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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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The KIPS Transactions:PartB
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Korea Information Processing Society
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Volume & Issues
Volume 13B, Issue 7 - Dec 2006
Volume 13B, Issue 6 - Dec 2006
Volume 13B, Issue 5 - Oct 2006
Volume 13B, Issue 4 - Aug 2006
Volume 13B, Issue 3 - Jun 2006
Volume 13B, Issue 2 - Apr 2006
Volume 13B, Issue 1 - Feb 2006
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Modular neural network in prediction of protein function
Hwang Doo-Sung ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 1~6
DOI : 10.3745/KIPSTB.2006.13B.1.001
The prediction of protein function basically make use of a protein-protein interaction map based on the concept of guilt-by-association. The method however cannot determine the functions of proteins in case that the target protein does not interact with proteins with known functions directly. This paper studies protein function prediction considering the given problem as a K-class classification problem and proposes a predictive approach utilizing a modular neural network. The proposed method uses interaction data and protein related attributes as well. The experimental results demonstrate that the proposed approach can predict the functional roles of Yeast proteins whose interaction knowledge is not known and shows better performance than the graph-based models that use protein interaction data.
An Efficient Face Recognition Using First Moment of Image and Basis Images
Cho Yong-Hyun ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 7~14
DOI : 10.3745/KIPSTB.2006.13B.1.007
This paper presents an efficient face recognition method using both first moment of image and basis images. First moment which is a method for finding centroid of image, is applied to exclude the needless backgrounds in the face recognitions by shifting to the centroid of face image. Basis images which are the face features, are respectively extracted by principal component analysis(PCA) and fixed-point independent component analysis(FP-ICA). This is to improve the recognition performance by excluding the redundancy considering to second- and higher-order statistics of face image. The proposed methods has been applied to the problem for recognizing the 48 face images(12 persons*4 scenes) of 64*64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed methods has a superior recognition performances(speed, rate) than conventional PCA and FP-ICA without preprocessing, the proposed FP-ICA has also better performance than the proposed PCA. The city-block has been relatively achieved more an accurate similarity than Euclidean or negative angle.
Research on Intelligent Game Character through Performance Enhancements of Physics Engine in Computer Games
Choi Jong-Hwa ; Shin Dong-Kyoo ; Shin Dong-Il ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 15~20
DOI : 10.3745/KIPSTB.2006.13B.1.015
This paper describes research on intelligent game character through performance enhancements of physics engine in computer games. The algorithm that recognizes the physics situation uses momentum back-propagation neural networks. Also, we present an experiment and its results, integration methods that display optimum performance based on the physics situation. In this experiment on integration methods, the Euler method was shown to produce the best results in terms of fps in a simulation environment with collision detection. Simulation with collision detection was shown similar fps for all three methods and the Runge-kutta method was shown the greatest accuracy. In the experiment on physics situation recognition, a physics situation recognition algorithm where the number of input layers (number of physical parameters) and output layers (destruction value for the master car) is fixed has shown the best performance when the number of hidden layers is 3 and the learning count number is 30,000. Since we tested with rigid bodies only, we are currently studying efficient physics situation recognition for soft body objects.
Web Change Detection System Using the Semantic Web
Cho Boo-Hyun ; Min Young-Kun ; Lee Bog-Ju ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 21~26
DOI : 10.3745/KIPSTB.2006.13B.1.021
The semantic web is an emerging paradigm in the information retrieval and Web-based system. This paper deals with a Web change detection system which employs the semantic web and ontology. While existing Web change detection systems detect the syntactic change, the proposed system focuses on the detection of the semantic change. The system detects the change only when the web has semantic change. To achieve this, the system employs the domain-specific ontology (e.g., computer science professional person information in the paper). The Web pages regarding before and after change are converted according to the ontology. Then the comparison is performed. The experimental result shows the semantic-based change detection is more useful than the syntax-based change detection.
An Algorithm of Score Function Generation using Convolution-FFT in Independent Component Analysis
Kim Woong-Myung ; Lee Hyon-Soo ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 27~34
DOI : 10.3745/KIPSTB.2006.13B.1.027
In this study, we propose this new algorithm that generates score function in ICA(Independent Component Analysis) using entropy theory. To generate score function, estimation of probability density function about original signals are certainly necessary and density function should be differentiated. Therefore, we used kernel density estimation method in order to derive differential equation of score function by original signal. After changing formula to convolution form to increase speed of density estimation, we used FFT algorithm that can calculate convolution faster. Proposed score function generation method reduces the errors, it is density difference of recovered signals and originals signals. In the result of computer simulation, we estimate density function more similar to original signals compared with Extended Infomax and Fixed Point ICA in blind source separation problem and get improved performance at the SNR(Signal to Noise Ratio) between recovered signals and original signal.
