• Korea-Australia Rheology Journal
• /
• v.21 no.1
• /
• pp.1-12
• /
• 2009

This paper reports the suitability of a domain decomposition technique for the hybrid simulation of dilute polymer solution flows using Eulerian Brownian dynamics and Radial Basis Function Networks (RBFN) based methods. The Brownian Configuration Fields (BCF) and RBFN method incorporates the features of the BCF scheme (which render both closed form constitutive equations and a particle tracking process unnecessary) and a mesh-less method (which eliminates element-based discretisation of domains). However, when dealing with large scale problems, there appear several difficulties: the high computational time associated with the Stochastic Simulation Technique (SST), and the ill-condition of the system matrix associated with the RBFN. One way to overcome these disadvantages is to use parallel domain decomposition (DD) techniques. This approach makes the BCF-RBFN method more suitable for large scale problems.

• Water Engineering Research
• /
• v.5 no.2
• /
• pp.101-109
• /
• 2004

Aggregating information by combining forecasts from two or more forecasting methods is an alternative to using forecasts from just a single method to improve forecast accuracy. This paper describes the development and use of a monthly inflow forecast model based on an optimal linear combination (OLC) of forecasts derived from naive, persistence, and Ensemble Streamflow Prediction (ESP) forecasts. Using the cross-validation technique, the OLC model made 1-month ahead probabilistic forecasts for the Chungju multi-purpose dam inflows for 15 years. For most of the verification months, the skill associated with the OLC forecast was superior to those drawn from the individual forecast techniques. Therefore this study demonstrates that OLC can improve the accuracy of the ESP forecast, especially during the dry season. This study also examined the value of the OLC forecasts in reservoir operations. Stochastic Dynamic Programming (SDP) derived the optimal operating policy for the Chungju multi-purpose dam operation and the derived policy was simulated using the 15-year observed inflows. The simulation results showed the SDP model that updated its probability from the new OLC forecast provided more efficient operation decisions than the conventional SDP model.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.643-653
• /
• 2017

The paper presents controller optimization algorithm for a 12-pulse voltage source converter (VSC) based high voltage direct current (HVDC) system. To get an optimum algorithm, three methods namely conventional-Zeigler-Nichols, linear-golden section search (GSS) and stochastic-particle swarm optimization (PSO) are applied to control of 12 pulse VSC based HVDC system and simulation results are presented to show the best among the three. The performance results are obtained under various dynamic conditions such as load perturbation, non-linear load condition, and voltage sag, tapped load fault at points-of-common coupling (PCC) and single-line-to ground (SLG) fault at input AC mains. The conventional GSS and PSO algorithm are modified to enhance their performances under dynamic conditions. The results of this study show that modified particle swarm optimization provides the best results in terms of quick response to the dynamic conditions as compared to other optimization methods.

• Journal of the Korean Statistical Society
• /
• v.24 no.1
• /
• pp.197-218
• /
• 1995

We first consider the random censorship model of survival analysis. Efron (1981) introduced two equivalent bootstrap methods for censored data. We propose a new bootstrap scheme, called Method 3, that acts conditionally on the censoring pattern when making inference about aspects of the unknown life-time distribution F. This article contains (a) a motivation for this refined bootstrap scheme ; (b) a proof that the bootstrapped Kaplan-Meier estimatro fo F formed by Method 3 has the same limiting distribution as the one by Efron's approach ; (c) description of and report on simulation studies assessing the small-sample performance of the Method 3 ; (d) an illustration on some Danish data. We also consider the model in which the survival times are censered by death times due to other caused and also by known fixed constants, and propose an appropriate bootstrap method for that model. This bootstrap method is a readily modified version of the Method 3.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.9
• /
• pp.91-98
• /
• 1997

In this paper, a new pixel decimation algorithm for the estimation of motion vector is proposed. In traditional methods, the computational cost can be reduced since only part of the pixels are used for motion vector calculation. But these methods limits the accuracy ofmotion vector because of the same reason. We derive a selection criteria of subsampled pixels that can reduce the probablity of false motion vector detection based on stochastic point of view. By using this criteria, a new pixel decimation algorithm that can reduce the prediction error with similar computational cost is presented. The simulation results applied to standard images haveshown that the proposed algorithm has less mean absolute prediction error than conventional pixel decimation algorithm.

