• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.184-189
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• 2008

In this paper, the sound data either from the sampled or streamed source are utilized for generating a map in the video game play for the dynamiccal use of sound data and synesthesia to users. When users can generate sound in real-time or put the sampled source, it is analyzed and re-processed through Fourier transformation to show the 3D map in dynamic shape over time. We interpolate the generated data to enable the game agents and objects to move.

• Journal of Sensor Science and Technology
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• v.21 no.4
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• pp.249-255
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• 2012

Among the human senses, olfactory information still does not have a proper data presentation method unlike that regarding vision and auditory information. It makes presenting the sense of smell into multimedia information impossible, which may be an exploratory field in human computer interaction. In this paper, we propose an olfactory information presentation method, which is a way to use smell as multimedia information, and show an application for scent generation and odor display using a web service. The olfactory information can present smell characteristics such as intensity, persistence, hedonic tone, and odor description. The structure of data format based on olfactory information can also be organized according to data types such as integer, float, char, string, and bitmap. Furthermore, it can be used for data transmitting via a web service and for odor display using a scent generator. The scent generator, which can display information of smell, is developed to generate 6 odors using 6 aroma solutions and a diluted solution with 14 micro-valves and a micropump. Throughout the experiment, we confirm that the remote user can grasp information of smell transmitted by messenger service and request odor display to the computer controlled scent generator. It contributes to enlarge existing virtual reality and to be proposed as a standard reference method regarding olfactory information presentation for future multimedia technology.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.565-578
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• 2006

Repeated measurement data between two group is often used in the field of medicine study. In this paper, we suggest a method for comparison of the trend between two groups based on repeated measurement data. First, we estimate regression coefficient of linear regression model from each subject and generate samples using the regression coefficient estimated previous. And then, we test the difference between two groups by unpaired t-test, Wilcoxon rank sum test and placement test using generated samples. Monte Carlo Simulation is adapted to examine the power and experimental significance levels of several methods in various combinations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3548-3557
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• 2010

Security enhancement technologies are required to preventing phishing and pharming attacks on Internet banking. One-time password(OTP) should be used with certificate for enhancing user authentication and security performance. However, existing OTP technique is weak on MITM(Man-In-The-Middle) attack and synchnonization should be provided on OTP system. Therefore, more advanced mechanism such as combining biometic data with OTP can be suggested to enhancing security on authentication system. In this paper, we designed and implemented a multifactor authentication system using one-time biometric template to generate unique authentication data after adapting biometric transform on each user's biometric data.

• Journal of the Korea Computer Graphics Society
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• v.5 no.2
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• pp.43-52
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• 1999

In this paper, we present an animation toolkit based on motion mapping technique in a graphics user interface that can represent data structures necessary for generating character motions. The motion mapping means that an animation sequence generated once can be mapped to another object directly according a data structure in the graphics user interface. Users can generate animation sequences interactively using a mouse. These are obtained automatically by modifying motion data structures interactively. Compared with other conventional tools, the toolkit has different features that two hierarchical structures necessary for representing modeling and animation data are managed independently each other, and that animations generated can be applied to any other characters by connecting the two hierarchical structures in the user interface.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.3
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• pp.123-137
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• 1993

In this paper, an integrated approach is proposed to generate gouging-free Cartesian tool paths for machining sculptured surfaces from 3D measurement data. The integrated CAD/CAM system consists of two modules : offset surface module an Carteian tool path module. The offset surface module generates an offset surface of an object from its 3D measurement data, using an offsetting method and a surface fitting method. The offsetting is based on the idea that the envelope of an inversed tool generates an offset surface without self-intersection as the center of the inversed tool moves along on the surface of an object. The surface-fitting is the process of constructing a compact representation to model the surface of an object based on a fairly large number of data points. The resulting offset surtace is a composite Bezier surface without self-intersection. When an appropriate tool-approach direction is selected, the tool path module generates the Cartesian tool paths while the deviation of the tool paths from the surface stays within the user-specified tolerance. The tool path module is a two-step process. The first step adaptively subdivides the offset surface into subpatches until the thickness of each subpatch is small enough to satisfy the user-defined tolerance. The second step generates the Cartesian tool paths by calculating the intersection of the slicing planes and the adaptively subdivided subpatches. This tool path generation approach generates the gouging-free Cartesian CL tool paths, and optimizes the cutter movements by minimizing the number of interpolated points.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.297-310
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• 2002

Liang and Zeger proposed generalized estimating equations(GEE) for analyzing repeated data which is discrete or continuous. GEE model can be extended to model for repeated categorical data and its estimator has asymptotic multivariate normal distribution in large sample sizes. But GEE is based on large sample asymptotic theory. In this paper, we study the properties of GEE estimators for repeated ordinal data in small sample sizes. We generate ordinal repeated measurements for two groups using two methods. Through Monte Carlo simulation studies we investigate the empirical type 1 error rates, powers, relative efficiencies of the GEE estimators, the effect of unequal sample size of two groups, and the performance of variance estimators for polytomous ordinal response variables, especially in small sample sizes.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.268-274
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• 2003

Most currently known molecular structures were determined by X-ray crystallography or Nuclear Magnetic Resonance (NMR). These methods generate a large amount of structure data, even far small molecules, and consist mainly of three-dimensional atomic coordinates. These are useful for analyzing molecular structure, but structure elements at higher level are also needed for a complete understanding of structure, and especially for structure prediction. Computational approaches exist for identifying secondary structural elements in proteins from atomic coordinates. However, similar methods have not been developed for RNA due in part to the very small amount of structure data so far available, and extracting the structural elements of RNA requires substantial manual work. Since the number of three-dimensional RNA structures is increasing, a more systematic and automated method is needed. We have developed a set of algorithms for recognizing secondary and tertiary structural elements in RNA molecules and in the protein-RNA structures in protein data banks (PDB). The present work represents the first attempt at extracting RNA structure elements from atomic coordinates in structure databases. The regularities in the structure elements revealed by the algorithms should provide useful information for predicting the structure of RNA molecules bound to proteins.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.2
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• pp.135-139
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• 2009

We present a new ensemble learning method that employs the set of region experts, each of which learns to handle a subset of the training data. We split the training data and generate experts for different regions in the feature space. When classifying a data, we apply a weighted voting among the experts that include the data in their region. We used ten datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as Bagging and Adaboost. We used SMO, Naive Bayes and C4.5 as base learning algorithms. As a result, we found that the performance of our method is comparable to that of Adaboost and Bagging when the base learner is C4.5. In the remaining cases, our method outperformed the benchmark methods.

• Smart Structures and Systems
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• v.8 no.1
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• pp.69-92
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• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.