dedicated computing facility, related research activity and the team members are collectively called 'DEEP-South' (DEep Ecliptic Patrol of Southern sky). DEEP-South observation is being made during the off-season for exoplanet search, yet part of the telescope time is shared in the period between when the Galactic bulge rises early in the morning and sets early in the evening. We present here the observation mode, strategy, software, test runs, early results, and the future plan of DEEP-South.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권11호
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• pp.527-536
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• 2017

최근 스마트 그리드 산업의 발달과 더불어 효과적인 에너지 관리 시스템의 필요성이 커지고 있다. 특히, 전기 부하 및 에너지 요금 감소를 위해서는 정확한 전력수요 예측과 그에 따른 효과적인 스마트 그리드 운영 전략이 필요하다. 본 논문에서는 보다 정확한 전력수요 예측을 위하여, 수요 시한 기준으로 수집된 전력 사용 데이터를 고시간 해상도로 분할하고, 이에 적합한 인공 신경망 기반의 전력수요 예측 모델을 구축하고자 한다. 예측 모델의 정확도를 향상시키기 위하여 우선, 수열 형태의 시계열 데이터가 가지는 주기성을 제대로 반영하지 못하는 기계 학습 모델의 문제점을 해결하고자, 시계열 데이터를 2차원 공간의 연속적인 데이터로 변환한다. 더욱이, 고시간 해상도에 따른 온도나 습도 등 외부 요인들의 보다 정확한 반영을 위해 이들에 대해서도 선형 보간법을 사용하여 세분화된 시점에서의 값을 추정하여 반영한다. 마지막으로, 구성된 특성 벡터에 대해 주성분 분석 수행을 통하여 불필요한 외부 요인을 제거한다. 예측 모델의 성능을 평가하기 위해서 5겹 교차 검증을 수행하였다. 실험 결과 모든 고시간 해상도에서 성능 향상을 보였으며, 특히 3분 해상도의 경우 3.71%의 가장 낮은 오차율을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• 천문학회보
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• 제46권2호
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• pp.45.3-46
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• 2021

Supernovae classes have been defined phenomenologically, based on spectral features and time series data, since the specific details of the physics of the different explosions remain unrevealed. However, the number of these classes is increasing as objects with new features are observed, and the next generation of large-surveys will only bring more variety to our attention. We apply the machine learning technique of multi-label classification to the spectra of supernovae. By measuring the probabilities of specific features or 'tags' in the supernova spectra, we can compress the information from a specific object down to that suitable for a human or database scan, without the need to directly assign to a reductive 'class'. We use logistic regression to assign tag probabilities, and then a feed-forward neural network to filter the objects into the standard set of classes, based solely on the tag probabilities. We present STag, a software package that can compute these tag probabilities and make spectral classifications.

• 대한원격탐사학회지
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• 제37권3호
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• pp.409-418
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• 2021

토지피복도는 국토정책, 환경정책을 위한 의사결정 근거 자료로 활용되는 매우 중요한 자료이다. 토지피복도는 원격탐사 자료를 활용하여 제작되는데, 이때 사용되는 데이터의 취득 시기에 따라 동일한 지역을 대상으로 하더라도 분류 결과가 달라질 수 있다. 본 연구에서는 단시기 데이터의 분류 정확도를 개선하기 위해 다중시기 위성영상을 활용하였으며 계절에 따른 지표면의 분광 반사 특성 차이를 딥러닝 알고리즘의 하나인 U-Net 모델에 학습시켜 분류하였다. 또한 단시기 분류 결과와 정확도 비교를 통해 분류 정확도의 향상 정도를 비교하였다. 구역 내에 30%의 녹지와 한강을 포함하여 다양한 토지피복으로 이루어진 서울특별시를 연구대상지로 설정하고 2020년 분기별 Sentinel-2 위성영상을 산출하였다. 대한민국 환경부에서 작성한 세분류 토지피복도를 활용하여 U-Net 모델을 학습시켰다. 학습한 U-Net 모델을 통해 단시기, 2시기, 3시기, 4시기로 모델을 학습하여 분류한 결과, 단시기를 제외하고 토지피복도 분류 정확도 확보기준인 75%를 상회하는 81%, 82% 79%의 정확도를 나타냈다. 이를 통해 다중 시계열 학습을 통해 토지피복의 분류 정확도 향상이 가능하다는 것을 확인하였다.

