• Structural Engineering and Mechanics
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• v.26 no.6
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• pp.673-685
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• 2007

Reinforced concrete beams can be strengthened in a structural retrofit process by attaching steel plates to their sides by bolting. Whilst bolting produces a confident degree of shear connection under conditions of either static or seismic overload, the plates are susceptible to local buckling. The aim of this paper is to investigate the local buckling of unilaterally-restrained plates with point supports in a generic fashion, but with particular emphasis on the provision of the restraints by bolts, and on the geometric configuration of these bolts on the buckling loads. A numerical procedure, which is based on the Rayleigh-Ritz method in conjunction with the technique of Lagrange multipliers, is developed to study the unilateral local buckling of rectangular plates bolted to the concrete with various arrangements of the pattern of bolting. A sufficient number of separable polynomials are used to define the flexural buckling displacements, while the restraint condition is modelled as a tensionless foundation using a penalty function approach to this form of mathematical contact problem. The additional constraint provided by the bolts is also modelled using Lagrange multipliers, providing an efficacious method of numerical analysis. Local buckling coefficients are determined for a range of bolting configurations, and these are compared with those developed elsewhere with simplifying assumptions. The interaction of the actions in bolted plates during buckling is also considered.

• Smart Media Journal
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• v.4 no.1
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• pp.16-24
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• 2015

In this paper, we propose a method for multi-touch tracking using MRF-based particle filters and gesture likelihood estimation Each touch (of one finger) is considered to be one object. One of frequently occurring issues is the hijacking problem which means that an object tracker can be hijacked by neighboring object. If a predicted particle is close to an adjacent object then the particle's weight should be lowered by analysing the influence of neighboring objects for avoiding hijacking problem. We define a penalty function to lower the weights of those particles. MRF is a graph representation where a node is the location of a target object and an edge describes the adjacent relation of target object. It is easy to utilize MRF as data structure of adjacent objects. Moreover, since MRF graph representation is helpful to analyze multi-touch gestures, we describe how to define gesture likelihoods based on MRF. The experimental results show that the proposed method can avoid the occurrence of hijacking problems and is able to estimate gesture likelihoods with high accuracy.

• Journal of the Society of Naval Architects of Korea
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• v.29 no.2
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• pp.115-122
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• 1992

A sandwich element is a special Hybrid structural form of the composite construction, which is consisted of three main parts : thin, stiff and relatively high density faces separated by a thick, light, and weaker core material. In a sandwich construction, the shear deformation of the faces. Therefore, in the calculation of the bending stiffness, the shear effect should be included. In this paper, the minimum weight is selected as an object function, as the weight critical structures are usually composed of these kind of construction. To obtain the minimum weight of sandwich panel, the principle of minimum potential energy is used and as for the design constraints, the allowable bending stress of face material, the allowable shear stress of core material, the allowable value of panel deflection and the wrinkling stress of faces are adopted, as well as the different boundary conditions. For the engineering purpose of sandwich panel design, the results are tabulated, which are calculated by using the nonlinear optimization technique SUMT.

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.278-281
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• 2011

This system can predict the maximum output about all illumination levels so that the PV system designer can design the system having the best efficiency. For the output prediction exact about the solar cell, that is the device the basis most in the PV system, the basis has to be in order to try this way. The solution based on Lambert W-function are presented to express the transcendental current-voltage characteristic containing parasitic power consuming parameters like series and shunt resistances. A simple and efficient method for the extraction of a single current-voltage (I-V) curve under the constant illumination level is proposed. With the help of the Lambert W function, the explicit analytic expression for I is obtained. And the explicit analytic expression for V is obtained. This analytic expression is directly used to fit the experimental data and extract the device parameters. The I-V curve of the solar cell was expressed through the modeling using Lambert W-function and the numerical formula where there is the difficulty could be logarithmically expressed This method expresses with the I-V curve through the modeling using Lambert W-function which adds other loss ingredients to the equation2 as to the research afterward. And the solar cell goes as small and this I-V curve can predict the power penalty in the system unit.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.409-422
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• 2013

For stability design and P-${\Delta}$ analysis of steel frames with semi-rigid connections, the explicit form of the exact tangential stiffness matrix of a generalized semi-rigid frame element having rotational and translational connections is firstly derived using the stability functions. And its elastic and geometric stiffness matrix is consistently obtained by Taylor series expansion. Next depending on connection types of semi-rigidity, the corresponding tangential stiffness matrices are degenerated based on penalty method and static condensation technique. And then numerical procedures for determination of effective buckling lengths of generalized semi-rigid frames members and P-${\Delta}$ and shortly addressed. Finally three numerical examples are presented to demonstrate the validity and accuracy of the proposed method. Particularly the minimum braced frames and coupled buckling modes of the corresponding frames are investigated.

• Journal of the Korean Society for Railway
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• v.18 no.2
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• pp.149-156
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• 2015

It is necessary to determine real path for urban railway users in order to allocate revenues between urban railway organizations. Out of several algorithms used to determine optimal routes, the k-path algorithm based on link-label has been utilized. However, this k-path algorithm has certain flaws. For example, optimal route can change depending on the travel and walking time, transfer penalty, and so on. Also, this algorithm cannot take into account the characteristics of users, who prefer express train to normal trains. In this paper, an algorithm is proposed to determine one single optimal route based on the traffic card data. This method also can search for an optimal path in conjunction with the railway timetable.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.1
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• pp.116-131
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• 2013

The resistance performance of an icebreaking cargo vessel in pack ice conditions was investigated numerically and experimentally using a recently developed finite element (FE) model and model tests. A comparison between numerical analysis and experimental results with synthetic ice in a standard towing tank was carried out. The comparison extended to results with refrigerated ice to examine the feasibility of using synthetic ice. Two experiments using two different ice materials gave a reasonable agreement. Ship-ice interaction loads are numerically calculated based on the fluid structure interaction (FSI) method using the commercial FE package LS-DYNA. Test results from model testing with synthetic ice at the Pusan National University towing tank, and with refrigerated ice at the National Research Council's (NRC) ice tank, are used to validate and benchmark the numerical simulations. The designed ice-going cargo vessel is used as a target ship for three concentrations (90%, 80%, and 60%) of pack ice conditions. Ice was modeled as a rigid body but the ice density was the same as that in the experiments. The numerical challenge is to evaluate hydrodynamic loads on the ship's hull; this is difficult because LS-DYNA is an explicit FE solver and the FSI value is calculated using a penalty method. Comparisons between numerical and experimental results are shown, and our main conclusions are given.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• Smart Structures and Systems
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• v.25 no.3
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.269-276
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• 2021

Recently, smart sensors are used in various environments, and the implementation of ad-hoc sensor networks (ASNs) is a hot research topic. Unfortunately, traditional sensor network routing algorithms focus on specific control issues, and they can't be directly applied to the ASN operation. In this paper, we propose a new routing protocol by using the Q-learning technology, Main challenge of proposed approach is to extend the life of ASNs through efficient energy allocation while obtaining the balanced system performance. The proposed method enhances the Q-learning effect by considering various environmental factors. When a transmission fails, node penalty is accumulated to increase the successful communication probability. Especially, each node stores the Q value of the adjacent node in its own Q table. Every time a data transfer is executed, the Q values are updated and accumulated to learn to select the optimal routing route. Simulation results confirm that the proposed method can choose an energy-efficient routing path, and gets an excellent network performance compared with the existing ASN routing protocols.