• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.609-618
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• 2013

This paper proposes a fast image segmentation method that gives high segmentation performance as graph-cut based methods. Graph-cut based image segmentation methods show high segmentation performance, however, the computational complexity is high to solve a computationally-intensive eigen-system. This is because solving eigen-system depends on the size of square matrix obtained from similarities between all pairs of pixels in the input image. Therefore, the proposed method uses the small-size square matrix, which is obtained from all the similarities among regions obtained by segmenting locally an image into several regions by graph-based method. Experimental results show that the proposed multi-scale image segmentation method using the algebraic multi-grid shows higher performance than existing methods.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.521-529
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• 2007

In this paper, we introduce a music recommendation system that automatically recommends music according to users' musical tastes. The recommendation system uses a graph-based collaborating filtering in which similarities between musics are saved as a graph, and so it can perform fast recommendation based on the implicit preference information. It also has capability of recommending music according to users' dynamically changing preferences as well as users' static preferences. The recommendation server is implemented as an independent server using Java, and communicates with clients according to a specified protocol. A demo web site has been built by using the server and music download data from actual users, and the accuracy of recommendation has been measured through experiments.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.671-680
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• 2008

Motion capture devices have been utilized in producing several contents, such as movies and video games. However, since motion capture devices are expensive and inconvenient to use, motions segmented from captured data was recycled and synthesized to utilize it in another contents, but the motions were generally segmented by contents producers in manual. Therefore, automatic motion segmentation is recently getting a lot of attentions. Previous approaches are divided into on-line and off-line, where ow line approaches segment motions based on similarities between neighboring frames and off-line approaches segment motions by capturing the global characteristics in feature space. In this paper, we propose a graph-based high-level motion segmentation method. Since high-level motions consist of repeated frames within temporal distances, we consider similarities between neighboring frames as well as all similarities among all frames within the temporal distance. This is achieved by constructing a graph, where each vertex represents a frame and the edges between the frames are weighted by their similarity. Then, normalized cuts algorithm is used to partition the constructed graph into several sub-graphs by globally finding minimum cuts. In the experiments, the results using the proposed method showed better performance than PCA-based method in on-line and GMM-based method in off-line, as the proposed method globally segment motions from the graph constructed based similarities between neighboring frames as well as similarities among all frames within temporal distances.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.2000-2013
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• 2016

The purpose of this study is to validate the validity of the methodology for analyzing the space with complex characteristics and to evaluate the existing spatial structure analysis theory. Seven example models are designed and analyzed data of spatial syntax analysis and visibility graph analysis. And analyzed the agent-based model using two analytical methods: the adjacent space and the whole spatial connection. The results of this study are as follows. Based on the analysis of the agent - based model for perfectly freewalking, the validity of the method is verified in terms of predictive ability and effectiveness. Agent-based models can be simulated considering various variables, so realistic predictions will be possible and a new biography of complex systems can be met.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.117-131
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• 2016

The development of LBS(location-based services) due to the widespread mobile environment highlights the importance of POI(point of interest) information. The accurate and up-to-date POI has to be ensured to reflect the information of rapidly changing places. For the efficient construction of POI, here we propose the novel construction system for t he place information. This system is based on crowd-sourcing in which a great number of users participate. In addition, we utilize the graph data model to build the new concept of the place information covering the wide areas extending from the specific point. Moreover, the implementation of the new system applying the graph data model and crowd-sourcing is realized in this paper. That is, this study suggests the whole new concept of the place information and shows the clustering and the renewal of the place information through crowd-sourcing.

• Journal of KIISE
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• v.44 no.6
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• pp.643-648
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• 2017

Among Wi-Fi based indoor positioning systems, fingerprinting localization is the most common technique with high precision. However, construction of the initial radio map and the update process require considerable labor and time effort. To address this problem, we propose an efficient method that constructs the initial radio map at each vertex based on a graph. In addition, we introduce a method to update the radio map automatically by mapping signal data acquired from users to the reference point created on each edge. Since the proposed method collects signal data manually only at the vertex of the graph to build the initial radio map and updates it automatically, our proposed method can dramatically reduce labor and time effort, which are the disadvantages of the conventional fingerprinting method. In our experimental study, we show validity of our radio map update method by comparing with the actual reference point data. We also show that our proposed method is able to construct the radio map with an accuracy of about 3.5m by automatically updating the radio map.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.974-983
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• 2005

