• Smart Structures and Systems
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• v.29 no.6
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• pp.799-805
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• 2022

Permanent magnet synchronous motors (PMSMs) are widely used in systems requiring high control precision, efficiency, and reliability. Predicting the remaining useful life (RUL) with health monitoring of PMSMs prevents catastrophic failure and ensures reliable operation of system. In this study, a model-based method for predicting the RUL of PMSMs using phase current and vibration signals is proposed. The proposed method includes feature selection and RUL prediction based on a particle filter with a degradation model. The Paris-Erdogan model describing micro fatigue crack propagation is used as the degradation model. An experimental set-up to conduct accelerated life test, capable of monitoring various signals was designed in this study. Phase current and vibration data obtained from an accelerated life test of the PMSMs were used to verify the proposed approach. Features extracted from the data were clustered based on monotonicity and correlation clustering, respectively. The results identify the effectiveness of using the current data in predicting the RUL of PMSMs.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.405-410
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• 2004

In mid-90's, the Korean government introduced the GIS(Geographic Information System) to digitalize every topography of national land and thereby, index locations and attributes of various urban facilities to construct a system whereby every information could be managed and operated in an integrated way, but the reliability of such geographic information has yet to be tested, much less its modification, complementation and maintenance. Under such circumstances, this study was aimed at constructing a reference point infrastructure for Seoul and Kyonggi area and comparing the data obtained from the GPS operation and various facility location data with the existing GIS data to address the problems of GIS operation and suggest their solutions. As a result of calculating the GPS reference point data and analyzing the deviations of the unknown point data in comparison with the fixed point ones, it was found that the horizontal location values were reliable within +/- 5cm, but that the above-sea level values varied as much as 1.4m depending on the deployment of pre-set fixed points determined by the direct level gauging. In addition, as a result of directly surveying major facilities around the roads based on the coordinates of the urban reference points networked with such a reference point system to check their conformity to existing data, it was confirmed that the difference was as wide as 2m. Such differences may be attributable to the fact that the data with their geographic information not confirmed are used as basic data for GIS. Hence, this study suggests the ways to set the absolute geographic data based on reference points and test the reliability of existing data and thereby, suggests a methods to solve the problems.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.125-132
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• 2010

With recent advancement of remote sensing technology, a variety of data acquisition over the same area is achievable. An automated co-registration of heterogeneous airborne images is a critical step for change detection. This paper describes an automatic method for co-registration between digital image and existing building model. Optimal building models for co-registration purpose are extracted as primitives from existing building model database. A set of homologous features between straight lines extracted from aerial digital image and model primitive are computed based on geometric similarity function. With obtained homologous features, EO parameter is recomputed using least square method. The result shows that die suggested method automatically co-register two data set in a reliable manner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2704-2719
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• 2022

Lately, neo-Chinese style furniture has been frequently noticed by product design professionals for the big part it played in promoting traditional Chinese culture. This article is an attempt to use big data semantic analysis method to provide effective design research method for neo-Chinese furniture design. By using big data mining program TEXTOM for big data collection and analysis, the data obtained from typical websites in a set time period will be sorted and analyzed. On the basis of "neo-Chinese furniture" samples, key data will be compared, classification analysis of overall data, and horizontal analysis of typical data will be performed by the methods of word frequency analysis, connection centrality analysis, and TF-IDF analysis. And we tried to summarize according to the related views and theories of the design. The research results show that the results of data analysis are close to the relevant definitions of design. The core high-frequency vocabulary obtained under data analysis, such as popular, furniture, modern, etc., can provide a reasonable and effective focus of attention for the designs. The result obtained through the systematic sorting and summary of the data can be a reliable guidance in the direction of our design. This research attempted to introduce related big data mining semantic analysis methods into the product design industry, to supply scientific and objective data and channels for studies on design, and to provide a case on the practical application of big data analysis in the industry.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2003.11a
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• pp.322-327
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• 2003