Active Fusion Model with Robustness against Partial Occlusions
Lee Joong-Jae ; Lee Geun-Soo ; Kim Gye-Young ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 35~46
DOI : 10.3745/KIPSTB.2006.13B.1.035
The dynamic change of background and moving objects is an important factor which causes the problem of occlusion in tracking moving objects. The tracking accuracy is also remarkably decreased in the presence of occlusion. We therefore propose an active fusion model which is robust against partial occlusions that are occurred by background and other objects. The active fusion model is consisted of contour-based md region-based snake. The former is a conventional snake model using contour features of a moving object and the latter is a regional snake model which considers region features inside its boundary. First, this model classifies total occlusion into contour and region occlusion. And then it adjusts the confidence of each model based on calculating the location and amount of occlusion, so it can overcome the problem of occlusion. Experimental results show that the proposed method can successfully track a moving object but the previous methods fail to track it under partial occlusion.
Content-Based Image Retrieval using Region Feature Vector
Kim Dong-Woo ; Song Young-Jun ; Kim Young-Gil ; Ah Jae-Hyeong ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 47~52
DOI : 10.3745/KIPSTB.2006.13B.1.047
This paper proposes a method of content-based image retrieval using region feature vector in order to overcome disadvantages of existing color histogram methods. The color histogram methods have a weak point that reduces accuracy because of quantization error, and more. In order to solve this, we convert color information to HSV space and quantize hue factor being purecolor information and calculate histogram and then use thus for retrieval feature that is robust in brightness, movement, and rotation. Also we solve an insufficient part that is the most serious problem in color histogram methods by dividing an image into sixteen regions and then comparing each region. We improve accuracy by edge and DC of DCT transformation. As a result of experimenting with 1,000 color images, the proposed method has showed better precision than the existing methods.
Model-Based Object Recognition using PCA & Improved k-Nearest Neighbor
Jung Byeong-Soo ; Kim Byung-Gi ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 53~62
DOI : 10.3745/KIPSTB.2006.13B.1.053
Object recognition techniques using principal component analysis are disposed to be decreased recognition rate when lighting change of image happens. The purpose of this thesis is to propose an object recognition technique using new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. And the object recognition algorithm proposed here represents more enhanced recognition rate using improved k-Nearest Neighbor. In this thesis, we proposed an object recognition algorithm which creates object space by pre-processing and being learned image using histogram equalization and median filter. By spreading histogram of test image using histogram equalization, the effect to change of illumination is reduced. This method is stronger to change of illumination than basic PCA method and normalization, and almost removes effect of illumination, therefore almost maintains constant good recognition rate. And, it compares ingredient projected test image into object space with distance of representative value and recognizes after representative value of each object in model image is made. Each model images is used in recognition unit about some continual input image using improved k-Nearest Neighbor in this thesis because existing method have many errors about distance calculation.
PPEditor: Semi-Automatic Annotation Tool for Korean Dependency Structure
Kim Jae-Hoon ; Park Eun-Jin ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 63~70
DOI : 10.3745/KIPSTB.2006.13B.1.063
In general, a corpus contains lots of linguistic information and is widely used in the field of natural language processing and computational linguistics. The creation of such the corpus, however, is an expensive, labor-intensive and time-consuming work. To alleviate this problem, annotation tools to build corpora with much linguistic information is indispensable. In this paper, we design and implement an annotation tool for establishing a Korean dependency tree-tagged corpus. The most ideal way is to fully automatically create the corpus without annotators` interventions, but as a matter of fact, it is impossible. The proposed tool is semi-automatic like most other annotation tools and is designed to edit errors, which are generated by basic analyzers like part-of-speech tagger and (partial) parser. We also design it to avoid repetitive works while editing the errors and to use it easily and friendly. Using the proposed annotation tool, 10,000 Korean sentences containing over 20 words are annotated with dependency structures. For 2 months, eight annotators have worked every 4 hours a day. We are confident that we can have accurate and consistent annotations as well as reduced labor and time.
Translation Disambiguation Based on `Word-to-Sense and Sense-to-Word` Relationship
Lee Hyun-Ah ;
The KIPS Transactions:PartB, volume 13B, issue 1, 2006, Pages 71~76
DOI : 10.3745/KIPSTB.2006.13B.1.071
To obtain a correctly translated sentence in a machine translation system, we must select target words that not only reflect an appropriate meaning in a source sentence but also make a fluent sentence in a target language. This paper points out that a source language word has various senses and each sense can be mapped into multiple target words, and proposes a new translation disambiguation method based on this `word-to-sense and sense-to-word` relationship. In my method target words are chosen through disambiguation of a source word sense and selection of a target word. Most of translation disambiguation methods are based on a `word-to-word` relationship that means they translate a source word directly into a target wort so they require complicate knowledge sources that directly link a source words to target words, which are hard to obtain like bilingual aligned corpora. By combining two sub-problems for each language, knowledge for translation disambiguation can be automatically extracted from knowledge sources for each language that are easy to obtain. In addition, disambiguation results satisfy both fidelity and intelligibility because selected target words have correct meaning and generate naturally composed target sentences.