• International Journal of Ocean System Engineering
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• v.2 no.1
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• pp.16-23
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• 2012

In this paper we discussed a new methodology for Urdu Character Recognition system using Dempster-Shafer theory which can powerfully estimate the similarity ratings between a recognized character and sampling characters in the character database. Recognition of character is done by five probability calculation methods such as (similarity, hamming, linear correlation, cross-correlation, nearest neighbor) with Dempster-Shafer theory of belief functions. The main objective of this paper is to Recognition of Urdu letters and numerals through five similarity and dissimilarity algorithms to find the similarity between the given image and the standard template in the character recognition system. In this paper we develop a method to combine the results of the different distance measurement methods using the Dempster-Shafer theory. This idea enables us to obtain a single precision result. It was observed that the combination of these results ultimately enhanced the success rate.

• Journal of Computing Science and Engineering
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• v.11 no.3
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• pp.100-108
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• 2017

In the field of Internet of Things (IoT), smarter embedded devices offer functions via web services. The Quality-of-Service (QoS) prediction is a key measure that guarantees successful IoT service applications. In this study, a collaborative filtering method is presented for predicting response time of IoT service due to time-awareness characteristics of IoT. First, a calculation method of service response time similarity between different users is proposed. Then, to improve prediction accuracy, initial similarity values are adjusted and similar neighbors are selected by a similarity threshold. Finally, via a densified user-item matrix, service response time is predicted by collaborative filtering for current active users. The presented method is validated by experiments on a real web service QoS dataset. Experimental results indicate that better prediction accuracy can be achieved with the presented method.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.471-472
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• 2014

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.4
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• pp.324-328
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• 2012

With the recent advances in information retrieval, cross-media retrieval has been attracting lot of attention, but several issues remain problems such as constructing effective correlations, calculating similarity between different kinds of media objects. To gain better cross-media retrieval performance, it is crucial to mine the semantic correlations among the heterogeneous multimedia data. This paper introduces a new method for cross-media retrieval which uses ontology to organize different media objects. The experiment results show that the proposed method is effective in cross-media retrieval.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.680-687
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• 2005

This study presents a schema matching technique which can be applied to XML semantic model of structural calculation reports of steel-box bridges. The semantic model of structural calculation documents was developed by extracting the optimized common elements from the analyses of various existing structural calculation documents, and the standardized semantic model was schematized by using XML Schema. In addition, the similarity measure technique and the relaxation labeling technique were employed to develop the schema matching algorithm. The former takes into account the element categories and their features, and the latter considers the structural constraints in the semantic model. The standardized XML semantic model of steel-box bridge's structural calculation documents called target schema was compared with existing nonstandardized structural calculation documents called primitive schema by the developed schema matching algorithm Some application examples show the importance of the development of standardized target schema for structural calculation documents and the effectiveness and efficiency of schema matching technique in the examination of the degree of document standardization in structural calculation reports.

• Journal of Digital Contents Society
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• v.5 no.1
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• pp.7-11
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• 2004

There are many problems that must solve to construct next generation high-speed communication network. Among these, item that must consider basically is characteristics analysis of traffic that nows to network Traffic characteristics of many Internet services that is offered present have shown that network traffic exhibits at a wide range of scals-self-similarity. Self-similarity is expressed by long term dependency, this is contradictory concept with Poisson model that have relativity short term dependency. Therefore, first of all, for design and dimensioning of next generation communication network, traffic model that are reflected burstiness and self-similarity is required. Here self-similarity can be characterized by Hurst parameter. In this paper, the calculation equation is derived considering queueing delay and self-similarity of data traffic art compared with simulation results.

• Journal of information and communication convergence engineering
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• v.11 no.1
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• pp.62-68
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• 2013

This paper proposes a method for identification of harmful video images based on the degree of harmfulness in the video content. To extract harmful candidate frames from the video effectively, we used a video color extraction method applying a projection map. The procedure for identifying the harmful video has five steps, first, extract the I-frames from the video and map them onto projection map. Next, calculate the similarity and select the potentially harmful, then identify the harmful images by comparing the similarity measurement value. The method estimates similarity between the extracted frames and normative images using the critical value of the projection map. Based on our experimental test, we propose how the harmful candidate frames are extracted and compared with normative images. The various experimental data proved that the image identification method based on the 2-dimensional projection map is superior to using the color histogram technique in harmful image detection performance.

• Korean Journal of Microbiology
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• v.23 no.2
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• pp.157-165
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• 1985

Bacterial identification was performed with morphological, physiological and biochemical tests to the isolates from the mudflat of 30cm depth sampled in Nakdong river estuary in March and June, 1985. Flavobacterium and Cnterobacteriaceae were regarded as dominants. Pseudomonas, Bacillus, Micrococcus, Vibrio, Aerococcus, Aerononas, Acinetobacter and Staphylococcus were founded in various depth. Vertical composition of bacterial genera in March was more diversiform than that of June. Character analysis was carried out with the calculation of similarity index (S). At a level of 85% similarity, the isolates were clustered into 5 groups and ungrouped 2 strains. Classifying groups of bacterial strains with determination schemes and groups from similarity index were in good agreement.

• Journal of Pharmaceutical Investigation
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• v.33 no.3
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• pp.245-253
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• 2003

Dissolution profile comparsions can be done by virtue of the similarity factor $(f_2)$. It is a logarithmic reciprocal square root transformation of the sum of squared error of % dissolution differences between two profiles at several time points. It gives information on the degree of similarity between the two profiles: An $f_2$ value between 50 and 100 suggests the similarity/equivalence of the two dissolution curves being compared. The objective of this report was to provide a careful examination on the $f_2$ metrics in detail. It was shown that $f_2$ values exceeded 50, when relative differences in % dissolved between two products were less than 15% at all time points. The similarity factor value was also found to be greater than 50, in cases when absolute % dissolution differences were below 10% at all time points. Interestingly, the $f_2$ value was changed by the number of the time points selected for calculation. In particular, $f_2$ tended to have higher values, when the $f_2$ metrics used a large number of time points in which % dissolved reached plateau. Finally, since the similarity factor was a sample statistics, it was impossible to infer type I/II errors and sampling error. Despite certain limitations inherited in the $f_2$ metrics, it was easy and convenient to evaluate how similar the two dissolution profiles were.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.125-137
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• 2011

Recently the research on the system using images taken from camera phones as input is actively conducted. This paper proposed a system that shows wine pictures which are similar to the input wine label in order. For the calculation of the similarity of images, the representative color of each cell of the image, the recognized text color, background color and distribution of feature points are used as the features. In order to calculate the difference of the colors, RGB is converted into CIE-Lab and the feature points are extracted by using Harris Corner Detection Algorithm. The weights of representative color of each cell of image, text color and background color are applied. The image similarity is calculated by normalizing the difference of color similarity and distribution of feature points. After calculating the similarity between the input image and the images in the database, the images in Database are shown in the descent order of the similarity so that the effort of users to search for similar wine labels again from the searched result is reduced.