• Computational Structural Engineering
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• v.1 no.1
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• pp.113-122
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• 1988

A practical model for predicting the risk of fatigue failure of steel highway bridges is developed in this study. The proposed model is derived from fatigue reliability methods by incorporating various factors which may affect the fatigue life of bridges. The fatigue reliability function is assumed to follow the Weibull distribution. The computational form of the Weibull is adopted from Ang-Munse's approach that includes all the statistical uncertainties of the fatigue life of steel members and the stress ranges under variable amplitude loadings. The model accounts for the variation in ADTT, the change in stress history and the effects of inspections, which may occur during the serivce life of bridges. Stress range histograms are collected from the random stress spectra based on the field measurements of an existing bridge, and, thus, the resulting stress range frequency distribution is modelled with a beta distribution. The results of applications of the proposed fatigue analysis methods to an existing bridge show that the proposed models with the computer program developed for numerical computations can be used as a practical tool for the fatigue rating or for the predictions of the remaining fatigue life of deteriorated existing steel bridges.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.795-797
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• 2005

During the past few years, various traffic-flow forecasting models, i.e. an ARIMA, an ANN, and so on, have been developed to predict more accurate traffic flow. However, these models analyze historical data in an attempt to predict future value of a variable of interest. They make use of the following basic strategy. Past data are analyzed in order to identify a pattern that can be used to describe them. Then this pattern is extrapolated, or extended, into the future in order to make forecasts. This strategy rests on the assumption that the pattern that has been identified will continue into the future. So ARIMA or ANN models with its traditional architecture cannot be expected to give good predictions unless this assumption is valid; The statistical models in particular, the time series models are deficient in the sense that they merely extrapolate past patterns in the data without reflecting the expected irregular and infrequent future events Also forecasting power of a single model is limited to its accurate. In this paper, we compared with an ANN model and ARIMA model and tried to combine an ARIMA model and ANN model for obtaining a better forecasting performance. In addition to combining two models, we also introduced judgmental adjustment technique. Our approach can improve the forecasting power in traffic flow. To validate our model, we have compared the performance with other models. Finally we prove that the proposed model, i.e. ARIMA + ANN + Judgmental Adjustment, is superior to the other model.

• Journal of Food Hygiene and Safety
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• v.18 no.4
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• pp.237-242
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• 2003

Sequences of industrial microbial counts of foods shows irregular fluctuating patterns as adeinition of fluctuating microbial counts(FMC). Recently, it beame clear that the FMC was considered as having a lognormal distribution as a first order approximation. Application of lognormal distribution to the industrial microbial counts could produce useful information in practice. This study is intended to verift the application method of lognormal idstribution in FMC. The one year's records for microbial counts of frozen dumplings from two companies were obtained, and the statistical analysis was carried out to estimate the frequencies of future events where counts exceeding selected levels and to compare the sanitation level of the two companies. The results showed that this spplication method enable translation of irregular recourds of microbial counts into an useful information such as te actual probalities of outburst of a given level and the quantitative predictions of potential hazards in the processing.

• Advances in environmental research
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• v.6 no.4
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• pp.265-279
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• 2017

The climate change has made adverse effects on land surface temperature for many regions of the world. Several climatic studies focused on different downscaling techniques for climatological parameters of different regions. For statistical downscaling of any hydrological parameters, conventional Neural Network Models were used in common. However, it seems that in any modeling study, uncertainty is a vital aspect when making any predictions about the performance. In this paper, Gamma Test is performed to determine the data length selection for training to minimize the uncertainty in model development. Another measure to improve the data quality and model development are wavelet transforms. Hence, Gamma Test with Wavelet decomposed Feedforward Neural Network (GT-WNN) model is developed and tested for downscaled land surface temperature of Patna Urban, Bihar. The results of GT-WNN model are compared with GT-FFNN and conventional Feedforward Neural Network (FFNN) model. The effectiveness of the developed models is illustrated by Root Mean Square Error and Coefficient of Correlation. Results showed that GT-WNN outperformed the GT-FFNN and conventional FFNN in downscaling the land surface temperature. The land surface temperature is forecasted for a period of 2015-2044 with GT-WNN model for Patna Urban in Bihar. In addition, the significance of the probable changes in the land surface temperature is also found through Mann-Kendall (M-K) Test for Summer, Winter, Monsoon and Post Monsoon seasons. Results showed an increasing surface temperature trend for summer and winter seasons and no significant trend for monsoon and post monsoon season over the study area for the period between 2015 and 2044. Overall, the M-K test analysis for the annual data shows an increasing trend in the land surface temperature of Patna Urban.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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• pp.554-569
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• 2017

Sensitivity analysis on $PM_{10}$ forecasting simulations was carried out by using two different initial and boundary conditions of meteorological fields: NCEP/FNL (National Centers for Environmental Prediction/Final Analysis) reanlaysis data and NCEP/GFS (National Centers for Environmental Prediction/Global Forecast System) forecasting data, and the comparisons were made between two different simulations. The two results both yielded lower $PM_{10}$ concentrations than observations, with relatively lower biased results by NCEP/FNL than NCEP/GFS. We explored the detailed individual meteorological variables to associate with $PM_{10}$ prediction performance. With the results of NCEP/FNL outperforming GFS, our conclusion is that no particular significant bias was found in temperature fields between NCEP/FNL and NCEP/GFS data, while the overestimated wind speed by NCEP/GFS data influenced on the lower $PM_{10}$ concentrations simulation than NCEP/FNL, by decreasing the duration time of high-$PM_{10}$ loaded air mass over both coastal and metropolitan areas. These comparative characteristics of FNL against GFS data such as maximum 3~4 m/s weaker wind speed, $PM_{10}$ concentration control with the highest possible factor of 1.3~1.6, and one or two hour difference of peak time for each case in this study, were also reflected into the results of statistical analysis. It is implying that improving the surface wind speed fluctuation is an important controlling factor for the better prediction of $PM_{10}$ over Korean Peninsula.

