• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.28-37
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• 2015

Owing to the variability of large-scaled wind power system, the development of wind farm management technologies and related compensation methods have been receiving attention. To provide an accurate and reliable output power, certain wind farm adopts a specified management system including a wind prediction model and grid expectation solutions for considering grid condition. Those technologies are focused on improving the reliability and stability issues of wind farms, which can affect not only nearby system devices but also a voltage condition of utility grid. Therefore, to adapt the develop management system, an expectation process about voltage condition of Point of Common Coupling should be integrated in operating system for responding system requirements in real-time basis. This paper introduce a grid imposing method for a real-time based wind farm management system. The expected power can be transferred to the power flow section and the required quantity about reactive power can be calculated through the proposed system. For the verification process, the gauss-seidel method is introduced in the Matlab/Simulink for analysing power flow condition. The entire simulation process was designed to interwork with PSCAD for verifying real power system condition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.67-71
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• 2009

Using driving simulator, we analyzed the driving behavior of an older driver on intersection and measured the pychological load to HRV. As a results, older drivers started to enter the more complex intersection on a great distance and on low velocity for safety driving. On the other hand, the value of HRV would be lower on more complex intersections. It suggested that an older driver allowed for his own losses of physiological and cognitive function and recognized low level of driving confidence relatively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.161-170
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• 2008

Interest is growing in applying simulation models for the South Texas conditions, to better assess crop water use and production with different crop management practices. The Environmental Policy Integrated Climate (EPIC) model was used to evaluate its application as a decision support tool for irrigation management of com (Zea mays L.) in South Texas of the U.S. We measured actual crop evapotranspiration (ETc) using a weighing lysimeter, soil moisture using a neutron probe, and grain yield by field sampling. The model was then validated using the measured data. Simulated ETc using the Hargreaves-Samani equation was in agreement with the lysimeter measured ETc. Simulated soil moisture generally matched with the measured soil moisture. The EPIC model simulated the variability in grain yield with different irrigation regimes with $r^2$value of 0.69 and root mean square error of $0.5\;ton\;ha^{-1}$. Simulation results with farm data demonstrate that EPIC can be used as a decision support tool for com under irrigated conditions in South Texas. EPIC appears to be effective in making long term and pre-season decisions for irrigation management of crops, while reference ET and phenologically based crop coefficients can be used for inseason irrigation management.

• International Journal of Reliability and Applications
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• v.8 no.1
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• pp.111-124
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• 2007

In this paper, we apply insightful statistical reliability tools to manage and seek improvements in the strengths of Oriented Strand Board (OSB). As a part of the OSB manufacturing process, the product undergoes destructive testing at various intervals to determine compliance with customers' specifications. Workers perform these tests on sampled cross sections of the OSB panel to measure the tensile strength, also called internal bond (IB), in pounds per square inches until failure. Additional stiffness strength tests include parallel and perpendicular elasticity indices (EI), which are taken from cross sectional samples of the OSB panel in the parallel and perpendicular directions with respect to the orientation of the wood strands. We explore both graphically and statistically these "pressure-to-failures" of OSB. Also, we briefly comment on reducing sources of variability in the IB and EI of OSB.

• International Journal of Reliability and Applications
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• v.8 no.2
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• pp.153-173
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• 2007

Wood-plastic composites (WPC) are gaining market share in the building industry because of durability/maintenance advantages of WPC over traditional wood products and because of the removal of chromated copper arsenate (CCA) pressure-treated wood from the market. In order to ensure continued market share growth, WPC manufacturers need greater focus on reliability, quality, and cost. The reliability methods outlined in this paper can be used to improve the quality of WPC and lower manufacturing costs by reducing raw material inputs and minimizing WPC waste. Statistical methods are described for analyzing stiffness (tangent modulus of elasticity: MOE) and flexural strength (modulus of rupture: MOR) test results on sampled WPC panels. Descriptive statistics, graphs, and reliability plots from these test data are presented and interpreted. Sources of variability in the MOE and MOR of WPC are suggested. The methods outlined may directly benefit WPC manufacturers through a better understanding of strength and stiffness measures, which can lead to process improvements and, ultimately, a superior WPC product with improved reliability, thereby creating greater customer satisfaction.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.47-63
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• 2019

