• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• ETRI Journal
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• v.35 no.3
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• pp.431-438
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• 2013

This paper presents a new realization of the implicit root-mean-square (RMS) detector, employing three second-generation current conveyors and MOS transistors. The proposed circuit can be applied in measuring the RMS value of complex, periodic signals, represented in the form of the Fourier series. To verify the theoretical analysis, circuit Simulation Program with Integrated Circuit Emphasis simulations and experiment results are included, showing agreement with the theory.

• 유체기계공업학회:학술대회논문집
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• 2002.12a
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• pp.273-278
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• 2002

The characteristics of tip vortex within a blade tip region were examined experimentally in various flow coefficients by the way of changing tip clearance and blade stagger angle in an axial Low Speed Research Compressor(LSRC). The objective was to identify the unsteady pressure distribution in the blade passage by ensemble average technique acquired from high-frequency response pressure transducers and the tip vortex by root mean square value(RMS value). Data were reduced statistically using phase-lock technique for detailed pressure distributions.

• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1565-1571
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• 2005

Images reconstructed with Maximum-Likelihood Expectation-Maximization (MLEM) algorithm have been observed to have checkerboard effects and have noise artifacts near edges as iterations proceed. To compensate this ill-posed nature, numerous penalized maximum-likelihood methods have been proposed. We suggest a simple algorithm of applying edge detecting process to the MLEM and Bayesian Expectation-Maximization (BEM) to reduce the noise artifacts near edges and remove checkerboard effects. We have shown by simulation that this algorithm removes checkerboard effects and improves the clarity of the reconstructed image and has good properties based on root mean square error (RMS).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.101-104
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• 1995

A acoustic emission (AE) sensor has been used to monitor tool were during milling process. The relation between tool wear and AE RMS (Root mean Square) signal was investigated experimentally. A avaliable monitoring index for monitoring toolwear was newly extracted form AE RMS. And on-line monitoring program was developed. The proposed monitoring system has verified experimentally by roughing end milling titanium alloy with TIN coated HSS tool.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.192-196
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• 2001

This work describes the modeling of cutting process for feedback control based on signal processing in end-milling. Here, cutting force is used to design control model by a variety of schemes which are moving average, ensemble average, peak value, root mean square and analog low-pass filtering. It is expected that each model offers its own peculiar advantage in following cutting force control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.172-177
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• 1998

An experimental study on the prediction of grinding wheel wear by dual detection methods was conducted by the laser displacement and acoustic emission(AE) system. The laser displacement sensor was located above the head of the grinding wheel and the AE sensor was set under the workpiece, where the wheel were condition can be detected. It was found that the dual detection methods by laser displacement system and AE system made it possible to predict the wheel wear. From the experiments, the root mean square(RMS) values both methods was found to be proportional to the grinding wheel wear.

• Computers and Concrete
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• v.14 no.3
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• pp.233-245
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• 2014

In this paper, a rule based Mamdani type fuzzy logic model for prediction of slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes were discussed. In the model steel rebar diameters and development lengths were used as inputs. The FL model and experimental results, the coefficient of determination R2, the Root Mean Square Error were used as evaluation criteria for comparison. It was concluded that FL was practical method for predicting slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.56-60
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• 1996

An experimental study on the grinding temperature and Acoustic Emission(AE) signals due to the different shapes of wheel was conducted. The grinding characteristics by slotted shapes of wheel changed by width and helical angle, were compared with those by general one. Lower grinding temperature was obtained for 30$^{\circ}$ helical angle with 10mm width, Root Mean Square(RMS) values of AE signals were higher for slotted wheel rather than general one.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.817-825
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• 1997

Images reconstructed with EM algorithm have been observed to have checkerboard effects and have large distortions near edges as iterations proceed. We suggest a aimple algorithm of applying line process to the EM and Bayesian EM to reduce the distortions near edges. We show by simulation that this algorithm improves the clarity of the reconstructed image and has good properties based on root mean square error.