• Journal of KIISE:Computing Practices and Letters
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• v.6 no.6
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• pp.635-642
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• 2000

In this paper, we proposed an efficient document conversion mechanism to provide a adaptive web document to mobile terminals. We also proposed a RHTML(Reduced HTML) to archive the adaptive tag reduction. Markup error correction process in the proposed adaptive document conversion mechanism converts a HTML(HyperText Markup Language) document into a XML(Extensible Markup Language) application document. This. process makes web document easy to handle with a DOM (Document Object Mode)) as the tree model and removes the hardware overhead in mobile terminals. Also, tag reduction process provides the adaptive web document with three DTD(Document Type Definition)s in the RHTML.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.949-956
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• 2020

It's important to measure fuel consumption of vehicles. It's possible to monitor green house gas from vehicles for various traffic conditions with the measured data. It's effective to eco-drive for drivers with fuel consumption data also. There's a display of fuel consumption in the modern vehicles, but it's not useful to get the data from the display. An estimating method for fuel consumption of a vehicle is suggested in the study. It's a simple but an effective method using GPS data. The GPS data(speed, acceleration, road slope) and vehicle data(weight, frontal area, model year, certified fuel economy) is necessary to estimate the fuel consumption for the method. It calculates driving resistance force to estimate engine power. Then it estimates the necessary fuel consumption to maintain the engine power with fuel-power conversion factor. The conversion factor is corrected with certified fuel economy, model year and rated power. The precision of the methods is checked with road test data. The test driving data was measured with GPS and OBD. The error of the estimated fuel consumption for the measured one is about 1.8%. But the error is large for the 1000 and 100 data number from the total data number of about 10,000. The error is from the larger change range of the GPS data than the one of the measured fuel consumption data. But the proposed estimating method is useful to percept the fuel consumption change for better fuel economy with simple gadget like smart phone or other GPS instruments.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.63-73
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• 2000

Internet becomes very common tool for communication and data sharing. Virtual reality(VR) on web browser, and virtual prototyping and virtual manufacturing is widely used in many engineering folds. The reduction of overall development process and error minimization during data conversion becomes very crucial where sharing data via Internet and VR. This paper deals with the advantage and disadvantage of VRML format used in RP(Rapid Prototyping), and a software for RP data preparation is developed. If VRML format as an international standard for VR, is replaced with STL format, the weak points of STL format can be overcome and the technique related to virtual prototyping and virtual manufacturing can be addressed more systematically by sharing the data. The system developed in this work shows a good window to get access to a more realistic observation of an object fur an RP system from a long remote sites in a more systematic way.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.4
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• pp.207-216
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• 2014

Total organic carbon (TOC) is an important indicator used as an direct biological index in the research field of the marine carbon cycle. It is possible to produce the sufficient TOC estimation data by using the Chemical Oxygen Demand(COD) data because the available TOC data is relatively poor than the COD data. The outlier detection and treatment (removal) should be carried out reasonably and objectively because the equation for a COD-TOC conversion is directly affected the TOC estimation. In this study, it aims to suggest the optimal regression model using the available salinity, COD, and TOC data observed in the Korean coastal zone. The optimal regression model is selected by the comparison and analysis on the changes of data numbers before and after removal, variation coefficients and root mean square (RMS) error of the diverse detection methods of the outlier and influential observations. According to research result, it is shown that a diagnostic case combining SIQR (Semi - Inter-Quartile Range) boxplot and Cook's distance method is most suitable for the outlier detection. The optimal regression function is estimated as the TOC(mg/L) = $0.44{\cdot}COD(mg/L)+1.53$, then determination coefficient is showed a value of 0.47 and RMS error is 0.85 mg/L. The RMS error and the variation coefficients of the leverage values are greatly reduced to the 31% and 80% of the value before the outlier removal condition. The method suggested in this study can provide more appropriate regression curve because the excessive impacts of the outlier frequently included in the COD and TOC monitoring data is removed.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.5
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• pp.415-421
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• 2014

This study proposes a model-based predictive control for interleaved multi-phase DC/DC converters. The power values necessary to adjust the output voltage in the succeeding are predicted using a converter model. The output power is controlled by selecting the optimal duty cycle. The proposed method does not require controller loops and modulators for converter switching. This method can control the converter by calculating the optimal duty cycle, which minimizes the error between the reference and actual output voltage. The effectiveness of the proposed method is verified through simulations and experiments.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.347-358
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• 2019