In genetic algorithms with lotus-based encoding, static gene reordering is to locate the highly related genes closely together. It helps the genetic algorithms to create and preserve the schema of high-quality effectively. In this paper, we propose a static reordering framework for linear locus-based encoding. It differs from existing reorderings in that it is independent of problem-specific knowledge. It makes a complete graph where weights represent the interelationship between each pair of genes. And, it transforms the graph into a unweighted sparse graph by choosing the edges having relatively high weight. It finds a gene reordering by graph search method. Through the wide experiments about several problems, the method proposed in this paper shows significant performance improvement as compared with the genetic algorithm that does not rearrange genes.

• School Mathematics
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• v.4 no.2
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• pp.161-192
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• 2002

This paper investigated the teaching and teaming of contents-related graphing in Korean secondary textbooks and suggested the improved methods of graph instruction through this analysis. reification-the case of function, In Harel, G., Dubinsky(Eds.), The Concept of Function : Aspects of Epistemology and Pedagogy Textbooks are analyzed from the viewpoint of the proportion of graphing contents, their sequencing, the proportion of each domain in graphing activities (interpretation vs. construction, quantitative vs. qualitative aspect, local vs. global aspect) and tasks (prediction, translation, scaling), and the difference in the graphing contents between the sixth and the seventh curriculum. This analysis demonstrates that graphing contents are increasing in textbooks, therefore the high school textbooks appear in almost every content area. The graphing activities concentrate on the construction, the quantitative aspects, and the local aspects, and are gradually focusing on the interpretation and global aspects of high school textbooks. Furthermore, most of graphing tasks favor translation. In contrast, the current seventh curriculum includes a balance of interpretation and construction activities and has more global aspects than the sixth curriculum based textbooks; however, the qualitative approach still rarely appears. For the graphing tasks, translation is still prevalent, but the importances of prediction tasks based on graph have increased in comparison with the sixth curriculum textbooks. Further, the seventh curriculum based textbooks are designed to stimulate more dynamic graphing instruction by introducing new tools such as graphing calculators and computer software. We suggest that the qualitative and global aspects should be emphasized in early graph instruction, a variety of graph activities in realistic contexts should be performed, and educational technology such as graphing calculator and computer can be efficient to implement these ideas.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.601-612
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• 2006

This study proposes RAG (Region Adjancency Graph)-based image segmentation for large imagery in remote sensing. The proposed algorithm uses CN-chain linking for computational efficiency and multi-window operation of sliding structure for memory efficiency. Region-merging due to RAG is a process to find an edge of the best merge and update the graph according to the merge. The CN-chain linking constructs a chain of the closest neighbors and finds the edge for merging two adjacent regions. It makes the computation time increase as much as an exact multiple in the increasement of image size. An RNV (Regional Neighbor Vector) is used to update the RAG according to the change in image configuration due to merging at each step. The analysis of large images requires an enormous amount of computational memory. The proposed sliding multi-window operation with horizontal structure considerably the memory capacity required for the analysis and then make it possible to apply the RAG-based segmentation for very large images. In this study, the proposed algorithm has been extensively evaluated using simulated images and the results have shown its potentiality for the application of remotely-sensed imagery.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.1-8
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• 2024

In the API gateway, API composition is an essential function that can reduce the number of client calls and prevent over-fetching and under-fetching. API composition that operate with IMJ (In-Memory Join) consume a lot of resources, putting a burden on the performance of the API gateway. In this paper, to improve the problem of IMJ-style API composition, we propose SAPIC (Stream-based API Composition), which delivers the data to be composed to the client by streaming. SAPIC calls each MSA API that makes up the client response data and immediately streams the received response data to the client, reducing the resource consumption of the API gateway and providing faster response time compared to IMJ. As a result of a comparison experiment with GraphQL, a representative API combination technology, SAPIC recorded a maximum CPU occupancy rate of approximately 21 to 70 % lower, a maximum heap usage rate of approximately 16 to 74 % lower, and a throughput rate that was 1 to 2.3 times higher than GraphQL.