• 동력기계공학회지
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• 제17권5호
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• pp.86-93
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• 2013

This paper proposes the used of genetic algorithm for optimizing PI controller and describes the dynamic modeling simulation for the permanent magnet synchronous motor driven by simplified vector control with the aid of MATLAB-Simulink environment. Furthermore, three kinds of error criterion minimization, integral absolute error, integral square error, and integral time absolute error, are used as objective function in the genetic algorithm. The modeling procedures and simulation results are described and presented in this paper. Computer simulation results indicate that the genetic algorithm was able to optimize the PI controller and gives good control performance of the system. Moreover, simplified vector control on permanent magnet synchronous motor does not need to regulate the direct axis component current. This makes simplified vector control of the permanent magnet synchronous motor very useful for some special applications that need simple control structure and low cost performance.

• Journal of the Korean Statistical Society
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• 제8권1호
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• pp.23-36
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• 1979

In the multiple linear regression model a class of weighted least absolute error estimaters, which minimize the sum of weighted absolute residuals, is proposed. It is shown that the weighted least absolute error estimators with Wilcoxon scores are equivalent to the Koul's Wilcoxon type estimator. Therefore, the asymptotic efficiency of the proposed estimator with Wilcoxon scores relative to the least squares estimator is the same as the Pitman efficiency of the Wilcoxon test relative to the Student's t-test. To find the estimates the iterative weighted least squares method suggested by Schlossmacher is applicable.

• 한국군사과학기술학회지
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• 제22권5호
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• pp.674-686
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• 2019

In this paper, we will discuss the absolute angular error control mode for the Gun/Turret driving system. The Gun/Turret driving controller receives absolute angular error calculated from the fire control system (FCS). Thus, the Gun/Turret driving controller is subjected to step command to cause residual vibration and system unstable. In order to reduce residual vibration and to ensure the system stability, we propose an error motion profile method with two types of trapezoidal and S-Curve. The validity of the proposed error motion profile method is confirmed via simulation by observing that the resulting position error, driving power, and power density satisfied the control performance.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2556-2563
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• 2021

Metaheuristic algorithms can work well in solving or optimizing problems, especially those that require approximation or do not have a good analytical solution. Particle swarm optimization (PSO) is one of these algorithms. The response quality of these algorithms depends on the objective function and its regulated parameters. The nonlinear nature of the pressurized light-water nuclear reactor (PWR) dynamics is a significant target for PSO. The two-point kinetics model of this type of reactor is used because of fission products properties. The proportional-integral-derivative (PID) controller is intended to control the power level of the PWR at a short-time transient. The absolute error (IAE), integral of square error (ISE), integral of time-absolute error (ITAE), and integral of time-square error (ITSE) objective functions have been used as performance indexes to tune the PID gains with PSO. The optimization results with each of them are evaluated with the number of function evaluations (NFE). All performance indexes achieve good results with differences in the rate of over/under-shoot or convergence rate of the cost function, in the desired time domain.

• 산업경영시스템학회지
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• 제38권3호
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• pp.159-168
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• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

• 에너지공학
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• 제27권4호
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• pp.103-110
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• 2018

본 논문에서는 Mean Absolute Percentage Error (이하 MAPE)와 Symmetric Mean Absolute Percentage (이하 sMAPE)의 새로운 접근법을 이용한 시계열 예측 모델의 평가 방법을 소개한다. MAPE, sMAPE에는 다음과 같은 문제점이 있다. 데이터 집합에서 관측 값이 0일 경우 평가할 수 없고, 관측 값이 0에 매우 가깝다면 과도한 평가 값을 측정한다. 관측 값과 예측 값 간에 동일한 오차를 가지더라도 다른 값으로 평가하는 문제도 가지고 있다. 동일한 오류 값이 과대 예측되었는지 아니면 과소 예측되었는지에 따라 다른 평가 값을 측정하거나 관측 값의 부호와 예측 값의 부호가 서로 다르면 그 오차는 평가 값에 반영되지 않는다. 이러한 문제는 Maximum Mean Absolute Percentage Error (이하 mMAPE)에 의해 해결하였다. 우리는 MAPE 평가 방법의 분모에서 관측 값을 사용하는 대신 최대 절대 값을 사용했다. 최대 절대 값이 1보다 작으면 분모를 제거하여 0 값이 정의되지 않은 문제와 미세한 값일 경우 과대 측정되는 문제를 해결하였다. Beijing PM2.5의 온도 데이터와 시뮬레이션 데이터를 통해 mMAPE와 다른 평가 방법들의 결과 값을 비교하였으며, 위의 문제들을 해결할 수 있음을 검증하였다.

• The Journal of the Acoustical Society of Korea
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• 제16권3E호
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• pp.62-68
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• 1997

In this paper, a modified adaptive sign (MAS) algorithm based on a mixed norm error criterion is proposed. The mixed norm error criterion of be minimized is constructed as a combined convex function of the mean-absolute error and the mean-absolute error to the third power. A convergence analysis of the MAS algorithm is also presented. Under a set of mild assumptions, a set of nonlinear evolution equations that characterizes the statistical mean and mean-squared behavior of the algorithm is derived. Computed simulations are carried out to verify the validity of our derivations.

• International Journal of Advanced Culture Technology
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• 제3권2호
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• pp.161-170
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• 2015

Mean separated histogram equalization in order to preserve the original mean brightness has been proposed. To provide the minimum mean brightness error after the histogram modification, the input image's histogram is successively divided by the factor of 2 until the mean brightness error is satisfied the defined threshold. Then each divided group or sub-histogram will be independently equalized based on the proportional input mean. To provide the overall minimum mean brightness error, each group will be controlled by adding some certain pixels from the adjacent grey level of the next group for giving its mean near by the corresponding the divided mean. However, it still exists some little error which will be put into the next adjacent group. By successive dividing the original histogram, we found that the absolute mean brightness error is gradually decreased when the number of group is increased. Therefore, the error threshold is assigned in order to automatically dividing the original histogram for obtaining the desired absolute mean brightness error (AMBE). This process will be applied to the color image by treating each color independently.

• 한국정밀공학회지
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• 제19권9호
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• pp.51-58
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• 2002

Vehicle dynamics control systems are. complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed is good results in normal conditions. But the estimation error in severe braking is discontented. In this paper, we estimate the absolute vehicle speed by using the wheel speed data from standard 50-tooth anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used. Ten algorithms are verified experimentally to estimate the absolute vehicle speed and one of those is perfectly shown to estimate the vehicle speed with a 4% error during a braking maneuver.

• 한국인터넷방송통신학회논문지
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• 제19권5호
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• pp.79-85
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• 2019

본 연구에서는 Long Short Term Memory (LSTM) 신경망과 Gated Recurrent Unit(GRU) 신경망을 Internet of Things (IoT) 파워미터에 적용하여 단기 전력사용량 예측방법을 제안하고, 실제 가정의 전력사용량 데이터를 토대로 예측 성능을 분석한다. 성능평가 지표로써 Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Percentage Error (MPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE)를 이용한다. 실험 결과는 GRU 기반의 모델이 LSTM 기반의 모델에 비해 MAPE 기준으로 4.52%, MPE 기준으로 5.59%만큼의 성능개선을 보였다.