• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.2
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• pp.53-64
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• 2018

This study aims to quantitatively evaluate the conservation priorities of unexecuted urban parks in Seoul both from an ecological and public perspective. To this end, two methodologies, ecological network analysis based on graph and circuit theory and accessibility analysis, were employed in order to assess ecological connectivity of and public accessibility to unexecuted parks, respectively. This study applied linkage-mapping methods (shortest path and current flow betweenness centrality) of connectivity analysis to an integrated map of landscape permeability. The population-weighted accessibility to unexecuted parks was measured based on a negative exponential distance decay function. As a result, for both ecological connectivity and accessibility, Gwanaksan, Suraksan, and Bulamsan urban natural parks are found to be the most important (rank 1-3) to be conserved. For these sites, inner park areas with conservation priorities for connectivity and accessibility were identified. The findings of the study can be used for giving conservation priority to the unexecuted urban parks in terms of long-term sustainable urban planning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.199-204
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• 2017

In this study, we present a home appliance load identification algorithm. The algorithm utilizes complex sensory data in order to improve the existing NIALM using total power usage information. We define the influence graph between the appliance status and the measured sensor data. The device identification prediction result is calculated as the weighted sum of the predicted value of the sensor data processing algorithm and the predicted value based on the total power usage. We evaluate proposed algorithm to compare appliance identification accuracy with the existing NIALM algorithm.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.709-726
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• 2018

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.237-251
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• 2023

This study explores the possibility of automated scoring for scientific graph answers by designing an automated scoring model using convolutional neural networks and applying it to students' kinematics graph answers. The researchers prepared 2,200 answers, which were divided into 2,000 training data and 200 validation data. Additionally, 202 student answers were divided into 100 training data and 102 test data. First, in the process of designing an automated scoring model and validating its performance, the automated scoring model was optimized for graph image classification using the answer dataset prepared by the researchers. Next, the automated scoring model was trained using various types of training datasets, and it was used to score the student test dataset. The performance of the automated scoring model has been improved as the amount of training data increased in amount and diversity. Finally, compared to human scoring, the accuracy was 97.06%, the kappa coefficient was 0.957, and the weighted kappa coefficient was 0.968. On the other hand, in the case of answer types that were not included in the training data, the s coring was almos t identical among human s corers however, the automated scoring model performed inaccurately.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.333-338
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.

• Journal of Korea Game Society
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• v.12 no.1
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• pp.123-132
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• 2012

There are many of games using smart devices. Voice recognition is can be useful way for input. In the game, voice have to be quickly recognized, at the same time it have to be manipulated promptly as well. In this study, we developed the optimized real-time phoneme recognition using max flow matching that it can be efficiently used in the game field. Firstly, voice wavelength is transformed to FFT, secondly, transformed value is made by a graph in Z plane, thirdly, data is extracted in specific area, and then data is saved in database. After all the value is recognized using weighted bipartite max flow matching. This way would be useful method in game or robot field when researchers hope to recognize the fast voice recognition.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.1
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• pp.17-23
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• 2004

This paper proposes to determine word senses in Korean language processing by corpus-based ontology learning. Our approach is a hybrid method. First, we apply the previously-secured dictionary information to select the correct senses of some ambiguous words with high precision, and then use the ontology to disambiguate the remaining ambiguous words. The mutual information between concepts in the ontology was calculated before using the ontology as knowledge for disambiguating word senses. If mutual information is regarded as a weight between ontology concepts, the ontology can be treated as a graph with weighted edges, and then we locate the least weighted path from one concept to the other concept. In our practical machine translation system, our word sense disambiguation method achieved a 9％ improvement over methods which do not use ontology for Korean translation.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.139-148
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• 2022

Research into wireless sensor networks (WSNs) is a trendy issue with a wide range of applications. With hundreds to thousands of nodes, most wireless sensor networks interact with each other through radio waves. Limited computational power, storage, battery, and transmission bandwidth are some of the obstacles in designing WSNs. Clustering and routing procedures have been proposed to address these concerns. The wireless sensor network's most complex and vital duty is routing. With the Greedy Perimeter Stateless Routing method (GPSR), an efficient and responsive routing protocol is built. In packet forwarding, the nodes' locations are taken into account while making choices. In order to send a message, the GPSR always takes the shortest route between the source and destination nodes. Weighted directed graphs may be constructed utilising four distinct distance metrics, such as Euclidean, city block, cosine, and correlation distances, in this study. NS-2 has been used for a thorough simulation. Additionally, the GPSR's performance with various distance metrics is evaluated and verified. When compared to alternative distance measures, the proposed GPSR with correlation distance performs better in terms of packet delivery ratio, throughput, routing overhead and average stability time of the cluster head.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.593-603
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• 2009

In this paper, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with an active stereo camera. The proposed system consists of three modules: detection, tracking and silhouette extraction. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap is estimated in advance and then this was effectively incorporated into the graph cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data and it was shown to detect and track multiple people very well and also produce high quality silhouettes. The proposed system can assist in gesture and gait recognition in field of Human-Robot Interaction (HRI).

• Spatial Information Research
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• v.14 no.4 s.39
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• pp.391-401
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• 2006

We propose a method for geographically enhanced representation of social networks and implement a Web-based 3D visualization of geographical contexts in social networks. A renovated social network graph is illustrated by using two key components: (i) GWCMs (geographically weighted centrality measures) that reflect the differences in interaction intensity and spatial proximity among nodes and (ii) MSNG (map-integrated social network graph) that incorporates the GWCMs and the geographically referenced arrangement of nodes on a choroplethic map. For the integrated 3D visualization of the renovated social network graph, we employ X3D (Extensible 3D), a standard 3D authoring tool for the Web. An experimental case study of regional R&D collaboration provides a visual clue to geographical contexts in social networks including how the social centralization relates to spatial centralization.