• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.39 no.1
• /
• pp.63-71
• /
• 2019

The two-fluid model proposed by Herman and Prigogine is useful for analyzing macroscopic traffic flow in a network. The two-fluid model is used for analyzing a network through the relationship between the ratio of stopped vehicles and the average moving speed of the network, and the two-fluid model has also been applied in the urban transportation network where many signalized or unsignalized intersections existed. In general, the average travel speed and moving speed of a network decrease, and the ratio of stopped vehicles and low speed vehicles in network increase as the traffic demand increases. This study proposed the two-fluid model considering congested and uncongested traffic situations. The critical velocity and the weight factor for congested situation are calibrated by minimizing the root mean square error (RMSE). The critical speed of the Seoul network was about 34 kph, and the weight factor of the congestion on the network was about 0.61. In the proposed model, $R^2$ increased from 0.78 to 0.99 when compared to the existing model, suggesting that the proposed model can be applied in evaluating network performances or traffic signal operations.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.7
• /
• pp.953-959
• /
• 2018

This paper deals with controlling surge oscillations of a mooring vessel system under large external disturbances such as wind, waves and currents. A control synthesis based on Sliding Mode Control (SMC) with a Super-Twisting Algorithm (STA) has been applied to suppress nonlinear surge oscillations of a two-point mooring system. Despite the advantages of robustness against parameter uncertainties and disturbances for SMC, chattering is the main drawback for implementing sliding mode controllers. First-order SMC shows convergence within the desired level of accuracy, in which chattering is the main obstacle related to the destructive phenomenon. Alternatively, STA completely eliminates chattering phenomenon with high accuracy even for large disturbances. SMC based on STA is an effective tool for the motion control of a nonlinear mooring system because it avoids the chattering problems of a first-order sliding mode controller. In addition, the error trajectories of controlled mooring systems implemented by means of STA form in the bounded region. Finally, the control gain effect of STA can be observed in sliding surface and position trajectory errors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.4
• /
• pp.233-242
• /
• 2019

ZHD (Zenith Hydrostatic Delay) model is important parameter in estimating of GNSS (Global Navigation Satellite System) PWV (Precipitable Water Vapor) along with weighted mean temperature. The ZWD (Zenith Wet Delay) is tend to accumulate the ZHD error, so that biases from ZHD will be affected on the precision of GNSS PWV. In this paper, we compared the accuracy of GNSS PWV with radiosonde PWV using three ZHD models, such as Saastamoinen, Hopfield, and Black. Also, we adopted the KWMT (Korean Weighted Mean Temperature) model and the mean temperature which was observed by radiosonde on the retrieval processing of GNSS PWV. To this end, GNSS observation data during one year were processed to produce PWVs from a total of 5 GNSS permanent stations in Korea, and the GNSS PWVs were compared with radiosonde PWVs for the evaluating of biases. The PWV biases using mean temperature estimated by the KWMT model are smaller than radiosonde mean temperature. Also, we could confirm the result that the Saastamoinen ZHD which is most used in the GNSS meteorology is not valid in South Korea, because it cannot be exclude the possibility of biases by latitude or height of GNSS station.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.9 no.4
• /
• pp.11-22
• /
• 1999

Least-mean-square(LMS) adaptive filters have proven to be extremely useful in a number of signal processing tasks. However LMS adaptive filter suffer from a slow rate of convergence for a given steady-state mean square error as compared to the behavior of recursive least squares adaptive filter. In this paper an efficient signal interference control technique is introduced to improve the convergence speed of LMS algorithm with tap weighted vectors updating which were controled by reusing data which was abandoned data in the Adaptive transversal filter in the scheme with data recycling buffers. The computer simulation show that the character of convergence and the value of MSE of proposed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of LMS algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.1
• /
• pp.134-145
• /
• 2021

It is common to optimize the propulsion control system through a so-called tuning process that modifies the parameter values of the propulsion control software during a ship commissioning. However, during this process, if the error of the initial setting value is large, the tuning time may take too long, or the propulsion equipment can be seriously damaged. Therefore, we conducted research on the design of a propulsion controller that applied a Processor lever controller even for inexperienced people with relatively little experience in tuning propulsion control software to be able to reduce the tuning time while protecting the propulsion system. Through simulation, by comparing the execution result of propulsion control lever commands through the PI controller without applying the Processor lever controller. We analyzed the improvement of the Overshoot and propulsion performance. The simulation results showed that the safety of the propulsion system increased because Overshoot of approximately 9.74%, which occurred when the Processor lever function was not applied.

