• Transactions of the Korean Society of Automotive Engineers
• /
• v.22 no.7
• /
• pp.84-89
• /
• 2014

Not only the emission regulation of on-road vehicle engine, but also emission regulation of off-road engine have been reinforced. It is the reason of wide application of emission reduction technology for off-road engines. In this study, optimization of engine parameters (Injector hole number, Injection timing and EGR rate) for reduction of NOx and smoke emissions were conducted by using the analysis of sensitivity and S/N ratio of Taguchi method(DOE). As results, this paper shows optimum value of the parameters for NOx and smoke emission reduction. From the result of reproducibility verification, it is final that the prediction value of NOx and smoke has the error of below 10%. Consequently, the method and results of this study will be used for quantitative reference to EGR control mapping in next study.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.5 s.170
• /
• pp.28-36
• /
• 2005

A gerotor pump is suitable for oil hydraulics of machine tools, automotive engines, compressors, constructions and other various applications, which are highly accepted by designers. Especially the pump is an essential machine element of an automotive engine to feed lubricant oil. However, related industries do not have necessary technology to design and optimize the pump and paid royalties of rotor profile on an advanced country. Also, gerotor pumps with unsettled design parameters have not been sufficiently analyzed from a theoretical view of design. Therefore, it is still very difficult for the pump designer and manufacturer to decide the specifications for the required gerotor pump by users. In this study, the design optimization has been carried out to determine the design parameters that maximize the specific flow rate and minimize the flow rate irregularity. Theoretical analyses and optimal design of the gerotor oil pump have been performed by mathematical base, numerical method and knowledge of kinematics. An automated design system of the tooth profile has been developed through Auto LISP language and CAD method considering various design parameters. Finally, an optimally designed model for a general type of a gerotor pump has been generated and experimentally verified for the pump performances.

• Journal of Magnetics
• /
• v.21 no.4
• /
• pp.544-553
• /
• 2016

Torque ripple minimization without decrease in average torque is a vital attribute in the design of Synchronous Reluctance (SynRel) motor. As the design of SynRel motor is an arduous task, which encompasses many design variables, this work first analyses the significance of the effect of varying the geometrical parameters on average torque and torque ripple and then proposes an extensive optimization procedure to obtain configurations with improved average torque and minimized torque ripple. A hardware prototype is fabricated and tested. The Finite Element Analysis (FEA) software tool used for validating the test results is MagNet 7.6.0.8. Multi Objective Particle Swarm Optimization (MOPSO) is used to determine the various designs meeting the requirements of reduced torque ripple and improved torque performance. The results indicate the efficacy of the proposed methodology and substantiate the utilization of MOPSO as a significant tool for solving design problems related to SynRel motor.

• Nuclear Engineering and Technology
• /
• v.52 no.12
• /
• pp.2709-2716
• /
• 2020

Compositions of large nuclear cores (e.g. boiling water reactors) are highly heterogeneous in terms of fuel composition, control rod insertions and flow regimes. For this reason, they usually lack high order of symmetry (e.g. 1/4, 1/8) making it difficult to estimate their neutronic parameters for large spaces of possible loading patterns. A detailed hyperparameter optimization technique (a combination of manual and Gaussian process search) is used to train and optimize deep neural networks for the prediction of three neutronic parameters for the Ringhals-1 BWR unit: power peaking factors (PPF), control rod bank level, and cycle length. Simulation data is generated based on half-symmetry using PARCS core simulator by shuffling a total of 196 assemblies. The results demonstrate a promising performance by the deep networks as acceptable mean absolute error values are found for the global maximum PPF (~0.2) and for the radially and axially averaged PPF (~0.05). The mean difference between targets and predictions for the control rod level is about 5% insertion depth. Lastly, cycle length labels are predicted with 82% accuracy. The results also demonstrate that 10,000 samples are adequate to capture about 80% of the high-dimensional space, with minor improvements found for larger number of samples. The promising findings of this work prove the ability of deep neural networks to resolve high dimensionality issues of large cores in the nuclear area.

• Journal of Korean Society on Water Environment
• /
• v.25 no.2
• /
• pp.245-255
• /
• 2009

CN values are changed by various surface condition, which is cover type or treatment, hydrologic condition, or percent impervious area, even the same combination of land use and hydrologic soil group. In this study, CN parameters were regionalized for Nakdong River Basin by Long-Term Hydrologic Impact Assessment (L-THIA) coupled with SCE-UA, which is one of the global optimization technique. Six watersheds were selected for calibration (optimization) and periodic validation and two watersheds for spatical validation as ungauged watershed within Nakdong River Basin. Nash-Sutcliffe (NS) values were 0.66~0.86 for calibration, 0.68~0.91 for validation, and 0.60 and 0.85 for ungauged watersheds, respectively. Urban area for the selected watersheds covered high impervious area with 85% for residential area and 92% for commercial/industrial/transportation area. Hydrologic characteristics for crop area was similar to row crop with contoured treatment and poor hydrologic condition. For the forested area, hydrologic characteristics could be clearly distinguished from the leaf types of plant. Deciduous, coniferous, and mixed forest showed low, moderate, and high runoff rates by representing wood with fair and poor hydrologic condition, and wood-grass combination with fair hydrologic condition, respectively. CN parameters from this study could be strongly recommended to be used to simulate runoff for ungauged watershed.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.4
• /
• pp.636-645
• /
• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• Water for future
• /
• v.29 no.5
• /
• pp.223-233
• /
• 1996

Daily streamflow model, DAWAST, considering the meteorologic and geographic characteristics of the Korean watersheds has been developed to simulate the daily streamflow with the input data of daily rainfall and pan evaporation. The model is the conceptual one with three sub-models which are optimization, generalization, and regionalization models. The conceptual model consists of three linear reservoirs representing the surface, unsaturated, and saturated soil zones and water balance analysis was carried out in each soil zones on a daily basis. Optimization model calibrates the parameters by optimization technique and is applicable to the watersheds where the daily streamflow data are available Generalization model predicts the parameters by regression equations considering the geographic, soil type, land use, and hydrogeologic characteristics of watershed and is appicable to ungaged medium or small watersheds. Regionalization model cites the parameters from the analysed ones considering river system, latitude and longitude, and is applicable to ungaged large watersheds.

• Journal of Wetlands Research
• /
• v.19 no.4
• /
• pp.423-432
• /
• 2017

This study used SSARR model to predict the flood hydrograph for the Miho stream in the Geum river basin. First, we performed the sensitivity analysis on the parameters of SSARR model to know the characteristics of the parameters and set the range. For the parameter calibration, optimization methods such as genetic algorithm, pattern search and SCE-UA were used. WSSR and SSR were applied as objective functions, and the results of optimization method and objective function were compared and analyzed. As a result of this study, flood prediction was most accurate when using pattern search as an optimization method and WSSR as an objective function. If the parameters are optimized based on the results of this study, it can be helpful for decision making such as flood prediction and flood warning.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.7A
• /
• pp.776-785
• /
• 2008

Compared with the existing cellular base station whose radio parameters are configured manually, the small base station named as self-configurable base station configures its radio parameters automatically by the central controller. When installing the self-configurable base station, it should be considered primarily that the seamless coverage for the target area is secured while the signal interference to the existing cellular service area must to be minimized. In order to achieve this, it is very important to select the correct radio parameters, e.g., transmission power and working frequency. In this work, we formulate and solve the optimization problem by using mixed integer programming to optimize the air parameter for the self-configurable base stations.

• Smart Structures and Systems
• /
• v.25 no.5
• /
• pp.605-617
• /
• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.