• 천문학회지
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• 제18권1호
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• pp.1-13
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• 1985

We present the data of photoelectric photometric observations of BW Vul carried out for four nights during the period of $1982{\sim}1984$. The light curves with asymmetric shape show a stillstand on the ascending branch at phase of ${\phi}{\approx}0.85$ just before the maximum light, and also the ampitude and shape of light curves are changed from night to night. Using all the published data, a new ephemeris of maximum time is derived, in which the period of light variation is $P=0^d.20102977$ and its increasing rate is 2.2 see/century.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.295-305
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• 2002

We shall propose several estimators for the shape and scale parameters in a generalized uniform distribution when both parameters are polynomial of a known exposure level, and obtain expectations and variances for their proposed estimators. And we shall compare numerically efficiencies for the several proposed estimators for the shape and scale parameters in a generalized uniform distribution in the small sample sizes.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2004년도 학술대회 논문집
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• pp.309-313
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• 2004

This paper presents an algorithm of detecting changed intervals with step-type shape in motor's speed response data. The proposed method is composed of 4 parts such as noise filtering, decision making of reference value's change, finding entrance point of steady state and detecting changed intervals. According to simulation results for three cases, we see that changed intervals can be found well.

• 품질경영학회지
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• 제27권4호
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• pp.153-166
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• 1999

In this paper, we address the Bayesian hypotheses testing for the shape parameter of weibull model. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors and use these results to analyze real data sets.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1107-1117
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• 2005

We consider the problem of estimating the scale and shape parameters in the Weibull distribution based on censored samples. We propose the approximate maximum likelihood estimators (AMLEs) of the scale and shape parameters in the Weibull distribution based on Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error (MSE).

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.586-590
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• 1990

In this paper we describe the shape measuring technique and system with a non-contractive sensor, composed of slit-ray projector and solid-state camera. For improving the accuracy and preventing measuring dead point, this sensor part is attached to the end of robot, and each sensing is executed after one step moving. By patching these sensing data, whole measuring data is constructed. The calibration between sensor and world coordinate is implemented through the specific calibration block by transformation matrix method. The result of experiment was satisfactory.

• 한국정밀공학회지
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• 제30권6호
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• pp.601-606
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• 2013

3D scanning is a technique to measure the 3D shape information of the object. Shape information obtained by 3D scanning is expressed either as point cloud or as polygon mesh type data that can be widely used in various areas such as reverse engineering and quality inspection. 3D scanning should be performed as accurate as possible since the scanned data is highly required to detect the features on an object in order to scan the shape of the object more precisely. In this study, we propose the method on finding the location of feature more accurately, based on the extended Biplane SNAKE with global optimization. In each iteration, we project the feature lines obtained by the extended Biplane SNAKE into each image plane and move the feature lines to the features on each image. We have applied this approach to real models to verify the proposed optimization algorithm.

• 건축역사연구
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• 제6권3호
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• pp.61-76
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• 1997

A study on the traditional houses of North Korea(I) have been published in 1996. This paper is written to supplement the preceding paper. This paper aims at collecting new data of traditional house in North Korea. But still being prohibited for the researchers of South Korea to approach to the field, I had to depend on the memories and experiences of the immigrants from North Korea who are now living in Kangwon and Incheon Province. Through the questionnaire and drawings, they described vivid memory of their old houses. I was able to add new data of 70 cases, which are significant and valuable as much as those of the real field are. Those data, including the exisiting data, are enough for me to analize statstically the regional charateristics, the differnces among economical classes, and the periodical change. It opens the way for verfying the existing theory. Regional charateristics of house in North Korea can be described as followings: a. Hamkyong-do ; Concentrating spaces into one building, Double-fold type plan, Including 'cheongju-kan' space, Weak fences b. Pyongan-do ; Concentrating spaces into two buildings, 二 shape buildings , Single-fold type plan, Strong fence c. Pyongannam-do to Myolak mountains; Concentraing spaces into two buildings, ㄱ, ㄷ shape buildings, Single-fold type plan, Strong fence d. Southern area of Myolak mountains; Concentrating spaces into one building ㅁ shape building, Single-fold type plan with wooden floor space

• 한국측량학회지
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• 제22권2호
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• pp.179-186
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• 2004

본 연구는 산업 사진측량을 이용하여 자동차의 외형을 정밀 측정하고, 사고 등의 원인으로 변형이 발생하였을 때 변형량을 측정하는 방법에 관한 것이다. 자동차의 외형을 정밀 측정하기 위하여 점군데이터를 취득할 수 있는 프로젝션 타겟을 사용함으로써 보다 곡면에 대한 정밀한 측정이 가능하도록 하였으며, 대상물 촬영 및 좌표해석을 통하여 타겟의 3차원 좌표를 취득하였다. 취득된 점군데이터는 불규칙 삼각망으로 표면을 형성하여 3차원 모델링을 하였다. 또한, 변형 발생부위를 정밀 측정함으로써 교통사고 분석 등에 효율적이고 정밀한 데이터를 제공할 수 있을 것이다.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.