• Survey Research
• /
• v.7 no.2
• /
• pp.53-69
• /
• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.

• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.450-453
• /
• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 steps. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional (3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with it’s initial centroid value as the outstanding cluster’s centroid value. The proposed segmentation algorithm complements the defect of FCM algorithm, being influenced upon initial centroid, by calculating cluster’s centroid accurately And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the results of single spectral analysis.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.537-537
• /
• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

• Journal of Korean Society of Rural Planning
• /
• v.23 no.2
• /
• pp.1-7
• /
• 2017

The purpose of this study is to develop a farm management diagnostic checklist form, which can be applied to any crops. First, upper indexes and subordinate indexes were identified through survey with expert, and weighted values for each subordinate index were calculated through AHP analysis. Second, as a reuslt of Analytic Hierarchy Process (AHP) analysis, marketing management (0.276) was found to be the most important index of all upper indexes. In the case of subordinate indexes, reflecting management evaluation (0.252) of management consciousness, quality enhancement efforts (0.332) of production management, locating new sales outlets (0.323) of marketing management, agriculture accounting (0.300) of finance management, and adjusting shipping dates (0.274) of risk management were found to be the highest. Third, the interval division using weight of farm receiving prices was higher discrimination in comparison to equal interval division of weighted values for each index. The newly developed farm management diagnostic checklist can be applied to any crops, as it utilizes indexes such as management consciousness, production management, marketing management, financial management, risk management, etc. based on professional opinions. In addition, it allows an objective evaluation of farm management situations by utilizing the weighted value of farm receiving prices.

• The Journal of the Korea Contents Association
• /
• v.9 no.12
• /
• pp.683-690
• /
• 2009

The purpose of this study is to evaluate the usefulness of high-b-values diffusion weighted magnetic resonance imaging for the preoperative detection of focal rectum cancers. 60patients with diffusion weighted imaging were evaluated for the presence of rectal cancers. Forty were male and twenty were female, and their ages ranged from 38 to 71 (mean, 56) years. Used equipment was 1.5Tesla MRI((GE, General Electric Medical System, Excite HD). Examination protocols were used the fast spin echo T2, T1 weighted imaging. All examination protocols were performed by the same location with diffusion weighted imaging for accuracy detection. The b-values used in DWI were 250, 500, 750, 1000. 1500, 2000$(s/mm^2)$. The rectum, bladder to tumor contrast-to-noise ratio (CNR) of MR images were quantitativlely analyzed using GE software Functool tool, four experienced radiologists and three radiotechnologists qualitatively evaluated image quality in terms of image artifacts, lesion conspicuity and rectal wall. These data were analysed by using ANOVA and Freedman test with each b-value(p<0.05). Contrast to noise ratio of rectum, bladder and tumor in b-value 1000 were 27.21, 24.44, respectively(p<0.05) and aADC value was $0.73\times10^{-3}$. As a qualitative analysis, the conspicuity and discrimination from the rectal wall of lesions were high results as $4.0\pm0.14$, $4.4\pm0.16$ on b-value 1000(p<0.05), image artifacts were high results as $4.8\pm0.25$ on b-value 2000(p<0.05). In conclusion, DWI was provided useful information with depicting the pre-operative detection of rectal cancers, High-b-value 1000 image was the most excellent DWI value.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.2
• /
• pp.562-574
• /
• 2012

Simulation validation techniques which have been employed in most studies are statistical analysis, which validate a model with mean or variance of throughput and resource utilization as an evaluation object. However, these methods have not been able to ensure the reliability of individual elements of the model well. To overcome the problem, the weighted F-measure method was proposed, but this technique also had some limitations. First, it is difficult to apply the technique to complex system environment with numerous values of interarrival time because it assigns a class to an individual value of interarrival time. In addition, due to unbounded weights, the value of weighted F-measure has no lower bound, so it is difficult to determine its threshold. Therefore, this paper propose weighted F-measure technique with cluster analysis to solve these problems. The classes for the technique are defined by each cluster, which reduces considerable number of classes and enables to apply the technique to various systems. Moreover, we improved the validation technique in the way of assigning minimum bounded weights without any lack of objectivity.

• Journal of Environmental Impact Assessment
• /
• v.18 no.1
• /
• pp.31-39
• /
• 2009

A lot of data which are used in environment analysis of air pollution have characteristics that are distributed continuously in space. In this point, the collected data value such as precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When geostatistical data analysis are needed, acquisition of the value in every point is the best way, however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the unknown values at unsampled locations based on observations. In this study, spatial interpolation method such as local trend surface model, IDW(inverse distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation accuracy, range of estimated value, RMSE, average error were analyzed with observation data. The Kriging and RBF methods had the higher accuracy than others.

