• Structural Engineering and Mechanics
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• v.20 no.1
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• pp.85-110
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• 2005

This paper aims at formulating various statistical models for the study of a ten year Weigh-in-Motion (WIM) data collected from various WIM stations in Hong Kong. In order to study the bridge live load model it is important to determine the mathematical distributions of different load affecting parameters such as gross vehicle weights, axle weights, axle spacings, average daily number of trucks etc. Each of the above parameters is analyzed by various stochastic processes in order to obtain the mathematical distributions and the Maximum Likelihood Estimation (MLE) method is adopted to calculate the statistical parameters, expected values and standard deviations from the given samples of data. The Kolmogorov-Smirnov (K-S) method of approach is used to check the suitability of the statistical model selected for the particular parameter and the Monte Carlo method is used to simulate the distributions of maximum value stochastic processes of a series of given stochastic processes. Using the statistical analysis approach the maximum value of gross vehicle weight and axle weight in bridge design life has been determined and the distribution functions of these parameters are obtained under both free-flowing traffic and dense traffic status. The maximum value of bending moments and shears for wide range of simple spans are obtained by extrapolation. It has been observed that the obtained maximum values of the gross vehicle weight and axle weight from this study are very close to their legal limitations of Hong Kong which are 42 tonnes for gross weight and 10 tonnes for axle weight.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.166-169
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model, and the GA is applied to identify this fuzzy model. The proposed method is compared with the AIMM algorithm in simulations.

• Journal of computational fluids engineering
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• v.13 no.1
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• pp.49-56
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• 2008

Chimera grid methods have been widely used in Computational Fluid Dynamics due to its simplicity in constructing grid systems over complex bodies, and suitability for unsteady flow computations with bodies in relative motion. However, the interpolation procedure for ensuring the continuity of the solution over overlapped regions fails when the so-called orphan cells are present. We have adopted the MLS(Moving Least Squares) method to replace commonly used linear interpolations in order to alleviate the difficulty associated with the orphan cells. MLS is one of the interpolation methods used in mesh-less methods. A number of examples with MLS are presented to show the validity and the accuracy of the method.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2382-2384
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulations.

• Structural Engineering and Mechanics
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• v.22 no.5
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• pp.517-539
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• 2006

Friction problems involving rubber components are frequently encountered in industrial applications. Their treatment within the framework of numerical simulations by means of the Finite Element Method (FEM) is the main issue of this paper. Special emphasis is placed on the choice of a suitable material model and the formulation of a contact model specially designed for the particular characteristics of rubber friction. A coupled thermomechanical approach allows for consideration of the influence of temperature on the frictional behavior. The developed tools are implemented in the commercial FE code ABAQUS. They are validated taking the sliding motion of a rubber tread block as example. Such simulations are frequently encountered in tire design and development. The simulations are carried out with different formulations for the material and the frictional behavior. Comparison of the obtained results with experimental observations enables to judge the suitability of the applied formulations on a structural scale.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.16-21
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• 2003

The accuracy in maneuvering target tracking using multiple models is resulted in by the suitability of each target motion model to be used. The interacting multiple model (IMM) method and the adaptive IMM (AIMM) method require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems, a genetic algorithm(GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to calculate the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulation.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.7
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• pp.709-715
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• 2015

Underwater gliders do not typically have separate propellers for forward motion. They generate propulsive forces based on the difference between their buoyancy and gravity. They can control the volume from the buoyancy engine to adjust the propulsive force. In addition, the attitude of the underwater glider is controlled by a rubberless motion controller. The motion controller can change the mass center and moment of inertia of the inner moving mass. Owing to the change in these parameters, the attitude of the underwater glider is changed. In this study, we derive nonlinear, six degree of freedom (DOF) mathematical models for the motion controller and buoyancy engine. Using these equations, we perform dynamic simulations of the proposed underwater glider, and verify the suitability of the design and dynamic performances of the proposed underwater glider. We then perform the motion control simulation for the pitch and roll angle, and analyze the dynamic performance according to the pitch and roll angles.

• Fashion & Textile Research Journal
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• v.20 no.4
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• pp.449-456
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• 2018

This study conducted a survey of members at sports climbing centers on the purchasing and wearing conditions of sports climbing wear and design preferences. 140 participants were in their 20's and 30's, and many of them engaged in sports climbing activities over 20 times a month. They put emphasis on the motion adaptability, durability, price and functional material when purchasing a climbing wear. However, only 23% of them used sports climbing wear. The reasons for wearing sports climbing wears were suitability for physical activity, comfort, and design. On the other hand, the reasons for not purchasing them were expensive prices and lack of designs. They experienced inconveniences at sleeves and waist in shirts. In case of pants, participants experienced inconveniences with the knee and thigh when they climbing. Concerning the damage of shirts, the majority experienced the elbow part was worn out, and the knee part was either worn out or torn at pants. Design preference results showed participants preferred loose-fit short-sleeved shirts that could cover half of the hip. In case of pants, they preferred basic-fit long trousers and basic hems with no functional characteristic. 3 sports climbing instructors answered that climbing wears should put emphasis on deodorization and antimicrobial effects as well as durability and suitability for physical activity. They also pointed out limitations in price and design and presented opinions about creating various sizes for different body parts by taking into account the growth of muscles.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.18-23
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• 2008

In this paper, we propose the input image selection-method to improve the reconstructed high-resolution (HR) image quality. To obtain ideal super-resolution (SR) reconstruction image, all input images are well-registered. However, the registration is not ideal in practice. Due to this reason, the selection of input images with low registration error (RE) is more important than the number of input images in order to obtain good quality of a HR image. The suitability of a candidate input image can be determined by using statistical and restricted registration properties. Therefore, we propose the proper candidate input Low Resolution(LR) image selection-method as a pre-processing for the SR reconstruction in automatic manner. In video sequences, all input images in specified region are allowed to use SR reconstruction as low-resolution input image and/or the reference image. The candidacy of an input LR image is decided by the threshold value and this threshold is calculated by using the maximum motion compensation error (MMCE) of the reference image. If the motion compensation error (MCE) of LR input image is in the range of 0 < MCE < MMCE then this LR input image is selected for SR reconstruction, else then LR input image are neglected. The optimal reference LR (ORLR) image is decided by comparing the number of the selected LR input (SLRI) images with each reference LR input (RLRI) image. Finally, we generate a HR image by using optimal reference LR image and selected LR images and by using the Hardie's interpolation method. This proposed algorithm is expected to improve the quality of SR without any user intervention.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.279-286
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• 2021

The purpose of this study is to compare the predictive power of the Bagging and Boosting algorithm of ensemble method based on the motion information that occurs in woman handball matches and to analyze the availability of motion information. To this end, this study analyzed the predictive power of the result of 15 practice matches based on inertial motion by analyzing the predictive power of Random Forest and Adaboost algorithms. The results of the study are as follows. First, the prediction rate of the Random Forest algorithm was 66.9 ± 0.1%, and the prediction rate of the Adaboost algorithm was 65.6 ± 1.6%. Second, Random Forest predicted all of the winning results, but none of the losing results. On the other hand, the Adaboost algorithm shows 91.4% prediction of winning and 10.4% prediction of losing. Third, in the verification of the suitability of the algorithm, the Random Forest had no overfitting error, but Adaboost showed an overfitting error. Based on the results of this study, the availability of motion information is high when predicting sports events, and it was confirmed that the Random Forest algorithm was superior to the Adaboost algorithm.