• Journal of Information Processing Systems
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• v.11 no.4
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• pp.573-582
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• 2015

Edit distance metrics are widely used for many applications such as string comparison and spelling error corrections. Hamming distance is a metric for two equal length strings and Damerau-Levenshtein distance is a well-known metrics for making spelling corrections through string-to-string comparison. Previous distance metrics seems to be appropriate for alphabetic languages like English and European languages. However, the conventional edit distance criterion is not the best method for agglutinative languages like Korean. The reason is that two or more letter units make a Korean character, which is called as a syllable. This mechanism of syllable-based word construction in the Korean language causes an edit distance calculation to be inefficient. As such, we have explored a new edit distance method by using consonant normalization and the normalization factor.

• International Journal of Contents
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• v.9 no.2
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• pp.1-7
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• 2013

This paper proposes an efficient matching algorithm based on ASIFT (Affine Scale-Invariant Feature Transform) which is fully invariant to affine transformation. In our approach, we proposed a method of reducing similar measure matching cost and the number of outliers. First, we combined the Manhattan and Chessboard metrics replacing the Euclidean metric by a linear combination for measuring the similarity of keypoints. These two metrics are simple but really efficient. Using our method the computation time for matching step was saved and also the number of correct matches was increased. By applying an Optimized Random Sampling Algorithm (ORSA), we can remove most of the outlier matches to make the result meaningful. This method was experimented on various combinations of affine transform. The experimental result shows that our method is superior to SIFT and ASIFT.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.863-875
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• 2017

The traditional text similarity measurement methods based on word frequency vector ignore the semantic relationships between words, which has become the obstacle to text similarity calculation, together with the high-dimensionality and sparsity of document vector. To address the problems, the improved singular value decomposition is used to reduce dimensionality and remove noises of the text representation model. The optimal number of singular values is analyzed and the semantic relevance between words can be calculated in constructed semantic space. An inverted index construction algorithm and the similarity definitions between vectors are proposed to calculate the similarity between two documents on the semantic level. The experimental results on benchmark corpus demonstrate that the proposed method promotes the evaluation metrics of F-measure.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.353-356
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• 2019

The main functionality of occupancy sensors is to determine the existence of humans in the space. If the space is occupied, a light is on and for vacancy, the light automatically turns off. In this letter, the functionality is realized by the utilization of color information. The color information of incoming people is saved. For outgoing people, their color distribution is compared with the saved information, thus providing the recognition of the outgoing people. For the comparison, four similarity metrics are examined to validate the proposed method.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.377-390
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• 2010

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. 2006. Our metrics are applied to an economic time-series dataset.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.99-109
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• 2007

To realize process-driven management, so many companies have been launching business process managementsystems. Business process is collection of standardized and structured tasks inducing value creation of acompany. Moreover, it is recognized as one of significant intangible business assets to achieve competitiveadvantages. This research introduces a novel approach of workflow process analysis, which has more and moresignificance as process-aware information systems are spreading widely into a lot of companies, In this paper, amethodology of workflow clustering based on process similarity has been proposed. The purpose of workflowclustering is to analyze accumulated process definitions in order to assist design of new processes andimprovement of existing ones. The proposed methodology exploits measures of structural similarity of workflowprocesses.The methodology has been experimented with synthetic process models for illustrating the implicationofworkflow clustering.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.42-48
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• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.199-206
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• 2012

Increment of mobile client's information request in wireless mobile networks requires a new method to manage and serve the streaming media object. This paper proposes a new object segment grouping method for enhancing the performance of streaming media services in wireless mobile networks. The proposed method performs the similarity metric for the partitioned object segments, and it process the disjunction, conjunction, and filtering for these metrics. This paper was to decided the partitioned group of object segments for these operation metrics, and it decided the performance of streaming media services. The simulation result showed that the proposed method has better performance in throughput, average startup latency, and cache hit ratio.

• Management Science and Financial Engineering
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• v.16 no.2
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• pp.53-65
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• 2010

Despite the overall success of neighbor-based CF methods, there are some fundamental questions about neighbor selection and prediction mechanism including arbitrary similarity, over-fitting interpolation weights, no trust consideration between neighbours, etc. This paper proposes a simple method to compute absolute interpolation weights based on similarity values. In order to supplement the method, two schemes are additionally devised for high-quality neighbour selection and trust metrics based on co-ratings. The former requires that one or more neighbour's similarity should be better than a pre-specified level which is higher than the minimum level. The latter gives higher trust to neighbours that have more co-ratings. Experimental results show that the proposed method outperforms the pure IBCF by about 8% improvement. Furthermore, it can be easily combined with other predictors for achieving better prediction quality.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.33-35
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• 2018

기존의 사람간의 유사성 측정 시스템은 적외선 빔이나 열 감지 영상 장치를 통해 측정하였다. 하지만 이와 같은 방법으로 측정하면 2명 이상의 객체를 분류해내는 기술은 제공하지 않는다. 이에 본 논문은 고정된 카메라를 이용하여 각 사람의 피부색과 옷차림 등의 RGB 정보를 이용한 사람 유사성 측정 기법을 제안한다. RGB카메라 영상을 통하여 객체의 RGB 히스토그램을 얻은 후 각 객체에 대해 Bhattacharyya metric, Cosine similarity, Jensen difference, Euclidean distance로 histogram similarity를 계산하여 객체 추적 및 유사성 측정을 통해 객체를 분류한다. 제안된 시스템은 C/C++를 기반으로 구현하여, 유사성 측정 성능을 평가하였다.