• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.307-310
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• 2000

The measuring method of large object using the pattern matching is discussed in the paper. It is hard and expensive to get the complete 3D data when the object is large or exceeds the limit of measuring devices. The large object is divided into several smaller areas and is scanned several times to get the data of all the pieces. These data are aligned to get the complete 3D data using the pattern matching method. The point pattern matching method and transform matrix algorithm are used for aligning. The laser slit beam and CCD camera is applied for experimental measurement. Visual C++ on Window98 is implemented in processing the algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.714-719
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• 2003

We propose a measuring method of large object using the pattern matching. It is hard and expensive to get the complete 3D data when the object is large and exceeds the limit of measuring devices. The large object is divided into several smaller areas and is scanned several times to get the data of all the pieces. These data are aligned to get the complete 3D data using the pattern matching method such as point pattern matching method and transform matrix algorithm. The laser slit beam and CCD camera are applied for the experimental measurement. Visual C＋＋ on Windows 98 is implemented in processing the algorithm.

• 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.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1118-1130
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• 2006

Existed pattern analysis algorithms in data streams environment have researched performance improvement and effective memory usage. But when new data streams come, existed pattern analysis algorithms have to analyze patterns again and have to generate pattern tree again. This approach needs many calculations in real situation that needs real time pattern analysis. This paper proposes a method that continuously analyzes patterns of incoming data streams in real time. This method analyzes patterns fast, and thereafter obtains real time patterns by updating previously analyzed patterns. The incoming data streams are divided into several sequences based on time based window. Informations of the sequences are inputted into a hash table. When the number of the sequences are over predefined bound, patterns are analyzed from the hash table. The patterns form a pattern tree, and later created new patterns update the pattern tree. In this way, real time patterns are always maintained in the pattern tree. During pattern analysis, suffixes of both new pattern and existed pattern in the tree can be same. Then a pointer is created from the new pattern to the existed pattern. This method reduce calculation time during duplicated pattern analysis. And old patterns in the tree are deleted easily by FIFO method. The advantage of our algorithm is proved by performance comparison with existed method, MILE, in a condition that pattern is changed continuously. And we look around performance variation by changing several variable in the algorithm.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.

• The Research Journal of the Costume Culture
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• v.19 no.5
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• pp.1049-1060
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• 2011

The purpose of this study was to develop a shirt pattern that enhances the functionality of golf swing motions. The pattern was made with 3D body surface shape data that changed according to dynamic golf postures. The data were collected from the golf swing motions. The 3D body surface data in golf swing postures piled up on the 3D surface data in a static posture. The results showed that the surface shape data changed more in the address, back swing, and finish postures than the other swing postures. The experimental pattern was developed with 3D surface scan data in those three golf swing motions. The pattern had raglan sleeves and the front-bodied piece was divided into two pieces with a princess line, which comes from armscye line of the address posture. The back bodice piece was divided into three pieces with a yoke line and a back princess line. The yoke line was made by back shoulder shape in the back swing posture. The level of comfort of the experimental garment and commercial golf shirts was evaluated by 38 women golfers. The experimental garment pattern was evaluated to be more comfortable in golf swing postures than commercial golf shirts.

• Korean Journal of Human Ecology
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• v.15 no.3
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• pp.481-490
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• 2006

Adjusting pressure level in the construction of athletes' tight-fitting garments by reducing the elastic knit pattern is a challenging subject, which influences the performance of the wearer directly. Therefore, in this study, relationship between the reduction rates of the basic pattern obtained from 3D human scan data and resultant clothing pressure was explored to improve the fit and pressure exerted by clothing. 3D scan data were obtained using Cyberware and they were transformed into a flat pattern using software based on Runge-Kutta method. Reduction rate was examined by subjective wear test as well as objective pressure measurement. As a result, difference in the length between the original 3D body scan data and the 2D tight-fitting pattern was 0.02$\sim$0.50cm (0.05$\sim$1.06%), which was within the range of tolerable limits in making clothes. Among the five garments, the 3T-pattern was superior in terms of subjective sensation and fit. The pressure of the 3T pattern was 2$\sim$4 gf/cm2 at five locations on the body, which is almost the same or a bit higher than that of Z-pattern. In the case of tight-fitting overall garment, the reduction rate of the pattern in the wale direction is more critical to the subjective sensation than the course direction. It is recommended that the reduction grading rules of course direction should be larger than that of Ziegert for a better fit of tight-fitting garments. In the case of wale direction, however, reduction grading rule should be kept the same as suggested earlier by Ziegert (1988).

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.91-102
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• 2018

The purpose of this study is to develop an excellent bodice prototype that is adapted to the body shape of middle-aged women using 3D measurement data. In the evaluation of appearance, S pattern 4.00, B pattern 2.80, E pattern 2.40, L pattern 1.40 were shown in order, and the best fit of S pattern was evaluated as excellent. As a result of looking at the color distribution chart to find out the amount, E pattern and S pattern were not space in the front bust, armhole, and the back waist line. The B pattern and the L pattern were marked in blue because of insufficiency space in the back neck. As a result of evaluation the amount of air gap in the clothing, the air gap of the bust was 0.12, which is the largest pattern of B. Next, the L pattern appears as a tight circle with smallest air gap in the order of the S pattern 0.096, the E pattern 0.08, and the L pattern 0.003. The S pattern was evaluated to be the most appropriate for the body shape of middle-aged women. But the waist and back were slightly tight. Middle-aged women have larger shoulder-related items and larger waist circumference. Therefore, when you set the perimeter item, you should add 1-2cm of space amount and give extra space to the circumference area.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.89-91
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• 2001

This paper presents the result of pattern classification of load demand for distribution transformer in domestic. The field data of load demand is measured using the load acquisition device and the measurement data is used for the database system for load management of distribution transformed. For the pattern classification, the load data and the customer information data are also used. The K-MEAN method is used for the pattern classification algorithm. The result of pattern classification is used for the 2-step format of load demand curve.