• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.99-107
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• 2009

This paper presents an enhanced hybrid model based on Independent Component Analysis(ICA) in order to features of numeric characters in license plates. ICA which is used only in high dimensional statistical features doesn't consider statistical features in low dimension and correlation between numeric characters. To overcome the drawbacks of ICA, we propose an improved ICA with the hybrid model using both Principle Component Analysis(PCA) and Linear Discriminant Analysis(LDA). Experiment results show that the proposed model has a superior performance in feature extraction and recognition compared with ICA only as well as other hybrid models.

• Proceedings of the KIEE Conference
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• 2006.10a
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• pp.36-37
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• 2006

Both negative and positive substrate bias effects on the structural properties and field-emission characteristics are investigated. carbon nanotubes (CNTs) are grown on Ni catalysts employing an inductively-coupled plasma chemical vapor deposition (ICP-CVD) method. Characterization using various techniques, such as field-emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM), Auger spectroscopy (AES), and Raman spectroscopy, shows that the physical dimension as well as the crystal quality of CNTs grown can be changed and controlled by the application of substrate bias during CNT growth. It is for the first time observed that the prevailing growth mechanism of CNTs, which is either due to tip-driven growth or based-on-catalyst growth, may be influenced by substrate biasing. It is also seen that negative biasing would be more effectively role in the vertical-alignment of CNTs compared to positive biasing. However, the CNTs grown under the positively bias condition display much better electron emission capabilities than those grown under negative bias or without bias. The reasons for all the measured data regarding the structural properties of CNTs are discussed to confirm the correlation with the observed field-emissive properties.

• Advances in concrete construction
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• v.3 no.2
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• pp.91-102
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• 2015

In this paper, the load-deflection analysis of the Carbon Fiber Reinforced Polymer (CFRP) strengthened Reinforced Concrete (RC) slab using Recurrent Neural Network (RNN) is investigated. Six reinforced concrete slabs having dimension $1800{\times}400{\times}120mm$ with similar steel bar of 2T10 and strengthened using different length and width of CFRP were tested and compared with similar samples without CFRP. The experimental load-deflection results were normalized and then uploaded in MATLAB software. Loading, CFRP length and width were as neurons in input layer and mid-span deflection was as neuron in output layer. The network was generated using feed-forward network and a internal nonlinear condition space model to memorize the input data while training process. From 122 load-deflection data, 111 data utilized for network generation and 11 data for the network testing. The results of model on the testing stage showed that the generated RNN predicted the load-deflection analysis of the slabs in acceptable technique with a correlation of determination of 0.99. The ratio between predicted deflection by RNN and experimental output was in the range of 0.99 to 1.11.

• Geomechanics and Engineering
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• v.11 no.5
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• pp.639-655
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• 2016

The effect of particle breakage and intermediate principal stress ratio on the behaviour of crushable granular assemblies under true triaxial stress conditions is studied using the discrete element method. Numerical results show that the increase of intermediate principal stress ratio $b(b=({\sigma}_2-{\sigma}_3)/({\sigma}_1-{\sigma}_3))$ results in the increase of dilatancy at low confining pressures but the decrease of dilatancy at high confining pressures, which stems from the distinct increasing compaction caused by breakage with b. The influence of b on the evolution of the peak apparent friction angle is also weakened by particle breakage. For low relative breakage, the relationship between the peak apparent friction angle and b is close to the Lade-Duncan failure model, whereas it conforms to the Matsuoka-Nakai failure model for high relative breakage. In addition, the increasing tendency of relative breakage, calculated based on a fractal particle size distribution with the fractal dimension being 2.5, declines with the increasing confining pressure and axial strain, which implies the existence of an ultimate graduation. Finally, the relationship between particle breakage and plastic work is found to conform to a unique hyperbolic correlation regardless of the test conditions.