• Communications for Statistical Applications and Methods
• /
• v.29 no.6
• /
• pp.629-640
• /
• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• Journal of Korea Water Resources Association
• /
• v.57 no.5
• /
• pp.311-320
• /
• 2024

Climate change has been intensifying drought frequency and severity. Such prolonged droughts reduce reservoir levels, thereby exacerbating drought impacts. While previous studies have focused on optimizing reservoir operations using historical data to mitigate these impacts, their scope is limited to analyzing past events, highlighting the need for predictive methods for future droughts. This research introduces a novel approach for predicting minimum inflow at the Seomjingang dam which has experienced significant droughts. This study utilized the Stochastic Analysis Modeling and Simulation (SAMS) 2007 to generate inflow sequences for the same period of observed inflow. Then we simulate reservoir operations to assess firm yield and predict minimum inflow through synthetic inflow analysis. Minimum inflow is defined as the inflow where firm yield is less than 95% of the synthetic inflow in many sequences during periods matching observed inflow. The results for each case indicated the firm yield for the minimum inflow is on average 9.44 m³/s, approximately 1.07 m³/s lower than the observed inflow's firm yield of 10.51 m³/s. The minimum inflow estimation can inform reservoir operation standards, facilitate multi-reservoir system reviews, and assess supplementary capabilities. Estimating minimum inflow emerges as an effective strategy for enhancing water supply reliability and mitigating shortages.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.24 no.6
• /
• pp.860-869
• /
• 2000

Numerical and experimental investigations are performed for the molecular transition and slip flows in pumping channels of a disk-type drag pump. The flow occurring in the pumping channel develops from the molecular transition to the slip flow traveling downstream. Two different numerical methods are used in this analysis: the first one is a continuum approach in solving the Navier-Stokes equations with slip boundary conditions, and the second one is a stochastic approach through the use of the direct simulation Monte Carlo method. In the experimental study, the inlet pressures are measured for various outlet pressures in the range of 0.1{\sim}4Torr. From the present study, the numerical results of predicting the performance, obtained by both methods, agree well with the experimental data for the range of Knudsen number $Kn{\leq}0.1$ (i.e., the slip flow regime). But the results from the second method only agree with the experimental data for Kn>0.1(i.e., the molecular transition regime)

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.05a
• /
• pp.373-378
• /
• 2006

Recently the technical advances and complexities have generated much of the difficulties in managing the project resources, for both scheduling and costing to accomplish the project in the most efficient manner. The project manager is frequently required to render judgments concerning the schedule and resource adjustments. This research develops an analytical model for a schedule-cost and risk analysis based on visual PERT/CPM. We used a three-step approach: 1) in the first step, a deterministic PERT/CPM model for the critical path and estimating the project time schedule and related resource planning and we developed a heuristic model for crash and stretch out analysis based upon a time-cost trade-off associated with the crash and stretch out of the project. 2) In second step, we developed web-based risk evaluation model for project analysis. Major technologies used for this step are AHP (analytic hierarchy process, fuzzy-AHP, multi-attribute analysis, stochastic network simulation, and web based decision support system. Also we have developed computer programs and have shown the results of sample runs for an R&D project risk analysis. 3) We developed an optimization model for project resource allocation. We used AHP weighted values and optimization methods. Computer implementation for this model is provided based on GUI-Type objective-oriented programming for the users and provided displays of all the inputs and outputs in the form of GUI-Type. The results of this research will provide the project managers with efficient management tools.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.5
• /
• pp.1743-1753
• /
• 2017

Since there are multiple random variables in the probabilistic load flow (PLF) calculation of distribution system containing distributed generation (DG) and electric vehicle charging load (EVCL), a Monte Carlo method based on composite sampling method is put forward according to the existing simple random sampling Monte Carlo simulation method (SRS-MCSM) to perform probabilistic assessment analysis of voltage quality of distribution system containing DG and EVCL. This method considers not only the randomness of wind speed and light intensity as well as the uncertainty of basic load and EVCL, but also other stochastic disturbances, such as the failure rate of the transmission line. According to the different characteristics of random factors, different sampling methods are applied. Simulation results on IEEE9 bus system and IEEE34 bus system demonstrates the validity, accuracy, rapidity and practicability of the proposed method. In contrast to the SRS-MCSM, the proposed method is of higher computational efficiency and better simulation accuracy. The variation of nodal voltages for distribution system before and after connecting DG and EVCL is compared and analyzed, especially the voltage fluctuation of the grid-connected point of DG and EVCL.