• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.379-388
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• 2019

Recently, a technique for acquiring spatial information data using UAV (Unmanned Aerial Vehicle) has been greatly developed. It is a very crucial issue of the GIS (Geographic Information System) mapping system that passes way point in the unmanned airframe and finally measures the accurate image and stable localization to the desired destination. Though positioning using DGPS (Differential Global Navigation System) or RTK-GPS (Real Time Kinematic-GPS) guarantee highly accurate, they are more expensive than the construction of a single positioning system using a single GPS. In the case of a low-priced single GPS system, the stability of the positioning data deteriorates. Therefore, it is necessary to supplement the uncertainty of the absolute position data of the UAV and to improve the accuracy of the current position data economically in the operating state of the UAV. The aim of this study was to present an algorithm enhancing the stability of position data in a single GPS mode of UAV with multiple GPS. First, the arrangement of multiple GPS receivers through the center of gravity of the UAV were examined. Next, MD (Mahalanobis Distance) is applied to detect instantaneous errors of GPS data in advance and eliminate outliers to increase the accuracy of previously collected multiple GPS data. Processing procedure for multiple GPS reception data by applying the center of the triangular method were presented to improve the position accuracy. Second, UAV navigation systems integrated multiple GPS through configuration of the UAV specifications were implemented. Using the unmanned airframe equipped with multiple GPS receivers, GPS data is measured with the TCM (Triangular Center Method). In addition, UAV equipped with multiple GPS were operated in study area and locational accuracy of multiple GPS of UAV with VRS (Virtual Reference Station) GNSS surveying were compared. The result showed that the error factors are compensated, and the error range are reduced, resulting in the reliability of the corrected value. In conclusion, the result in this paper is expected to realize high-precision position estimation at low cost in UAV using multiple low-cost GPS receivers.

• Journal of the Korean Geotechnical Society
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• v.38 no.10
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• pp.17-29
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• 2022

Recently, the interest in integrated underground geospatial information mapping (IUGIM) to ensure the safety of underground spaces and facilities has been increasing. Because IUGIM is used in the fields of underground space development and underground safety management, the up-to-dateness and accuracy of information are critical. In this study, IUGIM and field data were classified, and the accuracy of IUGIM was improved by spatial interpolation. A spatial interpolation technique was used to process borehole data in IUGIM, and a quantitative evaluation was performed with mean absolute error and root mean square error through the cross-validation of seven interpolation results according to the technique and model. From the cross-validation results, accuracy decreased in the order of nonuniform rational B-spline, Kriging, and inverse distance weighting. In the case of Kriging, the accuracy difference according to the variogram model was insignificant, and Kriging using the spherical variogram exhibited the best accuracy.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.219-227
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• 2021

This paper proposed an Orthogonal Frequency Division Multiplexing (OFDM) based biomimetic communication method using a dolphin whistle which covertly transmits communication signals to allies. The proposed method divides the dolphin whistle into several time slots corresponding to a number of OFDM symbols, and modulates the communication signal by mapping differential phase shift keying (DPSK) symbols into subcarriers that have the frequency bands of the dolphin whistle in each slot. The advantages of the proposed method are as follows: In the conventional Chirp Spread Spectrum (CSS) and Frequency Shift Keying (FSK) based biomimetic communication methods, the discontinuity of the frequency contour is large, but the proposed method can reduce the discontinuity. Even if the modulation order is increased, the degradation of the mimicking performance is small. The computer simulations demonstrate that the Bit Error Rate (BER) and mimicking performance of the proposed method are better performance than those of the conventional CSS and FSK.

• Journal of Korean Society of Steel Construction
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• v.12 no.5 s.48
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• pp.605-615
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• 2000

Cerebellar model articulation controller (CMAC) is introduced and used for the identification of structural dynamic model. CMAC has fascinating features in learning speed. It can learn structural response within a few seconds. Therefore it is suitable for the real time identification structures. Real time identification is required in the control of structure which may be damaged or undergo severe change in mechanical properties due to shrinkage or relaxation etc. In numerical examples, it is shown that CMAC trained with the dynamic response of three-story building can predict responses under not trained earthquakes with allowable error. Finally, CMAC has great potential in structural and control engineering.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.19-26
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• 2003

As recently, high-resolution satellite images of 1m spatial resolution are opened to the public and able to be used commercially, the studies that make ortho-images using them and apply to digital mapping and database of geo-spatial information system are having been progressed actively. Therefore, the purposes of this study are to establish the auto-extraction methods and to develope algorithms for automatically extracting buildings out of man-made structures, after making the IKONOS ortho-image. As the result of this study, we can extract buildings automatically at 72% out of the whole buildings. And we have analyzed the error trend by means of the comparison with ortho-image, digital map and drawing result, then we could know that obtain the good result for extraction of the building through the methods and algorithms of this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Journal of the Korea Computer Graphics Society
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• v.17 no.2
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• pp.1-9
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• 2011

In recent researches, several LOD techniques are used for real-time visualization of large sized terrain data. However, during mesh simplification, geometry popping may occur in consecutive frames, because of the geometric error. We propose an efficient method for reducing the geometry popping using roughness map and bias map. A roughness map and a bias map are used to move vertices of the terrain mesh to appropriate position where they minimize the geometry errors. A roughness map and a bias map are represented as a texture suitable for GPU processing. Moving vertices using bias map is processed on the GPU, so the high-speed visualization can be possible.

• Journal of Environmental Science International
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• v.26 no.4
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• pp.411-420
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• 2017

This research aims at comparing the accuracy of flood discharge estimation. For this, we focused on the Oedo watershed of Jeju Island and compared flood discharge by analyzing the values as follows: (1) the concentration of the lumped model (HEC-HMS) and distributed model (Vflo), and (2) the in-situ data using Fixed Surface Image Velocimetry (FSIV). The flood discharge estimation from the HEC-HMS model is slightly larger than the Vflo model results. This result shows that the estimations of the HEC-HMS are larger than the flood discharge data by 4.43 to 36.24% and that of the Vflo are larger by 8.49 to 11%. In terms of the error analysis at the peak discharge occurrence time of each mapping, HEC-HMS is one hour later than the measured data, but Vflo is almost the same as the measured data.

• The Journal of Korea Robotics Society
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• v.7 no.1
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• pp.29-34
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• 2012

This paper suggests a new observation model for Extended Kalman Filter based Simultaneous Localization and Mapping (EKF-SLAM). Since the EKF framework linearizes non-linear functions around the current estimate, the conventional line model has large linearization errors when a mobile robot locates faraway from its initial position. On the other hand, the model that we propose yields less linearization error with respect to the landmark position and thus suitable in a large-scale environment. To achieve it, we build up a three-dimensional space by adding a virtual axis to the robot's two-dimensional coordinate system and extract a plane by using a detected line on the two-dimensional space and the virtual axis. Since Jacobian matrix with respect to the landmark position has small value, we can estimate the position of landmarks better than the conventional line model. The simulation results verify that the new model yields less linearization errors than the conventional line model.