• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.9
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• pp.960-966
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• 2007

High-efficient machining, which means to machine a part in the least amount of time, is the most effective tool to improve productivity. In this study, a new feed optimization method based on virtual manufacturing was proposed to realize the high-efficient machining in turning process through the cutting power regulation. The cutting area was evaluated by using the Boolean intersection operation between the cutting tool and workpiece. And the cutting force and power were predicted from the cutting parameters such as feed, depth of cut, spindle speed, specific cutting force, and so on. Especially, the reliability of the proposed optimization method was validated by comparing the predicted and measured cutting forces. The simulation results showed that the proposed optimization method could effectively enhance the productivity in turning process.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.97-106
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• 2011

Both an optimal design of the engine operating strategy and fuel economy prediction technique for a HEV under the vehicle driving condition are very crucial for the development of vehicle fuel economy performance. Thus, in this study, engine operating characteristics of PRIUS III were analyzed with vehicle running conditions and the correlations between vehicle tractive power and fuel consumption were introduced. As a result, fuel economy performance of PRIUS III with various test modes were predicted and verified. Errors of predicted fuel economy were between -5% and -1%.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• The Korean Journal of Nuclear Medicine
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• v.39 no.4
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• pp.257-262
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• 2005

Purpose: Gastric emptying scan (GES) is usually acquired up to 2 hours. Our study investigated whether a fraction of meal-retention in the stomach at 120 minutes (FR120) was predicted from the data measured for 90 minutes by using non-linear curve fitting. We aimed at saving the delayed imaging by utilizing mathematical models. Materials and Methods: Ninety-six patients underwent GES immediately after taking a boiled egg with 74 MBq (2 mCi) Tc-99m DTPA. The patients were divided into Group I ($T_{1/2}\;{\leq}90\;min$) and Group II ($90\;min). Group I (n=51) had 21 men and 30 women, and Group II (n=45) 15 men and 30 women. There was no significant difference in age and sex between the two groups. Simple exponential, power exponential, and modified power exponential curves were acquired from the measured fraction of meal-retention at each time (0, 15, 30, 45, 60, 75, and 90 min) by non-linear curve fitting ($MATLAB^{\circledR}$ 5.3) and another simple exponential fitting was performed on the fractions at late times (60, 75, and 90 min). A predicted FR120 was calculated from the acquired functional formulas. A correlation coefficient between the measured FR120 and the predicted FR120 was computed ($MedCalc^{\circledR}$ 6.0). Results: Correlation coefficients(r) between the measured FR120 and the predicted FR120 of each mathematical functions were as follows: simple exponential function (Group I: 0.8558, Group II: 0.5982, p<0.0001), power exponential function (Group I: 0.8755, Group II: 0.6008, p<0.0001), modified power exponential function (Group I: 0.8892, Group II: 0.5882, p<0.0001), and simple exponential function at the late times(Group I: 0.9085, Group II: 0.6832, p<0.0001). In all the fitting models, the predicted FR120 were significantly correlated with the measured FR120 in Group I but not in Group II. There was no statistically significant difference in correlation among the 4 mathematical models. Conclusion: In the cases with $T_{1/2}\;{\leq}90\;min$, the predicted FR120 is significantly correlated with the measured FR120. Therefore, FR120 can be predicted from the data measured for 90 minutes by using non-linear curve fitting, saving the delayed imaging after 90 minutes when $T_{1/2}\;{\leq}90\;min$ is ascertained.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.474-481
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• 2017

When designing photovoltaic power plants in Korea, the prediction of photovoltaic power generation at the design phase is carried out using PVSyst, PVWatts (Overseas power generation prediction software), and overseas weather data even if the test site is a domestic site. In this paper, for a comparative study to predict power generation using weather information, domestic photovoltaic power plants in two regions were selected as target sites. PVsyst, which is a commercial power generation forecasting program, was used to compare the accuracy between the predicted value of power generation (obtained using overseas weather information (Meteonorm 7.1, NASA-SSE)) and the predicted value of power generation obtained by the Korea Meteorological Administration (KMA). In addition, we have studied ways to improve the prediction of power generation through comparative analysis of meteorological data. Finally, we proposed a revised solar power generation prediction model that considers climatic factors by considering the actual generation amount.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.59.2-59.2
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• 2011