Lambertian cloud model (Lambertian Cloud Model) is the simplified cloud model which is used to effectively retrieve the vertical ozone distribution of the atmosphere where the clouds exist. By using the Lambertian cloud model, the optical characteristics of clouds required for radiative transfer simulation are parametrized by Optical Centroid Cloud Pressure (OCCP) and Effective Cloud Fraction (ECF), and the accuracy of each parameter greatly affects the radiation simulation accuracy. However, it is very difficult to generalize the vertical ozone error due to the OCCP error because it varies depending on the radiation environment and algorithm setting. In addition, it is also difficult to analyze the effect of OCCP error because it is mixed with other errors that occur in the vertical ozone calculation process. This study analyzed the ozone retrieval error due to OCCP error using two methods. First, we simulated the impact of OCCP error on ozone retrieval based on Optimal Estimation. Using LIDORT radiation model, the radiation error due to the OCCP error is calculated. In order to convert the radiation error to the ozone calculation error, the radiation error is assigned to the conversion equation of the optimal estimation method. The results show that when the OCCP error occurs by 100 hPa, the total ozone is overestimated by 2.7%. Second, a case analysis is carried out to find the ozone retrieval error due to OCCP error. For the case analysis, the ozone retrieval error is simulated assuming OCCP error and compared with the ozone error in the case of PROFOZ 2005-2006, an OMI ozone profile product. In order to define the ozone error in the case, we assumed an ideal assumption. Considering albedo, and the horizontal change of ozone for satisfying the assumption, the 49 cases are selected. As a result, 27 out of 49 cases(about 55%)showed a correlation of 0.5 or more. This result show that the error of OCCP has a significant influence on the accuracy of ozone profile calculation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.70-75
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• 2014

Super-resolution near-field structure (super-RENS) read-out samples are affected by a nonlinear and noncausal channel, which results in inter-symbol interference (ISI). In this study, we investigate asymmetry or domain bloom in super-RENS in terms of equalization. Domain bloom is caused by writing process in optical recording. We assume in this work that the asymmetry symbol conversion scheme is to generate asymmetric symbols, and then a linear finite impulse response filter can model the read-out channel. For equalizing this overall nonlinear channel, the read-out signals are deconvolved with the finite impulse response filter and its output is decided based on the decision rule table that is developed from the asymmetry symbol conversion scheme. The proposed equalizer is investigated with the simulations and the real super-RENS samples in terms of raw bit error rate.

• Current Optics and Photonics
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• v.8 no.3
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• pp.300-306
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• 2024

To solve the nozzle swing angle non-contact measurement problem, we present a nozzle pose estimation algorithm involving weighted measurement uncertainty based on rotation parameters. Firstly, the instantaneous axis of the rocket nozzle is constructed and used to model the pivot point and the nozzle coordinate system. Then, the rotation matrix and translation vector are parameterized by Cayley-Gibbs-Rodriguez parameters, and the novel object space collinearity error equation involving weighted measurement uncertainty of feature points is constructed. The nozzle pose is obtained at this step by the Gröbner basis method. Finally, the swing angle is calculated based on the conversion relationship between the nozzle static coordinate system and the nozzle dynamic coordinate system. Experimental results prove the high accuracy and robustness of the proposed method. In the space of 1.5 m × 1.5 m × 1.5 m, the maximum angle error of nozzle swing is 0.103°.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.5
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• pp.62-68
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• 1985

Recently, because of its pecularity, the Residue Number System has been considerable interest in spacial purpose signal processing. In this paper, a realization of digital filter based on residue properties were considered. To get the residue code directly from the analog input, a direct analog to residue conversion scheme were proposed. Finally error analysis is discussed and scaling error model is established.

• Journal of Korean Society of Forest Science
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• v.98 no.4
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• pp.409-416
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• 2009

Forest biomass estimation is essential for greenhouse gas inventories and terrestrial carbon accounting. Remote sensing allows for estimating forest biomass over a large area. This study was conducted to estimate forest biomass and to produce a forest biomass map for Muju county using forest biomass conversion table developed by field plot data from the 5th National Forest Inventory and Landsat TM-5. Correlation analysis was carried out to select suitable independent variables for developing regression models. It was resulted that the height class, crown closure density, and age class were highly correlated with forest biomass. Six regression models were used with the combination of these three stand variables and verified by validation statistics such as root mean square error (RMSE) and mean bias. It was found that a regression model with crown closure density and height class (Model V) was better than others for estimating forest biomass. A biomass conversion table by model V was produced and then used for estimating forest biomass in the study site. The total forest biomass of the Muju county was estimated about 8.8 million ton, or 128.3 ton/ha by the conversion table.