• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.119-122
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• 2000

In this paper, we implement an elementary system to correct accent, pronunciation, and intonation in English spoken by non-native English speakers. In case of the accent evaluation, energy and pitch information are used to find stressed syllables, and then we extract the segment information of input patterns using a dynamic time warping method to discriminate and evaluate accent position. For the pronunciation evaluation. we utilize the segment information using the same algorithm as in accent evaluation and calculate the spectral distance measure for each phoneme between input and reference. For the intonation evaluation. we propose nine pattern of slope to estimate pitch contour, then we grade test sentences by accumulated error obtained by the distance measure and estimated slope. Our result shows that 98 percent of accent and 71 percent of pronunciation evaluation agree with perceptual measure. As the result of the intonation evaluation. system represent the similar order of grade for the four sentences having different intonation patterns compared with perceptual evaluation.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.79-85
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• 2019

In this paper, we propose a short-term power forecasting method by applying Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network to Internet of Things (IoT) power meter. We analyze performance based on real power consumption data of households. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean squared error (MSE), and root mean squared error (RMSE) are used as performance evaluation indexes. The experimental results show that the GRU-based model improves the performance by 4.52% in the MAPE and 5.59% in the MPE compared to the LSTM-based model.

• The Journal of the Acoustical Society of Korea
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• v.27 no.2E
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• pp.64-68
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• 2008

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square(VEDNSS LMS) algorithm. Adopting VEDNSS LMS results in higher system complexity, but noise is reduced providing fast convergence speed Mathematical analysis demonstrates that tap coefficient misadjustment converges. This is confirmed by computer simulation with the proposed algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.541-542
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• 2013

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.5
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• pp.93-99
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• 2002

In general, manufacturing error is originated from bad material, machine tool defection and tool breakage. When the manufacturing process is stable, the most of error come from the tool wear. In common on-machine measurement teaching probe and touch sensor are widely used however in this paper the electric touch point type automatic tool compensation system is developed the performance of it is validated and effective operating is proposed.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2006.08a
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• pp.109-109
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• 2006

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.293-297
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• 1998

A method for the evaluation and the compensation of the vertical milling machine is presented. The method used a mathmatical model of thermal deformation based on temperatur variations of the machine and the environment. It follows an empirical approach and requires low cost equipment to be applied. According to this study, machine error caused by thermal deformation will be reduced to about 1/6.

• Journal of Korean Society for Quality Management
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• v.22 no.3
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• pp.85-98
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• 1994

In current coordinate measuring machine practice, there are no commonly accepted sample sizes for estimating form errors which have a statistical confidence. Practically, sample size planning is important for the geometrical tolerance inspection using a coordinate measuring machine. We determine and validate appropriate sample sizes for form error estimation. Also, we develop form error estimation methods with certain confidence levels based on the obtained sample sizes in various form errors: straightness, flatness, circularity, and cylindericity.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.597-598
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• 2009