• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.7
• /
• pp.644-649
• /
• 2006

In this paper, a methodology for estimating the model parameters of a discrete-time system and tuning a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems regarding parameter estimation and controller tuning, pseudo-parallel genetic algorithms(PPGAs) are used. The parameters of a discrete-time system are estimated using both the model adjustment technique and a PPGA. The digital PID controller is described by the pulse transfer function and then its three gains are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.5
• /
• pp.839-844
• /
• 2008

A novel state of health estimation method for hybrid electric vehicle lithium battery using sliding mode observer has been presented. A simple R-C circuit method has been used for the lithium battery modeling for the reduced calculation time and system resources due to the simple matrix operations. The modeling errors of simple model are compensated by the sliding mode observer. The design methodology for state of health estimation using dual sliding mode observer has been presented in step by step. The structure of the proposed system is simple and easy to implement, but it shows robust control property against modeling errors and temperature variations. The convergence of proposed observer system has been proved by the Lyapunov inequality equation and the performance of system has been verified by the sequence of urban dynamometer driving schedule test. The test results show the proposed observer system has superior tracking performance with reduced calculation time under the real driving environments.

• Journal of Aerospace System Engineering
• /
• v.4 no.1
• /
• pp.19-22
• /
• 2010

The accuracy and integrity of global navigation satellite systems (GNSS) can be improved by using GNSS augmentation systems. Large ionospheric spatial gradient, during ionosphere storm, is a major threat for using GNSS augmentation systems by increasing the spatial decorrelation between a reference system and users. Ionosphere decorrelation behavior can be analyzed by using dual frequency GPS data. GNSS receivers have their own biases, called inter-frequency bias (IFB) between dual(P1 and P2) frequencies and they must be accurately estimated before computing ionosphere delays. GPS network data in Korea is used to compute each receiver's IFB, and their estimation accuracy and variability are analyzed. IFB estimation methodology to apply for ionosphere gradient analysis is discussed.

• Proceedings of the KIEE Conference
• /
• 1998.11a
• /
• pp.253-255
• /
• 1998

This paper presents the methodology of DSM potential evaluation and estimation of commercial sector in Korea. In general, the estimation process of the potential savings of DSM activities include the determination of baseline electricity consumption, the instantaneous technical potential (ITP), the phased technical potential (PTP), the economic potential (EP), and the achievable potential(AP). The purpose of this paper is to provide evaluation process of those DSM potential savings based on top-down approach and apply it to commercial sector.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
• /
• v.29 no.3
• /
• pp.163-174
• /
• 2023

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model's Mean Squared Error (MSE). Using both data from the literature and "synthetic" data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.

• Journal of Korean Society of Water and Wastewater
• /
• v.22 no.1
• /
• pp.159-168
• /
• 2008

A special field experiment has been carried out from March 2001 to June 2001 at the Changhowon in Kyunggi to investigate a better methodology for the estimation of dry deposition of pollutions applicable in Korea. In this study, dry deposition plate was used to measure of total and water soluble acidic mass fluxes, and CPRI(Coarse Particle Rotary Impactor), CI(Cascade Impactor) were also used to measure ambient concentrations in various particle size ranges. Sehmel-Hodgson model was used to estimate dry depostion velocity and Weibull probability distribution function was applied to get generalized particle size distribution for the size fractioned concentration data sampled by CPRI and CI. Atmospheric dry deposition fluxes of mass and ionic matters estimated by the various techniques(one-step, multi-step, equi-concentration, subdivision for only the coarse particle range, applying Weibull distribution function, etc.) were compared to flux data sampled by DDP. It was found out that the deposition fluxes estimation methodology calculated by the each particle size range devided by particle size distribution characteristics and the rapidly changed points of deposition velocity using Weibull probability distribution function was the most applicable.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.14 no.3
• /
• pp.208-213
• /
• 2004

This paper is concerned with the theoretical estimation of the single-input single-output(SISO) positive position feedback(PPF) controller and the derivation of the stability conditions for the multi-input multi-output (MIMO) PPF controller. Although the stability condition for the SISO PPF controller was derived in the earlier works, the question regarding the performance estimation of the SISO PPF controller has never been studied theoretically. Hence, the SISO PPF controller for the single degree-of-freedom system was first investigated and then control parameters including gain, the filter frequency, and the damping factor of the PPF controller were analyzed in detail thus providing the design methodology for the SISO PPF controller. In the case of real structure. there are infinite number of natural modes so that some modes are to be controlled by a limited number of actuator and sensor. Based on the theoretical results on the SISO PPF controller, the stability condition for the multi-input multi-output PPF controller was derived when only the few number of modes are to be controlled. The control spillover problem is also discussed in detail.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.13 no.5
• /
• pp.7-14
• /
• 2009

A methodology of design performance estimation for the supersonic impulse turbine was investigated. Relations of similarity condition and test nozzle area ratio were derived. Comparison of efficiencies between the turbines with real nozzle and test nozzle are made numerically and experimentally. The CFD results and test result confirmed that the turbine with test nozzle was able to predict real turbine performance. In addition, design performance of the supersonic impulse turbine also could be estimated using real nozzle in air-medium test. In this case, design efficiency was found at the pressure-ratio and velocity-ratio of similarity condition of test nozzle.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.50 no.6
• /
• pp.27-35
• /
• 2008

This study aims to estimate the optimal probability distribution of daily electricity generation by wind power, in order to contribute in rural green-village planning. Wind power generation is now being recognized as one of the most popular sources for renewable resources over the country. Although it is also being adapted to rural area for may reasons, it is important to estimate the magnitudes of power outputs with reliable statistical methodologies while applying historical daily wind data, for correct feasibility analysis. In this study, one of the well-known statistical methodology is employed to define the appropriate statistical distributions for monthly power outputs for specific rural areas. The results imply that the assumption of normal distributions for many cases may lead to incorrect decision-making and therefore lead to the unreliable feasibility analysis. Subjective methodology for testing goodness of fit for normal distributions on all the cases in this study, provides possibilities to consider the other various types of statistical distributions for more precise feasibility analysis.

• Journal of Korea Water Resources Association
• /
• v.37 no.9
• /
• pp.759-769
• /
• 2004

This study proposes a new methodology to derive the areal reduction factor (ARF) using mixed probability density functions. Estimation of ARFs requires using the simultaneous rainfall data over the basin, which is rarely available in general. The new methodology Proposed in this study uses more available daily rainfall data during a given period, so the mixed probability density functions should be introduced to explain both the rainfall intermittency and variability. This study applied the mixed gamma distribution for the derivation of ARFs for the Keum river basin, and found that the new method is easier for application as well as it provides very comparable results.