• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.9
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• pp.487-493
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• 2004

The electric power industry throughout the world is undergoing considerable changes from the vertically integrated utility structure to the deregulated market. However, the deregulated electricity market is operated with respect to theory of economical efficiency, and therefore, the system operator requires data with fast contingency ranking for security of the bulk power system. This paper compares the weather dependant probabilistic risk index(PRI) with the system performance index for power flow in the IEEE-RTS. The system performance index for power flow presents the power system stability. This paper presents fast calculation method for determining contingency ranking using the weather dependant probabilistic risk index(PRI). The probabilistic risk index can be classified into the case of normal and adverse weather. This paper proposes calculation method using the probabilistic risk index in determining contingency ranking required for security under the deregulated electricity market.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.35-43
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• 1992

In this paper fuzzy ranking method using dominance relation is suggested. This method is compared to the existing fuzzy number comparing methods. All ten methods with the suggested method are evaluated using six example based on four criteria.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.279-290
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• 2010

Technical countermeasures against debris flow should be established upon the risk level of the target location. Risk of debris flow should consider the hazard imposed by debris flow and vulnerability of the facilities to debris flow. In this research, we have defined the target location for risk evaluation and suggested scoring method of hazard of debris flow and vulnerability of road to debris flow. By defining risk rank into 6 categories in terms of possibility of damage during rainfall and using the risk scores of 46 debris flow cases, we have suggested risk ranking matrix. The method can be used in ranking the drainage basin adjacent to road by simply determining the hazard with vulnerability score and can be used for planning the debris flow countermeasures.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.113-115
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• 2003

The electric power industry throughout the world is undergoing considerable changes from the vertically integrated utility structure to the deregulated market. However, the deregulated electricity market is operated with respect to theory of economical efficiency, and therefore, the system operator requires data with fast contingency ranking for security of the bulk power system. This paper presents fast calculation method for determining contingency ranking using the weather dependant probabilistic risk index(PRI). The probabilistic risk index can be classified into normal weather and adverse weather. This paper proposes calculation method using the probabilistic risk index in determining contingency ranking requiring for security under the deregulated electricity market.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.2
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• pp.184-191
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• 1999

The k most vital arcs (k-MVA) of a maximum flow problem is defined as those k arcs whose simultaneous removal from the network causes the greatest decrease in the throughput capability of the remaining system between a specified pair of nodes. In this study, we present a method for determining all the k-MVA in maximum flow problem using a minimal cardinality cut algorithm and k-th minimal cut ranking algorithm. For ranking cardinality cuts, we use Hamacher's ranking algorithm for cut capacity and by comparing present residual capacity of cardinality cut with expected residual capacity of next cardinality cut, we also present termination condition for this algorithm. While the previous methods cannot find all the alternatives for this problem, a method presented here has advantage of determining all the k-MVA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4A
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• pp.403-410
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• 2008

In this paper, we propose a variant of the QRM-MLD signal detection method that is used for spatially multiplexed multiple antenna system. The original QRM-MLD signal detection method combines the QR decomposition with the M-algorithm, thereby significantly reduces the prohibitive hardware complexity of the ML signal detection method, still achieving a near ML performance. When the number of transmitter antennas and/or constellation size are increased to achieve higher bit rate, however, its increased complexity makes the hardware implementation challenging. In an effort to overcome this drawback of the original QRM-MLD, a number of variants were proposed. A most strong variant among them, in our opinion, is the ranking method, in which the constellation points are ranked and computation is performed for only highly ranked constellation points, thereby reducing the required complexity. However, the variant using the ranking method experiences a significant performance degradation, when compared with the original QRM-MLD. In this paper, we point out the reasons of the performance degradation, and we propose a novel variant that overcomes the drawbacks. We perform a set of computer simulations to show that the proposed method achieves a near performance of the original QRM-MLD, while its computational complexity is near to that of the QRM-MLD with ranking method.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.1-21
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• 2023

This study specifically reviewed the performance of the ranking schemes as an efficient feature selection method for text classification. Until now, feature ranking schemes are mostly based on document frequency, and relatively few cases have used the term frequency. Therefore, the performance of single ranking metrics using term frequency and document frequency individually was examined as a feature selection method for text classification, and then the performance of combination ranking schemes using both was reviewed. Specifically, a classification experiment was conducted in an environment using two data sets (Reuters-21578, 20NG) and five classifiers (SVM, NB, ROC, TRA, RNN), and to secure the reliability of the results, 5-Fold cross-validation and t-test were applied. As a result, as a single ranking scheme, the document frequency-based single ranking metric (chi) showed good performance overall. In addition, it was found that there was no significant difference between the highest-performance single ranking and the combination ranking schemes. Therefore, in an environment where sufficient learning documents can be secured in text classification, it is more efficient to use a single ranking metric (chi) based on document frequency as a feature selection method.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.123-137
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• 2007

The information space of semantic web comprised of various resources, properties, and relationships is more complex than that of WWW comprised of just documents and hyperlinks. Therefore, ranking methods in the semantic web should be modified to reflect the complexity of the information space. In this paper we propose a method of ranking query results from RDF(Resource Description Framework) knowledge bases. The ranking criterion is the importance of a resource computed based on the link structure of the RDF graph. Our method is expected to solve a few problems in the prior research including the Tightly-Knit Community Effect. We illustrate our methods using examples and discuss directions for future research.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.767-778
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• 2008

Comparison between two or more fuzzy numbers, along with their ranking, is an important subject discussed in scholarly articles. We endeavor in this paper to present a simple yet effective parametric method for comparing fuzzy numbers. This method offer significant advantages over similar methods, in comparing intersected fuzzy numbers, rendering the comparison between fuzzy numbers possible in different decision levels. In the process, each fuzzy number will be given a parametric value in terms of $\alpha$, which is dependent on the related $\alpha$-cuts. We have compared this method to Cheng's centroid point method [5] (The relation of calculating centroid point of a fuzzy number was corrected later on by Wang [12]). The proposed method can be utilized for all types of fuzzy numbers whether normal, abnormal or negative.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1454-1466
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• 2016

This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.