• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.2
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• pp.52-59
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• 2008

Recommendation ITU-R S.1062 specifies the performance of digital satellite systems. The performance objectives were given in terms of bit error probability divided by the average number of errors per burst versus percentage of time. This performance objective is highly dependent on the forward error correction(FEC) coding schemes used in the system. This implies that we need an effective way of estimating the error performance of a system by the given FEC scheme. In this paper, we derive theoretical formula to estimate performance measure of digital satellite systems defined in Recommendation ITU-R S.1062. We demonstrate various estimation results, and verify them by comparing to the simulation results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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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.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.345-352
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• 2012

Objective: The aim of this study is to evaluate the accuracy and reliability of Spine-Pelvis Monitor(SPM) that was developed to measure 3-dimensional motion of spine and pelvis using tilt sensor and gyro sensor. Background: The main cause of low back pain is very much associated with the task using the low back and pelvis, but no measurement technique can quantify the both spine and pelvis. Method: For testing the SPM, 125 angles from three anatomical planes were measured three times in order to evaluate the accuracy and reliability. The accuracy of SPM in measuring dynamic motion was evaluated using digital motion analysis system. The motion pattern captured by two measuring methods was compared with each other. In result, the percentage error and Cronbach coefficient alpha were calculated to evaluate the accuracy and reliability. Results: The percentage error was 0.35% in flexion-extension on sagittal plane, 0.43% in lateral bending on coronal plane, and 0.40% in twisting on transverse plane. The Cronbach coefficient alpha was 1.00, 0.99 and 0.99 in sagittal, coronal and transvers plane, respectively. Conclusion: The SPM showed less than 1% error for static measurement, and showed reasonably similar pattern with the digital motion system. Application: The results of this study showed that the SPM can be the measuring method of spine pelvis motion that enhances the kinematic analysis of low back dynamics.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• Korean Journal of Community Nutrition
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• v.2 no.1
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• pp.33-43
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• 1997

In order to investigate the dietary intakes and physical characteristics in college women smokers, interviews using questionnaires were done on 33 smokers and 42 nonsmokers residing in seoul area. General living habits, dietary habits, food consumption frequency and nutrient intake by quick estimation were investigated through direct interviews with subjects. Subjects height, weight and blood pressure were measured, and body fat percentage were statistically analyzed using Bio-electrical Impedence Fatness Analyzer(GIF-891). All data were statistically analyzed by SAS PC package program ; percentage or mean and standard error were examined for each item, and the significant difference was evaluated by chi-square test or Student's t-test at $\alpha$=0.05. In the analysis of taste and food preference, smokers consumed larger amount of alcohol and coffee than nonsmokers ; they also disliked sweet taste. The results of food consumption frequency data also showed that smokers consumed less fish, milk and fruits but consumed more instant foods than nonsmokers. As a result of anthropometric measurements, height, age, and 패요 fat percentage showed no difference, but there was a significant difference in weight, BMI, systolic blood pressure, and diastolic blood pressure. Energy intake in nonsmokers was 1640 ㎉/day(CHO : Pro : Fat=66.0 : 14.7 : 19.3), in smokers. Intakes of calcium, iron, vitamin C, vitamin A, vitamin B1, vitamin B2, and niacin in smokers were not significantly different from those of nonsmokers.

• Journal of Advanced Navigation Technology
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• v.17 no.4
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• pp.377-385
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• 2013

Since the late 1950s, concerted efforts to reduce the accident rate in aviation have yielded unprecedented levels of safety. Although, the overall accident rate has declined considerably over the years, unfortunately reductions in human error-related accidents in aviation have failed to keep pace with the reduction of accidents due to environmental and mechanical factors. Today, a very large percentage of all aviation are attributable, directly or indirectly, to some form of human error. As a result of many study, a range of prevention of human error have been developed. but each of kind is lack of a precision, effectiveness and seem to be considered for aspect of deficiency as an systematic accessibility. So, we're going to analysis the most effective and systematic prevention of human error and study on consolidating method for human error and aviation safety. In this study, several alternatives for the prevention of human errors a priority to understand and solve problems by identifying the implications for human error to be presented.

• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.29-34
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• 1996

In data transmission system, some data are more sensitive to channel errors. Unequal error protection method increases transmission reliability by protecting channel error sensitive data more than other data. However, this method cannot be directly applied to vector quantized signals which are designed by LBG algorithm that assumes no channel distortion in the design, process. Therefore, in this paper, to apply unequal error protection to vector quantized signals, we propose a method which systematically assigns binary indexes to code vectors. We applied the proposed method to the transmission of vector quantized first-order Gauss-Marcov signals assuming that the percentage of the important data is 50%

• Journal of Environmental Science International
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• v.27 no.7
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.7 s.262
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• pp.800-807
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• 2007

Autofrettage process is used for internal forming and sizing of cylinder designed to withstand high internal pressures. Once the tube is autofrettaged, it needs to be machined to its final dimensions both at the bore and its outer surface. This paper presents an analytical analysis and numerical analysis of machined compound cylinder using finite element code, ANSYS10.0. An analytical model for predicting the level of autofrettage following either inner, outer, or combined machining of the compound cylinder is developed for the autofrettage residual stress field is simulated by an autofrettaged pressure. The autofrettaged pressures are obtained by using trying-error method. As autofrettage percentage is 20 % and 40 %, the numerical results are found to be in almost agreement with the analytical ones. However, as autofrettage percentage is 60 %, the numerical results have a little difference with the analytical ones.