• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.369-380
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• 2004

In this paper, we propose a web page weighting scheme based on WordNet-based collaborative evaluation and hyperlink to improve the precision of web search engine. Generally search engines use keyword matching to decide web page ranking. 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 lenses, so that more accurate search is possible. Weighting of each web page by using collaborative evaluation and hyperlink is reflected in ranking. The experimental results with several keywords show that the precision of proposed system is improved compared to conventional search engines.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.366-373
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• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.55-62
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• 2009

Location based Services in the ubiquitous computing environment, namely u-LBS, use very large and skewed spatial objects that are closely related to locational information. It is especially essential to achieve fast search, which is looking for POI(Point of Interest) related to the location of users. This paper examines how to search large and skewed POI efficiently in the u-LBS environment. We propose the Dynamic-level Grid based R-Tree(DGR-Tree), which is an index for point data that can reduce the cost of stationary POI search. DGR-Tree uses both R-Tree as a primary index and Dynamic-level Grid as a secondary index. DGR-Tree is optimized to be suitable for point data and solves the overlapping problem among leaf nodes. Dynamic-level Grid of DGR-Tree is created dynamically according to the density of POI. Each cell in Dynamic-level Grid has a leaf node pointer for direct access with the leaf node of the primary index. Therefore, the index access performance is improved greatly by accessing the leaf node directly through Dynamic-level Grid. We also propose a K-Nearest Neighbor(KNN) algorithm for DGR-Tree, which utilizes Dynamic-level Grid for fast access to candidate cells. The KNN algorithm for DGR-Tree provides the mechanism, which can access directly to cells enclosing given query point and adjacent cells without tree traversal. The KNN algorithm minimizes sorting cost about candidate lists with minimum distance and provides NEB(Non Extensible Boundary), which need not consider the extension of candidate nodes for KNN search.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.168-175
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• 2013

Color consistency in stereoscopic contents is important for 3D display systems. Even with a stereo camera of the same model and with the same hardware settings, complex color discrepancies occur when acquiring high quality stereo images. In this paper, we propose an integrated color matching method that use cumulative histogram in global matching and estimated 3D-distance for the stage of local matching. The distance between the current pixel and the target local region is computed using depth information and the spatial distance in the 2D image plane. The 3D-distance is then used to determine the similarity between the current pixel and the target local region. The overall algorithm is described as follow; First, the cumulative histogram matching is introduced for reducing global color discrepancies. Then, the proposed local color matching is established for reducing local discrepancies. Finally, a weight-based combination of global and local matching is computed. Experimental results show the proposed algorithm has improved global and local error correction performance for stereoscopic contents with respect to other approaches.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.27-34
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• 2016

In the train system, railway vehicles are connected in a line. Therefore, this feature should be considered in composing network topology in a train system. Besides, inter-car communication should be distinguished from in-car communication. As for the inter-car communication, the hybrid topology was proposed to use rather than the conventional ring, star, daisy-chain, and bus topologies. In the hybrid topology, a number of cars are bound to be a group. Then star topology is used for the communication in a group and daisy-chain topology is used for the communication between groups. Hybrid topology takes the virtue of both star and daisy-chain topologies. Hence it maintains communication speed with reducing the number of connecting cables between cars. Therefore, it is important to choose the number of cars in a group to obtain higher performance. In this paper, we focus on the optimization of hybrid topology for railway cars. We first assume that the size of data and the frequency of data production for each car is identical. We also assume that the importance for the maximum number of cables to connect cars is variable as well as the importance of the communication speed. Separated weights are granted to both importance and we derive the optimum number of cars in a group for various number of cars and weights.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.1
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• pp.15-25
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• 2005

Embedded systems have a set of tasks to execute. These tasks can be implemented either on application specific hardware or as software running on a specific processor. The design of an embedded system involves the selection of hardware software resources, Partition of tasks into hardware and software, and performance evaluation. An accurate estimation of execution time for extreme cases (best and worst case) is important for hardware/software codesign. A tighter estimation of the execution time bound nay allow the use of a slower processor to execute the code and may help lower the system cost. In this paper, we consider an ARM-based embedded system and developed a tool to estimate the tight boundary of execution time of a task with loop bounds and any additional program path information. The tool we developed is based on an exiting timing analysis tool named 'Cinderella' which currently supports i960 and m68k architectures. We add a module to handle ARM ELF object file, which extracts control flow and debugging information, and a module to handle ARM instruction set so that the new tool can support ARM processor. We validate the tool by comparing the estimated bound of execution time with the run-time execution time measured by ARMulator for a selected bechmark programs.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.719-729
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• 2000

