• Kyungpook Mathematical Journal
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• v.55 no.1
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• pp.127-135
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• 2015

We determine two-interval normalized tight frame wavelet sets for real dilation $d{\in}(1,{\infty})$, and characterize all symmetric normalized tight frame wavelet sets. We also construct a normalized tight frame wavelet set which has an infinite number of components accumulating at the origin. These normalized tight frame wavelet sets and their closures possess the same measure. Finally an example of a normalized tight frame wavelet set is provided whose measure is strictly less than the measure of its closure.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.136-139
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• 2010

We derived Stop-and-go normalized DD, dual-mode normalized Sato, dual-mode NCMA blind equalization algorithm for complex data in this research. And then, the convergence characteristics of the proposed SG-NDD, dual-mode NSato blind equalization algorithms are compared with those of SG-DD, dual-mode Sato algorithms. In general, the normalized blind equalization algorithms have better convergence characteristics than the conventional algorithms.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.255-258
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• 2003

This paper defines Normalized Term Frequency Weighting method for automatic text categorization by using Box-Cox, and then it applies automatic text categorization. Box-Cox transformation is statistical transformation method which makes normalized data. This paper applies that and suggests new term frequency weighting method. Because Normalized Term Frequency is different from every term compared by existing term frequency weighting method, it is general method more than fixed weighting method such as log or root. Normalized term frequency weighting method's reasonability has been proved though experiments, used 8000 newspapers divided in 4 groups, which resulted high categorization correctness in all cases.

• Journal of applied mathematics & informatics
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• v.32 no.1_2
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• pp.227-245
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• 2014

Suppose G is a simple graph. The ${\ell}$-eigenvalues ${\delta}_1$, ${\delta}_2$,..., ${\delta}_n$ of G are the eigenvalues of its normalized Laplacian ${\ell}$. The normalized Laplacian Estrada index of the graph G is dened as ${\ell}EE$ = ${\ell}EE$(G) = ${\sum}^n_{i=1}e^{{\delta}_i}$. In this paper the basic properties of ${\ell}EE$ are investigated. Moreover, some lower and upper bounds for the normalized Laplacian Estrada index in terms of the number of vertices, edges and the Randic index are obtained. In addition, some relations between ${\ell}EE$ and graph energy $E_{\ell}$(G) are presented.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.337-339
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• 1992

This paper deals with the tuning method of PID controls for process controls. It introduces the normalized process gain and the normalized process dead-time for processes based on Ziegler-Nichols tuning methods. In the case of PID auto-tuning, the first, this method applies Ziegler-Nichols tuning method and introduces the set-point weighting for reducing overshoot in the large normalized process gain or small normalized process dead-time, the second, this method is modified and includes the set-point weighting in the small normalized process gain or large normalized process dead-time. In the case of PI auto-tuning, this method is modified for reducing overshoot. This paper obtains empirical data with Ziegler-Nichols methods for refined Ziegler-Nichols tuning methods.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.75-80
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• 2019

In machine learning, the performance of the system depends upon the nature of input data. The efficiency of the system improves when the behavior of the input data changes from un-normalized to normalized form. This paper experimentally demonstrated the performance of KNN, SVM, LDA and NB on Alzheimer's dataset. The dataset undertaken for the study consisted of 3 classes, i.e. Demented, Converted and Non-Demented. Analysis shows that LDA and NB gave an accuracy of 89.83% and 88.19% respectively in both the cases whereas the accuracy of KNN and SVM improved from 46.87% to 82.80% and 53.40% to 88.75% respectively when input data changed from un-normalized to normalized state. From the above results it was observed that KNN and SVM show significant improvement in classification accuracy on normalized data as compared to un-normalized data, whereas LDA and NB reflect no such change in their performance.

• Kyungpook Mathematical Journal
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• v.58 no.1
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• pp.167-182
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• 2018

In this paper, we prove the optimal inequalities for the generalized normalized ${\delta}$-Casorati curvature and the normalized scalar curvature for different submanifolds in generalized (${\kappa},{\mu}$)-space forms. The proof is based on an optimization procedure involving a quadratic polynomial in the components of the second fundamental form. We also characterize the submanifolds on which equalities hold.

• ETRI Journal
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• v.33 no.3
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• pp.401-406
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• 2011

This paper presents a fast block-matching algorithm based on the normalized cross-correlation, where the elimination order is determined based on the gradient magnitudes of subblocks in the current macroblock. Multilevel Cauchy-Schwartz inequality is derived to skip unnecessary block-matching calculations in the proposed algorithm. Also, additional complexity reduction is achieved re-using the normalized cross correlation values for the spatially neighboring macroblock because the search areas of adjacent macroblocks are overlapped. Simulation results show that the proposed algorithm can improve the speed-up ratio up to about 3 times in comparison with the existing algorithm.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2243-2245
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• 1998

In this paper, we treat image segmentation as a graph partitioning problem. and use the normalized cut for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different graphs as well as the total similarity within the groups. The minimization of this criterion can formulated as a generalized eigenvalues problem. We have applied this approach to segment static image. This criterion can be shown to be computed efficiently by a generalized eigenvalues problem

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.4
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• pp.196-202
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• 2020

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91~0.92, GNDVI were 0.92~0.94, NGRDI were 0.71~0.85 and NDREI were 0.84~0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.