• Ocean and Polar Research
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• v.39 no.1
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• pp.13-21
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• 2017

Temporal changes in the number of zooplankton species are important information for understanding basic characteristics and species diversity in marine ecosystems. The aim of the present study was to estimate the optimal monitoring frequency (OMF) to guarantee and predict the minimum number of species occurrences for studies concerning marine ecosystems. The OMF is estimated using the temporal number of zooplankton species through bi-weekly monitoring of zooplankton species data according to operational taxonomic units in the Tongyoung coastal sea. The optimal model comprises two terms, a constant (optimal mean) and a cosine function with a one-year period. The confidence interval (CI) range of the model with monitoring frequency was estimated using a bootstrap method. The CI range was used as a reference to estimate the optimal monitoring frequency. In general, the minimum monitoring frequency (numbers per year) directly depends on the target (acceptable) estimation error. When the acceptable error (range of the CI) increases, the monitoring frequency decreases because the large acceptable error signals a rough estimation. If the acceptable error (unit: number value) of the number of the zooplankton species is set to 3, the minimum monitoring frequency (times per year) is 24. The residual distribution of the model followed a normal distribution. This model can be applied for the estimation of the minimal monitoring frequency that satisfies the target error bounds, as this model provides an estimation of the error of the zooplankton species numbers with monitoring frequencies.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.13-27
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• 2013

Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.

• Convergence Security Journal
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• v.6 no.3
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• pp.145-153
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• 2006

Infinite failure NHPP models for a record value satisfies mode proposed in the literature exhibit either monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, propose comparative study of software reliability model using Erlang distribution, Rayleigh and Gumbel distribution. Equations to estimate the parameters using maximum likelihood estimation of infinite failure NHPP model based on failure data collected in the form of interfailure times are developed. For the sake of proposing distribution, we used to the special pattern. Analysis of failure data set using arithmetic and Laplace trend tests, goodness-of-fit test, bias tests is presented.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.1-9
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• 2014

The inverse Rayleigh model distribution and Rayleigh distribution model were widely used in the field of reliability station. In this paper applied using the finite failure NHPP models in order to growth model. In other words, a large change in the course of the software is modified, and the occurrence of defects is almost inevitable reality. Finite failure NHPP software reliability models can have, in the literature, exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, proposes the inverse Rayleigh and Rayleigh software reliability growth model, which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on mean square error (MSE) and coefficient of determination($R^2$), for the sake of efficient model, were employed. In order to insurance for the reliability of data, Laplace trend test was employed. In many aspects, Rayleigh distribution model is more efficient than the reverse-Rayleigh distribution model was proved. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can helped.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.3
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• pp.326-333
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• 2003

In the measurement using DMA and CPC in series, there is some time delay for particles classified in DMA to detect in CPC. During this time, the DMA time-response changes due to the velocity profile of sampling tube and the diffusion of particles in the volume that exists between the DMA exit and the detector of ultra-fine CPC. This is called mixing effect. In the accelerated measurement methods like the TSI -SMPS, the size distribution is obtained from the correlation between the time-varying electrical potential of the DMA and the corresponding particle concentrations sampled in DMA. If the DMA time -response changes during this delay time, this can cause the error of a size distribution measured by this accelerated technique. The kernel function considering this mixing effect using the residence time distribution is proposed by Russell et al. In this study, we obtained a size distribution using this kernel to compare to the result obtained by the commercial accelerated measurement system, TSI -SMPS for verification and considered the errors that result from the mixing effect with the geometric mean diameters of originally sampled particles, using virtually calculated responses obtained with this kernel as input data.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.12
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• pp.858-866
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• 2012

A numerical study was performed to predict the performance of a fin-and-tube condenser. A condenser model was developed and verified by comparing the simulation results with experimental data for a R410A condenser in a residential air-conditioning system. The prediction error was 0.07% and -5.77% for the condenser capacity and pressure drop, respectively. In simulation results, the capacity and pressure drop of the condenser with even air velocity distribution were 0.67% and 12.93% higher than those with uneven distribution of air velocity. It was predicted by the model that the refrigerant distribution at the condenser inlet to the two first passes was not significantly influenced by the air distribution. The simulation results presented that the 1.49% of capacity and 64.6% of pressure drop were reduced by replacing helical microfin tubes with smooth tubes for the condenser.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.5-10
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• 2006

The purpose of this study is to measure the droplet distribution and Sauter mean diameter(SMD) of biodiesel fuel, using the immersion sampling method. This method involves using an optical microscope and a CCD camera, to take an image of the droplets. These images are then measured by using a 'Sigma Scan' processing program. The results of the above experiment are summarized as followed ; (1) There can be as much as a 10% error rate when measuring the diameter of these droplets, using the image processing method and the naked eye. (2) The result of droplet size distribution test, TVO(transesterified vegetable oil) big size droplet distribution were increased at ambient pressure $6kg/cm^2$. (3) When ambient pressure increased $6kg/cm^2$ above, SMD variation of TVO and UVO(used vegetable oil) 30 are small. (4) On Rosin-Rammler analysis, droplets size distribution of UVO(used vegetable oil) 30 uniform more than TVO 20 on ambient pressure $1kg/cm^2$.

