• Journal of The Korean Astronomical Society
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• v.37 no.4
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• pp.151-157
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• 2004

It is investigated quantitative relations between the magnetic storm magnitude and the solar wind parameters such as the Interplanetary Magnetic Field (hereinafter, IMF) magnitude (B), the southward component of IMF (Bz), and the dynamic pressure during the main phase of the magnetic storm with focus on the role of the interplanetary shock (hereinafter, IPS) in order to build the space weather fore-casting model in the future capable to predict the occurrence of the magnetic storm and its magnitude quantitatively. Total 113 moderate and intense magnetic storms and 189 forward IPSs are selected for four years from 1998 to 2001. The results agree with the general consensus that solar wind parameter, especially, Bz component in the shocked gas region plays the most important role in generating storms (Tsurutani and Gonzales, 1997). However, we found that the correlations between the solar wind parameters and the magnetic storm magnitude are higher in case the storm happens after the IPS passing than in case the storm occurs without any IPS influence. The correlation coefficients of B and $BZ_(min)$ are specially over 0.8 while the magnetic storms are driven by IPSs. Even though recently a Dst prediction model based on the real time solar wind data (Temerin and Li, 2002) is made, our correlation test results would be supplementary in estimating the prediction error of such kind of model and in improving the model by using the different fitting parameters in cases associated with IPS or not associated with IPS rather than single fitting parameter in the current model.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.141-147
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• 2018

This study analyzed the early retired employees who stayed in company no longer than 3 years based on a certain company's personality evaluation result data. The predicted model was analyzed by dividing into two categories; the manufacture group and the R&D group. Independent variables were selected according to the stepwise method. A logistic regression model was selected as a prediction model among various supervised learning methods, and trained through cross-validation to prevent over-fitting or under-fitting. The accuracy of the two groups were confirmed by the confusion matrix. The most influential factor for early retirement in the manufacture group was revealed as "immersion," and for the R&D group appeared as "antisocial." In the past, people concentrated on collecting data by questionnaire and identifying factors that are highly related to the retirement, but this study suggests a sustainable early retirement prediction model in the future by analyzing the tangible outcome of the recruitment process.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.542-551
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• 2000

In this paper, a novel technique is presented for automatic brain region segmentation in single channel MR image data sets for 3D visualization and analysis. The method detects brain contours in 2D and 3D processing of four steps. The first and the second make a head mask and an initial brain mask by automatic thresholding using a curve fitting technique. The stage 3 reconstructs 3D volume of the initial brain mask by cubic interpolation and generates an intermediate brain mask using morphological operation and labeling of connected components. In the final step, the brain mask is refined by automatic thresholding using curve fitting. This algorithm is useful for fully automatic brain region segmentation of T1-weighted, T2-weighted, PD-weighted, SPGR MRI data sets without considering slice direction and covering a whole volume of a brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 in comparison with manual drawing in similarity index.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.249-252
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• 2008

The solutions are expressed in terms of a double integral transformation over wavenumber and frequency. The complete solution is considered in such a full wave theory approach. This method can handle a larger number of plane layers. Therefore, the result of FK method is very similar to real data. Using the models that were modified in velocity and Q value with depth by iterative process from a model (Kang and Park, 2006) and considered as one of the best models in Korean Peninsula, the synthetic data are simulated for explosions and earthquakes of North Korea. This study notes that the wave shape of the synthetic data is very dependent on Q value, velocities, and thickness of sedimentary layers. Comparing between the real and the synthetic, fitting well in arrival time of first arrival and wave shape causes us to arrive at an indication that the model is very close representation of upper crustal structure and simulations are well done in amplitude fitting and in identification of phases of local and regional waves.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.235-238
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• 2007

본 논문에서는 기존의 클러스터 기반 퍼지 모델트리에서 트리의 깊이에 따른 over-fitting으로 인한 훈련 및 검증데이터의 일관성 문제점을 해결하기 위해 상호 노드간의 정보를 고려하는 방법을 제안하고자 한다. 제안된 방법은 우선 입력과 출력변수의 속성을 고려한 퍼지 클러스터링에 의해 중심벡터를 계산한 후, 중심벡터들과 입력 속성간의 소속도를 이용하여 구간 분할된 영역별로 각각의 선형모델을 구축한다. 예측 단계에서는 입력된 데이터가 잎노드에 도달하는 노드간의 중심벡터와 입력 데이터간의 거리값에 따른 소속도를 계산한 후 최종적으로 무게 중심법을 이용하여 출력값을 예측하게 된다. 제안된 방법의 우수성을 보이기 위해 다양한 벤치마크 데이터를 대상을 실험한 결과, 기존의 클러스터 기반 퍼지 모델트리보다 향상된 성능을 보임을 알 수 있었다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.183-186
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• 2007

Hyperspectral remote sensing data contain plenty of information about objects, which makes object classification more precise. Over the past several years, different algorithms for the classification of hyperspectral remote sensing images have been developed. In this study, we proposed method based on absorption band extraction and Gravity type model to solve hyperspectral image classification problem. In contrast to conventional methods that are based on correlation techniques, this method is simple and more effective. The proposed approach was tested to evaluate its effectiveness. The evaluation was done by comparing the results of preexiting SFF(Spectral Feature Fitting) classification method. The evaluation results showed the proposed approach has a good potential in the classification of hyperspectral images.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2001.11a
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• pp.134-137
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• 2001

The cure kinetics of a cycloalipatic epoxy / anhydride / Co(II) system for a no-flow underfill encapsulant, has been studied by using a differential scanning calorimetry(DSC) under isothermal and dynamic conditions over the temperature range of $160^{\circ}C ~220^{\circ}C$. The kinetic analysis was carried out by fitting dynamic/isothermal heating experimental data to the kinetic expressions to determine the reaction parameters, such as order of reaction and reaction constants. Diffusion-controlled reaction has been observed as the cure conversion increases and successfully analyzed by incorporating the diffusion control term into the rate equation. The prediction of reaction rates by the model equation corresponded well to experimental data at all temperature.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.189-194
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• 2002

As a environment of SCM is issued recently, many enterprises are concerned about inventory control for an efficiency of operation and reliablity. The goal of them is to match up to improving of facilities and redoing of inventory cost. To put an efficiency in the operation rate of facilites, A moderate provision of spare parts for that would come into a matter. This study designs two-echelon spare parts distribution system and sees over the relationship between operating rate and inventories of spare parts. and then it determines fitting inventories and investment cost considering an operating rate.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.80-90
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• 1994

In this paper, a high-resolution algorithm for detecting the orientation and position of the SMD and an algorithm for compensating the position and skew angle of the PCB are proposed. The proposed algorithm for the first topic consists of two parts. Its first part is a preprocessing step. in which corner points of the SMD are detected and they are grouped. Then the coarse angle of the principal axis is obtained by line fitting. The second part is a main processing step, in which the fuzzy Hough transform over the limited range of angles is applied to the corner points to detect precisely the orientation of the SMD. The position of the SMD is determined by using its four corner points. The proposed algorithm for the second topic is the one which detects a rotation angle and translation parameters of the PCB using a template matching method. The computer simulation shows that the parametes obtained by proposed algorithms are more precise than those by the several conventional methods considered. The proposed algorithms can be applied to the fast and accurate automatic inspection systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.787-790
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• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.