• Investigative Magnetic Resonance Imaging
• /
• v.20 no.4
• /
• pp.241-249
• /
• 2016

Purpose: To evaluate the added value of diffusion weighted imaging (DWI) to computed tomography (CT) for detecting pancreatic abnormality in patients with clinically suspected acute pancreatitis (AP). Materials and Methods: 203 patients who underwent abdomen CT and subsequent DWI to do a workup for epigastric pain were analyzed. Two blinded radiologists independently performed an interval reading based on CT image sets first, then based on combined CT and DWI image sets. The diagnostic criterion on DWI was the increased signal intensity in the pancreas to that of the spleen. For quantitative analysis, the third radiologist measured ADC value of the pancreas in each patient. Results: For AP (n = 43), the sensitivity for detecting pancreatic abnormality increased, from 42% to 70% for reader 1 (P < 0.05) and from 44% to 72% for reader 2 (P < 0.05). For borderline pancreatitis (n = 42), the sensitivity also increased, from 10% to 26% for reader 1 (P < 0.05) and from 7% to 29% for reader 2 (P < 0.05). The mean ADC values (unit, ${\times}10^{-3}mm^2/s$) were significantly different among the three groups (for AP, $1.09{\pm}0.16$; for borderline pancreatitis, $1.28{\pm}0.2$; for control, $1.46{\pm}0.15$, P < 0.05). Conclusion: Sensitivity for detecting pancreatic abnormality increased significantly after adding DWI to CT in patients with clinically suspected AP.

• The International Journal of Costume Culture
• /
• v.12 no.1
• /
• pp.38-51
• /
• 2009

The goal of this study is to define the types of the lifestyles of senior consumers and identify the differences in the properties of apparel products and the apparel attitudes. To collect the data for this study, questionnaires for the research were distributed from November 20, 2006 to December 15, 2006 to those over 50 living in Seoul, Pusanand Kyunggi and 302 questionnaires were used for the data analysis. The results of the study are as follows. First, six factors were extracted which were "Pursuit of Self-development", "Pursuit of Active Life", "Pursuit of Material", "Pursuit of Diversity", "Pursuit of Family-oriented" and "Pursuit of Recreational Life" after factor analysis of lifestyles recognized by the senior consumers that participated in this study. Second, the lifestyles of the senior consumers were categorized into "Consumption-oriented Type", "Personal Satisfaction-oriented Type", "Family Weighted Type" and "Recreation-oriented Type." Third, three factors were extracted which were "Symbolical Property", "Functional Property" and "Customer Support Property" after conducting the factor analysis on the properties of apparel products. Fourth, significant differences were shown in apparel properties by the lifestyle types of senior consumers in the symbolical property and the customer support property. The "symbolical property" was shown highest in "recreation-oriented type" and lowest in the "family weighted type." The customer support property was shown highest in the "family weighted type" and lowest in the "recreation-oriented type", showing the opposite result. Fifth, significant differences were shown in apparel attitudes by the lifestyle types of senior consumers in "Fashion Innovativeness", "Apparel Involvement" and "Apparel Necessity." The "fashion innovativeness" was shown highest in the "recreation-oriented type" and lowest in the "family weighted type." The apparel involvement and the necessity for apparel for senior citizens was shown high in the "recreation-oriented type" and this showed that the senior consumers valuing recreation also value fashion, have high apparel involvement and feel the necessity for apparel for senior citizens.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.19
• /
• pp.8271-8277
• /
• 2014

Aims: Apparent diffusion coefficient (ADC) values of nodes in diffusion-weighted imaging (DWI) are widely used in differentiating metastatic from non-metastatic lymph nodes. The purpose of this meta-analysis was to demonstrate whether DWI could contribute to the precise diagnosis of breast cancer (BC) with and without lymph node metastasis (LNM). Materials and Methods: English and Chinese electronic databases were searched for relevant studies followed by a comprehensive literature search. Two reviewers independently assessed the methodological quality of the included trials based on the quality assessment of diagnostic accuracy studies (QUADAS). Summary odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were calculated. Results: Final analysis of 624 BC subjects (patients with LNM = 254, patients without LNM = 370) were incorporated into the current meta-analysis from 9 eligible cohort studies. Combined ORs of ADCs suggested that ADC values in BC patients without LNM were higher than in patients with LNM (OR=0.56, 95%CI: 0.11-1.01, p=0.015). Subgroup analysis stratified by country indicated a low ADC value in BC patients with LNM rather than those without LNM among Chinese (OR=1.27, 95%CI: 0.89-1.66, p<0.001), Italians (OR=0.75, 95%CI: 0.13-1.38, p=0.018), and Egyptians (OR=1.27, 95%CI: 0.71-1.84, p<0.001). The findings of subgroup analysis by MRI machine type revealed that ADC values from diffusion MRI may be potential diagnostic indicators for BC using Non-Philips 1.5T (OR=1.10, 95%CI: 0.84-1.36, p<0.001). Conclusions: The main findings of our meta-analysis demonstrated that increased signal intensity on DWI and decreased signals on ADC are helpful in diagnosis of BC patients with or without LNM. DWI could therefore be an important imaging investigation in patients suspected of BC.