• International Journal of Automotive Technology
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• v.8 no.2
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• pp.165-177
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• 2007

Predictions of diesel particulate filtration are typically made by modeling of a particle collection, and providing particle trapping levels in terms of a pressure drop. In the present study, a series of single channel diesel particulate filter (DPF) experiments are conducted, the pressure traces are inversely analyzed and essential filtration parameters are deducted for model closure. A DPF filtration model is formulated with a non-linear description of soot cake regression. Dependence of soot cake porosity, packing density, permeability, and soot density in filter walls on convective-diffusive particle transportation is examined. Sensitivity analysis was conducted on model parameters, relevant to the mode of transition. Soot cake porosity and soot packing density show low degrees of dispersion with respect to the Peclet number and have asymptotes at 0.97 and $70\;kg/m^3$, respectively, at high Peclet number. Soot density in the filter wall, which is inversely proportional to filter wall Peclet number, controls the filtration mode transition but exerts no influence on termination pressure drop. The percolation constant greatly alters the extent of pressure drop, but is insensitive to volumetric flow rate or temperature of exhaust gas at fixed operation mode.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.2
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• pp.119-126
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• 2009

LCVA has an advantage that its natural frequency can be easily controlled by changing the area ratio of the vertical column and horizontal part. The previous studies investigated the dynamic characteristics of the LCVA under harmonic load. This study experimentally obtained the first and second mode natural frequencies of the LCVA from shaking table tests using white noise and compared the values with the ones by previous study. Test results show that the measured first mode natural frequency of the LCVA has a different value compared with calculated one. The effective length($L_e$) was revised using by power equation. In the case01 to 19, the standard deviation($S_r$) is 4.7292 and the coefficient of correlation(r) is 0.9856. In the case21 to 61, the standard deviation($S_r$) is 14.2143 and the coefficient of correlation(r) is 0.9935. The second mode frequency increases with the increasing area ratio, which is due to the sloshing motion effect resulting from the large area of the vertical column.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.669-674
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• 2006

The optimal control of heat pump performance for heating mode operation was investigated. Fuzzy logic was applied to control the heating performance of heat pump system and superheat at compressor discharge was taken as a control variable. Regression model was adapted to determine the optimal points where COP is maximized. Optimization of fuzzy rule table was investigated to improve operation performance of heat pump system. Experiments were carried out using original fuzzy table and the modified fuzzy rule table for heating mode operation of heat pump system. The results show that control performance of heat pump system with the modified fuzzy rule table was better than that with the original rule table.

• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.5
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• pp.383-389
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• 2014

POD analysis has been done to investigate the internal flow characteristics using LES calculation results of hybrid rocket combustion chamber. The special emphasis was put on the change in the mode energy distribution caused by the installation of diaphragm compared to the baseline case. Also the comparison was made to investigate the effect of wall blowing on the changes in the mode energy between the regions near and far from the diaphragm. For baseline case, POD results clearly distinguish the primary mode containing most of flow energy from the rest of flow modes (2-9 mode) depicting small scale modes. Also, the increase in the energy of flow modes 2-5 is responsible for the formation of relatively large scale structures due to diaphragm. In addition, the comparison of mode energy distributions of flow fields with diaphragm shows similar patterns in both wall blowing and no blowing case. This implies that the local increase in regression rate just after the diaphragm is directly associated with the increase in energy distributions of 2-5 modes.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.2
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• pp.10-17
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• 2004

A real depth of cut in deformed zone has larger than an ideal depth of cut. So the heat generated during grinding operation makes the deformation of a workpiece surface as convex farm. Consequently the workpiece surface remains a geometric error as concave form after cooling In this study, the grinding force and the geometric error were examined in surface grinding. Through magnitude and mode of geometric error were evaluated according to grinding conditions, an optimal grinding condition was proposed to minimize the geometric error In addition, the relationship between the geometric error and the grinding force was examined. Due to least square regression, It was possible to predict the geometric error by using the grinding force.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1109-1110
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• 2008

It is required to optimize the system operation efficiency to allocate maintenance task and period using systemic maintenance process. To allocate maintenance task and period must analysis the failure distribution mode at first. In this paper, we introduce the linear regression analysis and estimate the failure distribution for the railroad signal equipment using that.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.2
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• pp.9-16
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• 2004

Because a generated heat during grinding operation makes a serious deformation on a ground surface as a convex form, a real depth of cut in deformed zone has larger than an ideal depth of cut. Consequently, the ground surface has a geometric error as a concave form after cooling the workpiece. In this study, the force and the geometric error of surface grinding were examined. From evaluating magnitude and mode of the geometric error according to grinding conditions, an optimal grinding condition was proposed to minimize the geometric error. In addiction the relationship between the geometric error and the grinding force was found out. Due to least square regression it was able to predict the geometric error by using the grinding force.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.139-151
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• 2002

This study is concerned with model selection and diagnostics for nonlinear regression model through Bayes factor. In this paper, we use informative prior and simulate observations from the posterior distribution via Markov chain Monte Carlo. We propose the Laplace approximation method and apply the Laplace-Metropolis estimator to solve the computational difficulty of Bayes factor.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.475-482
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• 2011

In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.