The volatility of major crypto currencies was examined and they are diagnosed whether they have a systematic risk or not, by estimating market beta representing systematic risk using GARCH( Generalized Auto Regressive Conditional Heteroskedastieity) model. First, the empirical results showed that their prices are very volatile over time because of the existence of ARCH and GARCH effects. Second, in terms of efficiency, asymmetric GJR model was estimated to be the most appropriate model because the standard error of a market beta was less than that of the OLS model and GARCH model. Third, the estimated market beta of Bitcoin using GJR model was less than 1 at 0.8791, showing that there is no systematic risk. However, unlike OLS model, the market beta of Ethereum and Ripple was estimated at 1.0581 and 1.1222, showing that there is systematic risk. This result shows that bitcoin is less dangerous than Ripple and Ethereum, and ripple is the most dangerous of all three crypto currencies. Finally, the major cryptocurrency found that the negative impact caused greater variability than the positive impact, causing bad news to fluctuate more than good news, and therefore good news and bad news had a different effect on the variability.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.343-350
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• 2016

Remaining lifetime prediction of the underground gas pipeline plays a key role in maintenance planning and public safety. One of main causes in the pipeline failure is metal corrosion. This paper deals with estimating the pipeline reliability in the presence of corrosion defects. Because a pipeline has uncertainty and variability in its operation, probabilistic approximation approaches such as first order second moment (FOSM), first order reliability method (FORM), second order reliability method (SORM), and Monte Carlo simulation (MCS) are widely employed for pipeline reliability predictions. This paper presents a fuzzy inference based reliability method (FIRM). Compared with existing methods, a distinction of our method is to incorporate a fuzzy inference into quantifying degrees of variability in corrosion defects. As metal corrosion depends on the service environment, this feature makes it easier to obtain practical predictions. Numerical experiments are conducted by using a field dataset. The result indicates that the proposed method works well and, in particular, it provides more advisory estimations of the remaining lifetime of the gas pipeline.

• Journal of Distribution Science
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• v.15 no.11
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• pp.21-29
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• 2017

Purpose - Relationship between farm and county losses determines whether the county program provides too little, too much, or similar amount of assistance relative to the loss on an individual farm. A review of the literature finds limited analysis of the determinants of this relationship. This paper conducts such an analysis using farm-level yield data. Research design, data, and methodology - Farm-level yield data from Illinois and Kansas farm business management associations are used for to calculate the correlation between farm and county loss and the share of farm loss systemic with county loss, and also for the regression analysis. Results - Average share of farm loss systemic with the county loss lies between 42% and 68%. The correlation between farm and county yield/revenue deviation from expected value is statistically significant in all four models. The coefficient is positive, implying the higher the correlation, the larger the share of farm loss that is systemic with the county loss. Conclusions - The findings of this study are consistent with the existing literature which argues that county variability may not be closely associated with farm variability. The findings of this study thus raise questions about the efficacy of area yield and revenue insurance products in helping farmers manage their risk.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.1
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• pp.63-71
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• 2008

Local meteorological conditions exert a strong influence over day-to-day variations in air pollutant concentrations. Therefore, the meteorological fluctuations have to be removed in order for air quality planners and managers to examine underlying emissions-related trends and make better air quality management decisions for future. In this study, the meteorologically adjusted $PM_{10}$ trends in Seoul are investigated over the period $1999{\sim}2006$ using Kolmogorov-Zurbenco (KZ) filter. The result indicated that meteorologicaJ variability accounts for about 25% of $PM_{10}$ variability. Both the meteorologically adjusted and unadjusted Jong-term daily $PM_{10}$ concentrations had a significant downward trends and the difference between the meteorologically adjusted and unadjusted was small. So it was assumed that in long-term daily $PM_{10}$ changes, localized changes in emissions is more important than the changes caused by meteorological conditions.

• Environmental Engineering Research
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• v.19 no.3
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• pp.223-228
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• 2014

We investigated seasonal algae variability and its influence on water quality in an agricultural reservoir. We observed that maximal total phosphorus (TP) loads were 1,715 kg from farmland in September, which changed the ecosystem in the reservoir. At this time, Dictyosphaerium pulchellum as green algae dominated. Aulacoseira ambigua as diatom became the most dominant from November 2010 to May 2011. The diatom was sensitive to water temperature. Microcystis spp. and Oscillatoria spp. as cyanobacteria were dominant during summer and fall, and sank down to the sediment during winter and spring. Increasing water temperature during summer causes anaerobic conditions in the sediment, leading to regrowth of cyanobacteria. With regard to the ratio of total nitrogen (TN) to TP, green algae and cyanobacteria were dominant at TN:TP ratios less than 20-30. Diatoms were dominant at TN:TP ratios greater than 30-40. Statistical analysis indicated that diatom growth was dependent on water temperature and TN concentration. TP concentration and water temperature were the key factors for the growth of cyanobacteria and green algae. From these results of this study, the management of land-use was an important parameter for improving water quality in the agricultural reservoir.