• Journal of Wetlands Research
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• v.11 no.2
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• pp.107-121
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• 2009

Many rainfall-runoff models have been used for the flood forecasting. However, the determination of rainfall-runoff model parameters is very difficult. In this study, we investigated the efficiency of flood forecasting models by studying the optimization techniques for parameter calibration of SFM, Tank, and SSARR models. We analyzed the correlations between parameters in optimization techniques, then classified the parameters into parameter groups. For this we applied the sequential calibration method through the sensitivity analysis. As the results of the analysis, the parameter groups clibration method showed better result for peak flow and clibtation time.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1173-1177
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• 1990

Cerebellar Model Linear Associator Net(CMLAN), a kind of neuro-net based adaptive control function generator, was applied to the problem of direct inverse calibration of three and six d.o.f. POMA 560 robot. Since CMLAN autonomously maps and generalizes a desired system function via learning on the sampled input/output pair nodes, CMLAN allows no knowledge in system modeling and other error sources. The CMLAN based direct inverse calibration avoids the complex procedure of identifying various system parameters such as geometric(kinematic) or nongeometric(dynamic) ones and generates the corresponding desired compensated joint commands directly to each joint for given target commands in the world coordinate. The generated net outputs automatically handles the effect of unknown system parameters and dynamic error sources. On-line sequential learning on the prespecified sampled nodes requires only the measurement of the corresponding tool tip locations for three d.o.f. manipulator but location and orientation for six d.o.f. manipulator. The proposed calibration procedure can be applied to any robot.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.195-198
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• 2001

We developed the portable blood analysis system, which can be measured pH of the blood. This system is composed to electronic circuit, mechanism, and system software. Electronic circuit is composed to the sensor, pre-amp part, temperature regulation part, fluid sensing part, A/D(analog to digital) conversion part, main and peripheral device processing part. And the mechanism is composed to the flow cell and the liquid flow part. The liquid flow part is consisted of blood and washing control system under the control of the 6-channel solenoid valve and syringe rump. The system software is composed to measurement program, calibration program, washing and diagnostic program. The program of each routine is designed as sequential process for an efficiency. And the portable pH analysis system used two-point calibration method using the two types of corrective liquid. As a result, we obtained the calibration curve and calculated the value of pH. For verifying the system, we confirmed the output voltage of the sensor, and estimated reappearance of system using the standard liquid.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.1
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• pp.134-143
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• 2008

The hardware scatters as well as the engine parameters calibration have strong influences on exhaust emissions in recent diesel engines. In this research DoE(Design of Experiments) optimizations were done to study the possibility of minimizing the emission deviations caused by flow rate scatters of the injectors. It has been shown that the optimization of engine calibration, which minimizes the emission deviations, is feasible by establishing a target function representing the emission deviations for test results of maximum, mean and minimum flow rate injectors. It has also been shown that optimization of both emission deviations and emission level is possible by sequential DoE optimizations of the target functions representing the emission level and the emission deviations respectively with the appropriate boundary limits.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.7
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• pp.643-651
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• 2015

Multi-axis magnetic and accelerometer sensor are widely used in consumer product such as smart phones. The vector output of multi-axis sensors have errors on each axis such as offset error, scale error, non-orthogonality. These errors cause many problems on the performance of the applications. In this paper, we designed the effective inline compensation algorithm for calibrating of 3 axis sensors using ellipsoid for mass production of multi-axis sensors. The outputs with those kinds of errors can be modeled by ellipsoid, and the proposed algorithm makes sequential mappings of the virtual ellipsoid to perfect sphere which is calibrated function of the sensor on three-dimensional space. The proposed calibrating process composed of four main stages and is very straightforward and effective. In addition, another imperfection of the sensor such as the drift from temperature can be easily inserted in each mapping stage. Numerical simulation and experimental results shows great performance of the proposed compensation algorithm.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.3
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• pp.295-301
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• 2018

This study compares optimization algorithms for efficient estimations of ship's hydrodynamic coefficients. Two constrained algorithms, the interior point and the sequential quadratic programming, are compared for the estimation. Mathematical optimization is designed to get optimal hydrodynamic coefficients for modelling a ship, and benchmark data are collected from sea trials of a training ship. A calibration for environmental influence and a sensitivity analysis for efficiency are carried out prior to implementing the optimization. The optimization is composed of three steps considering correlation between coefficients and manoeuvre characteristics. Manoeuvre characteristics of simulation results for both sets of optimized coefficients are close to each other, and they are also fit to the benchmark data. However, this similarity interferes with the comparison, and it is supposed that optimization conditions, such as designed variables and constraints, are not sufficient to compare them strictly. An enhanced optimization with additional sea trial measurement data should be carried out in future studies.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.47-57
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• 2018

