• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2104-2107
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• 2003

In this paper, we propose the method Model based Non-Rigid Moving Object Tracking. Motion based method becomes difficult to predict precisely when motion gets larger, so that we can solve such difficultly with regarding the moving object as a model. In the model based method, it should be concerned about setting initial model and updating its model in each frame. We used SNAKE in a way to set the initial model, and also proposed a modified SNAKE to handle the previous SNAKE problems. Moreover, with the elliptical setting, we made the initializing process automatically which is highly subject to change in measuring the performance of SNAKE. We used the Hausdorff distance to identify models in each frame. Through our experiments, our Proposed algorithm does effective work in Non-Rigid Moving Object Tracking.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1988.10a
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• pp.7-12
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• 1988

This study presents rational methods for probability-based estimates of basic design wind speeds in Korea and develops a risk-bases nation-wide map of design wind speeds. The paper examines the fitting of the Type-I extreme model to maximum yearly non-typhoon wind data from long-term records based on the conventional method and to maximum monthly nod-typhoon wind data from short-term records following Grigorin's approach. The paper also reviews the applicability of the method using short records of about 5 years. The basic design wind speeds for typhoon and non-typhoon wind at a station are made to be obtained from a mixed model which is given as a product of typhoon and non-typhoon extreme wind distributions. A practical method which is based on the fitting of the Type I model to records or typhoon and non-typhoon mixed wind data at a station is also preposed in this study.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.499-505
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• 2006

Regularlization approach to regression can be easily found in Statistics and Information Science literature. The technique of regularlization was introduced as a way of controlling the smoothness properties of regression function. In this paper, we have presented a new method to evaluate linear and non-linear fuzzy regression model based on Tanaka's model using the idea of regularlization technique. Especially this method is a very attractive approach to model non -linear fuzzy data.

• Speech Sciences
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• v.10 no.3
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• pp.225-239
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• 2003

In this paper, we develop a keyword spotting system using vocabulary-independent speech recognition technique, and investigate several non-keyword modeling and post-processing methods to improve its performance. In order to model non-keyword speech segments, monophone clustering and Gaussian Mixture Model (GMM) are considered. We employ likelihood ratio scoring method for the post-processing schemes to verify the recognition results, and filler models, anti-subword models and N-best decoding results are considered as an alternative hypothesis for likelihood ratio scoring. We also examine different methods to construct anti-subword models. We evaluate the performance of our system on the automatic telephone exchange service task. The results show that GMM-based non-keyword modeling yields better performance than that using monophone clustering. According to the post-processing experiment, the method using anti-keyword model based on Kullback-Leibler distance and N-best decoding method show better performance than other methods, and we could reduce more than 50% of keyword recognition errors with keyword rejection rate of 5%.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.377-382
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• 2005

This paper presents a new method for the attitude control of planar space robots. In order to control highly constrained non-linear system such as a 3D space robot, the analytical formulation for the system with complex dynamics and effective control methodology based on the formulation, are not always obtainable. In the proposed method, correspondingly, a non-analytical but effective self-organizing modeling method for controlling a highly constrained system is proposed based on a polynomial data mining algorithm. In order to control the attitude of a planar space robot, it is well known to require inputs characterized by a special pattern in time series with a non-deterministic length. In order to correspond to this type of control paradigm, we adopt the Model Predictive Control (MPC) scheme where the length of the non-deterministic horizon is determined based on implementation cost and control performance. The optimal solution to finding the size of the input pattern is found by a solving two-stage programming problem.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.3
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• pp.329-337
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• 2001

This simulator is developed for the analysis of a MOSFET based on Thermally Coupled Energy Transport Model(TCETM). The simulator has the ability to calculate not only stationary characteristics but also non - stationary characteristics of a MOSFET. It solves basic semiconductor devices equations including Possion equation, current continuity equations for electrons and holes, energy balance equation for electrons and heat flow equation, using finite difference method. The conventional semiconductor device simulation technique, based on the Drift-Diffusion Model (DDM), neglects the thermal and other energy-related properties of a miniaturized device. I, therefore, developed a simulator based on the Thermally Coupled Energy Transport Model (TCETM) which treats not only steady-state but also transient phenomena of such a small-size MOSFET. In particular, the present paper investigates the breakdown characteristics in transient conditions. As a result, we found that the breakdown voltage has been largely underestimated by the DDM in transient conditions.

• Structural Engineering and Mechanics
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• v.52 no.5
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• pp.997-1017
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• 2014

According to the structure characteristics of the non-uniform beam bridge, a practical model for calculating the vibration equation of the non-uniform beam bridge is given and the application scope of the model includes not only the beam bridge structure but also the non-uniform beam with added masses and elastic supports. Based on the Bernoulli-Euler beam theory, extending the application of the modal perturbation method and establishment of a semi-analytical method for solving the vibration equation of the non-uniform beam with added masses and elastic supports based is able to be made. In the modal subspace of the uniform beam with the elastic supports, the variable coefficient differential equation that describes the dynamic behavior of the non-uniform beam is converted to nonlinear algebraic equations. Extending the application of the modal perturbation method is suitable for solving the vibration equation of the simply supported and continuous non-uniform beam with its arbitrary added masses and elastic supports. The examples, that are analyzed, demonstrate the high precision and fast convergence speed of the method. Further study of the timesaving method for the dynamic characteristics of symmetrical beam and the symmetry of mode shape should be developed. Eventually, the effects of elastic supports and added masses on dynamic characteristics of the three-span non-uniform beam bridge are reported.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3950-3964
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• 2017

With the increasing popularity of 3D technology such as 3D printing, 3D modeling, etc., there is a growing need to search for similar models on the internet. Matching non-rigid shapes has become an active research field in computer graphics. In this paper, we present an efficient and effective non-rigid model retrieval method based on topological structure and local volume. The integral geodesic distances are first calculated for each vertex on a mesh to construct the topological structure. Next, each node on the topological structure is assigned a local volume that is calculated using the shape diameter function (SDF). Finally, we utilize the Hungarian algorithm to measure similarity between two non-rigid models. Experimental results on the latest benchmark (SHREC' 15 Non-rigid 3D Shape Retrieval) demonstrate that our method works well compared to the state-of-the-art.

• Journal of the Korean Society for Railway
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• v.5 no.1
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• pp.40-47
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• 2002

In this study, and analytical method for ride sensitivity analysis of a train with non-linear suspension elements are proposed. Non-linear characteristics of springs and dampers for primary and secondary suspensions of a train are parameterized using polynomial interpolation. Vertical dynamic model of a three-body train running on straight rail with the predetermined roughness expressed in terms of spectral density function is set up and its equations of motion for ride analysis are derived. Using the direct differentiation method, sensitivity equations of the vertical dynamic model with respect to design parameters associated with non-linearity of suspensions are obtained. Based on the sensitivity analysis, improvement of ride is achieved by varying appropriate suspension parameters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.