Although there are many activities on the construction of wind farm to produce amount of power from the wind, in practice power productions are not as much as its expected capabilities. This is because a lack of both the prediction of wind resources and the aerodynamic analysis on turbines with far wake effects. In far wake region, there are velocity deficits and increases of the turbulence intensity which lead to the power losses of the next turbine and the increases of dynamic loadings which could reduce system's life. The analysis on power losses and the increases of fatigue loadings in the wind farm is needed to prevent these unwanted consequences. Therefore, in this study velocity deficits have been predicted and aerodynamic analysis on turbines in the far wake is carried out from these velocity profiles. Ainslie's eddy viscosity wake model is adopted to determine a wake velocity and aerodynamic analysis on wind turbines is predicted by the numerical methods such as blade element momentum theory(BEMT) and vortex lattice method(VLM). The results show that velocity recovery is more rapid in the wake region with higher turbulence intensity. Since the velocity deficit is larger when the turbine has higher thrust coefficient, there is a huge aerodynamic power loss at the downstream turbine.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.11a
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• pp.168-169
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• 2005

The optimum design and the performance analysis software called POSEIDON for the HAWT (Horizontal Axis Wind Turbine) was developed by use of BEMT. The Prandtl's tip loss theory was adopted to consider the blade tip loss. The lift and the drag coefficient of S-809 airfoil were predicted via X-FOIL and also the post stall characteristics of S-809 were estimated by the Viterna's equations. All the predicted aerodynamic characteristics are fairly well agreed with the wind tunnel test results, performed by Sommers in Delft university of technology. The rated power of the testing rotor is 20kW(FIL-20) at design conditions. The experimental aerodynamic parameters and the X-FOIL data were used for the power prediction of the FIL-20 respectively. The comparison results shows good agreement in power prediction.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.6
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• pp.669-676
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• 2006

The optimum design and the performance analysis software called POSEIDON for the HAWT (Horizontal Axis Wind Turbine) is developed by use of BEMT, which is the standard computational technique for prediction of power curves of wind turbines. The Prandtl's tip loss theory is adopted to consider the blade tip loss. The lift and the drag coefficient of S-809 airfoil are predicted via X-FOIL and the post stall characteristics of S-809 also are estimated by the Viterna's equations.$^{[13]}$ All the predicted aerodynamic characteristics are fairly well agreed with the wind tunnel test results. performed by Sommers in Delft university of technology. The rated power of the testing rotor is 20kW(FIL-20) at design conditions. The experimental aerodynamic parameters and the X-FOIL data are used for the power Prediction of the FIL-20 respectively The comparison results shows good agreement in power prediction.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.782-785
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• 2006

The Power Flow Analysis(PFA) can be effectively used to predict structural vibration in medium-to-high frequency range. In this paper, Power Flow Finite Element Method (PFFEM) based on PFA has been used to predict the vibration of an automobile door. The predicted results for the frequency response function of the door have been compared with corresponding experimental results. In the experiment, the automobile door has been divided into several subsystems and the loss factor of each subsystem has been measured. The input mobility at a source point has been also measured. The data for the loss factors and the input mobility have been used as the input data to predict the vibration of the automobile door with PFFEM. The frequency response functions have been measured over the surface of the door. The comparison between the experimental results and the predicted results for the frequency response functions showed that PFFEM could be an effective tool to predict the structural vibration.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.10
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• pp.660-666
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• 2015

Surface acoustic wave(SAW) device is used for transporting and patterning micro-scale particles such as cells. In this research, velocity of particles was investigated moved by SAW device with two types of interdigital electrode transducers(IDTs) under various conditions. SAW devices which have single IDTs and double IDTs were designed and fabricated. On the previous studies, resultant velocities of particles were predicted considering output power and power ratio between IDTs-shape. For more accurate prediction, power loss in SAW device and a power difference between two types of IDTs-shape were considered. Maximum error between the test results and predicted values was 5 % so the power loss must be considered in the velocity prediction of the particles.