• Geomechanics and Engineering
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• v.9 no.2
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• pp.145-167
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• 2015

Digital simulation has recently become the preferred method for designing complex and dynamic systems. Simulation packages provide interactive, block-diagram environment for modeling and simulating dynamic models. The block diagrams in simulation models are flowcharts which describe the components of dynamic systems and their interaction. This paper is the first part of the study for determining the seismic behavior of soil systems. The aim of this part is to present the constructed block diagrams for discrete-time analysis of seismic site amplification in layered media for vertically propagating shear waves. Detailed block diagrams are constructed for single and multiple soil layers by considering wave propagation with and without damping, respectively. The block diagrams for recursive filter to model attenuation in discrete-time form are also constructed. Finite difference method is used for strain calculation. The block diagrams are developed by utilizing Simulink which is a software add-on to Matlab.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.433-447
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• 1999

검사관련 능력과 문항점수사이의 관계를 모형화하기 위해 사용한 순환모형에서 관측불능인 능력상대변수를 비롯한 모든 변수들이 범주형 변수라 가정하자. 이 범주형 자료를 위한 모수추정문제를 다루기 위해 EM 방법을 이용했는데, EM 방법은 사용하기에 편리하지만 순환모형에 대한 추정값이 적절하지 않는 경우가 발생한다. 그 주된 원인중의 하나로 초기값 선정의 잘못을 들 수 있는데, 본 논문에서는 이 외에 구조상의 결함도 그 원인이 됨을 경험적으로 보았다. 따라서 구조적 결함을 먼저 해결하면 보다 효과적인 초기값을 선정할 수 있으리가 기대한다.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.203-207
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• 1992

In this paper, a new GPC(Generalized Predictive Control) algorithm which is robust to disturbances isproposed. This controller minimizes the LQ cost function when the disturbance maximizes this cost function. The solution is obtained from the min-max problem which can be solved by differential game theory and has the non-recursive form which does not use the Riccati equation. Its another solution for state space models is investigated.

• School Mathematics
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• v.18 no.3
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• pp.625-645
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• 2016

This study clarified the big ideas related to teaching addition and subtraction of fractions with different denominators based on quantitative reasoning with unit and recursive partitioning. An analysis of this study urged us to re-consider the content related to the addition and subtraction of fraction. As such, this study analyzed textbooks and teachers' manuals developed from the fourth national mathematics curriculum to the most recent 2009 curriculum. In addition and subtraction of fractions with different denominators, it must be emphasized the followings: three-levels unit structure, fixed whole unit, necessity of common measure and recursive partitioning. An analysis of this study showed that textbooks and teachers' manuals dealt with the fact of maintaining a fixed whole unit only as being implicit. The textbooks described the reason why we need to create a common denominator in connection with the addition of similar fractions. The textbooks displayed a common denominator numerically rather than using a recursive partitioning method. Given this, it is difficult for students to connect the models and algorithms. Building on these results, this study is expected to suggest specific implications which may be taken into account in developing new instructional materials in process.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.4
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• pp.465-474
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• 2012

In this study, a dynamic simulation model that considers many spherical particles and multibody dynamics (MBD) entities is developed. Plenteous spherical particles are solved using the Discrete Element Method (DEM) technique and simulated on a GPU board in a PC. A fast algorithm is used to calculate the Hertzian contact forces between many spherical particles, and NVIDIA CUDA is used to increase the calculation speed. The explicit integration method is applied to solve the many spheres. MBD entities are simulated by recursive formulation. Constraints are reduced by recursive formulation, and the implicit generalized alpha method is applied to solve the dynamic model. A new algorithm is developed to simulate the DEM and MBD models simultaneously. As a numerical example, a truck car model and gear model are developed. The results show that the proposed algorithm using a general-purpose GPU in a PC has many advantages.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.497-500
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• 2002

When one is interested in the dynamics of a mechanical system with electric motors, the force generated by the motor is generally considered as only an applied torque or force independent of mechanical state variables such as velocity. For a system operated in non-steady dynamic conditions, however, the usual analysis approach may fail to predict some characteristics in the dynamic behaviors because of electromechanical coupling effects. In this paper, we propose dynamics analysis model in which dc motor dynamics with the electromechanical coupling effects are embedded to mechanical dynamics models. The do motor is modeled based on its equivalent circuit model and included in the dynamics solving algorithm which we developed before, called generalized recursive dynamics formula. The developed dynamic analysis model is effective and realistic for analysis of electromechanical dynamics of a system with do motors. The developed model is evaluated by constructing and simulating the flexible antennas of an artificial satellite driven by do motors.

• Fibers and Polymers
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• v.6 no.1
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• pp.55-65
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• 2005

Extensible elastica solutions of two-dimensional deflection of crimped fiber cantilever of circular wavy crimp were obtained for one end clamped boundary under concentrated, inclined and dead tip load Fiber was also regarded as a linear elastic material. Crimp was described as a combination of semicircular arcs smoothly connected with each other having con­stant curvature of all the same magnitude and alternative sign. Also the inclined load direction was taken into account. The solutions were expressed as the recursive forms of integrals in two different cases, which can also be transformed to elliptic integrals respectively. Comparing the data with inextensible ones was carried out. Consequently in the solution, the normal strain of neutral axis is expressed in terms of cross-sectional area, second moment of area and normalized load parameter. Examples of the circular cross-sectioned fiber are presented. As a result, the differences of normalized load between inexten­sible and extensible elastica solutions when the radius ratio becomes 0.1 were maximum $\Lambda$ = 0.1.