• Journal of Industrial Technology
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• v.19
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• pp.179-187
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• 1999

The computer pattern sewing machine is an automatic sewing machine that is controlled by an input pattern. Even a novice can run this machine for various tasks fast and reliably such as sewing a button, a belt ring and an airbag, etc. The pattern processing software, which is the main software of this machine, is for editing and modifying pattern data by online teaching or off-line editing, setting up parameters, and calculate a moving distance of working area on the x-y axes. In this paper we propose an algorithm to generate pattern data for sewing by simplifying image data. The pattern data are composed of outline data like dot, line, circle, arc, curve, etc. We need converting this data into sewing data which involve sewing parameter, moving distance of working are an the x-y axes, thread, spindle speed.

• Journal of Sensor Science and Technology
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• v.15 no.6
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• pp.433-441
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• 2006

Because of the low power and low rate of a sensor network, outlier is frequently occurred in the time series data of sensor network. In this paper, we suggest periodic pattern analysis that is applied to the time series data of sensor network and predict outlier that exist in the time series data of sensor network. A periodic pattern is minimum period of time in which trend of values in data is appeared continuous and repeated. In this paper, a quantization and smoothing is applied to the time series data in order to analyze the periodic pattern and the fluctuation of each adjacent value in the smoothed data is measured to be modified to a simple data. Then, the periodic pattern is abstracted from the modified simple data, and the time series data is restructured according to the periods to produce periodic pattern data. In the experiment, the machine learning is applied to the periodic pattern data to predict outlier to see the results. The characteristics of analysis of the periodic pattern in this paper is not analyzing the periods according to the size of value of data but to analyze time periods according to the fluctuation of the value of data. Therefore analysis of periodic pattern is robust to outlier. Also it is possible to express values of time attribute as values in time period by restructuring the time series data into periodic pattern. Thus, it is possible to use time attribute even in the general machine learning algorithm in which the time series data is not possible to be learned.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1424-1434
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• 2009

DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.838-841
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• 2005

DNA Chip is able to show DNA-Data that includes diseases of sample to User by using complementary characters of DNA. So this paper studied Neural Network algorithm for Image data processing of DNA-chip. DNA chip outputs image data of colors and intensities of lights when some sample DNA is putted on DNA-chip, and we can classify pattern of these image data on user pc environment through artificial neural network and some of image processing algorithms. Ultimate aim is developing of pattern classifying algorithm, simulating this algorithm and so getting information of one's diseases through applying this algorithm. Namely, this paper study artificial neural network algorithm for classifying pattern of image data that is obtained from DNA-chip. And, by using histogram, gradient edge, ANN and learning algorithm, we can analyze and classifying pattern of this DNA-chip image data. so we are able to monitor, and simulating this algorithm.

• Journal of the Korean Home Economics Association
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• v.46 no.9
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• pp.137-146
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• 2008

This study was conducted in order to spread out lower body 3D scan data of men in their twenties. The aim was to achieve slacks pattern with ease allowance through comparison with existing flat patterns. For conversion of 3D scan data into 20 pattern, reference lines were established by using Rapid Foam in 3D shape analysis software. 2C-AN program and Yuka CAD were used to convert 20 pattern earned with straight posture of 3D scan data into slacks pattern by using Triangle Simplification & Runge-Kutta Method. In order to achieve this we needed to set a line 9cm below the hip line, to array vertex of each block to crease line while maintaining the horizontal line. And then we needed to set ease allowance in back crotch and to set waist circumference or hip circumference ease allowance in side seam of slacks. Results showed that long front crotch length can be achieved if 3D scan data is compared with 20 existing flat pattern. Slacks pattern that raise front crotch by about 1.5cm compared to back crotch and also possess ease allowance in back crotch area are great in appearance evaluation.

• The Research Journal of the Costume Culture
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• v.12 no.2
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• pp.290-299
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• 2004

The purpose of this study is to testify the pattern making method to develop the men's basic bodice pattern using 3-dimensional body scan data. The experimental patterns were made by adding wearing ease on flattened body scan data and tracing the outlines of it. The experimental bodice pattern were composed of front, back, and side panels. To compare the difference between the experimental pattern and traditional pattern, two pattern making methods were compared. Two sets of basic bodice patterns were made for each of the 10 male subjects: a set of pattern was made by experimental method and the other set was made by Bunka pattern making method. The experimental and traditional patterns were measured at 13 dimensions. The results show that there was a difference between the experimental patterns and traditional patterns at the front length, back length, front width, front neck width, back neck width, and back neck depth. The fit was also compared for both patterns. The results of the fit test show that the experimental patterns were superior to the traditional patterns at the fit of neck, shoulder, and armhole. The experimental pattern making method was expected to be useful for mass customization.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.183-190
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• 2012

Mobile web applications are different from desktop web applications because of their small screen size and small user input devices. Therefore user interface designers have spent their effort and time to re-design the user interface of mobile web applications to meet these differences. In this paper, we introduce five user interface patterns for mobile web applications to reduce their effort and time. Two of them are for utilizing small screen size efficiently, and they are space overloading pattern and data filtering pattern. These patterns enable designers to reduce screen usage. The other three patterns - data suggestion pattern, input reuse pattern, and incremental data input pattern - are for helping users' data input on mobile devices. These three patterns enable users to reduce direct data input. Our work will help user interface designers develop mobile web interface to utilize screen space efficiently and get data with less errors and less efforts from users.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.589-598
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• 2015

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.

• Journal of Fashion Business
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• v.11 no.3
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• pp.35-45
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• 2007

This study examined how to create 2D basic pattern of individuals by means of 3-D body figure, which is to develop a flat of individual basic pattern directly from the 3-D body scan data of each subject using that of the upper body of a male adult. In terms of methodology, this study adopted 3D body scan data on system and body to make examinations in the following steps: 1. Standard point and line were set on human body, along with 3-D definition points(feature points). 2. PB was created by modifying horizontal and longitudinal section of scan data. 3. Ways to set reserve were established in the findings of PB planar development. Respective developed flat patterns were compared with pattern findings in previous studies by means of sensory evaluation. As a result, it was found that both system and body model are basic pattern and belong to appropriate pattern as semi-tight-fit basic pattern with overall appropriate tolerances. Thus, this study came to a conclusion that it is feasible and valid to develop theories for flat development as considered herein.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.526-539
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• 2012

This paper proposes a high capacity data hiding method using high frequence secret data indexing algorithm. Many novel data hiding methods based on LSB and PVD methods were presented to enlarge hiding capacity and provide an imperceptible quality. In this paper, first, calculating data iteration frequency of the secret message and make up the high frequency data index matrix (HFDT) using high frequence data's location information. Next, HFDT uses to that data hiding process on the cover image and recovering process on the stego image. The experimental results demonstrate the efficiency of the proposed high frequency secret data indexing method. For the data hiding method, experiments are conducted for four cases: 2 pattern secret data (2PD), 4 pattern secret data (4PD), 8 pattern secret data (8PD) and higher pattern secret data (HPD). When comparing the proposed method with other data hiding methods, for the HPD case, the results show that the proposed method has a good PSNR and more capacity, and for the other case, the results show that the proposed method has a higher PSNR and larger capacity.