• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.27-34
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• 2009

Recently, power saving with high performance is one of the hot issues in the mobile systems. Various technologies are introduced to achieve low-power processors, which include sub-micron semiconductor fabrication, voltage scaling, speed scaling and etc. In this paper, we introduce a new method that reduces of energy loss at the data cache. Our methods take the benefits in terms of speed and energy loss using selective way precharging of way prediction with concurrent way selecting. By the simulation results, our method achieves 10.2% energy saving compared to the way prediction method, and 56.4% energy saving compared to the common data cache structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6A
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• pp.371-378
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• 2003

In Code Division Multiple Access (CDMA) system, the cell capacity is defined as the number of available channels in a cell, which is limited by the interferences. When a new call is accepted at its home cell, this adds the interference to the home and its neighboring cells. This paper proposes a call admission control based on mobile transmission power prediction. The home cell has enough capacity to admit new call and if home cell would have admitted a new call, it calculates the mobile transmission power. Also, its neighboring cell can predict the amount of interference using the predicted mobile transmission power. Thus, the new mobile is accepted by its home cell if QoS(Quality Of Service) is guaranteed in its neighboring cells. The simulation result shows that the proposed scheme largely reduces the outage probability in the neighboring cells.

• Journal of Korea Multimedia Society
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• v.24 no.9
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• pp.1251-1260
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• 2021

This paper discusses missing data processing using simple moving average (SMA) and kalman filter. Also SMA and kalman predictive value are made a comparative study. Time series analysis is a generally method to deals with time series data in photovoltaic field. Photovoltaic system records data irregularly whenever the power value changes. Irregularly recorded data must be transferred into a consistent format to get accurate results. Missing data results from the process having same intervals. For the reason, it was imputed using SMA and kalman filter. The kalman filter has better performance to observed data than SMA. SMA graph is stepped line graph and kalman filter graph is a smoothing line graph. MAPE of SMA prediction is 0.00737%, MAPE of kalman prediction is 0.00078%. But time complexity of SMA is O(N) and time complexity of kalman filter is O(D²) about D-dimensional object. Accordingly we suggest that you pick the best way considering computational power.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1448-1456
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• 2022

This paper studies a model to diagnose efficiency reduction of inverter using Multilayer Perceptron(MLP). In this study, two inverter data which started operation at different day was used. A Multilayer Perceptron model was made to predict photovoltaic power data of the latest inverter. As a result of the model's performance test, the Mean Absolute Percentage Error(MAPE) was 4.1034. The verified model was applied to one-year-old and two-year-old data after old inverter starting operation. The predictive power of one-year-old inverter was larger than the observed power by 724.9243 on average. And two-year-old inverter's predictive value was larger than the observed power by 836.4616 on average. The prediction error of two-year-old inverter rose 111.5572 on a year. This error is 0.4% of the total capacity. It was proved that the error is meaningful difference by t-test. The error is predicted value minus actual value. Which means that PV system actually generated less than prediction. Therefore, increasing error is decreasing conversion efficiency of inverter. Finally, conversion efficiency of the inverter decreased by 0.4% over a year using this model.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.24-34
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• 2021

This paper predicts the speed-power-rpm relationship in regular head waves using various indirect methods: load variation, direct powering, resistance and thrust identity, torque and revolution, thrust and revolution, and Taylor expansion methods. The subject ship is KVLCC2. The wave conditions are the regular head waves of λ/LPP = 0.6 and 1.0 with three wave steepness ratios at three ship speeds of 13.5, 14.5 and 15.5 knots (design speed). In the case of λ/LPP = 0.6 at design speed, two more wave steepness ratios have been taken into consideration. The indirect methods have been evaluated through comparing the speed-power-rpm relationships with those obtained from the resistance and self-propulsion tests in calm water and in waves. The load variation method has been applied to predict propulsive performances in waves, and to derive overload factors (ITTC, 2018). The overload factors have been applied to obtain propulsive efficiency and propeller revolution. The thrust and revolution method (ITTC, 2014) has been modified.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.749-756
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• 2018

In this study, we developed a prediction model for greenhouse control using machine learning technique. The prediction model was developed using measured data (2016) on greenhouse in the Protected Horticulture Research Institute. In order to improve the predictive performance of model and to ensure the reliability of data, the dimension of the data was reduced by correlation analysis. The dataset were divided into spring, summer, autumn, and winter considering the seasonal characteristics. An artificial neural network, recurrent neural network, and multiple regression model were constructed as a machine leaning based prediction model and evaluated by comparative analysis with real dataset. As a result, ANN showed good performance in selected dataset, while MRM showed good performance in full dataset.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• JSTS:Journal of Semiconductor Technology and Science
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• v.9 no.4
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• pp.187-191
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• 2009

This paper proposes a high-performance architecture of the H.264 intra prediction circuit. The proposed architecture uses the 4-input and 2-input common computation units and common registers for fast and efficient prediction operations. It avoids excessive power consumption by the efficient control of the external and internal memories. The implemented circuit based on the proposed architecture can process more than 60 HD ($1,920{\times}1,088$) image frames per second at the maximum operating frequency of 101 MHz by using 130 nm standard cell library.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1140-1143
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• 2007

The purpose of this study is the improvement of the prediction model of highway noise. It includes the measurement and analysis of predicted noise levels by various programs in types of road and environments. The results of the measurement are compared with the noise levels predicted by improved highway noise prediction model and domestic prediction models, (Improved highway noise prediction model was considered ASJ-2003, ISO-9613 part2 and noise power of road surface types at Korean highway road.)