• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2003.09a
• /
• pp.295-302
• /
• 2003

A quantitative approach for the retrofit prioritization of bridges is developed based on the damage risk of seismic vulnerable components. In the developed approach, seismic damage risk is estimated in the probabilistic perspectives with an analytical bridge model, which can consider various phenomena found in the seismic behaviors of girder-type bridges and damage models of various vulnerable components. Based on the total cost due to failure of structural components, weighting factors are proposed. Finally, the ranking index and retrofit priority of bridges are estimated from the overall damage risk and weighting factors of bridges. As a result, the retrofit priority of four PSC girder bridges is evaluated by using the proposed approach. The vulnerable components in need of seismic retrofit are selected accordingly. From simulated results, the validity of the proposed approach is verified by comparison with the existing approach. In addition, the proposed approach is found to be appropriate in evaluating the priority of existing bridges.

• The Journal of the Acoustical Society of Korea
• /
• v.30 no.6
• /
• pp.324-329
• /
• 2011

In this paper, we propose a novel approach to improve the performance of a statistical model-based voice activity detection (VAD) which is based on the conditional maximum a posteriori (CMAP) with deviation. In our approach, the VAD decision rule is expressed as the geometric mean of likelihood ratios (LRs) based on adapted threshold according to the speech presence probability conditioned on both the speech activity decisions and spectral deviation in the pervious frame. Experimental results show that the proposed approach yields better results compared to the CMAP-based VAD using the LR test.

• Structural Engineering and Mechanics
• /
• v.23 no.3
• /
• pp.217-232
• /
• 2006

A crack model for the fracture in concrete based on meshless method is proposed in this paper. The cracks in concrete are classified into micro-cracks or macro-cracks respectively according to their widths, and different numerical approaches are adopted for them. The micro-cracks are represented with smeared crack approach whilst the macro-cracks are represented with discrete cracks that are made up with additional nodes and boundaries. The widely used meshless method, Element-free Galerkin method, is adopted instead of finite element method to model the concrete, so that the discrete crack approach is easier to be implemented with the convenience of arranging node distribution in the meshless method. Rotating-Crack-Model is proved to be preferred over Fixed-Crack-Model for the smeared cracks of this composite crack model due to its better performance on mesh bias. Numerical examples show that this composite crack model can take advantage of the positive characteristics in the smeared and discrete approaches, and overcome some of their disadvantages.

• Smart Structures and Systems
• /
• v.32 no.1
• /
• pp.61-81
• /
• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Journal of Information Processing Systems
• /
• v.18 no.2
• /
• pp.268-281
• /
• 2022

The development of information technology is bringing many changes to everyday life, and machine learning can be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data are essential processes in applying machine learning to real-world problems. As a method of processing data in machine learning, we propose an approach based on applying multiple linear regression models by interlacing data to the task of classifying similar software. Linear regression is widely used in estimation problems to model the relationship between input and output data. In our approach, multiple linear regression models are generated by training on interlaced feature data. A combination of these multiple models is then used as the prediction model for classifying similar software. Experiments are performed to evaluate the proposed approach as compared to conventional linear regression, and the experimental results show that the proposed method classifies similar software more accurately than the conventional model. We anticipate the proposed approach to be applied to various kinds of classification problems to improve the accuracy of conventional linear regression.

• Journal of Korea Water Resources Association
• /
• v.49 no.2
• /
• pp.165-175
• /
• 2016

In order to examine the applicability, the evaporation estimation approaches based on solar radiation are classified into 3 different model groups (Model groups A, B, and C) in this study. Each group is tested in the 6 study stations (Seoul, Daejeon, Jeonju, Busan, Mokpo, and Jeju). The model parameters of each model group are estimated and verified with measured pan evaporation data. The applicability of verified model groups are compared with results of Penman (1948) combination approach. Nash-Sutcliffe (N-S) efficiency coefficients greater than 0.663 in all study stations indicate satisfactory estimates of evaporation. On the other hand, in the model verification process, N-S efficiency coefficients greater than 0.526 in all study stations indicate also satisfactory estimates of evaporation. However, N-S efficiency coefficients in all study cases except Model groups B and C in Busan are less than those of Penman (1948) combination approach. Therefore, it is concluded in this study that the evaporation estimation approaches based on solar radiation have capability to replace Penman (1948) combination approach for the estimation of evaporation in case that some meteorological data (wind speed, relative humidity) are missing or not measured.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.23 no.3
• /
• pp.123-133
• /
• 1998

A cell design model based on operation sequence is proposed for maximizing the total parts flow within cells considering the data of Process plans for parts, Production volume, and cell size. A relationship between machines is calculated on the basis of the process plans for parts obtained from process plan sheets. Then the machines are classified into machine cells using the relationship. The model is formulated as a 0-1 integer programming and a genetic algorithm approach is developed to solve the model. The developed approach is tested and Proved using actual industrial data. Experimental results indicate that the approach is appropriate for large-size cell design problems efficiently.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.1100-1102
• /
• 2000

Most systems in tile real world are non-linear and can be represented by the non-linear autoregressive moving average (NARMA) model. The extension of fuzzy system for modeling the system that is represented by NARMA model will be proposed in this paper. Here, fuzzy basis function (FBF) is used as fuzzy NARMA(p,q) model. Then, an approach to Identify fuzzy NARMA models based on fuzzy basis functions is proposed. The efficacy of the proposed approach is shown from experimental results.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.9
• /
• pp.43-50
• /
• 2018

In this paper, we propose a hexagon model-based efficient beacon frame scheduling approach for wireless sensor networks. The existing beacon frame scheduling approaches use a lot of slots and subslots for the beacon frame scheduling. Thus, the data from source nodes are not efficiently delivered to a sink node. Also in case a sink node needs to broadcast a beacon frame to the nodes in the network, delivering the beacon frame to the network nodes is not efficient as well. Thus, to solve the problem, we use a hexagon model to find the number of slots and subslots for the beacon frame scheduling. By using them for the beacon frame scheduling, the proposed approach performs better than other approaches in terms of the data transmission delay, the number of received data, the beacon transmission delay and the number of relaying the beacon frames.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.490-495
• /
• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.