• Journal of Advanced Marine Engineering and Technology
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• 제28권1호
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• pp.83-89
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• 2004

Flare nut is an important Part that used to joint a brake tube-end in automobiles. It was made of SWCH 10A by machining. But we studied to make it by metal forming. The main focus of this paper is to investigate an optimal forging processes for flare nut using the DEFORM$^{TM}$-3D. commercially available finite element code and tests. Actually an explicit finite element analysis of the flare nut forging processes has been carried out to predict an optimal shape of the flare nut and its results were reflected in the tests of the forging processes design for flare nut. The simulation results which had obtained from finite element analysis were contributed to the forging processes design for flare nut. An optimal shape of nave nut showed agreements with test results. Furthermore. this paper should contribute to a development of the forging process for a variety of parts.s.

• 대한원격탐사학회지
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• 제39권6_1호
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• pp.1389-1399
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• 2023

Field Line of sight Automated Radiance Exposure (FLARE) 시스템을 이용하여 다목적위성3호/3A호의 절대복사 검보정 연구를 수행하였다. FLARE는 미국의 Labshphere사에 의해 개발된 시스템으로 SPecular Array Radiometric Calibration (SPARC) 개념을 적용한 것이다. FLARE는 거울처럼 반사하는 거울 타겟을 사용하여 산란되는 복사에너지의 원인 요소들을 최소화시킨 단순한 복사보정 방법을 제공한다. FLARE 시스템이 장착된 사이트를 통과하는 다목적위성3호/3A호를 이용한 영상자료 획득을 위해 2021년 7월 5일부터 7월 15일 사이에 필드캠페인을 진행하였다. 기상 상황 때문에 여러 번의 관측 자료 가운데 2개의 다목적위성3호 관측자료만이 유효한 샘플 영상으로 확인되었다. FLARE 시스템과 다목적위성3호 관측 자료를 바탕으로 절대복사 검보정 계수를 산출하였다. 7월 7일과 7월 13일 획득된 2개의 FLARE 관측 자료를 통해 계산된 결과는 근적외 채널을 제외하고 1% 이내의 매우 유사한 결과를 보여 주었다. 2021년 8월 획득된 다목적위성3호/3A호 자료를 추가하여 분석한 결과, 현재의 메타 데이터에 할당된 위성들의 이득값들과는 상당한 차이를 보였다. 제한된 획득자료로 인해 FLARE 시스템을 실제 운영 중인 다목적위성3호/3A호에 대한 절대복사 검보정 계수 산출 용도로 사용하기 위해서는 추가적인 연구가 필요할 것으로 판단된다.

• 천문학회지
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• 제36권spc1호
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• pp.49-54
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• 2003

We introduce the two-dimensional spectral observations of solar flares using the Solar Tower Tele-scope of Nanjing University, China. In particular, we introduce three typical events and the methods used to analyze the data. (1) The flare of November 11, 1998, which is a limb flare. We derive the temperature and density within the flaring loop using non-LTE calculations. The results show that the loop top may be hotter and denser than other parts of the loop, which may be a result of magnetic reconnect ion above the loop. (2) The flare of March 10, 2001, which is a white-light flare that shows an emission enhancement at the near infrared continuum. We propose a model of non-thermal electron beam heating plus backwarming to interpret the observations. (3) The flare of September 29, 2002, which shows unusual line asymmetries at one flare kernel. The line asymmetries are caused by an upward moving plasma that is accelerated and heated during the flare development.

• 천문학회보
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• 제42권2호
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• pp.60.1-60.1
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• 2017

In this study we apply Convolution Neural Network(CNN) to solar flare occurrence prediction with various parameter options using the 00:00 UT MDI images from 1996 to 2010 (total 4962 images). We assume that only X, M and C class flares correspond to "flare occurrence" and the others to "non-flare". We have attempted to look for the best options for the models with two CNN pre-trained models (AlexNet and GoogLeNet), by modifying training images and changing hyper parameters. Our major results from this study are as follows. First, the flare occurrence predictions are relatively good with about 80 % accuracies. Second, both flare prediction models based on AlexNet and GoogLeNet have similar results but AlexNet is faster than GoogLeNet. Third, modifying the training images to reduce the projection effect is not effective. Fourth, skill scores of our flare occurrence model are mostly better than those of the previous models.

