• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.265-285
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• 2006

In text categorization a certain level of correctness of labels assigned to training documents is assumed without solid knowledge on that of real-world collections. Our research attempts to explore the quality of pre-assigned subject categories in a real-world collection, and to identify the relationship between the quality of category assignment in training set and text categorization performance. Particularly, we are interested in to what extent the performance can be improved by enhancing the quality (i.e., correctness) of category assignment in training documents. A collection of 1,150 abstracts in computer science is re-classified by an expert group, and divided into 907 training documents and 227 test documents (15 duplicates are removed). The performances of before and after re-classification groups, called Initial set and Recat-1/Recat-2 sets respectively, are compared using a kNN classifier. The average correctness of subject categories in the Initial set is 16%, and the categorization performance with the Initial set shows 17% in $F_1$ value. On the other hand, the Recat-1 set scores $F_1$ value of 61%, which is 3.6 times higher than that of the Initial set.

• Bulletin of the Korean Mathematical Society
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• v.55 no.4
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• pp.1161-1178
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• 2018

In this paper, we consider the Cauchy problem for a class of nonlinear sixth-order wave equation. The global existence and the finite time blow-up for the problem are proved by the potential well method at both low and critical initial energy levels. Furthermore, we present some sufficient conditions on initial data such that the weak solution exists globally at supercritical initial energy level by introducing a new stable set.

• Journal of KIISE:Databases
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• v.27 no.1
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• pp.90-100
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• 2000

We propose, in this paper, a new query expansion technique with term reweighting. All terms in the documents feedbacked from a user, excluding stopwords, are selected as candidate terms for query expansion and reweighted using the relevance degree which is calculated from the term-distribution similarity between a candidate term and each term in initial query. The term-distribution similarity of two terms is a measure on how similar their occurrence distributions in relevant documents are. The terms to be actually expanded are selected using the relevance degree and combined with initial query to construct an expanded query. We use KT-set 1.0 and KT-set 2.0 to evaluate performance and compare our method with two methods, one with no relevance feedback and the other with Dec-Hi method which is similar to our method. based on recall and precision.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.46-53
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• 2019

When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.390-401
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• 2014

ISO 15926 is an international standard for integration of lifecycle data for process plants including oil and gas facilities. From the viewpoint of information modeling, ISO 15926 Parts 2 provides the general data model that is designed to be used in conjunction with reference data. Reference data are standard instances that represent classes, objects, properties, and templates common to a number of users, process plants, or both. ISO 15926 Parts 4 and 7 provide the initial set of classes, objects, properties and the initial set of templates, respectively. User-defined reference data specific to companies or organizations are defined by inheriting from the initial reference data and the initial set of templates. In order to support the extension of reference data and templates, an editor that provides creation, deletion and modification functions of user-defined reference data is needed. In this study, an editor for reference data based on ISO 15926 was developed. Sample reference data were encoded in OWL (web ontology language) according to the specification of ISO 15926 Part 8. iRINGTools and dot15926Editor were benchmarked for the design of GUI (graphical user interface). Reference data search, creation, modification, and deletion functions were implemented with XML (extensible markup language) DOM (document object model), and SPARQL (SPARQL protocol and RDF query language).

• Journal of the Korean Society of Visualization
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• v.2 no.2
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• pp.45-51
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• 2004

Initial flame development and propagation were visualized under different fuel injection timings to relate the initial flame development to the engine stability in a port injection SI engine. Experiments were performed in an optical single cylinder engine modified from a production engine and images were captured through the quartz window mounted in the piston by an intensified CCD camera. Stratification state was controlled by varying injection timing. Under each injection condition, the flame images were captured at the pre-set crank angles. These were averaged and processed to characterize the flame. The flame stability was estimated by the weighted average of flame area, luminosity, and standard deviation of flame area. Results show that stratification state according to injection timing did not affect on the direction of flame propagation. The flame development and the initial flame stability are strongly dependent on the stratified conditions and the initial flame stability governs the engine stability and lean misfire limit.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.354-359
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• 2007

A new level set based topology optimization employing inner-front creation algorithm is presented. In the conventional level set based topology optimization, the optimum topology strongly depends on the initial level set distribution due to the incapability of inner-front creation during optimization process. In the present work, an inner-front creation algorithm is proposed, in which the sizes, positions, and number of new inner-fronts during the optimization process can be globally and consistently identified. To update the level set function during the optimization process, the least-squares finite element method is employed. As demonstrative examples for the flexibility and usefulness of the proposed method, the level set based topology optimization considering lightweight design of 3D shell structure is carried out.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.501-509
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• 2011

In this paper, we propose an edge profile adaptive bi-directional diffusion interpolation method which consists of shock filter and level set. In recent years many interpolation methods have been proposed but all methods have some degrees of artifacts such as blurring and jaggies. To solve these problems, we adaptively apply shock filter and level set method where shock filter enhances edge along the normal direction and level set method removes jaggies artifact along the tangent direction. After the initial interpolation, weights of shock filter and level set are locally adjusted according to the edge profile. By adaptive coupling shock filter with level set method, the proposed method can remove jaggies artifact and enhance the edge. Experimental results show that the average PSNR and MSSIM of our method are increased, and contour smoothness and edge sharpness are also improved.

• Journal of Information Processing Systems
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• v.5 no.1
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• pp.1-4
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• 2009

Level set methods are the numerical techniques for tracking interfaces and shapes. They have been successfully used in image segmentation. A new variational level set evolving algorithm without re-initialization is presented in this paper. It consists of an internal energy term that penalizes deviations of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature. This algorithm can be easily implemented using a simple finite difference scheme. Meanwhile, not only can the initial contour can be shown anywhere in the image, but the interior contours can also be automatically detected.