• KIPS Transactions on Software and Data Engineering
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• v.7 no.6
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• pp.205-220
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• 2018

Software defect prediction is helpful for allocating valuable project resources effectively for software quality assurance activities thanks to focusing on the identified fault-prone modules. If historical data collected within a company is sufficient, a Within-Project Defect Prediction (WPDP) can be utilized for accurate fault-prone module prediction. In case a company does not maintain historical data, it may be helpful to build a classifier towards predicting comprehensible fault prediction based on Cross-Project Defect Prediction (CPDP). Since CPDP employs different project data collected from other organization to build a classifier, the main obstacle to build an accurate classifier is that distributions between source and target projects are not similar. To address the problem, because it is crucial to identify effective similarity measure techniques to obtain high performance for CPDP, In this paper, we aim to identify them. We compare various similarity measure techniques. The effectiveness of similarity weights calculated by those similarity measure techniques are evaluated. The results are verified using the statistical significance test and the effect size test. The results show k-Nearest Neighbor (k-NN), LOcal Correlation Integral (LOCI), and Range methods are the top three performers. The experimental results show that predictive performances using the three methods are comparable to those of WPDP.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.493-500
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• 2001

Video scene segmentation is fundamental role for content based video analysis. In this paper, we propose a new region based video scene segmentation method using continuity test for each object region which is segmented by the watershed algorithm for all frames in video data. For this purpose, we first classify video data segments into classes that are the dynamic and static sections according to the object movement rate by comparing the spatial and shape similarity of each region. And then, try to segment each sections by grouping each sections by comparing the neighbor section sections by comparing the neighbor section similarity. Because, this method uses the region which represented on object as a similarity measure, it can segment video scenes efficiently without undesirable fault alarms by illumination and partial changes.

• ETRI Journal
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• v.24 no.1
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• pp.23-30
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• 2002

MPEG-7 Visual Standard specifies a set of descriptors that can be used to measure similarity in images or video. Among them, the Edge Histogram Descriptor describes edge distribution with a histogram based on local edge distribution in an image. Since the Edge Histogram Descriptor recommended for the MPEG-7 standard represents only local edge distribution in the image, the matching performance for image retrieval may not be satisfactory. This paper proposes the use of global and semi-local edge histograms generated directly from the local histogram bins to increase the matching performance. Then, the global, semi-global, and local histograms of images are combined to measure the image similarity and are compared with the MPEG-7 descriptor of the local-only histogram. Since we exploit the absolute location of the edge in the image as well as its global composition, the proposed matching method can retrieve semantically similar images. Experiments on MPEG-7 test images show that the proposed method yields better retrieval performance by an amount of 0.04 in ANMRR, which shows a significant difference in visual inspection.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.39-55
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• 2013

It is a difficult problem to use keyword retrieval for low-quality Korean document images because these include adjacent characters that are connected. In addition, images that are created from various fonts are likely to be distorted during acquisition. In this paper, we propose and test a keyword retrieval system, using a support vector machine (SVM) for the retrieval of low-quality Korean document images. We propose a keyword retrieval method using an SVM to discriminate the similarity between two word images. We demonstrated that the proposed keyword retrieval method is more effective than the accumulated Optical Character Recognition (OCR)-based searching method. Moreover, using the SVM is better than Bayesian decision or artificial neural network for determining the similarity of two images.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.207-210
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• 2005

The Similarity Index[1] is a good Performance measure for the network reduction. It can be applied to the network reduction In the zone categorized by the nodal prices. This paper deals with a zonal reduction method based on the similarity indices. The proposed method was verified by IEEE 39 bus test system.

• IE interfaces
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• v.10 no.3
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• pp.189-196
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• 1997

This paper presents a cell formation approach for a cellular manufacturing system to minimize the inter-cell moves considering operation sequences. Two new factors are introduced: (1)flow-similarity(FS) for integrating direct/indirect inter-machine flow and similarity (2)machine cell-part moves (CPM) for exactly computing inter-cell moves. FS is used for combining machines and CPM is used for assigning the parts to the preliminary machine cells. In addition, we develop an aggregated heuristic algorithm to form manufacturing machine cells and assign the parts to those cells based on these concepts. We use performance criterion called total inter-cell moves(TICM), which is the total material flow between internal cells and external cells. Results of computational tests on a number of randomly generated test problems show that the suggested heuristic is superior to existing methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.250-252
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• 2022

As immersive video contents have started to emerge in the commercial market, research on it is required. For this, efficient coding methods for immersive video are being studied in the MPEG-I Visual workgroup, and they released Test Model for Immersive Video (TMIV). In current TMIV, the patches are packed into atlas in order of patch size. However, this simple patch packing method can reduce the coding efficiency in terms of 2D encoder. In this paper, we propose patch packing method which pack the patches into atlases by using the similarity of each patch for improving coding efficiency of 3DoF+ video. Experimental result shows that there is a 0.3% BD-rate savings on average over the anchor of TMIV.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.374-377
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• 2017

A cold-flow test equipment was designed to carry out the performance verification of TDACS. For that purpose, the pressure dynamics in the solid rocket motor combustor and the cold-flow test was modeled, and the response time showing the dynamic characteristics of each model was obtained. In this paper, the system response time of the cold-flow test was designed to be equal to that of the motor, making the dynamic response in cold-flow and hot gas condition to be similar.

• ETRI Journal
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• v.25 no.5
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• pp.401-406
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• 2003

A biometric system determines the identity of a person by measuring physical features that can distinguish that person from others. Since biometric features have many variations and can be easily corrupted by noises and deformations, it is necessary to apply machine learning techniques to treat the data. When applying the conventional machine learning methods in designing a specific biometric system, however, one first runs into the difficulty of collecting sufficient data for each person to be registered to the system. In addition, there can be an almost infinite number of variations of non-registered data. Therefore, it is difficult to analyze and predict the distributional properties of real data that are essential for the system to deal with in practical applications. These difficulties require a new framework of identification and verification that is appropriate and efficient for the specific situations of biometric systems. As a preliminary solution, this paper proposes a simple but theoretically well-defined method based on a statistical test theory. Our computational experiments on real-world data show that the proposed method has potential for coping with the actual difficulties in biometrics.