• Journal of the Korean Solar Energy Society
• /
• v.37 no.1
• /
• pp.81-90
• /
• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1993.10a
• /
• pp.482-487
• /
• 1993

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO $_{2}$ Arc Welding using the finite element method. The validity of the above results is demonstrated by experimental elastic stress relief method which is called Holl Drilling Method. The first part of numarical analysis performs a three-dimensional transient heat transfer anslysis, and the second part then uses results of the first part and performs a three-dimensional transient thermo-clasto-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method were used to train a backpropagation neural network to predict residual stress. Architecturally, the finite element method were used to train a backpropagation voltage and the current, a hidden layer to accommodate failure mechanism mapping, and an output layer for residual stress. The trained network was then applied to the prediction of residual stress in the four specimens. The results of predicted residual stress have been very encouraging.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1521-1521
• /
• 2001

The objective of the research being presented was to construct and calibrate a spectrometer for the analysis of single kernels of corn. In light of the difficulties associated with capturing the spatial variability in composition of corn kernels by single-beam spectrometry, a hyperspectral imaging spectrometer was constructed with the intention that it would be used to analyze single kernels of corn for the prediction of moisture and oil content. The spectrometer operated in the range of 750- 1090 nanometers. After evaluating four methods of standardizing the output from the spectrometer, calibrations were made to predict whole-kernel moisture and oil content from the hyperspectral image data. A genetic algorithm was employed to reduce the number of wavelengths imaged and to optimize the calibrations. The final standard errors of prediction during cross-validation (SEPCV) were 1.22% and 1.25% for moisture and oil content, respectively. It was determined, by analysis of variance, that the accuracy and precision of single-kernel corn analysis by hyperspectral imaging is superior to the single kernel reference chemistry method (as tested).

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.12
• /
• pp.78-86
• /
• 1999

The improvement of the water level control is important since it will prevent the steam generator trip so that improve the reliability and credibility of operation system. In this paper, the closed loop system identification is performed which can be used for the system monitoring and prediction of the system response. The model also can be used for the prediction control. Irving model is used as a steam generator model. The plant is an open loop unstable and non-minimum phase system. Fuzzy controller stabilize the system and the stable controller stabilize the system and the stable closed loop system is identified using neural networks. The obtained neural network model is validated using the untrained input and output. The results of computer simulation show the obtained Neural Network model represents the closed loop system well.

• Journal of the Korean Society of Visualization
• /
• v.1 no.2
• /
• pp.58-70
• /
• 2003

Meteorological data contains observation and numerical weather prediction model output data. The computerized analysis and visualization of meteorological data often requires very high computing capability due to the large size and complex structure of the data. Because the meteorological data is frequently formed in multi-variables, 3-dimensional and time-series form, it is very important to visualize and analyze the data in 3D spatial domain in order to get more understanding about the meteorological phenomena. In this research, we developed interactive 3-dimensional visualization techniques for visualizing meteorological data on a PC environment such as volume rendering, iso-surface rendering or stream line. The visualization techniques developed in this research are expected to be effectively used as basic technologies not only for deeper understanding and more exact prediction about meteorological environments but also for scientific and spatial data visualization research in any field from which three dimensional data comes out such as oceanography, earth science, and aeronautical engineering.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.10a
• /
• pp.672-675
• /
• 1995

Because of a sudden growth of the research of fatigue failure, recent machines or structures have been designed by damage tolerance design in many fields. Consequently, it is the most primary factor to clarity the specific character of fatique failure in the design of machines or structures considering reliability. A statistical analysis is required to analyze the outcome of an experiment or a life estimate by reason of that fatigue failure contains lots of random elements. Reliability analysis which has tukenn the place of the existing analyses in the consideration of the uncertainty of a material, is a very efficient way. Even reliability analysis, however, is not a perfect way to analyses the uncertainties of all the materials. This thesis would refer to a newly conceived data analysis that the coefficient of a system could cause the ambiguity of the relationship of an input and output.

• Environmental Engineering Research
• /
• v.21 no.4
• /
• pp.355-364
• /
• 2016

Beijing, as a cradle of modern industry and the third largest metropolitan area in China, faces more responsibilities to adjust industrial structure and mitigate carbon emissions. The purpose of this study is aimed at predicting and comparing industrial carbon emissions of Beijing in ten scenarios under different policy focus, and then providing emission-cutting recommendations. In views of various scenarios issues, system dynamics has been applied to predict and simulate. To begin with, the model has been established following the step of causal loop diagram and stock flow diagram. This paper decomposes scenarios factors into energy structure, high energy consumption enterprises and growth rate of industrial output. The prediction and scenario simulation results shows that energy structure, carbon intensity and heavy energy consumption enterprises are key factors, and multiple factors has more significant impact on industrial carbon emissions. Hence, some recommendations about low-carbon mode of Beijing industrial carbon emission have been proposed according to simulation results.

• Journal of Biosystems Engineering
• /
• v.33 no.1
• /
• pp.63-68
• /
• 2008

This study was carried out to find a useful method to predict hardness of tomato using optical spectrum data. Optical spectrum of reflectance and transmittance data were collected processed by 9 kind of preprocessing methods-normalizations of mean, maximum and range, SNV (standard normal variate), MSC (multiplicative scatter correction), the first derivative and second derivative of Savitzky-Golay and Norris-Gap. With the preprocessed and non-processed original spectrum data, prediction models of hardness of tomato were developed using analytical tools of PLS (partial least squares) and MLR (multiple linear regression) and tested for their validation. The test of validation resulted that the analytical tools of PLS and MLR output similar performances while the transmittance spectra showed much better result than the reflectance spectra.

• Journal of the Korea Society for Simulation
• /
• v.4 no.1
• /
• pp.13-24
• /
• 1995

In general, simulation and analytic method are used for real system analysis. However, or date, there has been only the theoretical works on each approach. Therefore it is required that we study on the relationship between each approaches to obtain more reliable and correct system analysis results. In this paper, using SPN(Stochasitc Petri Net) formalism, we propose the method of output prediction of the DEVS(Discrete Event system Specification) simulation. For this we suggest a transformation algorithm which transform SPN form DEVS formalism based on the event scheduling world view and a verification algorithm for it. We then show an example to apply it to the real system, such that the Grocery Store System.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.7 s.94
• /
• pp.1851-1863
• /
• 1993

A procedure for the prediction of the off-design performance of a gas turbine engine is proposed. The system performance at off-design speed is predicted by coupling the thermodynamic models of a compressor and a turbine. The off-design performance of a compressor is obtained using the stage-stackimg method, while the Ainlay-Mathieson method is used for a turbine. The procedure is applied to a single-shaft gas turbine and its predictability is found satisfactory. The results also show that the net work output increases with the increase of the turbine inlet temperature, while the thermal efficiency is marginal. The maximum thermal efficiency at design point is obtained between the highest pressure ratio and design pressure ratio.