• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.57-70
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• 2024

In order to present a predictive drift model, Jeju National University's training ship was tested for about 11 hours and 40 minutes, and 81 samples that selected one of the entire samples at ten-minute intervals were subjected to regression analysis after verifying outliers and influence points. In the outlier and influence point analysis, although there is a part where the wind direction exceeds 1 in the DFBETAS (difference in Betas) value, the CV (cumulative variable) value is 6%, close to 1. Therefore, it was judged that there would be no problem in conducting multiple regression analyses on samples. The standard regression coefficient showed how much current and wind affect the dependent variable. It showed that current speed and direction were the most important variables for drift speed and direction, with values of 47.1% and 58.1%, respectively. The analysis showed that the statistical values indicated the fit of the model at the significance level of 0.05 for multiple regression analysis. The multiple correlation coefficients indicating the degree of influence on the dependent variable were 83.2% and 89.0%, respectively. The determination of coefficients were 69.3% and 79.3%, and the adjusted determination of coefficients were 67.6% and 78.3%, respectively. In this study, a more quantitative prediction model will be presented because it is performed after identifying outliers and influence points of sample data before multiple regression analysis. Therefore, many studies will be active in the future by combining them.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.89-96
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• 2008

This paper investigates the effect of blade deformation, caused by manufacturing inaccuracies, on the performance of a 2-stage axial steam turbine. A high fidelity 3D coordinate Measurement Machine has been employed to obtain the exact geometrical model of the blades. A Streamline Curvature solver was used to predict the overall performance of the turbine. During the manufacturing process of the casts and of the blades themselves, several types of errors can occur which lead to a different geometry from that envisaged by the designer. The main objective of this study is to investigate the effect of those errors on the performance of a 2-stage experimental axial steam turbine. A high fidelity measurement of the actual geometry of both stator and rotor blades has been carried out, using a 3D Coordinate Measurement Machine. The cross sections of the blades obtained by the measurement were compared with those produced by the design process to evaluate the change in blade inlet/exit angles. In addition, the geometrical deviations from the initial design have been subjected to a statistical study in order to locate the nature of the error. The actual(measured) model has been used as input into a Streamline Curvature solver to evaluate its performance. Finally, a comparison with the performance plots of the original geometry has been carried out. A measurable change of efficiency as well as in the total power delivered by the turbine was found. This suggests that the accumulated error caused during the manufacturing procedure plays a significant role in the overall performance of the machine by making it less efficient by more than 1%. Reverse engineering techniques are proposed to predict and alleviate these errors leading thereby to a final design of each stage with improved performance.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.2
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• pp.87-93
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• 2018

This Study Propose A New Approach To Impliment A Intelligent Robot Control Based on Voice Recognition For Smart Factory Automation Since human usually communicate each other by voices, it is very convenient if voice is used to command humanoid robots or the other type robot system. A lot of researches has been performed about voice recognition systems for this purpose. Hidden Markov Model is a robust statistical methodology for efficient voice recognition in noise environments. It has being tested in a wide range of applications. A prediction approach traditionally applied for the text compression and coding, Prediction by Partial Matching which is a finite-context statistical modeling technique and can predict the next characters based on the context, has shown a great potential in developing novel solutions to several language modeling problems in speech recognition. It was illustrated the reliability of voice recognition by experiments for humanoid robot with 26 joints as the purpose of application to the manufacturing process.

• Journal of Korean Tunnelling and Underground Space Association
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• v.13 no.3
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• pp.243-260
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• 2011

To predict TBM performance in design stage is indispensable for its successful application. The NTNU model, one of the representative TBM performance prediction models uses two distinct parameters such as DRI and CLI obtained from three different tests on bored rock cores. Based on DRI and CLI, it is possible to predict TBM advance rate and cutter life in the NTNU model. In this study, NTNU testing methods and their related testing equipments were introduced to measure DRl and CLI for the NTNU model. Then, in order to derive their relationships, the two key parameters measured for 39 domestic rocks were compared with physico-mechanical properties of rock such as uniaxial compressive strength and quartz content. Lastly, the experimental results were also compared with NTNU database to verify their reliability.