Prefetching aims at reducing memory latency by fetching, in advance, data that are likely to be requested by the processor in a near future. The effectiveness of prefetching is determined by how accurate the prediction on the needed instructions and data is. Most previous studies on prefetching were limited to proposing a particular prefetch scheme and its performance evaluation, paying little attention to theoretical aspects of prefetching. This paper focuses on the theoretical aspects of instruction prefetching. For this purpose, we propose a clairvoyant prefetch model that makes use of perfect history information. Based on this theoretical model, we analyzed upper limits on the prefetch prediction accuracies of the SPEC benchmarks. The results show that the prefetch prediction accuracy is very high when there is no cache. However, as the size of the instruction cache increases, the prefetch prediction accuracy drops drastically. For example, in the case of the spice benchmark, the prefetch prediction accuracy drops from 53% to 39% when the cache size increases from 2Kbyte to 16Kbyte (assuming 16byte block size). These results indicate that as the cache size increases, most localities are captured by the cache and that instruction prefetching based on the information extracted from the references that missed in the cache suffers from prediction inaccuracies

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.83-95
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• 2018

With recent technological advances in the shipbuilding industry, vessels have been improved in size and performance. As a result, an accident such as grounding, caused by a single ship-to-ship collision, could lead to a large-scale maritime disaster. Considering the seriousness of the situation, the international community has been consistently updating the standards for Electronic Navigational Chart(ENC) to improve the maritime safety. S-57, the existing ENC standard governed by the International Hydrographic Organization(IHO), includes standards for generating conventional binary-type ENC data sets. The S-57 standard, however, has not been updated since the release of Version 3.1 in December 2000. Since then, the standard has failed to reflect technological development regarding maritime spacial information, which has been consistently improving. In an effort to address this concern, the IHO designated S-100, i.e., the next-generation ENC production standard. S-100 differs from S-57 in data exchange type. Contrary to the conventional ENC standards, which use binary-type data, S-10X, based on the next-generation ENC standards, uses ENC data composed of Feature Catalogue, Portrayal Catalogue, and GML. Considering this fact, it is necessary to update S-58, the ENC validation check standard, or designate a new standard for ENC validation checks. This study is developed own software to implement validation checks for new types of data, and identified improvement points based on the test results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.10
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• pp.902-909
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• 2015

In this paper, monovision based relative navigation for a satellite proximity operation is studied. The chaser satellite only uses one camera sensor to observe the target satellite and conducts image tracking to obtain the target pose information. However, by using only mono-vision, it is hard to get the depth information which is related to the relative distance to the target. In order to resolve the well-known difficulty in computing the depth information with the use of a single camera, the active contour method is adopted for the image tracking process. The active contour method provides the size of target image, which can be utilized to indirectly calculate the relative distance between the chaser and the target. 3D virtual reality is used in order to model the space environment where two satellites make relative motion and produce the virtual camera images. The unscented Kalman filter is used for the chaser satellite to estimate the relative position of the target in the process of glideslope approaching. Closed-loop simulations are conducted to analyze the performance of the relative navigation with the active contour method.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.389-395
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• 2015

Digital image provides many conveniences at the internet environment recently. A great number of applications, like Digital Library, Stock Image, Personal Image and Important Information, require the use of digital image. However it has fatal defect which is easy to be modified because digital image is only electronic file. Numerous digital image forgeries have become a serious problem due to the sophistication and accessibility of image editing software. Copy-Move forgery is the simplest type of forgery that involves copying portion of an image and paste it on different location within the image. There are many approaches to detect Copy-Move forgery, but all of them have their own limitations. In this paper, visual and invisible feature based forgery detection techniques are tested and analyzed. The analysis shows that pros and cons of these two techniques compensate each other. Therefore, a hybrid of visual based and invisible feature based forgery detection that combine the merits of both techniques is proposed. The experimental results show that the proposed algorithm has enhanced performance compared to individual techniques. Moreover, it provides more information about the forgery, like identifying copy and duplicate regions.