• Structural Engineering and Mechanics
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• v.88 no.2
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• pp.129-140
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• 2023

To increase the stiffness and strength of a reinforced concrete shear wall, steel plates are bolted to the sides of the wall. The general behavior of a composite concrete-steel shear wall is dependent on the buckling of the steel plates that should be prevented. In this paper, the unilateral buckling of steel plates of a composite shear wall is studied using the Rayleigh-Ritz method. To model the unilateral buckling of steel plate, the restraining concrete wall is described as an elastic foundation with high stiffness in compression and zero stiffness in tension. To consider the effect of bolt connections on the plate's buckling, a constrained optimization problem is solved by using Lagrange multipliers method. This process is used to obtain the critical elastic local buckling coefficients of unilaterally-restrained steel plates with various numbers of bolts, subjected to pure compression, bending and shear loading, and the interaction between them. Using these results, the spacing between shear bolts in composite steel plate shear walls is estimated and compared with the results of the AISC seismic provisions (2016). The results show that the AISC seismic provisions(2016) are overly conservative in obtaining the spacing between shear bolts.

• Structural Engineering and Mechanics
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• v.89 no.2
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• pp.213-223
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• 2024

This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory.

• Steel and Composite Structures
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• v.51 no.5
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• pp.473-485
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• 2024

This paper proposes a composite form of fuzzy adaptive control plan based on a robust observer. The fuzzy 2D control gains are regulated by the parameters in the LMIs. Then, control and learning performance indices with weight matrices are constructed as the cost functions, which allows the regulation of the trade-off between the two performance by setting appropriate weight matrices. The design of 2D control gains is equivalent to the LMIs-constrained multi-objective optimization problem under dual performance indices. By using this proposed smart tracking design via fuzzy nonlinear criterion, the data link can be further extended. To evaluate the performance of the controller, the proposed controller was compared with other control technologies. This ensures the execution of the control program used to track position and trajectory in the presence of great model uncertainty and external disturbances. The performance of monitoring and control is verified by quantitative analysis. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.1-11
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• 2007

H.264/AVC uses variable block sizes to achieve significant coding gain. It has 7 different coding modes having different motion compensation block sizes in Inter slice, and 2 different intra prediction modes in Intra slice. This fine-tuned new coding feature has achieved far more significant coding gain compared with previous video coding standards. However, extremely high computational complexity is required when rate-distortion optimization (RDO) algorithm is used. This computational complexity is a major problem in implementing real-time H.264/AVC encoder on computationally constrained devices. Therefore, there is a clear need for complexity reduction algorithm of H.264/AVC such as fast mode decision. In this paper, we propose a fast mode decision with early $P8\times8$ mode rejection based on block size activity using large block history map (LBHM). Simulation results show that without any meaningful degradation, the proposed method reduces whole encoding time on average by 53%. Also the hybrid usage of the proposed method and the early SKIP mode decision in H.264/AVC reference model reduces whole encoding time by 63% on average.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1053-1063
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• 2014

This study presents a sustainable design method that optimizes the embodied energy of concrete beam based on the concept of sustainable development that effectively utilizes natural resource and energy within the range that our succeeding generation can afford to utilize. In order to get the flexural strength carrying the ultimate load, concrete beam sections are designed by optimization that consists of the embodied energy as a objective function and the requirements of design code as constrained conditions. The sustainable design can be used to minimize the embodied energy consumed in material production, construction, operation, demolition of the infrastructure. As a result of comparison of the cost and the embodied energy optimizations based on practical beam sections, it is shown that 20% embodied energy saving and 35% $CO_2$ emission saving are achieved by sacrificing 10% cost increase. The sustainable design method provides a new effective methodology that manages the strength design concept based on cost minimization together with economic feasibility and sustainability. In addition, the method is expected to be applied to more various structural design practices.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.54-64
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• 2015

The modular construction has several advantages such as high quality of product, safe work condition and short construction duration. The manufacturing planning of modular construction should consider time frame of manufacturing, transport and erection process with limited resources (e.g., modular units, transporter and workers). The manufacturing planning of multi modular construction project manages the modular construction's characteristics and diversity of projects, as a type of modular unit, modular unit quantities, and date for delivery. However, current modular manufacturing planning techniques are weak in dealing with resource interactions and each project requirement in multi modular construction project environments. Inefficient allocation of resources during multi modular construction project may cause delays and cost overruns to construction operation. In this circumstance, this research suggest a manufacturing planning model for schedule optimization of multi project of modular construction, using genetic algorithm as one of the powerful method for schedule optimization with multiple constrained resources. Comparing to the result of the existed schedule of case study, setting optimized scheduling for multi project decrease the total factory producing schedule. By using proposed optimization tool, efficient allocation of resource and saving project time is expected.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.2
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• pp.97-104
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• 2018

In this study, a structure satisfying the fatigue constraint is designed by applying the topology optimization based on the phase field design method. In order to predict life based on the stress value, high cycle fatigue failure theory in which stress acts within the range of elastic limit is discussed and three fatigue theories of modified-Goodman, Smith-Watson-Topper and Gerber theory are applied. To calculate the global maximum stress, a modified P-norm stress correction method is used. As a result, it is possible to obtain topology optimization results that minimize the volume while satisfying the fatigue constraints. By applying the phase field design method, a simple shape with a minimized gray scale was obtained, and the maximum stress value acting on the optimization result became very close to the allowable stress value due to the modified P-norm stress method. While previous studies does not consider the stress correction factor, this study proposes the determination method regarding the stress correction factor considering loading effects related to axial stress components.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.463-472
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• 2000

In this paper, we consider the problem of realizing associative memories via cellular neural network(CNN). After introducing qualitative properties of the CNN model, we formulate the synthesis of CNN that can store given binary vectors with optimal performance as a constrained optimization problem. Next, we observe that this problem's constraints can be transformed into simple inequalities involving linear matrix inequalities(LMIs). Finally, we reformulate the synthesis problem as a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. Proposed method can be applied to both space varying template CNNs and space-invariant template CNNs. The validity of the proposed approach is illustrated by design examples.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.1032-1037
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• 2012

Augmented reality (AR) is augmented virtual information on the real world with real-time. And user can interact with information. In this paper, Marker-less tracking algorithm has been studied, for implement the augmented reality system on a mobile environment. In marker-less augmented reality, users do not need to attach the markers, and constrained the location. So, it's convenient to use. For marker-less tracking, I use the SURF algorithm based on feature point extraction in this paper. The SURF algorithm can be used on mobile devices because of the computational complexity is low. However, the SURF algorithm optimization work is not suitable for mobile devices. Therefore, in this paper, in order to the suitable tracking in mobile devices, the SURF algorithm was tested in a variety of environments. And ways to optimize has been studied.