• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.06b
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• pp.171-177
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• 1998

The part Ⅰ was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Unfrared (NIR) Reflectance analyzer. The purpose of this study(part Ⅱ) is to measure fundamental data required for the prediction of rice quality , and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undreid paddy powder by using Near Infrared (NIR) Reflectance analyzer. The results of this study were summarized as follows . The predicted values of protein contents obtained from the undried paddy powder were will correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to be lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.

• Korean Journal of Artificial Intelligence
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• v.6 no.1
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• pp.16-20
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• 2018

The development of modern information technology has increased the amount of big data about patients' information and diseases. In this study, we developed a prediction model of diabetes using the health examination data provided by the public data portal in 2016. In addition, we graphically visualized diabetes incidence by sex, age, residence area, and income level. As a result, the incidence of diabetes was different in each residence area and income level, and the probability of accurately predicting male and female was about 65%. In addition, it can be confirmed that the influence of X on male and Y on female is highly to affect diabetes. This predictive model can be used to predict the high-risk patients and low-risk patients of diabetes and to alarm the serious patients, thereby dramatically improving the re-admission rate. Ultimately it will be possible to contribute to improve public health and reduce chronic disease management cost by continuous target selection and management.

• Journal of the Optical Society of Korea
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• v.16 no.1
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• pp.57-62
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• 2012

Using continuous wave near-infrared spectroscopy, we measured time-resolved concentration changes of oxy-hemoglobin and deoxy-hemoglobin from the primary motor cortex following finger tapping tasks. These data were processed using partial least squares-discriminant analysis (PLS-DA) to develop a prediction model for a brain-computer interface. The tasks were composed of a series of finger tapping for 15 sec and relaxation for 45 sec. The location of the motor cortex was confirmed by the anti-phasic behavior of the oxy- and deoxy-hemoglobin changes. The results were compared with those obtained using the hidden Markov model (HMM) which has been known to produce the best prediction model. Our data imply that PLS-DA makes better judgments in determining the onset of the events than HMM.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.11
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• pp.2812-2822
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• 2000

Several methods have been developed to predict the rupture time of the boiler tubes in thermal power plant. However, existing life prediction methods give very conservative value at operating stress of power plant and rupture strain cannot be well estimated. Therefore, in this study, rupture time and strain prediction method accounting for creep, corrosion and heat transfer is newly proposed and compared with the current research results. The creep damage evolves by continuous cavity nucleation and constrained cavity growth. The corrosion damage evolves by steam side and fire side corrosion. The results showed good correlation between the theoretically predicted rupture time and the current research results. And rupture strain may be well estimated by using the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.253-256
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• 1996

Rainfall is one of the major and complicated elements of hydrologic system. Accurate prediction of rainfall is very important to mitigate storm damage. The neural network is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. In this dissertation, rainfall predictions by the neural network theory were presented. A multi-layer neural network was constructed. The network learned continuous-valued input and output data. The network was used to predict rainfall. The online, multivariate, short term rainfall prediction is possible by means of the developed model. A multidimensional rainfall generation model is applied to Seoul metropolitan area in order to generate the 10-minute rainfall. Application of neural network to the generated rainfall shows good prediction. Also application of neural network to 1-hour real data in Seoul metropolitan area shows slightly good predictions.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2676-2681
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• 2008

In previous studies, Convex cyclone are proposed to reduce pressure loss which are design cyclone wall with a single continuous curve. Studies about a prediction model for pressure loss and cut-size has focused on conventional cylinder-on-con cyclone. Therefore, the models do not perform well for uncommon design. In this study, a predict model for pressure loss and cut-size depend on cyclone wall curvature are developed. The tangential velocity below vortex-finder is obtained with consideration about friction area and momentum loss on the cyclone wall, and with this the variation of vortex-core and core velocity is obtained. Pressure loss is predicted using a Rankine vortex hypothesis. The prediction results are well agreed with experiments and CFD results.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.62-66
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• 2002

An attempt of using SOCMAC neural network for the prediction of a nonlinear sequence, which is generated by Mackey-Glass equation, is reported. The ,report shows the SOCMAC can handle a system with multi-dimensional continuous inputs, which has been considered very difficult, if not impossible, task to be implemented by a CMAC neural network because of a huge amount of memory required. Also, an improved training method based on the variable receptive fields is proposed. The Performance ranged somewhere around those of TDNN and BP neural networks.

• Journal of Sensor Science and Technology
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• v.28 no.2
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• pp.88-93
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• 2019

This paper is concerned with distributed fusion moving average prediction for continuous-time linear stochastic systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal local moving average predictors. The distributed fusion prediction algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The derivation of equations for error cross-covariances between the local predictors is the key of this paper. Example demonstrates effectiveness of the distributed fusion moving average predictor.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2C
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• pp.59-69
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• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• Transactions of Materials Processing
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• v.11 no.1
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• pp.24-35
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• 2002

Recent trend and future prospect of flat rolling of steel has been summarized based on the earlier reports. Key technology in the plate rolling is to have ultra fine microstructure having high resistance against crack propagation during application. Heavy accelerated cooling facility and high power rolling mill will be helpful to develope the high toughness steel. Precise modeling of properly prediction based on deformation and transformation imposed on microstructure of steel during processing is highly anticipated. For the hot strip rolling process, new trend is lies on the production of ultra-thin gauged hot strip to substitute cold rolled strip. For the substitution of cold rolled strip into hot rolled strip widely, high formable property of hot strip is highly required. For the formabilit, the ferritic rolling of extra low carbon steel under high lubricated condition is essential. Recently introduced semi-continuous thin slab and rolling mill line is very plausible to develope those kinds of products easily In the view groin facility combination. New idea to modify the existing continuous hot strip mill line to produce the ultra thin-gauged hot strip in an economic way is suggested in this report.