• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.263-266
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• 2007

This paper discusses the Bayesian statistical inference. This paper discusses the Bayesian inference, MCMC (Markov Chain Monte Carlo) integration, MCMC method, Metropolis-Hastings algorithm, Gibbs sampling, Maximum likelihood estimation, Expectation Maximization algorithm, missing data processing, and BMA (Bayesian Model Averaging). The Bayesian statistical inference is used to process a large amount of data in the areas of biology, medicine, bioengineering, science and engineering, and general data analysis and processing, and provides the important method to draw the optimal inference result. Lastly, this paper discusses the method of principal component analysis. The PCA method is also used for data analysis and inference.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2003.04a
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• pp.105.1-105
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• 2003

This study was performed to develop the precise and rapid method to distinguish the blended sesame oil through the electronic nose analysis. The sesame oil was blended with corn oil at the ratio of 95:5, 90:10, 80:20(w/w), respectively. Samples were then analyzed by gas chromatography, SPME-GC/MS and the electronic nose composed of 12 metal oxide sensors. The sensetivities(delta Rgas/Rair) of sensors by electronic nose was carried out with principal component analysis(PCA). The proportion of first principal component showed 98.76%. In this study, the electronic nose analysis could be used as a competent method to classify for genuine sesame oil.

• Computers and Concrete
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• v.20 no.1
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• pp.23-29
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• 2017

In this study engineered steel fibres used as reinforcement for concrete were characterized by number of key mechanical and spatial parameters, which are easy to measure and quantify. Such commonly used parameters as length, diameter, fibre intrinsic efficiency ratio (FIER), hook geometry, tensile strength and ductility were considered. Effective classification of various fibres was demonstrated using simple multivariate computations involving principal component analysis (PCA). Contrary to univariate data mining approach, the proposed analysis can be efficiently adapted for fast, robust and direct classification of engineered steel fibres. The results have revealed that in case of particular spatial/geometrical conditions of steel fibres investigated the FIER parameter can be efficiently replaced by a simple aspect ratio. There is also a need of finding new parameters describing properties of steel fibre more precisely.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.32 no.6
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• pp.803-808
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• 1999

Trend of sea level change has been analysed by using the tidal data gathered at the 12 tide stations along the coast of Korean peninsula. Analysis and prediction of the sea level change were performed by Principal Component Analysis (PCA). For the period of 20 years from 1976 to 1995, the trend generally shows a rising pattern such as 0.22 cm/yr, 0.29 cm/yr, and 0.59 cm/yr along the eastern, southern, and western coast of Korea, respectively. On the average the sea level around the Korean peninsula seems to be rising at a rate of 0.37 cm/yr. Adopting the average rate to the sea level prediction model proposed by EPA (Titus and Narrayanan, 1995), the sea level may be approximately 50$\~$60 cm higher than the present sea level by the end of the next century.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.339-355
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• 2015

Corrosion of prestressed concrete structures is one of the main challenges that engineers face today. In response to this national need, this paper presents the results of a long-term project that aims at developing a structural health monitoring (SHM) technology for the nondestructive evaluation of prestressed structures. In this paper, the use of permanently installed low profile piezoelectric transducers (PZT) is proposed in order to record the acoustic emissions (AE) along the length of the strand. The results of an accelerated corrosion test are presented and k-means clustering is applied via principal component analysis (PCA) of AE features to provide an accurate diagnosis of the strand health. The proposed approach shows good correlation between acoustic emissions features and strand failure. Moreover, a clustering technique for the identification of false alarms is proposed.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.529-534
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• 2003

Within industry induction motors have a broad application area to drive pumps, fans, elevators and electric trains. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.598-600
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• 2001

DNA chip technology enables us to monitor thousands of gene expressions per sample simultaneously. Typically, DNA microarray data has at least several thousands of variables (genes) wish relatively smal1 number of samples. Thus feature (gene) selection by dimensionality reduction is necessary for efficient data analysis. In this paper we employ the partial least squares (PLS) method for gene selection and the principal component analysis (PCA) method for classification. The useful behavior of the PLS is verified by computer simulations.

• Journal of Environmental Science International
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• v.29 no.12
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• pp.1199-1204
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• 2020

Lentinula edodes mushrooms cultivated under different relative humidities were wrapped at 4℃ and the results of storage characteristics were investigated. Changes in water content of fruiting bodies during the storage period showed the highest water content in fruit bodies harvested from the treatment with the highest relative humidity. The luminosity of the fresh fruiting bodies showed no significant change during the storage period, and the redness was higher in the relative humidity 95% treatment than in the other treatments. According to this study, the relative humidity of the pileus was 65%, and the content of Ergosterol was 0.67 ± 15 g / L at relative humidity of 65%, 80% and 95%. In addition, amino acid analysis and Principal Component Analysis (PCA) confirmed that methionine was the main cause of changes in storage time and relative humidity.

• Asian Journal of Innovation and Policy
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• v.13 no.1
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• pp.57-67
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• 2024

This paper analyzes the factors influencing the selection of supercomputing resources. Using the results of a survey targeting supercomputing resources in the public sector, a resource selection model was presented through logistic regression and principal component analysis methods. As a result of the analysis, it was confirmed that affiliation, purpose of use, size of research funding, possession of a supercomputer, and whether specialized services are needed have a significant impact on resource selection. In the future, we expect that the results of this study will be used in various ways to manage demand for supercomputing resources.