• Journal of the Optical Society of Korea
• /
• v.14 no.1
• /
• pp.22-27
• /
• 2010

Bit error rate (BER) monitoring is the ultimate goal of performance monitoring in all digital transmission systems as well as optical fiber transmission systems. To achieve this goal, optimization of the decision threshold must also be considered because BER is dependent on the level of decision threshold. In this paper, we analyze a pseudo-error counting scheme and propose an algorithm to achieve both BER monitoring and adaptive decision threshold optimization in optical fiber transmission systems. To verify the effectiveness of the proposed algorithm, we conduct computer simulations in both Gaussian and non-Gaussian distribution cases. According to the simulation results, BER and the optimum decision threshold can be estimated with the errors of < 20% and < 10 mV, respectively, within 0.1-s processing time in > 40-Gb/s transmission systems.

• Journal of the Korea Society for Simulation
• /
• v.26 no.3
• /
• pp.55-63
• /
• 2017

In R&D of artillery system, error budget method is used to predict artillery firing error without field firing test. The error budget method for artillery has been consistently developed but apply for practical R&D of the weapon system has been avoided because of lacks of error budget source information. The error budget source is composed of every detailed error components which affect total distance and deflection error of artillery, and most of them are difficult to be calculated or measured. Also with the inaccuracy of source information, simulated error result dose not reflect real firing error. To resolve that problem, an optimization algorithm is adopted to figure out error budget sources from existing filed firing test. The method of finding input parameter estimation which is commonly used in aerodynamics was applied. As an optimization algorithm, CMA-ES is used and presented in the paper. The error budget sources which are figured out by the presented method can be applied to compute ROC of new weapon systems and may contribute to an improvement of accuracy in artillery.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.25 no.3
• /
• pp.227-234
• /
• 2020

This paper presents the methods for the modeling and parameter optimization of the electric vehicle battery. The state variables of the battery are defined, and the test methods for battery parameters are presented. The state-space equation, which consists of four state variables, and the output equation, which is a combination of to-be-determined parameters, are shown. The parameter optimization method is the key point of this study. The least square of the modeling error can be used as an initial value of the multivariable function. It is equivalent to find the minimum value of the error function to obtain optimal parameters from multivariable function. The SIMULINK model is presented, and the 10-hour full operational range test results are shown to verify the performance of the model. The modeling error for 25 degrees is approximately 1% for full operational ranges. The comments to enhance modeling accuracy are shown in the conclusion.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.700-702
• /
• 2004

A local-step optimization method is proposed to supplement the global-step optimization methods which adopt online update mode of internal weights and error energy as stop criterion in learning of multilayer perceptrons (MLPs). This optimization method is applied to the standard online error backpropagation(EBP) and the performance is evaluated for a speaker verification system.

• ETRI Journal
• /
• v.45 no.1
• /
• pp.119-130
• /
• 2023

We propose a quantized gradient search algorithm that can achieve global optimization by monotonically reducing the quantization step with respect to time when quantization is composed of integer or fixed-point fractional values applied to an optimization algorithm. According to the white noise hypothesis states, a quantization step is sufficiently small and the quantization is well defined, the round-off error caused by quantization can be regarded as a random variable with identically independent distribution. Thus, we rewrite the searching equation based on a gradient descent as a stochastic differential equation and obtain the monotonically decreasing rate of the quantization step, enabling the global optimization by stochastic analysis for deriving an objective function. Consequently, when the search equation is quantized by a monotonically decreasing quantization step, which suitably reduces the round-off error, we can derive the searching algorithm evolving from an optimization algorithm. Numerical simulations indicate that due to the property of quantization-based global optimization, the proposed algorithm shows better optimization performance on a search space to each iteration than the conventional algorithm with a higher success rate and fewer iterations.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.10 no.2
• /
• pp.128-133
• /
• 2010

This paper presents a method for estimating the parameters of dynamic models for induction motor dominating loads. Using particle swarm optimization, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs, active and reactive power demands and satisfying the steady-state error criterion.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2004.10a
• /
• pp.75-78
• /
• 2004

A mesh generation algorithm adapted to the mesh density map using the Delaunay mesh generation technique is developed. In the finite element analyses of the forging processes, the numerical error increases as the process goes on because of discrete property of the finite elements or severe distortion of elements. Especially, in the region where stresses and strains are concentrated, the numerical discretization error will be highly increased. However, it is too time consuming to use a uniformly fine mesh in the whole domain to reduce the expected numerical error. Therefore, it is necessary to construct locally refined mesh at the region where the error is concentrated such as at the die corner. In this study, the point insertion algorithm is used and the mesh size is controlled by moving nodes to optimized positions according to a mesh density map constructed with a posteriori error estimation. An optimization technique is adopted to obtain a good position of nodes. And optimized smoothing techniques are also adopted to have smooth distribution of the mesh and improve the mesh element quality.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.5
• /
• pp.611-616
• /
• 2008

Error correcting codes (ECCs) are commonly used to protect against the soft errors. Single error correcting and double error detecting (SEC-DED) codes are generally used for this purpose. The proposed approach in this paper selectively reduced power consumption, delay, and area in single-error correcting, double error-detecting checker circuits that perform memory error correction. The multi-objective genetic algorithm is employed to solve the non -linear optimization problem. The proposed method allows that user can choose one of different non-dominated solutions depending on which consideration is important among them. Because we use multi-objective genetic algorithm, we can find various dominated solutions. Therefore, we can choose the ECC according to the important factor of the power, delay and area. The method is applied to odd-column weight Hsiao code which is well- known ECC code and experiments were performed to show the performance of the proposed method.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.2
• /
• pp.1-13
• /
• 2020

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering "demand spike" during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.45 no.1
• /
• pp.43-49
• /
• 2008

In this paper, a method using the Ant Colony Optimization(ACO) is proposed for reducing the power consumption of memory ECC checker circuitry which provide Single-Error Correcting and Double-Error Detecting(SEC-DED). The H-matrix which is used to generate SEC-DED codes is optimized to provide the minimum switching activity with little to no impact on area or delay using the symmetric property and degrees of freedom in constructing H-matrix of Hsiao codes. Experiments demonstrate that the proposed method can provide further reduction of power consumption compared with the previous works.