• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Aerospace Engineering and Technology
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• v.9 no.1
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• pp.50-59
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• 2010

In this paper, the alignment and navigation results by INGU(Inertial Navigation and Guidance Unit) onboard software and by Inertial Explorer which is a post-processing software specialized for IMU(Inertial Measurement Unit) are compared for identification of inertial sensor error models and estimation of alignment and navigation errors for KSLV-I INGU. For verification of the IMU error estimated by Kalman Filter of Inertial Explorer, the covariance parameters of inertial sensor error model state are identified by using stochastic error model of inertial sensors estimated by Allan variance and the alignment and navigation test with static condition and the land navigation test with dynamic condition are carried out. The validity of inertial sensor model for KSLV-I INGU is verified by comparison the alignment and navigation results of INGU on-board software and Inertial Explorer.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.5
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• pp.596-604
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• 2016

In this study, we used a stochastic approach for guaranteeing the reliability and robustness of the performance with regard to the design of polymer components, while taking into consideration the degradation properties and operating conditions in automobiles. Creep and tensile tests were performed for obtaining degradation properties. The Prony series, which described the viscoelastic models, were calculated to use the creep data by the Maxwell fluid model. We obtained the stress data from the frequency response analysis of the polymer components while considering the degradation properties. Limit state functions are generated by using these data. Reliability assessments are conducted under the variation of the degradation properties and area of frequency at peak response. For this study, the input parameters are assumed to be a normal distribution, and the reliability under the yield stress criteria is evaluated by using the Monte Carlo Simulation. As a result, the reliabilities, according to the three types of polymer materials in automotive components, are compared to each other and suggested the applicable possibility of polymeric materials in automobiles.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.55-63
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• 2013

This study investigates the trends and uncertainty of the impacts of climate change on paddy rice production in the Geumho river basin. The Long Ashton Research Station stochastic Weather Generator (LARS-WG) was used to derive future climate data for the Geumho river basin from 15 General Circulation models (GCMs) for 3 Special Report on Emissions Scenarios (SRES) (A2, A1B and B1) included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. The Food and Agricultural Organization (FAO) AquaCrop, a water-driven crop model, was statistically calibrated for the 1982 to 2010 climate. The index of agreement (IoA), prediction efficiency ($R^2$), percent bias (PBIAS), root mean square error (RMSE) and a visual technique were used to evaluate the adjusted AquaCrop simulated yield values. The adjusted simulated yields showed RMSE, NSE, IoA and PBIAS of 0.40, 0.26, 0.76 and 0.59 respectively. The 5, 9 and 15 year central moving averages showed $R^2$ of 0.78, 0.90 and 0.96 respectively after adjustment. AquaCrop was run for the 2020s (2011-2030), 2050s (2046-2065) and 2090s (2080-2099). Climate change projections for Geumho river basin generally indicate a hotter and wetter future climate with maximum increase in the annual temperature of $4.5^{\circ}C$ in the 2090s A1B, as well as maximum increase in the rainfall of 45 % in the 2090s A2. The means (and ranges) of paddy rice yields are projected to increase by 21 % (17-25 %), 34 % (27-42 %) and 43 % (31-54 %) for the 2020s, 2050s and 2090s, respectively. The A1B shows the largest rice yield uncertainty in all time slices with standard deviation of 0.148, 0.189 and $0.173t{\cdot}ha^{-1}$ for the 2020s, 2050s and 2090s, respectively.

• The KIPS Transactions:PartC
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• v.12C no.2 s.98
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• pp.281-288
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• 2005

It is generally accepted that self-similar (or fractal) Processes may provide better models for teletraffic in modem computer networks than Poisson processes. f this is not taken into account, it can lead to inaccurate conclusions about performance of computer networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A generator of pseudo-random self similar sequences, based on the SRA (successive random addition) method, is implemented and analysed in this paper. Properties of this generator were experimentally studied in the sense of its statistical accuracy and the time required to produce sequences of a given (long) length. This generator shows acceptable level of accuracy of the output data (in the sense of relative accuracy of the Hurst parameter) and is fast. The theoretical algorithmic complexity is O(n).

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.559-569
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• 2016

A long-run relationship of stock, monetary, realty markets, and business conditions has been suggested to exist due to internal and external shocks. This study investigates whether such a relationship really exists and then performs statistical tests to discern features of the long-run adjustment processes from short-run discrepancies because it is difficult to find studies that examine the market relationship. The comovement relationship of the whole market does not appear to hold for the entire study period; however, it is found to exist for the period before the financial crisis. Estimated error correction models show consistently declining equilibrium errors each period that suggests a recovering process of the long-run equilibrium from short-run secessions.