• Environmental Engineering Research
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• v.15 no.2
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• pp.63-70
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• 2010

In this study, the response surface method and experimental design were applied as an alternative to conventional methods for the optimization of coagulation tests. A central composite design, with 4 axial points, 4 factorial points and 5 replicates at the center point were used to build a model for predicting and optimizing the coagulation process. Mathematical model equations were derived by computer simulation programming with a least squares method using the Minitab 15 software. In these equations, the removal efficiencies of turbidity and total organic carbon (TOC) were expressed as second-order functions of two factors, such as alum dose and coagulation pH. Statistical checks (ANOVA table, $R^2$ and $R^2_{adj}$ value, model lack of fit test, and p value) indicated that the model was adequate for representing the experimental data. The p values showed that the quadratic effects of alum dose and coagulation pH were highly significant. In other words, these two factors had an important impact on the turbidity and TOC of treated water. To gain a better understanding of the two variables for optimal coagulation performance, the model was presented as both 3-D response surface and 2-D contour graphs. As a compromise for the simultaneously removal of maximum amounts of 92.5% turbidity and 39.5% TOC, the optimum conditions were found with 44 mg/L alum at pH 7.6. The predicted response from the model showed close agreement with the experimental data ($R^2$ values of 90.63% and 91.43% for turbidity removal and TOC removal, respectively), which demonstrates the effectiveness of this approach in achieving good predictions, while minimizing the number of experiments required.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.2
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• pp.150-155
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• 2012

One of the primary purposes of tugs is for the towing of other ships in salvage operations. In order to conduct such a task safely, the tug should be capable of generating the appropriate towing forces. Therefore the prediction of resistance against a towed ship during towing operation is a very important and essential procedure. This paper studies the ship resistance calculation program to predict towing force. The calculation program consists of the functions that calculate basic hull resistance in calm water, added resistance due to wind, drifting, hull roughness, waves, shallow water and currents. All predictions are calculated by statistical and empirical methods by graph or formula. The calculation results made by this program are compared with the results from the U.S. Navy's Towing Manual. These results confirm that this computer program is quite capable of appropriately predicting the resistance of damaged ships.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.47-55
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• 2000

In oreder to predict the productions of major crops such as rice, barely, soybean and potato in Kyongsang Puk Do as early as possible, an attempt has been made to develop some prediction model of crop yields, using the data from the Statistical Yearbooks of Agriculture, Forestry and Fisheries from 1966 through 1999. Among the various models considered, $y=\exp({\beta}_{0}+{\beta}_{1}t+{\epsilon})$ was best fit to the planted area of the crops and $y=\exp({\beta}_{0}+{\beta}_{1}t^{1/2}+{\beta}_{2}t+{\sum}^{p}_{i=1}{\beta}_{i}+_2x_i+{\epsilon})$ to the yields. The $R^{2}$ values for the planted areas were $0.9180{\sim}0.9505$, implying good prediction, while that for rice was 0.7234 and those for barley, soybean and potato were $0.8855{\sim}0.9098$, Predictions have also been made for the planted areas upto the year 2005 and yield for the year 2000.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.345-356
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• 2010

In this paper, we introduce the linear regression model taking the parametric spatial association structure into account and employ it to five-year averaged wind speed data measured at 460 meteorological monitoring stations in South Korea. From the prediction map obtained by the model with spatial association parameters, we can see that inland area has smaller wind speed than coastal regions. When comparing the spatial linear regression model with classical one by using one-leave-out cross-validation, the former outperforms the latter in terms of similarity between the observations and the corresponding predictions and coverage rate of 95% prediction intervals.

• Journal of the Korean Geotechnical Society
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• v.21 no.9
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• pp.65-75
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• 2005

Maximum shear modulus of soil is a principal parameter for the design of earth structures under static and dynamic loads. In this study, the statistical data of maximum shear moduli of silty sands in Songdo area in the west coast of Korea evaluated by various field and laboratory tests - standard penetration test (SPT), cone penetration test (CPT), self-boring pressuremeter test (SBPT), downhole test (DH), seismic cone penetration test (SCPT) and resonant column test (RC) were analyzed. Based on the measurement of shear moduli using DH which is known as maximum value at very small strain, the new empirical correlations between shear moduli and SPT or CPT values were proposed. Predictions of maximum shear moduli using the proposed correlations were compared with the data obtained from DH. The good agreement confirmed that the proposed correlations reasonably predicted the maximum shear moduli of silty sands in the area.