• Journal of Korea Trade
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• v.25 no.2
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• pp.58-74
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• 2021

Purpose - This paper research on the embodied carbon emission in Sino-Korea trade. It calculates and analyzes the carbon emission coefficient and specific carbon emissions in Sino-Korea trade from 2005 to 2014. Design/methodology - This paper conducted an empirical analysis for embodied carbon emission in Sino-Korea trade during the years 2005-2014, using a multi-region input-output model. First, direct and complete CO2 emission coefficient of the two countries were calculated and compared. On this basis, combined with the world input-output table, the annual import and export volume and sector volume of embodied carbon emission are determined. Then through the comparative analysis of the empirical results, the reasons for the carbon imbalance in Sino-Korea trade are clarified, and the corresponding suggestions are put forward according to the environmental protection policies being implemented by the two countries. Findings - The results show that South Korea is in the state of net trade export and net embodied carbon import. The carbon emission coefficient of most sectors in South Korea is lower than that of China. However, the reduction of carbon emission coefficient in China is significantly faster than that in South Korea in this decade. The change of Korea's complete CO2 emission coefficient shows that policy factors have a great impact on environmental protection. The proportion of intra industry trade between China and South Korea is relatively large and concentrated in mechanical and electrical products, chemical products, etc. These sectors generally have large carbon emissions, which need to be noticed by both countries. Originality/value - To the best knowledge of the authors, this study is the first attempt to research the embodied carbon emission of ten consecutive years in Sino-Korea Trade. In addition, In this paper, some mathematical methods are used to overcome the error problem caused by different statistical caliber in different databases. Finally, the accurate measurement of carbon level in bilateral trade will provide some reference for trade development and environmental protection.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.80-90
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• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.606-614
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• 2012

A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omnidirectional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

• The KSFM Journal of Fluid Machinery
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• v.17 no.5
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• pp.67-71
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• 2014

Recently, the cross flow turbine attracts more and more attention for its good performance over a large operating regime at off design point. This study adopts a very low head cross flow turbine that has barely been studied before, and investigates the effect of air layer on the performance of the cross flow turbine. As open duct is applied in this study and free surface model is used between the air layer and water, an engineering definition of efficiency, instead of traditional definition of efficiency, is used. As torque at the runner fluctuates up and down at a reasonable limit, statistical method is used. Pressure and water volume fraction contours are shown to present the characteristics of air-water flow. With constant air suction in the runner chamber, the water level gradually drops below the runner and efficiency of the turbine can be raised by 10 percent. All considered, the effect of air layer on the performance of turbine is considerable.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.1
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• pp.87-94
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• 2006

In this paper, we investigated a feasibility of applying highest and lowest temperatures of the next day forecasted from a meteorological observatory to operation of an air-conditioning system with thermal storage. First we investigated specific characteristics of the time series of forecasted temperatures and errors in Osaka from 1994 to 1996. Since the forecast error is not always small, it might be difficult to use the forecasted data without correction for the sizing and the control of the thermal storage system. On the other hand, the autocorrelation functions of the forecast errors decrease relatively slowly during high summer season when cooling thermal storage is required. Since the values of the autocorrelation function; for one day are larger than 0.4, not small, the forecast errors can be predicted by proper statistical analysis. Thus, the forecasted values of the highest temperatures for the next day were improved by using the stochastic time series models.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.1
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• pp.34-41
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• 1993

SUS304 is wellknown as difficult-to-machine materials. It is easy to appear workhardened, and workhardening is one of the causes of groove wear on the tool. In this paper, the author would like to compare the width of flank wear with that of groove wear, and to find whether the groove wear can be used as a criterion of a tool life. The design of the twelve tests provides three levels for each variable (speed: 200m/min, 118m/min, 70m/min; feed: 0.3mm/rev, 0.17mm/rev, 0.1mm/rev; depth of cut: 0.4mm, 0.28mm, 0.2mm). The study of tool-life testing by statistical technique follows usual most scientific sequence. So the tool-life predicting equation is calculated by the method of least squares. The overall adequacy of the model can be verified by the analysis of variance. The results obtained are as follows : 1) When SUS304 is cut in 200(m/min), the width of flank wear is much larger than that of groove wear. 2) In cutting speed 118m/min, flank wear is a little larger than groove wear and in the cutting speed 70m/min, the latter is a little larger so that it is reasonable to determine the tool life according the crierion by groove wear in the low cutting speed (less than 70m/min). 3) Owing to the burr the depth of engagement along the cutting edge is extended toward the shank.