• Journal of Korean Clinical Nursing Research
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• v.19 no.2
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• pp.275-284
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• 2013

Purpose: This study was aimed to find out the effect of core competency and teaching style on preceptor self-efficacy among preceptors. Methods: One hundred twelve nurses working at four university hospitals with previous preceptor experience participated in the survey. The data were analyzed by t-test, ANOVA, Pearson correlation coefficients and multiple regression. Results: The preceptors used 'judgment-initiative' teaching style most frequently, and reported the highest scores in the role model dimension of core competency. There were significant positive relations between age (r=.266, p=.005), clinical experience (r=.274, p=.004), preceptorship experience (r=.204, p=.032), core competency (r=.593, p<.001) and preceptor self- efficacy. But preceptor self-efficacy was not significantly different depending on the teaching style (F=0.72, p=.54). The most predictive factors of the preceptor self-efficacy were core competency and teaching style (judgment)(F=31.849, p<.001). The explained variance for preceptor self-efficacy was 35.9% in the model. Conclusion: The preceptor self-efficacy is essential for the preceptors' successful teaching experience and the clinical competency improvement of the entry level nurses. Management for an effective preceptor training program needs to focus on the improvement of core competency of preceptors, which will lead to strengthen their self-efficacy.

• Research in Community and Public Health Nursing
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• v.18 no.4
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• pp.601-610
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• 2007

Purpose: This study investigated attitudes toward general elders and elders with dementia among nursing students before beginning the clinical practicum. In addition, students' characteristics differentiating the attitudes were examined. Methods: Attitudes were measured with questionnaires developed for Asian culture at the beginning week of the first semester of the junior year in two baccalaureate programs. Responses from 120 out of 121 students were analyzed using Pearson correlation coefficient, paired t-test, ANOVA, and Scheffe test. Results: Students held negative attitudes toward both types of elders except for generosity dimension toward general elders showing a neutral attitude. Elders with dementia were evaluated more negatively than general elders in all the dimensions of vitality, generosity and flexibility. Vitality and generosity toward general elders were different according to intimacy and the degree of communication with elders. Generosity toward general elders was also different according to students' religious beliefs. Students with interest in elders/issues showed more negative attitudes of vitality and flexibility toward elders with dementia. Conclusions: Students in general had negative attitudes toward elders and more negative attitudes toward elders with dementia. We need to put more efforts into the entire nursing curriculum in order to improve attitudes toward elders with particular concern over attitudes toward elders with dementia.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.869-872
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• 2003

The thermal contamination of the Younggwang coastal marine ecosystem has been investigated using space borne thermal infrared data acquired over the period 1985-2003 by the Landsat and NOAA satellites. The analysis of AVHRR data brought out the general pattern and extension of thermal plume while TM data yielded more accurate information about the plume shape, dimension, dispersion direction etc. The examination of sea surface temperature (SST) computed from these images clearly indicates that the thermal plume extends 70 to100km southward during summer and 50 to70km northwestward during winter monsoons. The maximum plume temperature was 29$^{\circ}C$ in summer and 12$^{\circ}C$ in winter. The comparative analysis shows that the temperature retrieved from TM is slightly higher (1.8$^{\circ}C$, 3$^{\circ}C$ and 2.2$^{\circ}C$ for the images of 98/11/10, 99/05/05 and 99/05/21 respectively) than those derived from AVHRR data. The correlation coefficient between the TM-derived SST and AVHRR-derived SST was 0.72.

• Tribology and Lubricants
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• v.30 no.6
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• pp.378-383
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• 2014

This study predicts the of the hysteretic friction of a rubber block sliding on an SMA asphalt road. The friction of filled rubber on a rough surface is primarily determined by two elements:the viscoelasticity of the rubber and the multi-scale perspective asperities of the road. The surface asperities of the substrate exert osillating forces on the rubber surface leading to energy dissipation via the internal friction of the rubber when rubber slides on a hard and rough substrate. This study defines the power spectra at different length scales by using a high-resolution surface profilometer, and uses rubber and road surface samples to conduct friction tests. I consider in detail the case when the substrate surface has a self affine fractal structure. The theory developed by Persson is applied to describe these tests through comparison with the hysteretic friction coefficient relevant to the energy dissipation of the viscoelastic rubber attributable to cyclic deformation. The results showed differences in the absolute values of predicted and measured friction, but with high correlation between these values. Hence, the friction prediction model is an appropriate tool for separating the effects of each factor. Therefore, this model will contribute to clearer understanding of the fundamental principles of rubber friction.