• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.146-151
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• 2016

This study concentrates on the design of floater for 15kW-class wave energy converter that extracts the ocean energy by oscillating vertically along the wave motion. The floater connects to a arm structure that connects to a hydraulic cylinder, which drives a hydraulic generator. The study mainly focuses on the structural analysis of the floater. Previous studies have been conducted using a miniature model; however, this study focuses on the size selection of the floater for a full scale model. Static structural analysis is conducted using fine numerical grids. Due to the complexity of the whole model, it is analyzed as a separate component. There are several load cases for each floater size, and they are analyzed thoroughly for stress (von-mises, shear, and normal) and deformation. The initial design was conducted by scaling up from the miniature model of the previous study, and the final design has been redesigned by changing the thickness and internal support structure shape.

• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.423-426
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• 2009

POSTECH Graduate School of Wind Energy is trying to upgrade the PMSG used for Unison U50 to 900 kW class. Intensive optimization efforts are carried out the reduce the axial size and total weight of the generator while increasing the rated output to 900 kW. The generator features 3.32m stator inner radius, 671mm stator length, 84 pole, 25 rated rpm and 31.6kN/$m^2$ shear force density. To reduce the gross weight, the stronger magnetic material is applied with optimal magnet size resulting lowest cogging torque. Also, instead of stator skewing the stator, the magnet position along the circumference is optimized to further reduce the cogging torque. This scheme eliminates the stator skewing procedure and may enhance the productivity. This method also reduces the total harmonic distortion. In this report, upgrade method, no-load line to line voltage and phase voltage, cogging torque, loss calculations and thermal analysis are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.295-300
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• 1998

In this paper, we point out the possibility of the divergence of control input caused by the estimation error of delay-time when general iterative learning algorithms are applied to a class of linear dynamic systems with time-delay in which delay-time is not exactly measurable, and then propose a new type of iterative learning algorithm in order to solve this problem. To resolve the uncertainty of delay-time, we propose an algorithm using holding mechanism which has been used in digital control system and/or discrete-time control system. The control input is held as constant value during the time interval of which size is that of the delay-time uncertainty. The output of the system tracks a given desired trajectory at discrete points which are spaced auording to the size of uncertainty of delay-time with the robust property for estimation error of delay-time. Several numerical examples are given to illustrate the effeciency of the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.723-725
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• 2016

In Gen-2 Q-algorithm, if the number of tags is small and we let the initial $Q_{fp}$ be large, the number of empty slot will be large. On the other hand, if we let the initial $Q_{fp}$ be small in spite of many tags, almost all the slots will be collided. Also, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to select the weight based on the slot-count size of current query round through the tag number estimation and.

• Current Optics and Photonics
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• v.7 no.4
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• pp.435-442
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• 2023

These days, the size of a reflective telescope has been increasing for astronomical observation. An additional optical system usually assists a large ground telescope for image analysis or the compensation of air turbulence. To guide collimated light to the external optical system through a designated path, a coudé mirror is usually adopted. Including a collimator, a coudé mirror of a ground telescope is affected by gravity, depending on the telescope's pointing direction. The mirror surface is deformed by the weight of the mirror itself and its mount, which deteriorates the optical performance. In this research, we propose an optimization method for the coudé mirror assembly for a 1-m class ground telescope that minimizes the gravitational surface error (SFE). Here the mirror support positions and the sizes of the mount structure are optimized using finite element analysis and the response surface optimization method in both the horizontal and vertical directions, considering the telescope's altitude angle. Throughout the whole design process, the coefficients of the Zernike polynomials are calculated and their amplitude changes are monitored to determine the optimal design parameters. At the same time, the design budgets for the thermal SFE and the mass and size of the mount are reflected in the study.

