• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.69-74
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• 2006

The recent development of multimedia content-based retrieval technologies brings great attention of musician retrieval using features of a digital audio data among music information retrieval technologies. But the indexing techniques for music databases have not been studied completely. In this paper, we present a musician retrieval technique for audio features using the space split methods in the time-series pattern index file. We use features of audio to retrieve the musician and a time-series pattern index file to search the candidate musicians. Experimental results show that the time-series pattern index file using the rotational split method is efficient for musician retrievals in the time-series pattern files.

• Journal of Family Resource Management and Policy Review
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• v.8 no.1
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• pp.101-116
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• 2004

This study analyze using pattern of the Korean elderly leisure time to know their leisure lifestyle. In order to do this study, we analysed the time use dairy which were collected by Korean National Statistical Office in 1999 and analysis of data was done through mean of time, percentage of acting people hourly. The result of this study were as follows : 1) The elderly spend leisure time average 6 hours 34 minute in a day and enjoy leisure activities from A.M. 11:00 to P.M. 12:00 and from P.M. 8:00 to P.M:. 10:00 mainly. 2) The elderly spend leisure time for acquaintance, using mass media, sports leisure activity and dilettante life. Especially the elderly associate with more others than their family, use more TV than others and spend time for break and amusement as dilettante life.

• Fashion & Textile Research Journal
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• v.12 no.5
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• pp.582-590
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• 2010

Garment design needs an iterative manipulation of 2D patterns to generate a final sloper. Traditionally there have been two kinds of design methodologies such as the flat pattern method and the pattern draping method. But today, it is possible to combine the advantages from the two methods due to the realistic cloth simulation techniques. We devised a new garment design system which starts from 3D initial drape simulation result and then modifies the garment by editing the 2D flat patterns synchronously. With this interactive methodology using real-time pattern deformation technique, the designer can freely change a pattern shape by watching its 3D outlook in real-time. Also the final garment data were given relative coordinates with respect to the human anthropometric feature points detected by an automatic body feature detection algorithm. Using the relative human body coordinate system, the final garments can be re-used to an arbitrary body data without repositioning in the drape simulation. A female shirt was used for an example and a 3D body scan data was used for an illustration of the feature point detection algorithm.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.6 s.111
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• pp.627-633
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• 2006

The tire is considered as one of the important noise sources having an influence on vehicle's performance. The Pattern noise of a tire is the transmission sound of airborne noise. On smooth asphalt road, Pattern noise is amplified with the velocity. In recent, the study on the reduction of Pattern noise is energetically processed. Pattern noise is strongly related with pitch sequence. To reduce the pattern noise, tire's designer has to randomize the sequence of pitch. The FFT is a traditional method to evaluate the level of the randomization of the pitch sequence, but gives no information on time-varying, instantaneous frequency. In the study, we found that Time-Frequency transform is a useful method to non-stationary signal such as tire noise.

• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.23-32
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• 2013

The purpose of this study is to make effective mobile grid considered general environment, which can be summarized as irregular mobility, service exploration, data sharing, variety of machines, limit to the battery duration, etc. The data was extracted from the Dartmouth College. We analysed mobile use pattern of a specific group and applied pattern using hybrid method. As a result, we could adjust infra usage effectively and appropriately and cost cutting and increase satisfaction of user. In this study, by applying weighting method based on access time interval, we analysed use pattern added time variation with association rule during users in mobile grid environment. We proposed more stable way to manage patterns in a mobile grid environment that is being used as a hybrid form to process the data value received from the server in real time. Further studies are needed to get appropriate use pattern by group using use patterns of various groups.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.44-51
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• 2008

Pattern matching and pattern searching in time series data have been active issues in a number of disciplines. This paper suggests a novel pattern matching technology which can be used in the field of stock market analysis as well as in forecasting stock market trend. First, we define conceptual patterns, and extract data forming each pattern from given time series, and then generate learning model using Hidden Markov Model. The results show that the context-based pattern matching makes the matching more accountable and the method would be effectively used in real world applications. This is because the pattern for new data sequence carries not only the matching itself but also a given context in which the data implies.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2253-2261
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• 1999

ACK generates optimized code using the string pattern matching technique in pattern table generator and peephole optimizer. But string pattern matching method is not effective due to the many comparative actions in pattern selection. We designed and implemented the EM intermediate code optimizer using tree pattern matching algorithm composed of EM tree generator, optimization pattern table generator and tree pattern matcher. Tree pattern matching algorithm practices the pattern matching that centering around root node with refer to the pattern table, with traversing the EM tree by top-down method. As a result, compare to ACK string pattern matching methods, we found that the optimized code effected to pattern selection time, and contributed to improved the pattern selection time by about 10.8%.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.73-79
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• 2003

This paper proposes speaker recognition algorithm which includes both the pitch detection and the fuzzified pattern matching. This study utilizes pitch pattern using a pitch and speech parameter uses binary spectrum. In this paper. makes reference pattern using fuzzy membership function in order to include time variation width for non-utterance time and performs vocal track recognition of common character using fuzzified pattern matching.