• Journal of Distribution Science
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• v.13 no.11
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• pp.39-46
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• 2015

Purpose - In Korea, there has been a recent trend that shows housing prices have risen rapidly following the International Monetary Fund crisis. The rapid rise in housing prices is spreading recognition of this as a factor in housing price volatility. In addition, this raises the expectations of housing prices in the future. These expectations are based on the assumption that a relationship exists between the current housing prices and expected housing prices in the real estate industry. By performing an empirical analysis on the validity of the claim that an increase in current housing prices can be correlated with expected housing prices, this study examines whether a long-term equilibrium relationship exists between expected housing prices and existing housing prices. If such a relationship exists, the recovery of equilibrium from disequilibrium is analyzed to derive related implications. Research design, data, and methodology - The relationship between current housing prices and expected housing prices was analyzed empirically using the Vector Error Correction Model. This model was applied to the co-integration test, the long-term equilibrium equation among variables, and the causality test. The housing prices used in the analysis were based on the National Housing Price Trend Survey released by Kookmin Bank. Additionally, the Index of Industrial Product and the Consumer Price Index were also used and were obtained from the Bank of Korea ECOS. The monthly data analyzed were from January 1987 to May 2015. Results - First, a long-term equilibrium relationship was established as one co-integration between current housing price distribution and expected housing prices. Second, the sign of the long-term equilibrium relationship variable was consistent with the theoretical sign, with the elasticity of housing price distribution to expected housing price, the industrial production, and the consumer price volatility revealed as 1.600, 0.104,and 0.092, respectively. This implies that the long-term effect of expected housing price volatility on housing price distribution is more significant than that of the industrial production and consumer price volatility. Third, the sign of the coefficient of the error correction term coincided with the theoretical sign. The absolute value of the coefficient of the correction term in the industrial production equation was 0.006, significantly larger than the coefficients for the expected housing price and the consumer price equation. In case of divergence from the long-term equilibrium relationship, the state of equilibrium will be restored through changes in the interest rate. Fourth, housing-price volatility was found to be causal to expected housing price, and was shown to be bi-directionally causal to industrial production. Conclusions - Based on the finding of this study, it is required to relieve the association between current housing price distribution and expected housing price by using property taxes and the loan-to-value policy to stabilize the housing market. Further, the relationship between housing price distribution and expected housing price can be examined and tested using a sophisticated methodology and policy variables.

• Korean journal of applied entomology
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• v.55 no.4
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• pp.501-511
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• 2016

Predicting potential spatial distribution of Monochamus alternatus, a major insect vector of the pine wilt disease, is essential to the spread of the pine wilt disease. The purpose of this study was to predict future domestic spatial distribution of M. alternatus by using the CLIMEX model considering the temperature condition of the vector's life history. To predict current distribution of M. alternatus, the administrative divisions data where the pine wilt spots caused by M. alternatus were found from 2006 to 2014 and the 10-year mean climate observed data in 68 meteorological stations from 2006 to 2015 were used. Eight parameter sets were chosen based on growth temperature range of M. alternatus reported in preceding researches. Error matrix method was utilized to select and simulate the parameter sets showing the highest correlation with the actual distribution. Regarding the future distribution of M. alternatus, two periods of 2050s(2046-2055) and 2090s(2091-2100) were predicted using the projected climate data of RCP 8.5 Scenario generated from Korea Meteorological Administration. Overall results of M. alternatus distribution simulation were fit in the actual distribution; however, overestimation in Seoul Metropolitan area and Chungnam Region were shown. Gradual expansion of M. alternatus would be expected to nationwide from western and southern coastal areas of Korea peninsula.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.12
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• pp.1054-1061
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• 2015

Navigation accuracy, integrity, and safety of commercial Unmanned Aerial Vehicle (UAV) is becoming crucial as utilization of UAV in commercial applications is expected to increase. Recently, the concept of Local-Area Differential GNSS (LADGNSS) which can provide navigation accuracy and integrity of UAV was proposed. LADGNSS can provide differential corrections and separation distances for precise and safe operation of the UAV. In order to derive separation distances between UAVs, modeling of Flight Technical Error (FTE) is required. In most cases, FTE for civil aircraft has been assumed to be zero-mean normal distribution. However, this assumption can cause overconservatism especially for UAV, because UAV may use control and navigation equipments in wider performance range and follow more diverse path than standard airway for civil aircraft. In this research, flight experiments were carried out to understand the characteristics of FTE distribution. Also, this paper proposes to use Johnson distribution which can better describe heavy-tailed and skewed FTE data. Futhermore, Kolmogorov-Smirnov and Anderson-Darling tests were conducted to evaluate the goodness of fit of Johnson model.