• Journal of Wetlands Research
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• v.16 no.1
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• pp.103-112
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• 2014

Area average rainfall estimation is important to determine the exact amount of the available water resources and the essential input data for rainfall-runoff analysis. Like that, the necessary criterion for accurate area average rainfall estimate is the uniform spatial distribution of raingauge network. In this study, we suggest the spatial distribution evaluation methodology of raingauge network to estimate better area average rainfall and after the suggested method is applied to Han River and Geum River basin. The spatial distribution of rainfall network can be quantified by the nearest neighbor index. In order to evaluate the effects of the spatial distribution of rainfall network by each basin, area average rainfall was estimated by arithmetic mean method, the Thiessen's weighting method and estimation theory for 2013's rainfall event, and evaluated the involved errors by each cases. As a result, it can be found that the estimation error at the best basin of spatial distribution was lower than the worst basin of spatial distribution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.263-269
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• 2017

'Goal Model' is core knowledge of 'Autonomic Control System' suggested to minimize human interference in system management. 'Autonomic Control System' performs 'Monitoring-Analysis-Plan-Execution', that is the four step of 'Autonomic Control', based on 'Goal Model'. Therefore, it is necessary to quantify achievement ratio of 'Goal Model' of target system. Thus, this paper present 'Probabilistic Evaluation of Goal Model' for methodology how to quantify achievement ratio of 'Goal Model'. It comprises 3-steps including 'Goal modeling and weighting', 'Goal model monitoring', 'Goal model evaluation and analysis'. Through these research, we provide core knowledge for 'Autonomic Control system' and it is possible to increase the reliability of system by evaluating 'Goal model' with applying weight. As case study, we apply 'Goal model' to a 'Smart IoT Kit' and we demonstrate the validity of the suggested research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.69-74
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• 2018

Recently, personal information leakage has caused phishing and spam. Previously developed systems focus on preventing personal information leakage. Therefore, there is a problem that the leakage of personal information can not be discriminated if there is already leaked personal information. In this paper, we propose a personal information hazard classification system based on web document analysis that calculates the hazard. The system collects web documents from the Twitter server and checks whether there are any user-entered search terms in the web documents. And we calculate the hazard classification weighting of the personal information leaked in the web documents and confirm the authority of the Twitter account that distributed the personal information. Based on this, the hazard can be derived and the user can be informed of the leakage of personal information of the web document.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.4
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• pp.456-462
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• 2014

In this paper, we develop a facility management system which provides fault effect and replacement alarm function of each component for supporting effective maintenance of facility. To do this, we use weighting method to each component, calculate importance of each component, and make them to hierarchy structure using bill of materials of facility. Also, we draw fault cause and fault effect of components based on failure modes effects and criticality analysis, and define criteria of severity, occurrence and detection to get risk priority number. To apply these concepts, we develop and test the facility management system to verify its practicality. In the future, we expect the developed system to apply many domains such as maintenance of ship and offshore plant.

• Spatial Information Research
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• v.19 no.2
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• pp.29-36
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• 2011

This study performed quantitative assessment of the performance of adjustment models by a-priori analysis of the statistics of the surface parameter estimates against modeling factors. Lidar, airborne imagery, and SAR imagery have been used to acquire the earth surface elevation, where the shape properties of the surface need to be determined through neighboring observations around target location. In this study, parameters which are selected to be estimated are elevation, slope, second order coefficient. In this study, several factors which are needed to be specified to compose adjustment models are classified into three types: mathematical functions, kernel sizes, and weighting types. Accordingly, a-priori standard deviations of the parameters are computed for varying adjustment models. Then their corresponding confidence regions for both the standard deviation of the estimate and the estimate itself are calculated in association with probability distributions. Thereafter, the resulting confidence regions are compared to each other against the factors constituting the adjustment models and the quantitative performance of adjustment models are ascertained.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.243-248
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• 2013

In this paper, depth of foreground is analysed by focus and color analysis grouping for 2D/3D video conversion and depth of foreground progressing method is preposed by using focus and motion information. Candidate foreground image is generated by estimated movement of image focus information for extracting foreground from 2D video. Area of foreground is extracted by filling progress using color analysis on hole area of inner object existing candidate foreground image. Depth information is generated by analysing value of focus existing on actual frame for allocating depth at generated foreground area. Depth information is allocated by weighting motion information. Results of previous proposed algorithm is compared with proposed method from this paper for evaluating the quality of generated depth information.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.1-14
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• 2006

This study proposed a customer preference estimation model for production recommendation and a method to enhance the performance of product recommendation using the estimated customer preference information. That is, we suggested customer preference estimation model to estimate exactly customer's product preference with his behavior. This model shows the relationship of customer's behaviors with his preferences. The proposed estimation model is optimized by learning the relative weights of customer's behavior variables to have an effect on his preference and enables to estimate exactly his preference. To validate our proposed models, we collected virtual book store data and then made a comparative analysis of our proposed models and a benchmark model in terms of performance results of collaborative filtering for product recommendation. The benchmark model means a prior preference weighting model. The results of our empirical analysis showed that our proposed model performed better results than the benchmark model.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.255-268
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• 2019

Taking top-coal caving mining face (TCCMF) as research object, this paper considers the combination of top-coal and immediate roof as cushion layer to build the solution model of support resistance based on the theory of elastic foundation beam. Meanwhile, the physical and mechanical properties of coal-rock combination influencing on strata behaviors is explored. The results illustrate that the subsidence of main roof in coal wall increases and the first weighting interval decreases with the increase of top-coal and immediate roof thicknesses as well as the decrease of top-coal and immediate roof elastic modulus. Moreover, the overlying strata reflecting on support has negative and positive relationship with top-coal thickness and immediate roof thickness, respectively. However, elastic modulus has limit influence on the dead weight of top-coal and immediate roof. As a result, it has similar roles on the increase of total support resistance and overlying strata reflecting on support in the limit range of roof control distance. In view of sensitive analysis causing the change of total support resistance, it can be regards as the rank of three components as immediate roof weight > overlying strata reflecting on support > top coal weight. Finally, combined with the monitoring data of support resistance in Qingdong 828, the validity of support resistance determined based on elastic foundation beam is demonstrated, and this method can be recommended to adopt for support type selecting in TCCMF.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.217-223
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• 2019

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.307-318
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• 2021

The objective of this study is to analyze the performance of Islamic banks with the Maqashid Shariah approach. The analysis technique used is the Simple Additive Weighting Method (SAW) to solve multi-attribute decision problems. The sampling technique used was purposive sampling while the data came from the annual report of each bank. The results showed that the BTPN Shariah (BTPNS) and Bank Muamalat Indonesia (BMI) are ranked first and second respectively on the Maqashid Shariah Index (MSI) with values of 0.265429 and 0.237110 respectively. Panin Dubai Shariah Bank (PDSB) ranked third with an MSI value of 0.180733, followed by BCA Shariah which ranked fourth with an MSI value of 0.151299. BRI Shariah ranked fifth with an MSI value of 0.128606, followed by BNI Shariah which ranked sixth with an MSI value of 0.124661. Bank Mega Shariah ranked last with an MSI value of 0.087068. Furthermore, there is a relationship (correlation) between ROE, ROA, and OEOI and MSI since each data has a value of 0.000, 0.000, 0.050, and 0.001 respectively, which is smaller than the significance value of 0.05. On the other hand, NPF, TPF, and Asset Growth Rates do not correlate with the MSI since each data has a value of 0.051, 0.252, and 0.215 respectively which is greater than the significance value of 0.05.