• Genomics & Informatics
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• v.12 no.3
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• pp.87-97
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• 2014

Although the number of protein-coding genes is not highly variable between plant taxa, the DNA content in their genomes is highly variable, by as much as 2,056-fold from a 1C amount of 0.0648 pg to 132.5 pg. The mean 1C-value in plants is 2.4 pg, and genome size expansion/contraction is lineage-specific in plant taxonomy. Transposable element fractions in plant genomes are also variable, as low as ~3% in small genomes and as high as ~85% in large genomes, indicating that genome size is a linear function of transposable element content. Of the 2 classes of transposable elements, the dynamics of class 1 long terminal repeat (LTR) retrotransposons is a major contributor to the 1C value differences among plants. The activity of LTR retrotransposons is under the control of epigenetic suppressing mechanisms. Also, genome-purging mechanisms have been adopted to counter-balance the genome size amplification. With a wealth of information on whole-genome sequences in plant genomes, it was revealed that several genome-purging mechanisms have been employed, depending on plant taxa. Two genera, Lilium and Fritillaria, are known to have large genomes in angiosperms. There were twice times of concerted genome size evolutions in the family Liliaceae during the divergence of the current genera in Liliaceae. In addition to the LTR retrotransposons, non-LTR retrotransposons and satellite DNAs contributed to the huge genomes in the two genera by possible failure of genome counter-balancing mechanisms.

• Journal of the Korean Society of Costume
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• v.58 no.6
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• pp.54-68
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• 2008

The purpose of this study is to investigate the effect of dot pattern size(0.8, 1.8, 2.5, 5, 8), color combination(BG/R, Y/B), area-ratio on image formation. Sets of stimulus and response scales(7 point semantic) were used as experimental materials. The stimuli were 20 color pictures manipulated with the combination of dot pattern size, color combination, and area-ratio using computer simulation. The subjects were 240 female undergraduates living in Gyeongnam-do. Image factor of the stimulus was composed of 5 different components, visibility, attractiveness, cuteness, stability and high class image. In the cuteness, color combination, dot pattern size showed independent effect. In the stability, area-ratio, dot pattern size showed independent effect. Interaction effects of color and area-ratio combination was significant on cuteness. For visibility image 8cm yellow dot/blue background, for attractiveness image BG/R coloration, for cuteness image Y/B coloration and for stability image 0.8cm yellow dot/blue background were effective. According to the variation of dot pattern size, color combination and area-ratio, it was investigated that the images for a dress wearer were expressed diversely, were shown differently in image dimensions, and could be produced to different images.

• Journal of the Korean Society of Clothing and Textiles
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• v.11 no.3 s.25
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• pp.89-99
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• 1987

The purpose of this present study is not only to investigate mutal relationship between the preference of clothing design and the nature of Reflectiveness-Impulsiveness, but to offer clothing design for desirably developing of child, especially of preschool children. And the practical research was performed for 166 preschool children who are in kindergarten located in Seoul and are selected according to their sex and social class. This study used Kagan's MFFT and Personality Inventory of Hwang Eung-Yeon for the test of Reflectiveness-Impulsiveness, while for the preference of clothing design used the photo deck which is showed to children. The data analysis was based on frequency, percentage, mean, standard deviation, chi-square ($x^2$) test, t-test, F-test, and Duncan's Multiple Range Test. It can be concluded that girls rather than toys, high class children rather than middle or low class children were more reflective as a general trend. The relationship between the preference of clothing design and the nature of Reflectiveness and Impulsiveness is as follows : 1. In color, reflective boy liked blue series and reflective girl liked pastel coloring series, while they disgusted red series and non-coloring series. The group of impulsiveness liked red series, while they disgusted non-coloring series. 2. In color combination, group of reflectiveness liked similar color, while group of impulsiveness liked contrasting color. 3. In texture, group of reflectiveness liked soft and shiny texture, while they disgusted thick and rough texture. Also Group of impulsiveness liked soft texture. 4. In pattern, reflective boy liked lettering pattern and reflective girl liked flower pattern, while she disgusted lettering pattern. Impulsive boy liked stripe pattern and limpulsive girl liked flower pattern. 5. In pattern size, group of reflectivenss liked small size, while group of impulsiveness liked large one. 6. In style, group of reflectiveness liked formal style.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.