• International Journal of Highway Engineering
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• v.7 no.1 s.23
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• pp.49-61
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• 2005

The dynamic modulus of hot mix asphalt can be determined according to the different combinations of testing temperature and loading frequency. The superposition rule is adapted to get the master curve of dynamic modulus for each hot mix asphalt. There are couple of different methods to get the shift factor which is a key for making the master curve. In this paper, Arrehnius, 2002 AASHTO, and experimental method was employed to get the master curve. Evaluation of dynamic modulus for 25mm base course of hot mix asphalt with granite aggregate and two asphalt binders(AP-3 and AP-5) was carried out. Superpave Level 1 Mix Design with gyratory compactor was adopted to determine the optimum asphalt binder content(OAC) and the measured ranges of OAC were between 4.1% and 4.4%. UTM was used for laboratory test. The dynamic modulus and phase angle were determined by testing on UTM, with 5 different testing temperature(-10, 5, 20, 40, & $55^{\circ}C$) and 5 different loading frequencies(0.05, 0.1, 1, 10, 25 Hz). Using the measured dynamic modulus and phase angle, the input parameters of Sigmoidal function equation to represent the master curve were determined and these will be adopted in FEM analysis for asphalt pavements. The shift factor and activation energy for determination of master curve were calculated.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Journal of the Korean Geotechnical Society
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• v.24 no.12
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• pp.113-120
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• 2008

One-dimensional site response analysis is widely used to simulate the seismic site effects. The equivalent linear analysis, which is the most widely used type of site response analysis, is essentially a linear method. The method applies constant shear modulus and damping throughout the frequency range of the input motion, ignoring the dependence of the soil response on the loading frequency. A new type of equivalent linear analysis method that can simulate the frequency dependence of the soil behavior via frequency-strain curve was developed. Various forms of frequency-strain curves were proposed, and all curves were asserted to increase the accuracy of the solution. However, its validity has not been extensively proven and the effect of the shape of the frequency-strain curve is not known. This paper used two previously proposed frequency-strain curves and three additional curves developed in this study to evaluate the accuracy of the frequency-dependent equivalent linear method and the influence of the shape of the frequency-strain curves. In the evaluation, six recordings from three case histories were used. The results of the case study indicated that the shape of the frequency-strain curve has a dominant influence on the calculated response, and that the frequency dependent analysis can enhance the accuracy of the solution. However, a curve that results in the best match for all case histories did not exist and the optimum curve varied for each case. Since the optimum frequency-strain curve can not be defined, it is recommended that a suite of curves be used in the analysis.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.8
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• pp.14-25
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• 2002

In this paper, we propose a modified AFC algorithm which is suitable for the implementation of WCDMA reverse link receiver modem. To reduce the complexity, the modified CP-FDD algorithm named 'Normalized CP-FDD' is applied to the AFC loop. The proposed FDD algorithm overcomes the conventional CP-FDD's sensitivity to the variance of input signal amplitude and increases the linear range of S -curve. Therefore, offset frequency estimation using the proposed scheme can be more stable than the conventional method. Unlike IS-95, since pilot symbol in WCDMA is not transmitted continuously, we introduce a moving average filter at the FDD input to increase the number of cross-product. So, tracking speed and stability are improved. For more rapid frequency acquisition and tracking, we adopt a multi-stage tracking mode. Using NCO having ROM table structure, the frequency offset is compensated. We applied the proposed algorithm in the implementation of WCDMA base station modem successfully.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.657-663
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• 2018

In this study, shape optimization was performed to improve the vibration isolation capability of an anti-vibration rubber assembly, which is used in the field option cabin of agricultural tractors. A uniaxial tension test and biaxial tension test were performed to characterize the hyper-elastic material properties of rubber, and the data were used to calibrate the material model used in the finite element analyses. A field test was performed to quantify the input excitation from the tractor and the output response at the cabin frame. To account for the nonlinear behavior of rubber, static analyses were performed and the load-displacement curve of rubber was derived. The stiffness of the rubber was calculated from this curve and input to the harmonic analyses of the cabin. The results were verified using the test data. Taguchi's parameter design method was used to find the optimal shape of the anti-vibration rubber assembly, which indicated a shape with reduced stiffness. The vibration of the cabin frame was reduced by the optimization by as much as 35% compared to the initial design.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.441-447
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• 2002

