• Proceedings of the KSME Conference
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• 2004.04a
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• pp.19-24
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• 2004

A number of creep data were collected and filed for type 316LN stainless steels through literature survey and experimental data produced in KAERI. Using these data, polynomial equations for predicting creep life were obtained for Larson Miller (L-M), Qrr-Sherby-Dorn (O-S-D) and Manson-Haferd (M-H) parametric methods. In order to find out the suitability for them, the relative standard error (RSE) and standard error of estimate (SEE) values were obtained by statistical process of creep data. The O-S-D parameter showed better fitting to creep-rupture data than the L-M or the M-H parameters, and the three parametric methods did not generate the large difference in the SEE and the RSE values.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.41-48
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• 2005

In this paper, we propose a systematic procedure to determine standard time and cycle time in a TFT-LCD factory. The proposed procedure mainly consists of data preprocessing, hypothesis testing and Group technology. Data preprocessing extracts relevant data from large on-line data sets by eliminating corrupt and noisy data. Hypothesis test techniques have been used to determine whether the standard information has been changed. Also, Group technology has been applied to generate standard information for newly developed products. The proposed procedure has been successfully applied to the production information system of a TFT-LCD factory in Korea.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.63-80
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• 1992

The concept of rapid prototyping intended for a significant reduction in cost and lead time becomes even more practical with the recent development of various equipments to make the concept concrete. For the purpose of real application of commercially available SLA(stereolithography apparatus), this paper is intended to develop the standard conversion procedure from CAD data to the input data for SLA. While the procedure presented in this paper is based on CAD system "CATIA" and SLA of 3D systems, Inc., which are being used in authors' company DAEWOO Motor Co., Ltd., the basic concept of this paper can be applied to any other CAD systems and machines of using stereolithography process. The algorithm presented in this paper is classified into two stages-node sampling and triangulation. First of all, point data are sampled through the node sampling procedure, and then these are triangulated so that the input data for SLA operation is finally generated. The suggested method is devised in a way to meet the input requirements of SLA and more importantly consume less computation time and generate less number of input data for SLA.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.519-521
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• 2003

Within the paper an approach for the automatic extraction and reconstruction of buildings in urban built-up areas base on fusion of high-resolution satellite image and LIDAR data is presented. The presented data fusion scheme is essentially motivated by the fact that image and range data are quite complementary. Raised urban objects are first segmented from the terrain surface in the LIDAR data by making use of the spectral signature derived from satellite image, afterwards building potential regions are initially detected in a hierarchical scheme. A novel 3D building reconstruction model is also presented based on the assumption that most buildings can be approximately decomposed into polyhedral patches. With the constraints of presented building model, 3D edges are used to generate the hypothesis and follow the verification processes and a subsequent logical processing of the primitive geometric patches leads to 3D reconstruction of buildings with good details of shape. The approach is applied on the test sites and shows a good performance, an evaluation is described as well in the paper.

• Journal of Korean Society of Rural Planning
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• v.11 no.1 s.26
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• pp.9-15
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• 2005

Preservation of geometrical context of terrains in a digitized format is useful in handling and making modification to the data. Digitization of three-dimensional terrain still proves a great challenge due to heavy load of context required to retain details of topological and geometrical information. Methods of simplification, restoration and multi-level terrain generation are often employed to transform the original data into a compressed digital format. However, reduction of the stored data size comes at an expense of loss of details in the original data set. This article reports on an alternative scheme for simplification and restoration of terrain data. The algorithm utilizes the fact that the terrain formation and patterns can be predicted and modeled through the fractal algorithm. This method was used to generate multi-level terrain model based on NGIS digital maps with preserving geometrical context of terrains.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1271-1274
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• 2021

RF fingerprinting based on deep learning (DL) has gained interests as a means to improve the security of near field communication (NFC) by allowing identification of NFC tags based on unique physical characteristics. To achieve high accuracy in the identification of NFC tags, it is crucial to utilize a large number of training data, however it is hard to collect such dataset in practice. In this study, we have provided new methodology to generate RF waveform from NFC tags, i.e., data augmentation, based on a conditional generative adversarial network (CGAN). By using the RF waveform of NFC tags which is collected from the testbed with software defined radio (SDR), we have confirmed that the realistic RF waveform can be generated through our proposed scheme.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1035-1043
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• 2021

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.69-79
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• 2013

This paper presents a two-stage geostatistical integration approach that aims at downscaling of coarse scale remote sensing data. First, downscaling of the coarse scale sedoncary data is implemented using area-to-point kriging, and this result will be used as trend components on the next integration stage. Then simple kriging with local varying means that integrates sparse precise observation data with the downscaled data is applied to generate thematic information at a finer scale. The presented approach can not only account for the statistical relationships between precise observation and secondary data acquired at the different scales, but also to calibrate the errors in the secondary data through the integration with precise observation data. An experiment for precipitation mapping with weather station data and TRMM (Tropical Rainfall Measuring Mission) data acquired at a coarse scale is carried out to illustrate the applicability of the presented approach. From the experiment, the geostatistical downscaling approach applied in this paper could generate detailed thematic information at various finer target scales that reproduced the original TRMM precipitation values when upscaled. And the integration of the downscaled secondary information with precise observation data showed better prediction capability than that of a conventional univariate kriging algorithm. Thus, it is expected that the presented approach would be effectively used for downscaling of coarse scale data with various data acquired at different scales.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.