This study implemented the rainfall-runoff analysis of the Mekong River basin using the SWAT (Soil and Water Assessment Tool). The runoff analysis was simulated for 2000~2007, and 11 parameters were calibrated using the SUFI-2 (Sequential Uncertainty Fitting-version 2) algorithm of SWAT-CUP (Calibration and Uncertainty Program). As a result of analyzing optimal parameters and sensitivity analysis for 6 cases, the parameter ALPHA_BF was found to be the most sensitive. The reproducibility of the rainfall-runoff results decreased with increasing number of stations used for parameter calibration. The rainfall-runoff simulation results of Case 6 showed that the RMSE of Nong Khai and Kratie stations were 0.97 and 0.9, respectively, and the runoff patterns were relatively accurately simulated. The runoff patterns of Mukdahan and Khong Chaim stations were underestimated during the flood season from 2004 to 2005 but it was acceptable in terms of the overall runoff pattern. These results suggest that the combination of SWAT and SWAT-CUP models is applicable to very large watersheds such as the Mekong for rainfall-runoff simulation, but further studies are needed to reduce the range of modeling uncertainty.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.05a
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• pp.135-143
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• 2000

The COCOMO Ⅱ model is well-suited for the new software development life cycle such as non-sequential and rapid-development processes. The traditional regression approach based on the least square criterion is the most commonly used technique for empirical calibration in the COCOMO Ⅱ model. But it has a few assumptions frequently violated by software engineering data sets. It is true that the source data is also generally imprecise in reporting size, effort, and cost-driver ratings, particularly across different organizations. And that the outlier for the source data is a peculiarity and indicates a data pint To cope with difficulties, in this paper, we propose a new regression method for calibrating COCOMO Ⅱ post-architecture model based on the minimum relative erro(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the data in the empirical calibration. As the experimental results, It is evident that our proposed calibration method MRE was shown to be superior to the traditional regression approach for model calibration, as illustrated by the values obtained for standard deviation(^σ), and prediction at level L PRED(L) measures.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.2
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• pp.47-55
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• 2000

To date many software engineering cost models have been developed to predict cost, schedule, and effort of the software under development. The COCOMO Ⅱ is well- suited for the new software development life cycle such as non-sequential and rapid- development processes. The traditional regression approach based on the least square criterion is the most commonly used technique for empirical calibration in the COCOMO Ⅱ model. It has a few assumptions frequently violated by software engineering data sets. The source data is also generally imprecise in reporting size effort, and cost-driver ratings, particularly across different organizations. And that the outlier for the source data is a peculiarity and indicates a data point. To cope with difficulties, in this paper, we propose a new regression method for calibrating COCOMO Ⅱ post-architecture model based on the minimum relative error(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the data in the empirical calibration. As the experimental results, It is evident that our proposed calibration method MRE was shown to be superior to the traditional regression approach for model calibration, as illustrated by the values obtained for standard deviation(^σ), and prediction at level LPRED(L) measures.

• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.681-690
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• 2013

Watershed models have been increasingly used to support an integrated management of land and water, non-point source pollutants, and implement total daily maximum load policy. However, these models demand a great amount of input data, process parameters, a proper calibration, and sometimes result in significant uncertainty in the simulation results. For this reason, uncertainty analysis is necessary to minimize the risk in the use of the models for an important decision making. The objectives of this study were to evaluate three different uncertainty analysis algorithms (SUFI-2: Sequential Uncertainty Fitting-Ver.2, GLUE: Generalized Likelihood Uncertainty Estimation, ParaSol: Parameter Solution) that used to analyze the sensitivity of the SWAT(Soil and Water Assessment Tool) parameters and auto-calibration in a watershed, evaluate the uncertainties on the simulations of runoff and sediment load, and suggest alternatives to reduce the uncertainty. The results confirmed that the parameters which are most sensitive to runoff and sediment simulations were consistent in three algorithms although the order of importance is slightly different. In addition, there was no significant difference in the performance of auto-calibration results for runoff simulations. On the other hand, sediment calibration results showed less modeling efficiency compared to runoff simulations, which is probably due to the lack of measurement data. It is obvious that the parameter uncertainty in the sediment simulation is much grater than that in the runoff simulation. To decrease the uncertainty of SWAT simulations, it is recommended to estimate feasible ranges of model parameters, and obtain sufficient and reliable measurement data for the study site.