KSR-III(Korea Sounding Rocket - III), which is being developed by Space Technology R&D Division of KARI(Korea Aerospace Research Institute) will be launched in late 2002. It is a three-stage, liquid propellant rocket which can reach 250 km altitude and will carry out observation of ozone layer and scientific experiments, such as microgravity experiment, and atmospheric measurement. KSR-III is believed to be an intermediate to the launch vehicle capable of carrying a satellite to its orbit. Space Test Department of KARI performed GVT(Ground Vibration Test) fer KSR-III EM at Rocket Test Building of KARI. GVT is very important for predicting the behavior of rocket in its operation, developing flight control program and performing aerodynamic analysis. This paper gives an introduction of rocket GVT configuration and information on test procedures, techniques and results of It. In this test. to simulate free-free condition, test object hung in the air laterally by 4 bungee cords specially devised. For the excitation of test object, pure random signal by two electromagnetic shakers was used and total 22 frequency response functions were achieved. Polyreference parameter estimation was performed to identify the modal parameters with MIMO(Multi-Input-Multi-Output) method. As the result of the test, low frequency mode shapes and modal parameters below 60Hz were identified

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.11-22
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• 2000

It needs to extract the accurate topological characteristics and hydrological parameters of watershed in order to manage water resource efficiently. But, these data are processed yet by manual wok and simple operation in hydrologic fields. In this paper, we presented algorithm that could extract topological characteristics and hydrological parameters over watershed using GSIS and it gives the saving of data processing tin and the confidency of data. We presented coupling method between GSIS and hydrologic model by using extracted parameters into the input parameter of HEC-HMS hydrologic model. The extraction procedure of topological characteristics and hydrological parameters is as below. First, watershed and stream are extracted by DEM and curve unmber is extracted throughout the overlay of landuse map and soil map. Also, we extracted surface parameters like the length of the longest flow path and the slope of the longest flow path by Grid computation into watershed and stream. And we gave the method that could extract hydrologic parameters like Muskingum K and sub-basin lag tin by executing computation into surface parameters and average Sn curve number being extracted.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Soil and Groundwater Environment
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• v.6 no.2
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• pp.3-13
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• 2001

It has been known that nonlinear characteristics of sorption affect the transport behavior of water soluble pollutants in soils. However detailed experimental studies have not been performed to verify the effect of non-linearity of adsorption isotherm on transport of chemicals in porous media. In this research, the distortion of breakthrough curves of a cationic surfactant (cetylpyridinium chloride, CPC) in a engineered stainless steel column packed with glass beads were investigated. Glass beads with about 110 $\mu\textrm{m}$diameter coated with a thin n-decane film were used as the media providing the sorption surface for CPC. The CPC adsorption isotherm on the surface of n-decane from aqueous solution was a typical Langmuir type. The breakthrough curve of CPC using step Input showed a late breakthrough on the front side and early breakthrough on the back side accordance to the shape of the isotherm. The retardation factor of CPC was found to be a strong function of the input concentration, which also a manifestation of the non-linearity of the isotherm. The retardation factors for the CPC with step input agreed with those of pulse input that the maximum concentrations are controlled to be the same as the step input concentrations. This results support the validity of the unproven field practices of using hydrogeotracers with non-linear adsorption isotherms to determine the hydrogeological parameters, e.g., NAPL saturation, air-water or NAPL-water interfacial areas.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.109-125
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• 2004

In recent decades, microscopic simulation models have become powerful tools to analyze traffic flow on highways and to assist the investigation of level of service. The existing microscopic simulation models simulate an individual vehicle's speed based on a constant free-flow speed dominantly specified by users and driver's behavior models reflecting vehicle interactions, such as car following and lane changing. They set a single free-flow speed for a single vehicle on a given link and neglect to consider the effects of highway design elements to it in their internal simulation. Due to this, the existing models are limitted to provide with identical simulation results on both curved and tangent sections of highways. This paper presents a model developed to estimate the change of free-flow speeds based on highway design elements. Nine neural network models were trained based on the field data collected from seven different freeway curve sections and three different locations at each section to capture the percent changes of free-flow speeds: 100 m upstream of the point of curve (PC) and the middle of the curve. The model employing seven highway design elements as its input variables was selected as the best : radius of curve, length of curve, superelevation, the number of lanes, grade variations, and the approaching free-flow speed on 100 m upstream of PC. Tests showed that the free-flow speeds estimated by the proposed model were statistically identical to the ones from the field at 95% confidence level at each three different locations described above. The root mean square errors at the starting and the middle of curve section were 6.68 and 10.06, and the R-squares at these points were 0.77 and 0.65, respectively. It was concluded from the study that the proposed model would be one of the potential tools introducing the effects of highway design elements to free-flow speeds in simulation.