• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.6
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• pp.1546-1553
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• 1998

In this paper, we investigate a human identification scheme using visual cryptography, which can decode concealed images without and crytographic computations. T.Katoh & H.Imai proposed a secret sharing scheme which can conceal two query images into one displayed image for the identification. the generalized construction of the share matrix is proposed to conceal the multiple query images by stacking user slides o the displayed image. Furthermore, an extended methodis proposed in which group of slides can conceal an independent secret image as well as the pairs of slides can.

• Journal of Communications and Networks
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• v.16 no.2
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• pp.202-208
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• 2014

Wireless network information services allow end systems to discover heterogeneous networks and spectrum available for secondary use at or near their current location, helping them to cope with increasing traffic and finite spectrum resources. We propose a unified architecture that allows end systems to find nearby base stations that are using either licensed, shared or unlicensed spectrum across multiple network operators. Our study evaluates the performance and scalability of spatial databases storing base station coverage area geometries. The measurement results indicate that the current spatial databases perform well even when the number of coverage areas is very large. A single logical spatial database would likely be able to satisfy the query load for a large national cellular network. We also observe that coarse geographic divisions can significantly improve query performance.

• ETRI Journal
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• v.44 no.5
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• pp.780-793
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• 2022

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.55-58
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• 2008

RFID(Radio frequency IDentification) leader is collision of data, when recognizing the multiple tag the inside area. This collision became the cause which delays the tag recognition time of the leader. The protocol which prevents the delay of tag recognition time of the leader the place where representative it uses QT(Query Tree) algorithms, it uses a collision bit position from this paper and are improved QT-MTC(Query Tree with Multi-Tag Cognition) algorithms which it proposes. This algorithm stored the bit position which bit possibility and the collision where the collision happens occurs in the stack and goes round a tree the number of time which, it reduced could be identified two tags simultaneously in order, it was planned. A result of performance analysis, It compared in QT protocols and the this algorithm against the tag bit which is continued a tush efficiency improvement effect was visible.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.701-708
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• 2004

Existing contents-based video retrieval systems search by using a single method such as annotation-based or feature-based retrieval. Hence, it not only shows low search efficiency, but also requires many efforts to provide system administrator or annotator with a perfect automatic processing. Tn this paper, we propose an agent-based, and automatic and unified semantics-based video retrieval system, which support various semantics-retrieval of the massive video data by integrating the feature-based retrieval and the annotation-based retrieval. The indexing agent embodies the semantics about annotation of extracted key frames by analyzing a fundamental query of a user and by selecting a key-frame image that is ed by a query. Also, a key frame selected by user takes a query image of the feature-based retrieval and the indexing agent searches and displays the most similar key-frame images after comparing query images with key frames in the database by using the color-multiple-partition histogram techniques. Furthermore, it is shown that the performance of the proposed system can be significantly improved.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.577-590
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• 2003

The shape-based retrieval is defined as the operation that searches for the (sub) sequences whose shapes are similar to that of a query sequence regardless of their actual element values. In this paper, we propose a similarity model suitable for shape-based retrieval and present an indexing method for supporting the similarity model. The proposed similarity model enables to retrieve similar shapes accurately by providing the combination of various shape-preserving transformations such as normalization, moving average, and time warping. Our indexing method stores every distinct subsequence concisely into the disk-based suffix tree for efficient and adaptive query processing. We allow the user to dynamically choose a similarity model suitable for a given application. More specifically, we allow the user to determine the parameter p of the distance function $L_p$ when submitting a query. The result of extensive experiments revealed that our approach not only successfully finds the subsequences whose shapes are similar to a query shape but also significantly outperforms the sequence search.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1101-1112
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• 2004

In this paper, we propose a Web page weighting scheme using the user preference in each sense of query word to improve the performance of Web search. Generally search engines assign weights to a web page by using relevancy only, which is obtained by comparing the query word and the words in a web page. In the information retrieval from huge data such as the Web, simple word comparison cannot distinguish important documents because there exist too many documents with similar relevancy In this paper we implement a WordNet-based user interface that helps to distinguish different senses of query word, and constructed a search engine in which the implicit evaluations by multiple users are reflected in ranking by accumulating the number of clicks. In accumulating click counts, they are stored separately according to senses, so that more accurate search is possible. The experimental results with several keywords show that the precision of proposed system is improved compared to conventional search engines.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.116-124
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• 2011

In this paper, We implemented vessel USN middleware by server-side method considering characteristics of ocean environment. We designed multiple query process module in order to efficient process multidimensional sensor stream data and proposed optimized query plan using Mjoin query and hash table. This paper proposed method that context aware of vessel and manage considering characteristics of ocean. We decided to risk context using SVM algorithm in context awareness management module. As a result, we obtained about 87.5% average accuracy for fire case and about 85.1% average accuracy for vessel risk case by input 5,000 data sets and implemented vessel USN monitoring system.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.879-888
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• 2006

Recently many researchers have focused on management of Historical trajectories of moving objects in Euclidean spaces due to numerous sizes of accumulated data over time. However, the movement of moving objects in real applications generally has some constraints, for example vehicles on roads can only travel along connected road networks. In this paper, we propose an indexing method for trajectories of moving objects on road networks in order to process the network-based spatiotemporal range query. Our method contains the connect information of road networks to use the network distance for query processing, deals with trajectories which are represented by road segments in road networks, and manages them using multiple R-trees assigned per each road segment. Furthermore, it has a structure to be able to share R-tree among several road segments in large road networks. Consequently, we show that our method takes about 30% less in node accesses for the network-based spatiotemporal range query processing than other methods based on the Euclidean distance by experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.990-999
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• 2007

In this paper, we propose the region-based image retrieval method using JSEG which is a method for unsupervised segmentation of color-texture regions. JSEG is an algorithm that discretizes an image by color classification, makes the J-image by applying a region to window mask, and then segments the image by using a region growing and merging. The segmented image from JSEG is given to a user as the query image, and a user can select a few segmented regions as the query region. After finding the MBR of regions selected by user query and generating the multiple window masks based on the center point of MBR, we extract the feature vectors from selected regions. We use the accumulated histogram as the global descriptor for performance comparison of extracted feature vectors in each method. Our approach fast and accurately supplies the relevant images for the given query, as the feature vectors extracted from specific regions and global regions are simultaneously applied to image retrieval. Experimental evidence suggests that our algorithm outperforms the recent image-based methods for image indexing and retrieval.