• Structural Engineering and Mechanics
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• v.59 no.3
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• pp.455-473
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• 2016

Methods for the seismic demands evaluation of structures require iterative procedures. Many studies dealt with the development of different inelastic spectra with the aim to simplify the evaluation of inelastic deformations and performance of structures. Recently, the concept of inelastic spectra has been adopted in the global scheme of the Performance-Based Seismic Design (PBSD) through Capacity-Spectrum Method (CSM). For instance, the Modal Pushover Analysis (MPA) has been proved to provide accurate results for inelastic buildings to a similar degree of accuracy than the Response Spectrum Analysis (RSA) in estimating peak response for elastic buildings. In this paper, a simplified nonlinear procedure for evaluation of the seismic demand of structures is proposed with its applicability to multi-degree-of-freedom (MDOF) systems. The basic concept is to write the equation of motion of (MDOF) system into series of normal modes based on an inelastic modal decomposition in terms of ductility factor. The accuracy of the proposed procedure is verified against the Nonlinear Time History Analysis (NL-THA) results and Uncoupled Modal Response History Analysis (UMRHA) of a 9-story steel building subjected to El-Centro 1940 (N/S) as a first application. The comparison shows that the new theoretical approach is capable to provide accurate peak response with those obtained when using the NL-THA analysis. After that, a simplified nonlinear spectral analysis is proposed and illustrated by examples in order to describe inelastic response spectra and to relate it to the capacity curve (Pushover curve) by a new parameter of control, called normalized yield strength coefficient (${\eta}$). In the second application, the proposed procedure is verified against the NL-THA analysis results of two buildings for 80 selected real ground motions.

• Korean Journal of Optics and Photonics
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• v.17 no.3
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• pp.268-272
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• 2006

We have demonstrated the selection and the amplification of the components of an optical frequency comb using femto-second laser injection-locking technique. We used a mode-locked Ti:sapphire laser as a master laser and a single-mode diode laser as a slave laser. After passing through the interference filter with the center wavelength 794.7 nm and the transmittance bandwidth 1.5 nm, the optical frequency comb by mode-locked femto-second laser was injected into the slave laser. The injection-locked slave laser had $3{\sim}4$ multi-mode with the mode spacing 100.5 MHz, whichcorrespond to the repetition rate of a mode-locked Ti:sapphire laser. The power of the modes selected by femto-second laser injection-locking technique was amplified to several thousands times

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.84-91
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• 2003

KOMPSAT series use software-controlled unregulated bus system in which the main bus is directly connected to a battery and the duty-ratio for PWM switch is controlled by the on-board satellite software. This paper proposes a new power-stage circuit that can be available for modularization of the power regulator which is used at the software-controlled unregulated bus system satellite. And we analyze the proposed power-stage operation according to its operating modes and verify it by performing software simulation and hardware experiment using prototype. We construct a parallel-module converter which is composed of proposed power-stages and perform experiment to verify modular characteristics of the proposed power-stage. Finally, we verify the usefulness of the proposed power-stage by comparing above results with those of a parallel-module converter made of conventional power-stages.

• Geotechnical Engineering
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• v.10 no.3
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• pp.79-96
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• 1994

A tunnel design expert system entitled NESTED is developed using the artificial neural network. The expert system includes three neural network computer models designed for the stability assessment of underground openings and the estimation of correlation between the RMR and Q systems. The expert system consists of the three models and the computerized rock mass classification programs that could be driven under the same user interface. As the structure of the neural network, a multi -layer neural network which adopts an or ror back-propagation learning algorithm is used. To set up its knowledge base from the prior case histories, an engineering database which can control the incomplete and erroneous information by learning process is developed. A series of experiments comparing the results of the neural network with the actual field observations have demonstrated the inferring capabilities of the neural network to identify the possible failure modes and the support timing. The neural network expert system thus complements the incomplete geological data and provides suitable support recommendations for preliminary design of tunnels in rock masses.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.2080-2089
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• 2015

In 3D video compression, a disparity vector (DV) pointing a corresponding block position in an adjacent view is a key coding tool to exploit statistical correlation in multi-view videos. In this paper, neighboring block-based disparity vector (NBDV) is shown with detail algorithm descriptions and coding performance analysis. The proposed method derives a DV from disparity motion vector information, obtained from spatially and temporally neighboring blocks, and provides a significant coding gain about 20% BD-rate saving in a texture-video-first-coding scheme. The proposed DV derivation method is adopted into the recent 3D video coding standards such as 3D-AVC and 3D-HEVC as the state-of-the-art DV derivation method.