• Journal of the Korean GEO-environmental Society
• /
• v.23 no.8
• /
• pp.29-36
• /
• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Tunnel and Underground Space
• /
• v.32 no.6
• /
• pp.502-517
• /
• 2022

As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.5 no.6
• /
• pp.583-591
• /
• 2015

In order to evaluate the performance of TAS applied to the hybrid vehicle of the soft belt driven, acceleration performance and fuel consumption performance is to be superior to the existing vehicle. The key components of belt driven TAS(Torque Assist System), such as the engine, the motor and the battery, The key components of the driven belt TAS, such as the engine, the motor, and the battery, have a significant impact on fuel consumption performance of the vehicle. Therefore, in order to improve the efficiency at the point of view based on the overall system, the study of the power distribution algorithm for controlling the main source powers is necessary. In this paper, we propose the power distribution algorithm, applied the homogeneous analysis method in terms of fuel equivalent, for minimizing the fuel consumption. We have confirmed that the proposed algorithm is contribute to improving the fuel consumption performance satisfied the constraints considering the vehicle status information and the required power through the control parameters to minimize the fuel consumption of the engine. The optimization process of the proposed driving strategy can reduce the trial and error in the research and development process and monitor the characteristics of the control parameter quickly and accurately. Therefore, it can be utilized as a way to derive the operational strategy to minimize the fuel consumption.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.5D
• /
• pp.453-459
• /
• 2010

In the study of traffic safety, it is utmost important to obtain more reliable estimates of the expected crashes for a site (or a segment). The observed crashes have been mainly used as the estimate of the expected crashes in Korea, while the empirical Bayes (EB) estimates based on the Poisson-gamma mixture model have been used in the USA and several European countries. Although numerous studies have used the EB method for estimating the expected crashes and/or the effectiveness of the safety countermeasures, no past studies examine the difference in the estimation errors between the two estimates. Thus, this study compares the estimation errors of the two estimates using a Monte Carlo simulation study. By analyzing the crash dataset at 3,000,000 simulated sites, this study reveals that the estimation errors of the EB estimates are always less than those of the observed crashes. Hence, it is imperative to incorporate the EB method into the traffic safety research guideline in Korea. However, the results show that the differences in the estimation errors between the two estimates decrease as the uncertainty of the prior distribution increases. Consequently, it is recommended that the EB method be used with reliable hyper-parameter estimates after conducting a comprehensive examination on the estimated negative binomial model.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.4B
• /
• pp.363-374
• /
• 2008

Intensity-Duration-Frequency (IDF) curve that is essential to calculate rainfall quantiles for designing hydraulic structures in Korea is generally formulated by regression analysis. In this study, IDF curve derived by the cumulative distribution function ("IDF by CDF") of the proper probability distribution function (PDF) of each site is suggested, and the corresponding parameters of IDF curve are computed using genetic algorithm (GA). For this purpose, IDF by CDF and the conventional IDF derived by regression analysis ("IDF by REG") were computed for 22 Korea Meteorological Administration (KMA) rainfall recording sites. Comparisons of RMSE (root mean squared error) and RRMSE (Relative RMSE) of rainfall intensities computed from IDF by CDF and IDF by REG show that IDF by CDF is more accurate than IDF by REG. In order to accommodate the effect of the recent intensive rainfall of Korea, the rainfall intensities computed by the two IDF curves are compared with that by at-site frequency analysis using the rainfall data recorded by 2006, and the result from IDF by CDF show the better performance than that from IDF by REG. As a result, it can be said that the suggested IDF by CDF curve would be the more efficient IDF curve than that computed by regression analysis and could be applied for Korean rainfall data.