• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.1
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• pp.23-31
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• 2012

Variable-flux permanent-magnet machines (VFPM) are of great interest and many different machine topologies have been documented. This paper categorizes VFPM machine topologies with regard to the method of flux variation and further, in the case of hybrid excited machines with field coils, with regard to the location of the excitation sources. The different VFPM machines are reviewed and compared in terms of their torque density, complexity and their ability to vary the flux.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1341-1364
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• 2023

This article introduces a method for estimating the conditional hazard function of a real-valued response variable based on a functional variable. The method uses local linear estimation of the conditional density and cumulative distribution function and is applied to a functional stationary ergodic process where the explanatory variable is in a semi-metric space and the response is a scalar value. We also examine the uniform almost complete convergence of this estimation technique.

• Journal of Yeungnam Medical Science
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• v.13 no.2
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• pp.261-271
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• 1996

Introduction: Osteoporosis, the most common metabolic bone disorder, is a condition of reduced bone density and increased susceptibility to fractures. Osteoporosis is a major public health problem and a significant cause of morbidity in postmenopausal women. Therefore family physicians as primary care physicians are in a key position for preventing and treating this disorder. So we studied the factors affecting to bone mineral density in postmenopausal women. Materials and Methods: A total of 136 spontaneous postmenopausal women were participated in the study. They have measured spinal bone mineral density by dual energy x-ray absorptiometry from January 1992 to June 1995 at Yeungnam University Hospital. Age, height, weight, age at menarche and menopause, number of child and breast feeding child, history of oral pill ingestion, family history of osteoporosis, amount of milk and coffee ingestion, consumption of tobacco and alcohol and physical activity were assessed by qustionnaire and medical records. Results: The mean age is 55.2 and mean age at menopause is 47.9. Height, weight and physical activity were significantly positive correlated to bone mineral density. But age, duration after menopause and number of child were significantly negative correlated. Also age, height, weight, physical activity and duration after menopause were significantly correlated to % age-matched bone mineral density. In multiple regression analysis, which dependent variable is bone mineral density, duration after menopause, physical activity and weight were significant contributors. Duration after menopause is most the largest contributor. In multiple regression analysis, which dependent variable is % age-matched bone mineral density to adjust the age effect, physical activity and weight were significant contributors. Physical activity is most the largest contributor. Conclusions: Among factors affecting to BMD in postmenopausal women, physical activity and weight were more important factors. Therefore continuous physical activity is significant factor to prevent osteoporosis in postmenopausal women.

• The Mathematical Education
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• v.58 no.4
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• pp.531-543
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• 2019

Continuous probability distribution was one of the mathematics concept that students had difficulty. This study analyzed the definition and introduction of the continuous probability distribution under the 2009-revised curriculum and 2015-revised curriculum. In this study, the following subjects were studied. Firstly, definitions of continuous probability variable in 'Probability and Statistics' textbook developed under the 2009-revised curriculum and 2015-revised curriculum were analyzed. Secondly, introductions of continuous probability distribution in 'Probability and Statistics' textbook developed under the 2009-revised curriculum and 2015-revised curriculum were analyzed. The results of this study were as follows. First, 8 textbooks under the 2009-revised curriculum defined the continuous probability variable as probability variable with all the real values within a range or an interval. And 1 textbook under the 2009-revised curriculum defined the continuous probability variable as probability variable when the set of its value is uncountable. But all textbooks under the 2015-revised curriculum defined the continuous probability variable as probability variable with all the real values within a range. Second, 4 textbooks under the 2009-revised curriculum and 4 textbooks under 2015-revised curriculum introduced a continuous random distribution using an uniformly distribution. And 5 textbooks under the 2009-revised curriculum and 5 textbooks under the 2015-revised curriculum introduced a continuous random distribution using a relative frequency density.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.46-49
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• 2002

We propose some properties of hybrid number the addition of fuzzy number and random variable and test the fuzzy hypotheses by agreement index for the hybrid number.

• Journal of the Korean Statistical Society
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• v.20 no.1
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• pp.1-28
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• 1991

Considerable progress on the problem of data-driven bandwidth selection in kernel density estimation has been made recently. The goal of this paper is to provide an introduction to the methods currently available, with discussion at both a practical and a nontechnical theoretical level. The main setting considered here is global bandwidth kernel estimation, but some recent results on variable bandwidth kernel estimation are also included.

• International Journal of Concrete Structures and Materials
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• v.19 no.1E
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.5
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• pp.394-406
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• 2004

A split-plot designed experiment including four rice varieties and 10 nitrogen levels was conducted in 2003 at the Experimental Farm of Seoul National University, Suwon, Korea. Before heading, hyperspectral canopy reflectance (300-1100nm with 1.55nm step) and nine crop variables such as shoot fresh weight (SFW), leaf area index, leaf dry weight, shoot dry weight, leaf N concentration, shoot N concentration, leaf N density, shoot N density and N nutrition index were measured at 54 and 72 days after transplanting. Grain yield, total number of spikelets, number of filled spikelets and 1000-grain weight were measured at harvest. 14,635 narrow-band NDVIs as combinations of reflectances at wavelength ${\lambda}l\;and\;{\lambda}2$ were correlated to the nine crop variables. One NDVI with the highest correlation coefficient with a given crop variable was selected as the NDVI of the best fit for this crop variable. As expected, models to predict crop variables before heading using the NDVI of the best fit had higher $r^2$ (>10\%)$ than those using common broad- band NDVI red or NDVI green. The models with the narrow-band NDVI of the best fit overcame broad- band NDVI saturation at high LAI values as frequently reported. Models using NDVIs of the best fit at booting showed higher predictive capacity for yield and yield component than models using crop variables.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.213-220
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• 2015

In this paper, we present a reliability-based iterative proportionality-logic decoding algorithm for two classes of structured low-density parity-check (LDPC) codes. The main contributions of this paper include: 1) Syndrome messages instead of extrinsic messages are processed and exchanged between variable nodes and check nodes, which can reduce the decoding complexity; 2) a more flexible decision mechanism is developed in which the decision threshold can be self-adjusted during the iterative process. Such decision mechanism is particularly effective for decoding the majority-logic decodable codes; 3) only part of the variable nodes satisfying the pre-designed criterion are involved for the presented algorithm, which is in the proportionality-logic sense and can further reduce the computational complexity. Simulation results show that, when combined with factor correction techniques and appropriate proportionality parameter, the presented algorithm performs well and can achieve fast decoding convergence rate while maintaining relative low decoding complexity, especially for small quantized levels (3-4 bits). The presented algorithm provides a candidate for those application scenarios where the memory load and the energy consumption are extremely constrained.

• International Journal of Contents
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• v.11 no.2
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• pp.57-62
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• 2015

Relative entropy is a divergence measure between two probability density functions of a random variable. Assuming that the random variable has only two alphabets, the relative entropy becomes a cross-entropy error function that can accelerate training convergence of multi-layer perceptron neural networks. Also, the n-th order extension of cross-entropy (nCE) error function exhibits an improved performance in viewpoints of learning convergence and generalization capability. In this paper, we derive a new divergence measure between two probability density functions from the nCE error function. And the new divergence measure is compared with the relative entropy through the use of three-dimensional plots.