• Structural Engineering and Mechanics
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• v.52 no.3
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• pp.485-505
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• 2014

This study aimed to develop an approach to accurately predict the wind models and wind effects of large wind turbines. The wind-induced vibration characteristics of a 5 MW tower-blade coupled wind turbine system have been investigated in this paper. First, the blade-tower integration model was established, which included blades, nacelle, tower and the base of the wind turbine system. The harmonic superposition method and modified blade element momentum theory were then applied to simulate the fluctuating wind field for the rotor blades and tower. Finally, wind-induced responses and equivalent static wind loads (ESWL) of the system were studied based on the modified consistent coupling method, which took into account coupling effects of resonant modes, cross terms of resonant and background responses. Furthermore, useful suggestions were proposed to instruct the wind resistance design of large wind turbines. Based on obtained results, it is shown from the obtained results that wind-induced responses and ESWL were characterized with complicated modal responses, multi-mode coupling effects, and multiple equivalent objectives. Compared with the background component, the resonant component made more contribution to wind-induced responses and equivalent static wind loads at the middle-upper part of the tower and blades, and cross terms between background and resonant components affected the total fluctuation responses, while the background responses were similar with the resonant responses at the bottom of tower.

• Computers and Concrete
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• v.22 no.2
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• Steel and Composite Structures
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• v.23 no.6
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• pp.727-736
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• 2017

The application of carbon fiber reinforced polymer (CFRP) in steel structures primarily includes two categories, i.e., the bond-critical application and the contact-critical application. Debonding failure and buckling failure are the main failure modes for these two applications. Conventional electrometric techniques may not provide precise results because of the limitations associated with single-point contact measurements. A nondestructive full-field measurement technique is a valuable alternative to conventional methods. In this study, the digital image correlation (DIC) technique was adopted to investigate the bond behavior and buckling behavior of CFRP-steel composite members. The CFRP-to-steel bonded joint and the CFRP-strengthened square hollow section (SHS) steel column were tested to verify the suitability of the DIC technique. The stereo-DIC technique was utilized to measure continuous deformation. The bond-slip relationship of the CFRP-to-steel interface was derived using the DIC data. Additionally, a multi-camera DIC system consisting of four stereo-DIC subsystems was proposed and applied to the compressive test of CFRP-strengthened SHS steel column. The precise buckling location and CFRP delamination of the CFRP-strengthened SHS steel column were identified. The experimental results confirm that the stereo-DIC technique can provide effective measurements for investigating the behaviors of CFRP-steel composite members.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2165-2170
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• 2008

The cooling load in winter season is significant in many commercial buildings and hotels because of the usage of office equipments and the high efficiency of wall insulation. Therefore, the development of a multi-heat pump which can cover heating and cooling simultaneously for each indoor unit is required. In this study, the characteristics and performance of a simultaneous heating and cooling heat pump in the cooling-only and cooling-main operating mode was investigated experimentally with a variation of indoor air dry bulb temperature which is from $21^{\circ}C$ to $35^{\circ}C$. EEV opening was adjusted from 20% to 24% during the tests. When the indoor air temperature varied, the performance in the cooling-only mode was more sensitive to the temperature than in the cooling-main mode. The total capacity and COP were increased by 53.8% and 48.1%, respectively, in the cooling-main, while those were increased by 19.6% and 19.3% in the cooling-only mode. The performance differences between the two operating modes became larger at lower temperatures, especially for the COP.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.