Chul-Won Kim

Enhancing Text Document Clustering Using Non-negative Matrix Factorization and WordNet

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

Document Clustering Using Semantic Features and Fuzzy Relations

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

Design of an Alpine Skiing Game Using ActionScript 3.0

Flash is the most popular 2D animation and game development software, supporting vector and media technology at the core, which enables the development of small and pithy games. It is widely used in Web animation, courseware, TV commercials, game development, and other works of design. In this paper, we propose a control movement function and auxiliary functions for an alpine skiing game based on ActionScript 3.0. The control movement function is designed with moving phases (i.e., free fall, sliding, projectile, and landing). The auxiliary functions include drawing path, start/restart movement, and saving the highest score. In addition, for the visual design of our game, we designed animations in connection with a character and background. In order to facilitate testing the friction, users can input a chosen friction value. Without user input, the friction default is set at 0.97.

Design and Implementation of Flaw Image processing System for Automated Ultrasonic Testing System

In this study, an automated ultrasonic testing system and post signal and image processing techniques are developed in order to construct ultrasonic flaw images in weldments. Image processing algorithms are built into the flaw image processing system for the automated ultrasonic testing system. The developed signal and image analysis algorithms addressed in this study include an A-Scan data compression algorithm, ultrasonic image amplification algorithm and B-scan flaw image correction algorithm(SAFT). This flaw image processing system for the automated ultrasonic testing system can be applied to various inspection fields.