• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.16-22
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• 2019

Off-line programming and robot calibration through simulation are essential when setting up a robot in a robot automation production line. In this study, we developed a new robot calibration method to match the CAD data of the production line with the measurement data on the site using 3D scanner. The proposed method calibrates the robot using 3D point cloud data through Iterative Closest Point algorithm. Registration is performed in three steps. First, vertices connected by three planes are extracted from CAD data as feature points for registration. Three planes are reconstructed from the scan point data located around the extracted feature points to generate corresponding feature points. Finally, the transformation matrix is calculated by minimizing the distance between the feature points extracted through the ICP algorithm. As a result of applying the software to the automobile welding robot installation, the proposed method can calibrate the required accuracy to within 1.5mm and effectively shorten the set-up time, which took 5 hours per robot unit, to within 40 minutes. By using the developed system, it is possible to shorten the OLP working time of the car body assembly line, shorten the precision teaching time of the robot, improve the quality of the produced product and minimize the defect rate.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.21-30
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• 2024

In this paper, we propose a method to build a sample dataset of the features of eight sensor-only facilities built as infrastructure for autonomous cooperative driving. The feature extracted from point cloud data acquired by LiDAR and build them into the sample dataset for recognizing the facilities. In order to build the dataset, eight sensor-only facilities with high-brightness reflector sheets and a sensor acquisition system were developed. To extract the features of facilities located within a certain measurement distance from the acquired point cloud data, a cylindrical projection method was applied to the extracted points after applying DBSCAN method for points and then a modified OTSU method for reflected intensity. Coordinates of 3D points, projected coordinates of 2D, and reflection intensity were set as the features of the facility, and the dataset was built along with labels. In order to check the effectiveness of the facility dataset built based on LiDAR data, a common CNN model was selected and tested after training, showing an accuracy of about 90% or more, confirming the possibility of facility recognition. Through continuous experiments, we will improve the feature extraction algorithm for building the proposed dataset and improve its performance, and develop a dedicated model for recognizing sensor-only facilities for autonomous cooperative driving.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• Tunnel and Underground Space
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• v.19 no.1
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• pp.52-63
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• 2009

This is a study on large-scale rock joint roughness measurements using LIDAR (light detection and ranging) and the Split-FX point cloud processing software. The large-scale rock Joint Roughness Coefficient (JRC) is calculated using the maximum amplitude of joint asperities over the profile length on large-scale Joint surfaces of rock. As the profile length increases, JRC decreases due to scale-effects of rock specimens and is non-stationary. Also JRC shows anisotropy depending on the profile direction. The profile direction is measured relative to either dip or strike of the large-scale joint.

• Journal of the Korea Fashion and Costume Design Association
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• v.13 no.2
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• pp.47-62
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• 2011

The purpose of this study was to investigate the knitwear preferences and purchase behaviors of university students. The subjects were 493 university students in Daejeon and Chungnam Provinces. The method of this study was a survey and measurement instruments were 34 stimuli which were manipulated pullover patterns and shapes and self-administrated questionnaire with knitwear design preference items and knitwear purchase behavior items. Data were analyzed by factor analysis, frequency analysis, Cronbach'${\alpha}$, t-test, ANOVA and $Sch{\acute{e}}ffe$ test using SPSS program. The results of the study were as follows. First, university students most preferred achromatic colors, pastel tones, solid patterns, and 100% cotton. Second, as for knitwear purchase behaviors, university students considered the esthetical factor to be most important among 4 dimensions (comfortable, esthetical, economical, and conspicuous factors) as important purchase criteria, and they used internet web sites for knitwear purchases. Third, there were significant differences in preferred pullover shape depending on neckline shape, neck depth, sleeve shape and clothing length. University students preferred the classic pullover design with V neckline, normal neck depth, set-in sleeves and normal length. Fourth, university students preferred the cloud motif and riding man motif among the 9 Baekje traditional motifs and one point small pattern on the left chest and crosswise bending pattern for pattern arrangement.

