• East Asian mathematical journal
• /
• v.28 no.1
• /
• pp.13-23
• /
• 2012

In this paper, we present the geometric Hermite interpolation for planar Pythagorean-hodograph cubics for some general Hermite data.

• Journal of applied mathematics & informatics
• /
• v.8 no.3
• /
• pp.811.2-818
• /
• 2001

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2006.11a
• /
• pp.327-330
• /
• 2006

With the increasing requirements for the survey and development of the ocean, the demands on the of AUV(Autonomous Underwater Vehicle) technologies have been increased. Reconstruction and replay of the AUV motion on the basis of the data stored during the execution of mission, can help the development of control strategies for AUVs such as mission planning and control algorithms. While an AUV cruises for her mission, her attitude and position data are is recorded. The data can be used for visualization of the motion in off-line. However, because most of the position data gathered from acoustic sensors have long time-interval and include intermittent faulty signal, the replayed motion by the graphic simulator can not demonstrate the motion as a smooth movie. In this paper, interpolation methods are surveyed to reconstruct the AUV position data. Then, an efficient 3D visualization method for AUV motion using the interpolation method is proposed. Simulation results arc also included to verify the proposed method.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4A
• /
• pp.677-687
• /
• 2006

Kriging interpolation is one of the generally used interpolation techniques in Geostatistics field. This technique includes the experimental and theoretical variograms and the formulation of kriging interpolation. In contrast to the conventional least square method for stress recovery, kriging interpolation is based on the weighted least square method to obtain the estimated exact solution from the stress data at the Gauss points. The weight factor is determined by variogram modeling for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. In addition to this, the p-level is increased non-uniformly or selectively through a posteriori error estimation based on SPR (superconvergent patch recovery) technique, proposed by Zienkiewicz and Zhu, by auto mesh p-refinement. The cut-out plate problem under tension has been tested to validate this approach. It also provides validity of kriging interpolation through comparing to existing least square method.

• Journal of Korean Society for Atmospheric Environment
• /
• v.25 no.4
• /
• pp.339-346
• /
• 2009

The objectives of this study were to introduce spatial interpolation methods which have been applied in recent papers, to apply three methods (nearest monitor, inverse distance weighting, kriging) to domestic data (Ulsan cohort) as an example of estimating the personal exposure levels. We predicted the personal exposure estimates of 2,102 participants in Ulsan cohort using spatial interpolation methods based on information of their residential address. We found that there was a similar tendency among the estimates of each method. The correlation coefficients between predictions from pairs of interpolation methods (except for the correlation coefficient between nearest montitor and kriging of CO and $SO_2$) were generally high (r=0.84 to 0.96). Even if there are some limitations such as location and density of monitoring station, spatial interpolation methods can reflect spatial aspects of air pollutant and spatial heterogeneity in individual level so that they provide more accurate estimates than monitor data alone. But they may still result in misclassification of exposure. To minimize misclassification for better estimates, we need to consider individual characteristics such as daily activity pattern.

• Geophysics and Geophysical Exploration
• /
• v.25 no.3
• /
• pp.140-161
• /
• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.368-368
• /
• 2022

The Soil and Water Assessment Tool (SWAT) has been widely used to simulate the long-term hydrological conditions of a catchment. Two output variables, outflow and sediment yield have been widely investigated in the field of water resources management, especially in determining the conditions of ungauged subbasins. The presence of missing data in weather input data can cause poor representation of the climate conditions in a catchment especially for large or mountainous catchments. Therefore, in this study, a custom module was developed and evaluated to determine the efficiency of utilizing basic spatial interpolation methods in the estimation of weather input data. The module has been written in Python language and can be considered as a pre-processing module prior to using the SWAT model. The results of this study suggests that the utilization of the proposed pre-processing module can improve the simulation results for both outflow and sediment yield in a catchment, even in the presence of missing data.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.2
• /
• pp.290-298
• /
• 2008

This paper presents an efficient data storage and reconstruction method in data acquisition and processing of monitoring system. The proposed method extracts minimum data using an interpolation from raw data which are acquired from a target system. They are transferred and saved in a monitoring PC via TCP/IP communication, and then reconstructed as original signals. Therefore, it is possible to design an efficient monitoring system by the improved data communication speed due to the reduced communication packet, and it reduces the storage space. The algorithm for data acquisition and reconstruction is based on Cubic Hermite interpolation. To verify the validity of the proposed scheme, we presents simulation results compared with other interpolation based approaches. Finally, it is applied to a monitoring system for grid-connected photovoltaic power generation system to prove the high-performance of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.3A
• /
• pp.317-324
• /
• 2000

In this paper, we propose an interpolation technique to rain attenuation data which represents dynamic characteristics by time variations. By using this technique, it is possible to sample the rain attenuation data at an arbitrary time interval, and thus it would play an important role in developing adaptive transmission scheme for countermeasuring rain attenuation. We propose the interpolation technique which can synthesizes rain attenuation data by extracting the most proper parameters required to emulate the dynamic characteristics of rain attenuation. Interpolation results to measured data of I minute time interval will be demonstrated, and it is shown that more exact performance evaluation of adaptive transmission scheme to countermeasure rain attenuation can be achieved.

• Journal of Korean Society for Geospatial Information Science
• /
• v.10 no.5 s.23
• /
• pp.75-80
• /
• 2002

Depending upon geographical features and surrounding errors in the survey field, inaccurate positioning is inevitable in a kinematic DGPs survey. Therefore, a data inaccuracy detection algorithm and an interpolation algorithm are essential to meet the requirement of a digital map. In this study, GPS characteristics are taken into account to develop the data inaccuracy detection algorithm. Then, the data interpolation algothim is obtained, based on the feature type of the survey. A digital map for 20km of a rural highway is produced by the kinematic DGPS survey and the features of interests are lines associated with the road. Since the vertical variation of GPS data is relatively higher, the trimmed mean of vertical variation is used as criteria of the inaccuracy detection. Four cases of 0.5%, 1%, 2.5% and 5% trimmings have been experimented. Criteria of four cases are 69cm, 65cm, 61cm and 42cm, respectively. For the feature of a curved line, cublic spine interpolation is used to correct the inaccurate data. When the feature is more or less a straight line, the interpolation has been done by a linear polynomial. Difference between the actual distance and the interpolated distance are few centimeters in RMS.