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.621-632
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• 2004

Recent measurement studies of real teletraffic data in modern telecommunication networks have shown that self-similar (or fractal) processes may provide better models of teletraffic in modern telecommunication networks than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A new generator of pseu-do-random self-similar sequences, based on the fractional Gaussian nois and a wavelet transform, is proposed and analysed in this paper. Specifically, this generator uses Daubechies wavelets. The motivation behind this selection of wavelets is that Daubechies wavelets lead to more accurate results by better matching the self-similar structure of long range dependent processes, than other types of wavelets. The statistical accuracy and time required to produce sequences of a given (long) length are experimentally studied. This generator shows a high level of accuracy of the output data (in the sense of the Hurst parameter) and is fast. Its theoretical algorithmic complexity is 0(n).

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.81-92
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• 2019

The objective of this study is to evaluate hydrologic impacts of climate change according to downscaling methods using the Soil and Water Assessment Tool (SWAT) model at watershed scale. We used the APCC Integrated Modeling Solution (AIMS) for assessing various General Circulation Models (GCMs) and downscaling methods. AIMS provides three downscaling methods: 1) BCSA (Bias-Correction & Stochastic Analogue), 2) Simple Quantile Mapping (SQM), 3) SDQDM (Spatial Disaggregation and Quantile Delta Mapping). To assess future hydrologic responses of climate change, we adopted three GCMs: CESM1-BGC for flood, MIROC-ESM for drought, and HadGEM2-AO for Korea Meteorological Administration (KMA) national standard scenario. Combined nine climate change scenarios were assessed by Expert Team on Climate Change Detection and Indices (ETCCDI). SWAT model was established at the Mankyung watershed and the applicability assessment was completed by performing calibration and validation from 2008 to 2017. Historical reproducibility results from BCSA, SQM, SDQDM of three GCMs show different patterns on annual precipitation, maximum temperature, and four selected ETCCDI. BCSA and SQM showed high historical reproducibility compared with the observed data, however SDQDM was underestimated, possibly due to the uncertainty of future climate data. Future hydrologic responses presented greater variability in SQM and relatively less variability in BCSA and SDQDM. This study implies that reasonable selection of GCMs and downscaling methods considering research objective is important and necessary to minimize uncertainty of climate change scenarios.

• International Area Studies Review
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• v.14 no.3
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• pp.205-219
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• 2010

In general, industrial production involves large amounts of harmful by-product, causing serious pollution and ecological risk. Eco-efficiency, which indicates how efficient the economic activity is by considering both the environmental risks and economic performance, is a new concept for economic sustainability analysis and quantitative ecological risk analysis. Data envelopment analysis (DEA) has been widely used to examine eco-efficiency in recent years. Whereas traditional DEA models such as CCR and BCC model neglect undesirable outputs, this paper adopts a stochastic undesirable-output DEA model to measure the eco-efficiency of each province of China and Korea. The results indicate that whereas the eco-efficiency of the eastern region was the highest, that of the western region was the lowest. The reason may come from the negative external dis-economies of environment. And thus the governmental efforts for outreach on the underdeveloped regions should be promoted.

• Structural Engineering and Mechanics
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• v.78 no.1
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• pp.53-66
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• 2021

The determination of the damage index to reveal the performance level of a structure can constitute the seismic risk generalization approach based on the parametric analysis. This study implemented this concept to one kind of civil engineering structure that is the concrete gravity dam. Different cases of the structure exhibit their individual responses, which constitute different considerations. Therefore, this approach allows the parametric study of concrete as well as soil for evaluating the seismic nature in the generalized case. To ensure that the target algorithm applicable to most of the concrete gravity dams, a very simple procedure has been considered. In order to develop a correlated algorithm (by response surface methodology; RSM) between the ground motion and the structural property, randomized sampling was adopted through a stochastic method called half-fractional central composite design. The responses in the case of fluid-foundation-dam interaction (FFDI) make it more reliable by introducing the foundation as being bounded by infinite elements. To evaluate the seismic generalization of FFDI models, incremental dynamic analysis (IDA) was carried out under the impacts of various earthquake records, which have been selected from the Pacific Earthquake Engineering Research Center data. Here, the displacement-based damage indexed fragility curves have been generated to show the variation in the seismic pattern of the dam. The responses to the sensitivity analysis of the various parameters presented here are the most effective controlling factors for the concrete gravity dam. Finally, to establish the accuracy of the proposed approach, reliable verification was adopted in this study.