• Journal of Korea Water Resources Association
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• v.35 no.1
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• pp.109-124
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• 2002

This study is an effort to develop a stochastic model of precipitation series that preserves the pattern of occurrence of precipitation events throughout the year as well as several characteristics of the duration, amount, and intensity of precipitation events. In this study an event cluster model is used to describe the occurrence of precipitation events. A logarithmic negative mixture distribution is used to describe event duration and separation. The number of events within each cluster is also described by the Poisson cluster process. The duration of each event within a cluster and the separation of events within a single cluster are described by a logarithmic negative mixture distribution. The stochastic model for hourly precipitation occurrence process is fitted to historical precipitation data by estimating the model parameters. To allow for seasonal variations in the precipitation process, the model parameters are estimated separately for each month. an analysis of thirty-four years of historical and simulated hourly precipitation data for Seoul indicates that the stochastic model preserves many features of historical precipitation. The seasonal variations in number of precipitation events in each month for the historical and simulated data are also approximately identical. The marginal distributions for event characteristics for the historical and simulated data were similar. The conditional distributions for event characteristics for the historical and simulated data showed in general good agreement with each other.

• Smart Structures and Systems
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• v.20 no.1
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• pp.35-42
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• 2017

Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1205-1214
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• 2007

We define a skew-symmetric double Rayleigh distribution by a symmetric double Rayleigh distribution, and derive an approximate maximum likelihood estimator(AML) and a moment estimator(MME) of a skewed parameter in a skew-symmetric double Rayleigh distribution, and hence compare simulated mean squared errors of those two estimators. We also compare simulated mean squared errors of two proposed estimators of reliability in two independent skew-symmetric double Rayleigh distributions.

• Journal of Korean Society on Water Environment
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• v.28 no.4
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• pp.595-600
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• 2012

This study is to assess the application of SWAT-CUP(Soil and Water Assessment Tool-Calibration Uncertainty Programs) and to extend daily flow data from 8-day interval flow data which has been measured by Korean Ministry of Environment(MOE). Model sensitivity analysis and calibration were performed with sequential uncertainty fitting(SUIF-2), which is one of the programs interfaced with SWAT, in the package SWAT-CUP. The most sensitive parameters were SOL_K.sol, CH_N2.rte, CN2.mgt, SOL_BD.sol, ALPHA_BF.gw, ALPHA_BNK.rte, SOL_AWC.sol, CH_K2.rte, SFTMP.bsn, GW_DELAY.gw. Following the sensitivity analysis, SWAT-CUP calibration was carried out using 8-day interval flow data from January 2008 to December 2010. The results were then assessed based on the visual agreement and simulated flow plots and the performance statistics generated $R^2$ and NSE which are 0.71 and 0.61 respectively. Results of these statistics indicated that there was a good agreement between the observed and simulated flow. To extend daily flow data from 8-day interval flow data, parameters, which were estimated by SWAT-CUP, re-entered for SWAT model. As a result, the observed flow data were found to reflect the trend of simulated flow data. From these results, it is thought that this method could be used to provide daily flow data using 8-day interval flow data.

• Korean Journal of Remote Sensing
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• v.15 no.4
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• pp.357-365
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• 1999

The optical sensors of Electro-Optical Camera (EOC) and Ocean Scanning Multi-spectral Imager (OSMI) aboard the KOrea Multi-Purpose SATellite (KOMPSAT) will be placed in a sun synchronous orbit in late 1999. The EOC and OSMI sensors are expected to produce the land mapping imagery of Korean territory and the ocean color imagery of world oceans, respectively. Utilization of the EOC and OSMI data would encompass the various fields of science and technology such as land mapping, land use and development, flood monitoring, biological oceanography, fishery, and environmental monitoring. Readiness of data support for user community is thus essential to the success of the KOMPSAT program. As a part of testing such readiness prior to the KOMPSAT launch, we have performed the preliminary acceptance test for the KOMPSAT data processing system using the simulated EOC and OSMI data sets. The purpose of this paper is to demonstrate the readiness of the KOMPSAT data processing system, and to help data users understand how the KOMPSAT EOC and OSMI data are processed, archived, and provided. Test results demonstrate that all requirements described in the data processing specification have been met, and that the image integrity is maintained for all products. It is however noted that since the product accuracy is limited by the simulated sensor data, any quantitative assessment of image products can not be made until actual KOMPSAT images will be acquired.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• The Journal of the Acoustical Society of Korea
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• v.24 no.6
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• pp.325-333
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• 2005

This paper proposes a geoacoustic inversion method for the experimental data or MAPLE 2004 experiment conducted in the East Sea of Korea in 2004 and shows source tracking test results to validate the Proposed inversion method. An objective function is defined as a correlation function of the measured and the simulated transmission loss data. The measured transmission data were obtained using a multi-tonal towed source and VLA. The VFSA (Very Fast Simulated Annealing) is applied to the inversion Problem which optimizes the objective function. After performing the inversion process for the S frequencies tonal data independently. geoacoustic models are constructed. Finally matched-field source tracking is Performed using the inverted parameters to verify them.

• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.725-740
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• 2008

In this study a multi-site daily precipitation generator which generates the precipitation with similar spatial correlation, and at the same time, with conserving statistical properties of the observed data is developed. The proposed generator is intended to be a tool for down-scaling the data obtained from GCMs or RCMs into local scales. The occurrences of precipitation are simultaneously modeled in multi-sites by 2-parameter first-order Markov chain using random variables of spatially correlated while temporally independent, and then, the amount of precipitation is simulated by 3-parameter mixed exponential probability density function that resolves the issue of maintaining intermittence of precipitation field. This approach is applied to the Nakdong river basin and the observed data are daily precipitation data of 19 locations. The results show that spatial correlations of precipitation series are relatively well simulated and statistical properties of observed precipitation series are simulated properly.

• Journal of Software Engineering Society
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• v.24 no.2
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• pp.35-45
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• 2011

Simulated annealing has been studied a long times. And it is one of the effective techniques for test data generation. But basic SA methods showed bad performance because of neighborhood selection strategies in the case of large input domain. To overcome this limitation, we propose new neighborhood selection approach, Branch Distance. We performs case studies based on the proposed approach to evaluate it's performance and to compare it whit basic SA and Random test generation. The results of the case studies appear that proposed approach show better performance than the other approach.

• Structural Engineering and Mechanics
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• v.71 no.2
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• pp.175-183
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• 2019

This paper deals with damage detection in a girder bridge using transmissibility functions as input data to Artificial Neural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage in a bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridge was used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a moving truck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train the ANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network can be used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique to real bridge data is potentially high.