It is important to know the accurate radionuclide inventory of radioactive waste for the reliable management. However, estimation of radionuclide concentrations in drummed radioactive waste is difficult and unreliable because of difficulties of direct detection, high cost, and radiation exposure of sampling personnel. In order to overcome these difficulties, scaling factors (SFs) have been used to assess the activities of radionuclides that could not be directly analyzed. A radionuclide assay system has been operated at KORI site since 1996 and consolidated scaling factor method has played a dominant role in determination of radionuclides concentrations. However, some problems are still remained such as uncertainty of estimated scaling factor values, inaccuracy of analyzed sample values, and disparity between the actual and ideal correlation pairs and the others. Therefore, it needs to improve the accuracy of scaling factor values. The scope of this paper is focused on the improvement of accuracy and representativeness of calculated scaling factor values based on statistical techniques. For the selection of reliable activity determination method, the accuracy of estimated SF values for each activity determination method is compared. From the comparison of each activity determination methods, it is recommended that SF determination method should be changed from the arithmetic mean to the geometrical mean for more reliable estimation of radionuclide activity. Arithmetic mean method and geometric mean method are compared based on the data set in KORI system.

• Clean Technology
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• v.18 no.1
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• pp.31-37
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• 2012

Biomass has become a major feedstock for bioenergy and other bio-based products because of its renewability and environmental benefits. Various researches have been done in the prediction of crucial characteristics of biomass, including the active utilization of spectroscopy data. Near infrared (NIR) spectroscopy has been widely used because of its attractive features: it's non-destructive and cost-effective producing fast and reliable analysis results. This work developed the multivariate statistical scheme for predicting weight loss profiles based on the utilization of NIR spectra data measured for six lignocellulosic biomass types. Wavelet analysis was used as a compression tool to suppress irrelevant noise and to select features or wavelengths that better explain NIR data. The developed scheme was demonstrated using real NIR data sets, in which different prediction models were evaluated in terms of prediction performance. In addition, the benefits of using right pretreatment of NIR spectra were also given. In our case, it turned out that compression of high-dimensional NIR spectra by wavelet and then PLS modeling yielded more reliable prediction results without handling full set of noisy data. This work showed that the developed scheme can be easily applied for rapid analysis of biomass.

• Genomics & Informatics
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• v.4 no.3
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• pp.110-117
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• 2006

In microarray technology, many diverse experimental features can cause biases including RNA sources, microarray production or different platforms, diverse sample processing and various experiment protocols. These systematic effects cause a substantial obstacle in the analysis of microarray data. When such data sets derived from different experimental processes were used, the analysis result was almost inconsistent and it is not reliable. Therefore, one of the most pressing challenges in the microarray field is how to combine data that comes from two different groups. As the novel trial to integrate two data sets with batch effect, we simply applied standardization to microarray data before the significant gene selection. In the gene selection step, we used new defined measure that considers the distance between a gene and an ideal gene as well as the between-slide and within-slide variations. Also we discussed the association of biological functions and different expression patterns in selected discriminative gene set. As a result, we could confirm that batch effect was minimized by standardization and the selected genes from the standardized data included various expression pattems and the significant biological functions.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.307-319
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• 2013

With the rapid accumulation of microarray data, it is a significant challenge to integrate available data sets addressing the same biological questions that can provide more samples and better experimental results. Sometimes, different microarray platforms make it difficult to effectively integrate data from several studies and there is no consensus on which method is the best to produce a single and unified data set. Methods using median rank score, quantile discretization and standardization (which directly combine rescaled gene expression values) and meta-analysis (which combine the results of individual studies at the interpretative level) are reviewed. Real data examples downloaded from GEO are used to compare the performance of these methods and to evaluate if the combined data set detects more reliable information from the separated data sets or not.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.141-143
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• 2004

This paper represents 3D line segment extraction method, which can be used in generating 3D rooftop model. The core of our method is that 3D line segment is extracted by using line fitting of elevation data on 2D line coordinates of ortho-image. In order to use elevations in line fitting, the elevations should be reliable. To measure the reliability of elevation, in this paper, we employed the concept of self-consistency. We carry out the experiment of 3D line extraction using synthetic images generated from Avenches data set of Ascona aerial images.