• 한국실내디자인학회논문집
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• 제22권6호
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• pp.108-118
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• 2013

This study has analyzed the process of space information perceived and recognized through the estimation of observation frequency and number according to the time range of observation data acquired from observation experiment with the object of hospital lobby. The followings are the results analyzed at this study. First, the continual observation of 3 and 6 times was attentive and conscious for probing to find an object rather than for acquiring exact information and that of 9 times could be regarded as the time for acquiring visual appreciation. However, the repetitive occurrence of high and low frequencies can be thought of repetitive acts for visual appreciation. Second, the continual observation of 3 and 6 times had the highest observation frequency of II, while that of 9 times had the highest observation frequency of III. In case of 3 and 6 times, the observation frequency had the tendency to become a little higher after being low since V, and in case of 9 times it had the repetition of becoming low and high and from IX it characteristically got higher. This feature can be thought to be the process that the subject repeats the fixation and movement of observation at a visual activity for perception and recognition. In the process of first observation, the observation frequency was the highest after 20 seconds or so, but since then, it gets lower and repeatedly gets higher and lower as time passes. After 90 seconds, the frequency showed the tendency of getting higher continuously. Third, the examination of changing features of frequency may show the characteristics of exploration for and attention to space but if the observation frequency is not associated with observation times for analysis there will a limitation that the features of observation frequency cannot be clarified. Accordingly, the simultaneous analysis of both is very effective for estimating the observation characteristics seen at the processes of perception and recognition. Fourth, the general analysis of the both revealed: with the progress of observation time the discontinuous space exploration decreased, and as the observation time got longer the fixed attention to a specific spot increased. Fifth, in order to estimate the observation characteristics by the change of time range the observation frequency and times by trend line was analyzed, which approach seems to be an appropriate technique that can comprehensively show the overall flow of time series data.

• Journal of Astronomy and Space Sciences
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• 제30권4호
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• pp.315-320
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• 2013

Space geodetic techniques can be used to obtain precise shape and rotation information of the Earth. To achieve this, the representative combination solution of each space geodetic technique has to be produced, and then those solutions need to be combined. In this study, the representative combination solution of very long baseline interferometry (VLBI), which is one of the space geodetic techniques, was produced, and the variations in the position coordinate of each station during 7 years were analyzed. Products from five analysis centers of the International VLBI Service for Geodesy and Astrometry (IVS) were used as the input data, and Bernese 5.0, which is the global navigation satellite system (GNSS) data processing software, was used. The analysis of the coordinate time series for the 43 VLBI stations indicated that the latitude component error was about 15.6 mm, the longitude component error was about 37.7 mm, and the height component error was about 30.9 mm, with respect to the reference frame, International Terrestrial Reference Frame 2008 (ITRF2008). The velocity vector of the 42 stations excluding the YEBES station showed a magnitude difference of 7.3 mm/yr (30.2%) and a direction difference of $13.8^{\circ}$ (3.8%), with respect to ITRF2008. Among these, the 10 stations in Europe showed a magnitude difference of 7.8 mm/yr (30.3%) and a direction difference of $3.7^{\circ}$ (1.0%), while the 14 stations in North America showed a magnitude difference of 2.7 mm/yr (15.8%) and a direction difference of $10.3^{\circ}$ (2.9%).

• 한국전자거래학회지
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• 제21권4호
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• pp.1-14
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• 2016

시멘틱 검색은 검색 사용자의 인지적 노력을 최소화하면서 사용자 질의의 문맥을 이해하여 의미에 맞는 문서를 정확히 찾아주는 기술이다. 아직 시멘틱 검색 기술은 온톨로지 또는 시멘틱 메타데이터 구축의 난제를 갖고 있으며 상용화 사례도 매우 미흡한 실정이다. 본 논문은 기존 시멘틱 검색 엔진의 한계를 극복하기 위하여 이전 연구에서 고안한 위키피디아 기반의 시멘틱 텐서공간모델을 활용하여 새로운 시멘틱 검색 기법을 제안한다. 제안하는 시멘틱 기법은 문서 집합에 출현하는 '단어'가 텐서공간모델에서 '문서-개념'의 2차 텐서(행렬), '개념'은 '문서-단어'의 2차 텐서로 표현된다는 성질을 이용하여 시멘틱 검색을 위해 요구되는 온톨로지 구축의 필요성을 없앤다. 그럼에도 불구하고, OHSUMED, SCOPUS 데이터셋을 이용한 성능평가를 통해 제안 기법이 벡터공간모델에서의 기존 검색 기법보다 우수함을 보인다.