• Proceedings of the KIEE Conference
• /
• 2002.07b
• /
• pp.817-820
• /
• 2002

Since a squirrel cage induction motor by NEMA Design types is designed to withstand full-voltage starting, direct starting method can be the most economical one. Starting a squirrel cage motor from standstill by connecting it directly across the line may allow inush currents of approximately 500-600% of rated current at lagging power factor of 35-50%. For many of the large motors, the starting inrush current may be great enough to cause voltage dips, which may adversely affect the building's lighting system. Electric utilities also have restrictions on starting currents, so that voltage fluctuations can be held to prescribed limits. Therefore the need for choosing the most appropriate method of motor starting is quite essential. In this paper, we proposed a plan for the selection of the most appropriate motor starting method, first by way of numeric simulation using manufacturer's data and second by way of actual experience. So far, more often than not, the selection of motor starting method has been accomplished only as regards to the capacity of the motor and the frequency of starting and stopping. But nowadays such high-tech apparatus as soft starters are being developed, and we are on the position to give more attention to clarify the way of selection of the motor starting method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.5A
• /
• pp.457-463
• /
• 2011

This paper proposes theorthogonal reference vectors selection method of the subspace interference alignment. The proposed method selects multiple orthogonal reference vectors instead of using one reference vector for all users at the same time. The proposed scheme selects a reference vector which maximizes a sum-rate for a certain cell, generates orthogonal vectors to the previous selected vector and selects the one of orthogonal vectors whose sum rate is maximized for each cell. Larger channel gain and sum-rate than the previous method can be obtained by selection degree of freedom. The computer simulation demonstrates the proposed method gives higher sum-rate compared with that of the previous reference vector selection method.

• Proceedings of the KSR Conference
• /
• 2007.11a
• /
• pp.595-600
• /
• 2007

Greater expansion and more frequent operation of the railroad transportation system anticipated due to its characteristics including low cost, safety and mass transportation. Recently, effects on the railway structures due to expansion of newly constructed road, construction of subway, city gas pipeline, communication network, electric power network and construction of other railway underground crossing in accordance with urban planning and organization has influenced safe operation of trains. Accordingly, standard for selection of construction method that will enable construction of more economical and rational subway underground crossing structures by preventing problems occurring at the time of above construction works and accidents in safe operation of trains due to construction in advance is definitively necessary. Although there are numerous construction methods that can be applied at the time of construction of railway underground crossing, there are much difficulties in selection of appropriate construction method that considers characteristics of each construction method on non-excavation type construction method, train operation plan of number of operational routes and on-site circumstances. Therefore, this research aims to present rational standard for selection of construction method for such, and standard for slowdown speed and interception of train when passing the areas of slowdown in sectors under construction.

• ETRI Journal
• /
• v.34 no.3
• /
• pp.388-398
• /
• 2012

Three-dimensional (3D) memories using through-silicon vias (TSVs) will likely be the first commercial applications of 3D integrated circuit technology. A 3D memory yield can be enhanced by vertical redundancy sharing strategies. The methods used to select memory dies to form 3D memories have a great effect on the 3D memory yield. Since previous die-selection methods share redundancies only between neighboring memory dies, the opportunity to achieve significant yield enhancement is limited. In this paper, a novel die-selection method is proposed for multilayer 3D memories that shares redundancies among all of the memory dies by using additional TSVs. The proposed method uses three selection conditions to form a good multi-layer 3D memory. Furthermore, the proposed method considers memory fault characteristics, newly detected faults after bonding, and multiple memory blocks in each memory die. Simulation results show that the proposed method can significantly improve the multilayer 3D memory yield in a variety of situations. The TSV overhead for the proposed method is almost the same as that for the previous methods.

• Communications for Statistical Applications and Methods
• /
• v.29 no.3
• /
• pp.287-299
• /
• 2022

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.1
• /
• pp.73-82
• /
• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2006.04a
• /
• pp.211-216
• /
• 2006

The Constrained Linear Spectral Unmixing(CLSU) is investigated for sub-pixel image processing, Its result is the abundance map which mean fractions of endmember existing in a mixed pixel. Compared to the Linear Spectral Unmixing using least square method, CLSU uses the NNLS (Non-Negative Least Square) algorithm to guarantee that the estimated fractions are constrained. But, CLSU gets Into difficulty in image processing due to select endmember at a user's disposition. In this study, endmember selection optimization method using entropy in the error-image analysis is proposed. In experiments which is used hyperion image, it is shown that our method can select endmember number than CLSU based on unsupervised endemeber selection.

• Journal of Korean Society for Quality Management
• /
• v.27 no.3
• /
• pp.125-141
• /
• 1999

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability with Poisson prior information are studied. For model selection, we explored the relative error.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.1
• /
• pp.21-28
• /
• 2005

This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.505-508
• /
• 2003

The current paper proposes a new vertex selection scheme for polygon-based contour ceding. To efficiently characterize the shape of an object, we incorporate the curvature information in addition to the conventional maximum distance criterion in vertex selection process. The proposed method consists of “two-step procedure.” At first, contour pixels of high curvature value are selected as key vertices based on the curvature scale space (CSS), thereby dividing an overall contour into several contour-segments. Each segment is considered as an open contour whose end points are two consecutive key vertices and is processed independently. In the second step, vertices for each contour segment are selected using progressive vertex selection (PVS) method in order to obtain minimum number of vertices under the given maximum distance criterion ( $D_{MAX}$). Experimental results are presented to compare the approximation performances of the proposed and conventional methods.s.