• 대한조선학회논문집
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• 제40권3호
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• pp.37-44
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• 2003

In rough seas, bow-flare regions of the sea-going ships are subject to high impact pressures due to the bow-flare slamming and panting. And many ships suffer structural damages in that region, even though they were built under the bow structure strengthening rules of the ship classes. So, a new design method for bow-flare structure is highly required. In this paper, bow-flare damage analysis is performed for 17 ships (total number of damage/non-damage data is 782). Based on this analysis, a new design standard and method for bow-flare structure (shell plate, frame and web frame) is proposed. 80.4% of the present damage/non-damage data were well-explained by this new design standard.

• 천문학회지
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• 제34권4호
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• pp.337-339
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• 2001

One-dimensional hydrodynamic modeling of a protostellar flare loop is presented. The model consists of thermally isolated loop connecting the central core and the accretion disk. We found that the conductive heat flux of a flare heated the accretion disk up to coronal temperature and consequently the disk is evaporated and disappeard. This effect may explain the ovserved feature of the repeated flare from the young stellar object YLW 15.

• 천문학회보
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• 제40권1호
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• pp.64.3-64.3
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• 2015

We have developed a set of daily solar flare peak flux forecast models for strong flares using multiple linear regression and artificial neural network methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray flare peak flux and weighted total flux of previous day, and mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classification. For a training data set, we use the same number of 61 events for each C-, M-, and X-class from Jan. 1996 to Dec. 2004, while other previous models use all flares. For a testing data set, we use all flares from Jan. 2005 to Nov. 2013. The best three parameters related to the observed flare peak flux are weighted total flare flux of previous day (r = 0.51), X-ray flare peak flux (r = 0.48), and Mount Wilson magnetic classification (r = 0.47). A comparison between our neural network models and the previous models based on Heidke Skill Score (HSS) shows that our model for X-class flare is much better than the models and that for M-class flares is similar to them. Since all input parameters for our models are easily available, the models can be operated steadily and automatically in near-real time for space weather service.

• 천문학회보
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• 제42권2호
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• pp.60.2-61
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• 2017

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.77 for flare classification and 0.83 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

• 천문학회보
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• 제36권2호
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• pp.98-98
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• 2011

We have investigated solar flare probability depending on sunspot classification, its area, and its area change using solar white light data. For this we used the McIntosh sunspot groups with most flare-productive regions : DKI, DKC, EKI, EKC, FKI and FKC. For each group, we classified it into three sub-groups according to sunspot area change : increase, steady, and decrease. For sunspot data, we used the NOAA active region information for 11 years (from January 2000 to December 2010): daily sunspot class and its area corrected for the projection effect. As a result, we find that the mean flare rates and the flare probabilities for the "increase" sub-groups are noticeably higher than those for other sub-groups. In case of the (M+X)-class flares of 'kc' groups, the mean flare rates of the "increase" sub-groups are more than two times than those of the "steady" sub-groups. This is statistical evidence that magnetic flux emergence is an very important for triggering solar flares since sunspot area increase can be a good proxy of magnetic flux emergence. In addition, we have examined the relationship between sunspot area and solar flare probability. For this, we classified each sunspot group into two sub-groups: large and small. In the case of compact group, the solar flare probabilities noticeably increase with its area.

• 천문학회보
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• 제41권1호
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• pp.82-82
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• 2016

To find the relationship between solar flares and halo CMEs using stereoscopic observations, we investigate 182 flare-associated halo CMEs among 306 front-side halo CMEs from 2009 to 2013. We have determined the 3D parameters (radial speed and angular width) of these CMEs by applying StereoCAT to multi-spacecraft data (SOHO and STEREO). For this work, we use flare parameters (peak flux and fluence) taken from GOES X-ray flare list and 2D CME parameters (projected speed, apparent angular width, and kinetic energy) taken from CDAW SOHO LASCO CME catalog. Major results from this study are as follows. First, the relationship between flare peak flux (or fluence) and CME speed is almost same for both 2D and 3D cases. Second, there is a possible correlation between flare fluence and CME width, which is more evident in 3D case than 2D one. Third, the flare fluence is well correlated with CME kinetic energy (CC=0.63). Fourth, there is an upper limit of CME kinetic energy for a given flare fluence (or peak flux). For example, a possible CME kinetic energy ranges from 1030.6 to 1033 erg for a given X1.0 class flare. Our results will be discussed in view of the physical mechanism of solar eruptions.