• 혜화의학회지
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• 제16권2호
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• pp.199-210
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• 2007

Objective & Methods : The case report is to observe 27 case of venesection with negative pressure therapy(buhang) operated at CV17 (Danjuong) on sudden palpitation. We carried out to analyze the distribution and Martin's evaluation and weighted scale by sex & age, patient conditional grade, accompanied symptoms, duration, number of treatments as well. Results : 1. Forties is significantly distributed on sudden palpitation. 2. Depression instance out of many accompanied symptoms is significantly distributed. 3. Mean of operant frequency is significantly resulted on 10 or 11 times. 4. The improvement on the weighted scale is over one grade. 5. Younger is high on the assessment of treatment on Martin's evaluation and weighted scale by age, while older is low. 6. Lesser one year and more three years is some significantly resulted on the assessment of treatment on Martin's evaluation and weighted scale by duration. 7. Pleurodynia, insomnia, distress in the stomach are effective more 90%, and depression, asthma are more 80% on the assessment of treatment on Martin's evaluation by accompanied symptoms, while asthma and pleurodynia are very significantly resulted on the assessment of treatment on weighted scale. 8. The assessment of treatment on Martin's evaluation and weighted scale have no connection with operant frequency. Conclusion : These results suggest that venesection with negative pressure therapy(buhang) operated at CV17 (Danjuong) has a therapeutic effect on on sudden palpitation.

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.91-102
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• 2019

In this paper, we propose a new estimation method based on a weighted linear regression framework to obtain some estimators for unknown parameters in a two-parameter Rayleigh distribution under a progressive Type-II censoring scheme. We also provide unbiased estimators of the location parameter and scale parameter which have a nuisance parameter, and an estimator based on a pivotal quantity which does not depend on the other parameter. The proposed weighted least square estimator (WLSE) of the location parameter is not dependent on the scale parameter. In addition, the WLSE of the scale parameter is not dependent on the location parameter. The results are compared with the maximum likelihood method and pivot-based estimation method. The assessments and comparisons are done using Monte Carlo simulations and real data analysis. The simulation results show that the estimators ${\hat{\mu}}_u({\hat{\theta}}_p)$ and ${\hat{\theta}}_p({\hat{\mu}}_u)$ are superior to the other estimators in terms of the mean squared error (MSE) and bias.

• 대한건축학회논문집:계획계
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• 제35권1호
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• pp.47-54
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• 2019

This study proposes a new ERAM model with building population weighting. Previous studies of applying weightings on ERAM model on the scale of urban space were focused on the relationship between the street and the human behavior. However, this study focuses on the influences that buildings give to human behavior and develops a building population weighted ERAM model. This research starts by analyzing ERAM model to its basic compositions, which are adjacency matrix and row vector. It applies building population weighting to the row vector, while previous studies put weightings in the adjacency matrix. Building population weighted ERAM model calculates the building population weighting based on GIS data, which provides objective and massive data of buildings in the urban scale. For the verification of the model, Insa-dong and Myeong-dong were analyzed with both ERAM model and building population weighted ERAM model. The results were analyzed through the correlation test with actual pedestrian population data of the two districts. As a result, the explanation ability of building population weighted ERAM model for the pedestrian population turned out to be higher than the ERAM model. Since building population weighted ERAM model has the structure that can be combined with other weighted ERAM models, it is expected to develop a multi-weighted ERAM model with better explanation ability as a further study.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.255-256
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• 2008

Distributed localization algorithms are required for large-scale wireless sensor network applications. In this paper, we introduce an efficient algorithm, termed weighted neighbor-node distribution localization(WNDL), which emphasizes simple refinement and low system-load for low-cost and low-rate wireless sensors. We inspect WNDL algorithm through MATLAB simulation.

• Journal of the Korean Data and Information Science Society
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• 제26권2호
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• pp.535-545
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• 2015

In a various range of applications including hydrology, the type-I extreme value distribution has been extensively used as a probabilistic model for analyzing extreme events. In this paper, we introduce methods for estimating the scale parameter of the type-I extreme value distribution. A simulation study is performed to compare the estimators in terms of mean-squared error and bias, and the obtained results are provided.

• Journal of the Korean Statistical Society
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• 제32권3호
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• pp.289-298
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• 2003

We consider the estimation problem for the scale parameter of the Rayleigh distribution using weighted balanced loss function (WBLF) which reflects both goodness of fit and precision. Under WBLF, we obtain the optimal estimator which creates a kind of balance between Bayesian and non-Bayesian estimation. We also deal with the estimation of R ＝ P(Y < X) when Y and X are two independent but not identically distributed Rayleigh distribution under squared error loss function.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.313-320
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• 1999

In this paper, a translation, rotation and scale invariant system for the classification of closed 2D images using the bispectrum of a contour sequence and the weighted fuzzy mean method is derived and compared with the classification process using one of the competitive neural algorithm, called a LVQ(Learning Vector Quantization). The bispectrun based on third order cumulants is applied to the contour sequences of the images to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images and are fed into an classifier using weighted fuzzy mean method. The experimental processes with eight different shapes of aircraft images are presented to illustrate the high performance of the proposed classifier.

• 대한원격탐사학회지
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• 제33권4호
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• 수산경영론집
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• 제47권1호
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• pp.87-100
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• 2016

Suhyup Bank became to be subject to regulation of capital ratio by Basel III which was introduced in order to enhance stability of the financial institution. Accordingly, Suhyup Bank will require recapitalization. It is important to estimate the risk-weighted assets in calculating of Suhyup Bank's recapitalization scale. Therefor, this study aimed to present a scientific model as estimated the risk-weighted assets. Risk-weighted assets are calculated by applying different risk weights for loans, may have a certain relationship with the loans. Results show that the risk-weighted assets is affected by the previous year's risk-weighted assets and influenced the increase in loans during the year. Since the required basic capital adequacy ratio was specified, the risk-weighted assets should be predicted reasonably. Accordingly, on this study it was tried to derive the accounting equation to predict the risk-weighted assets based on management data of a bank since introduction of Basel III. As the risk-weighted assets were weighted differently according to the type of loans, if the accounting equation is derived by using the type of loans, then it would be helpful for the risk management of banks in the long-term. According to this, the increase of loan would be predicted on the basis of past management performance of Suhyup Bank, and for this reason, the future risk-weighted assets of Suhyup Bank were predicted. The result of this study was showed that 98.3% of risk-weighted assets of the previous year, 62.4% of the secured loan changes and 95.1% of the credit loan changes affected risk-weighted assets.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1877-1885
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• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.