• Applied Chemistry for Engineering
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• v.10 no.5
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• pp.743-747
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• 1999

The component polymer was modified to enable the formation of intermolecular hydrogen bonding in the immiscibile polystyrene(PS)/polymethacrylate(PMA) blends. The mole percentages of hydroxystyrene of the poly(styrene-co-4-hydroxystyrene) copolymer(modified polystyrene, MPS) were controlled to 7%, 10% and 18%, respectively. MPS was used with PMA to study the variation of the miscibility in blends. PMA which had such different length of side chain as methyl, butyl, hexyl and ethylhexyl, respectively, was selected to study the effect of side chain length on the formation of intermolecular hydrogen bonding. As the hydroxyl content of MPS increased, the formation of intermolecular hydrogen bonding increased. The length of side chain of PMA had enormous effect on the miscibility of blend as confirmed from the result of cloud point measurement. As the length of side chain increased, the formation and the strength of intermolecular hydrogen bonding decreased severely due to the steric effect and the increased chain mobility.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.52.2-52.2
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• 2015

SgrA* located in the center of the Milky Way is of great interest to understand the physics of supermassive black hole(SMBH) and the interaction of the G2 cloud around SgrA* with the accretion flow which was expected since 2013. In order to seize this rare opportunity, KVN and VERA Array (so called, KaVA) has started an intensive monitoring program of SgrA* at 22/43 GHz where scatter broadening is reduced compared to lower frequency VLBI observations. We present the results of KaVA SgrA* observation together with Takahagi (32m) and Yamaguchi (32m) telescopes at 22 GHz on March 24, 2013. We have tested both a standard amplitude calibration methods using the Tsys and antenna gain information and a template amplitude calibration method which uses a peak of H2O maser line of nearby maser source (SgrB2), and found that the latter method is useful when an accuracy of Tsys measurement or antenna gain of a telescope is poor. In our comparison, the difference between the two methods is around 20% (~5% for the KVN and ~15% for the VERA when the elevation is above $20^{\circ}$). We also imaged SgrA* with a total flux of ~0.7 Jy at 22GHz, and fitted an elliptical Gaussian model which has a size of ~2.5mas for major axis and ~1.7mas for minor axis, respectively.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1085-1092
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• 2009

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model and Neural Networks (NN) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Paju monitoring site in Korea. In the both ARE model and NN model, seven meteorological variables and four pollution variables are used as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$), Cobalt (CO), and Promethium 10 (PM10). The result showed that the NN model is generally better suited for describing the ozone concentration than the ARE model. However, the ARE model will be expected also good when we add the explanatory variables in the model.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.94-101
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• 2016

Satellite data for remote sensing technology has limitations, especially with visible range sensor, cloud and/or other environmental factors cause missing data. In this study, using land surface temperature data from the MODerate resolution Imaging Spectro-radiometer(MODIS), we developed retrieving methods for satellite missing data and developed three methods; mean bias, regression analysis and local variation method. These methods used the previous day data as reference data. In order to validate these methods, we selected a specific measurement ratio using artificial missing data from 2014 to 2015. The local variation method showed low accuracy with root mean square error(RMSE) more than 2 K in some cases, and the regression analysis method showed reliable results in most cases with small RMSE values, 1.13 K, approximately. RMSE with the mean bias method was similar to RMSE with the regression analysis method, 1.32 K, approximately.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.829-835
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• 2017

Recently, the health issues triggered by fine dust matters occurred in higher frequency. Having adverse effects on health, particulate matters affect the human body indoors as well as outdoors. There is thus a need for a system to measure the concentration of particulate matters and control harmful particulate matters for human health in the indoor spaces where people live. The present study applied Internet of Things(IoT) technologies in order to increase the efficiency of the conventional fine dust measurement system. Especially, for the bidirectional communication environment, directly construct a separate server and applied to the system instead of a free cloud server also we used it directly in the school lab and home. When the proposed system is used in schools and homes, it can recognize the indoor environment quickly and it is expected that this will gradually contribute to the health of the individual. Users can also check the server data outside and deal with the current indoor situations.