• Economic and Environmental Geology
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• v.14 no.3
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• pp.193-201
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• 1981

The problem of automatic inversion of apparent resistivity sounding curves resulting from horizontally layered earth models is solved using the least-squares technique. This method, which makes use of damped least-squares algorithm in conjunction with digital filtering technique, is found to be speedier and more accurate than the conventional curve-matching method. Four sounding curves were chosen to test the inversion scheme. The analysis of the theoretical sounding data associated with a three-layer model illustrates clear advantages over the conventional curve-matching method. The usefulness of the inversion method is also shown when applied to the actual field data. It was found that the best fit earth models coincide with the subsurface structures confirmed by drilling.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.17-22
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• 2021

In this paper, a machine learning-based automatic infographic recommendation system is proposed to improve the existing infographic production method. This system consists of a part that machine learning multiple infographic images and a part that automatically recommends infographics with artificial intelligence only by inputting basic data from the user. The recommended infographics are provided in the form of a library, and additional data can be input by drag & drop method. In addition, the infographic image is designed to be dynamically adjusted according to the size of the input data. As a result of analyzing the machine learning-based automatic infographic recommendation process, the matching success rate for layout and keyword was very high, and the matching success rate for type was rather low. In the future, a study to improve the matching success rate for the image type for each part of the infographic will be needed.

• Journal of Information Technology and Architecture
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• v.9 no.4
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• pp.357-363
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• 2012

Ontologies are being used in order to define common vocabularies to describe the elements of schemas involved in a particular application. The problem of finding correspondences between ontologies concepts, called ontology matching, consists in the discovery of correspondences between terms of vocabularies (represented by ontologies) used by various applications. The majority of solutions proposed in the literature, despite being fully automatic, has heuristic nature and may produce nonsatisfactory results. The problem intensifies when dealing with large data sources. The goal of this paper is to propose a method for generation and incremental refinement of correspondences between ontologies. The proposed approach uses filtering techniques, as well as user feedback to support the generation and refinement of such matches. For validation purposes, a tool was developed and some experiments were conducted.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.911-923
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• 2008

The neural networks (NNs) simulation has been designed to simulate and predict the vortex wavelength ${\lambda}_x^*$, lateral vortex spacing ${\lambda}_y^*$, and normalized maximum vorticity at the vortex center near the wake of square cylinders with different corner radii. The system was trained on the available data of the three cases, although this data is very little. Therefore, we designed the system to work in automatic way for finding the best network that has the ability to have the best test and prediction. The proposed system shows an excellent agreement with that of an experimental data in these cases. The technique has been also designed to simulate the other distributions not presented in the training set and predicted them with effective matching.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.47-49
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• 2003

Automatic land use and land cover change (LUCC) detection via remotely sensed imagery has a wide application in the area of LUCC research, nature resource and environment monitoring and protection. Under the condition that one time (T1) data is existed land use and land cover maps, and another time (T2) data is remotely sensed imagery, how to detect change automatically is still an unresolved issue. This paper developed a land use and land cover class knowledge guided method for automatic change detection under this situation. Firstly, the land use and land cover map in T1 and remote sensing images in T2 were registered and superimposed precisely. Secondly, the remotely sensed knowledge database of all land use and land cover classes was constructed based on the unchanged parcels in T1 map. Thirdly, guided by T1 land use and land cover map, feature statistics for each parcel or pixel in RS images were extracted. Finally, land use and land cover changes were found and the change class was recognized through the automatic matching between the knowledge database of remote sensing information of land use & land cover classes and the extracted statistics in that parcel or pixel. Experimental results and some actual applications show the efficiency of this method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.297-297
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• 2000

This paper proposed character recognition method by using DB Matching and Artificial Neural Network at the Gerber files. Gerber files are file for make PCB. But we also use the file to a program of extraction PCB position data. If the Gerber file recognized a character, the extraction PCB position data will be faster and also when the recognition rate is high, it can be possible to automatic extraction. We apply to the construction PCB Gerber file program and Simulation results are presented to verify the usefulness of the method.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.229-242
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• 2005

Photogrammetry is a current method of GIS data acquisition. However, as a matter of fact, a large manpower and expenditure for making detailed 3D spatial information is required especially in urban areas where various buildings exist. There are no photogrammetric systems which can automate a process of spatial information acquisition completely. On the other hand, LiDAR has high potential of automating 3D spatial data acquisition because it can directly measure 3D coordinates of objects, but it is rather difficult to recognize the object with only LiDAR data, for its low resolution at this moment. With this background, we believe that it is very advantageous to integrate LiDAR data and stereo CCD images for more efficient and automated acquisition of the 3D spatial data with higher resolution. In this research, the automatic urban object recognition methodology was proposed by integrating ultra highresolution stereo images and LiDAR data. Moreover, a method to enable more reliable and detailed stereo matching method for CCD images was examined by using LiDAR data as an initial 3D data to determine the search range and to detect possibility of occlusions. Finally, intellectual DSMs, which were identified urban features with high resolution, were generated with high speed processing.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.2
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• pp.177-184
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• 2013

This paper proposes an enhanced Simultaneous Localization and Mapping (SLAM) algorithm based on matching laser image and Extended Kalman Filter (EKF). In general, laser information is one of the most efficient data for localization of mobile robots and is more accurate than encoder data. For localization of a mobile robot, moving distance information of a robot is often obtained by encoders and distance information from the robot to landmarks is estimated by various sensors. Though encoder has high resolution, it is difficult to estimate current position of a robot precisely because of encoder error caused by slip and backlash of wheels. In this paper, the position and angle of the robot are estimated by comparing laser images obtained from laser scanner with high accuracy. In addition, Speeded Up Robust Features (SURF) is used for extracting feature points at previous laser image and current laser image by comparing feature points. As a result, the moving distance and heading angle are obtained based on information of available points. The experimental results using the proposed laser slam algorithm show effectiveness for the SLAM of robot.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.591-597
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• 2014

A wide variety of image application methods, such as aerial image, terrestrial image, terrestrial laser, and stereo image point are currently under investigation to develop three-dimensional 3D geospatial information. In this study, matching points, which are needed to build a 3D model, were examined under diverse weather conditions by analyzing the stereo images recorded by closed circuit television (CCTV) cameras installed in the U-City. The tests on illuminance and precipitation conditions showed that the changes in the number of matching points were very sensitively correlated with the changes in the illuminance levels. Based on the performances of the CCTV cameras used in the test, this study was able to identify the optimal values of the shutter speed and iris. As a result, compared to an automatic control mode, improved matching points may be obtained for images filmed using the data obtained through this test in relation to different weather and illuminance conditions.

• ETRI Journal
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• v.33 no.4
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• pp.537-546
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• 2011

This paper introduces an automated method for building height recovery through the integration of high-resolution satellite images and digital vector maps. A cross-correlation matching method along the vertical line locus on the Ikonos images was deployed to recover building heights. The rational function models composed of rational polynomial coefficients were utilized to create a stereopair of the epipolar resampled Ikonos images. Building footprints from the digital maps were used for locating the vertical guideline along the building edges. The digital terrain model (DTM) was generated from the contour layer in the digital maps. The terrain height derived from the DTM at each foot of the buildings was used as the starting location for image matching. At a preset incremental value of height along the vertical guidelines derived from vertical line loci, an evaluation process that is based on the cross-correlation matching of the images was carried out to test if the top of the building has reached where maximum correlation occurs. The accuracy of the reconstructed buildings was evaluated by the comparison with manually digitized 3D building data derived from aerial photographs.