• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.12
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• pp.724-735
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• 2003

In recent high-performance superscalar processors, the result value of an instruction is predicted to improve instruction-level parallelism by breaking data dependencies. Using those predicted values, instructions are speculatively executed and substantial performance can be gained. It, however, requires additional power consumption due to the frequent access and update of the value prediction table. In this paper, first, the trade-off between the performance improvement and the increased power consumption for value prediction is measured and analyzed. And, in order to reduce additional power consumption without performance loss, the technique of controlling speculative execution with confidence counter and predicting useful instructions is developed. Also, in order to prove the validity, a tool is developed that can simulate processor behavior at cycle-level and measure total energy consumption and power consumption per cycle.

• Tribology and Lubricants
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• v.33 no.1
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• pp.9-14
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• 2017

This paper presents a rotordynamic analysis and the operation of a power turbine applied to a 250 kW super-critical $CO_2$ cycle. The power turbine consists of a turbine wheel and a shaft supported by two fluid film bearings. We use a tilting pad bearing for the power turbine owing to the high speed operation, and employ copper backing pads to improve the thermal management of the bearing. We conduct a rotordynamic analysis based on the design parameters of the power turbine. The dynamic coefficients of the tilting pad bearings were calculated based on the iso-thermal lubrication theory and turbine wheel was modeled as equivalent inertia. The predicted Cambell diagram showed that there are two critical speeds, namely the conical and bending critical speeds under the rated speed. However, the unbalance response prediction showed that vibration levels are controlled within 10 mm for all speed ranges owing to the high damping ratio of the modes. Additionally, the predicted logarithmic decrement indicates that there is no unstable mode. The power turbine uses compressed air at a temperature of $250^{\circ}C$ in its operation, and we monitor the shaft vibration and temperature of the lubricant during the test. In the steady state, we record a temperature rise of $40^{\circ}C$ between the inlet and outlet lubricant and the measured shaft vibration shows good agreement with the prediction.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.23-30
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• 2022

Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM's performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.

• Industrial Engineering and Management Systems
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• v.15 no.2
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• pp.148-155
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• 2016

Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.2
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• pp.580-590
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• 1995

To design and develop a turbocharged engine, ti needs that many study must be preceded about the characteristics of engine performance. Especially, a basic data about deciding target power is urgently needed for which is practically useful for engine design. The power output of turbo-charged engine is dominated by engine speed, displacement, compression ratio, air fuel ratio and charge pressure ratio. Therefore, the independent effect of these factors on power output was clarified from experiment, and the experimental equation to predict the power was founded from there results. The predicted power output from the experimental equation was well coincided with power measured through experiment.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.499-508
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• 2008

Frequency regulation in off-normal conditions has been an important problem in electric power system design/operation and is becoming much more significant today due to the increasing size, changing structure and complexity of interconnected power systems. Increasing economic pressures for power system efficiency and reliability have led to a requirement for maintaining power system frequency closer to nominal value. This paper presents a decentralized frequency control framework using a modified low-order frequency response model containing a proportional-integral(PI) controller. The proposed framework is suitable for near-normal and emergency operating conditions. An $H_{\infty}$ control technique is applied to achieve optimal PI parameters, and an analytic approach is used to analyse the system frequency response for wide area operating conditions. Time-domain simulations with a multi-area power system example show that the simulated results agree with those predicted analytically.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.114-120
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• 2014

In this paper, a method for multi-physics analysis of the temperature-dependent properties of an oil-immersed transformer is discussed. To couple thermal fields with electromagnetic and fluid fields, an algorithm employing a user defined function (UDF) is proposed. Using electromagnetic analysis, electric power loss dependent on temperature rise is calculated; these are used as input data for multi-physics analysis in order to predict the temperature rise. A heat transfer coefficient is applied only at the outermost boundary between transformer and the atmosphere in order to reduce the analysis region. To verify the validity of the proposed method, the predicted temperature rises in high-voltage (HV) and low-voltage (LV) windings and radiators were compared with the experimental values.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.132-138
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• 2000

Complex gear shifting and high speed-reduction ratio reduce the transmission efficiency in power driveline of agricultural tractors. According to a field test, the power transmission efficiency of a tractor in transporting operations was estimated about 70%. However, the actual efficiency was found by the experiment to fluctuate in a range of 56 to 87%. Therefore, the constant efficiency model commonly used for a simulation of power drivelines is not likely to simulate its performance more accurately. In order to predict power transmission efficiency more accurately, a new model was proposed and the new concepts of the maximum efficiency and sticking torque were introduced. The error mean between the measured and the predicted efficiencies was about 2.3% in mean. The new model reflecting the transmission characteristics in the power driveline of tractors could be used to analyze and predict the power transmission performance of tractors more accurately.

• Journal of Biosystems Engineering
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• v.27 no.1
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• pp.33-38
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• 2002

The indicated power of Stirling engine was affected by the heat exchange effectiveness of the regenerator. The temperature difference of working fluid between the expansion and the compression space of Stilting engine depends on the heat exchange effectiveness of the regenerator. The influence of the temperature ratio of expansion space to compression space of Stirling engine on the indicated power was analyzed by using Schmidt analysis in this study. In the Stirring engine, as the temperature ratio increased, the indicated power generally decreased. Therefor, it is necessary to develope the regenerator of high effectiveness. The actual indicated power was shown 64.9 percent of the predicted indicated power in maximum and 47.2 percent of that in minimum due to increased dead volume of engine, the loss of flow friction and heat transfer in the regenerator.