• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.83-97
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• 2002

An experimental model which considers post-peak behaviors and pre-peak damage characteristics representing changes of elastic moduli in each damage level was developed. From experiments, some damage thresholds of rocks were determined, and regression analyses were carried out in order to represent changes of elastic moduli in each damage level as functions of confining pressure. In addition, it was intended to simulate post-peak behaviors with Hoek-Brown constants, $m_r\;and\;s_r$ for post-failure. The developed experimental model was implemented into $FLAC^{2D}$ by a FISH function. From results of parametric studies on Hoek-Brown constants for post-peak, it was revealed that uniaxial compressive strength more highly depends upon $s_r$, although it depends on both $m_r\;and\;s_r$. It was also shown that the post-peak slopes of stress-stain curves depend mainly on $m_r$. When the optimum models obtained from parametric studies were applied to numerical analysis, they predicted maximum strengths obtained from experiments and well simulated stiffness changes due to damage levels.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.1
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• pp.31-37
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• 2013

This study aims to find the optimal skirt dimensions for a composite pressure vessel with a separated dome part. The size optimization for the skirt structure of the composite pressure vessel was conducted using a sub-problem approximation method and batch processing codes programmed using ANSYS Parametric Design Language (APDL). The thickness and length of the skirt part were selected as design variables for the optimum analysis. The objective function and constraints were chosen as the weight and the displacement of the skirt part, respectively. The numerical results showed that the weight of the skirt of a composite pressure vessel with a separated dome part could be reduced by a maximum of 4.38% through size optimization analysis of the skirt structure.

• Steel and Composite Structures
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• v.45 no.2
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• pp.175-191
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• 2022

A review of previous studies shows that although there is a considerable difference between buckling loads of structures under follower and non-follower lateral loads, only the buckling load of FGM elliptical cylindrical shell under non-follower lateral load was investigated in the literature. This study is the first to obtain the buckling load of elliptical FGM cylindrical shells under follower lateral load and also make a comparison between buckling loads of elliptical FGM cylindrical shells under follower and non-follower lateral loads. Moreover, this research is the first one to derive the load potential function of elliptical cylindrical shell. In this regard, the FGM cylindrical elliptical shell was modeled using the semi-analytical finite strip method and based on the First Shear Deformation Theory (FSDT). The shell is discretized by strip elements aligned in the longitudinal direction. The Lagrangian and harmonic shape functions were considered in the circumference and longitudinal directions, respectively. The buckling pressure of the shell under follower and non-follower lateral loads was obtained from eigenvalue problem. The results obtained from the model were compared with those presented in the literature to evaluate the validity of the model. A comparison index was defined to compare the buckling loads of the shell under follower and non-follower lateral load. A parametric study was carried out to investigate the effects of material properties and shell geometry characteristics on the comparison index. For the elliptical cylindrical shells with length-to-radius ratio greater than 16 and major-to-minor axis ratio greater than 0.6, the comparison index reaches to more than 20 percent which is significant. Moreover, the maximum difference is about 30 percent in some cases. The results obtained from the parametric study indicate that the buckling load of long elliptical cylindrical shell under non-follower load is not reliable.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1125-1138
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• 2014

In this study, we applied an advanced non-parametric low-flow frequency analysis using boundary kernel by Representative Concentration Pathways (RCPs) climate change scenarios through Arc-SWAT long-term runoff model simulation at the Gwangdong storage reservoir located in Taeback, Gangwondo. The results show that drought frequency under RCPs was expected to increase due to reduced runoff during the near future, and the variation of low-flow time series was appeared greatly under RCP8.5 scenario, respectively. The result from drought frequency of Median flow in the near future (2030s) compared historic period, the case of 30-year low-flow frequency was increased (the RCP4.5 shows +22.4% and the RCP8.5 shows +40.4%), but in the distant future (2080s) expected increase of drought frequency due to the reduction of low-flow (under RCP4.5: -4.7% and RCP8.5: -52.9%), respectively. In case of Quantile 25% flow time series data also expected that the severe drought frequency will be increased in the distant future by reducing low-flow (the RCP4.5 shows -20.8% to -60.0% and the RCP8.5 shows -30.4% to -96.0%). This non-parametric low-flow frequency analysis results according to the RCPs scenarios have expected to consider to take advantage of as a basis data for water resources management and countermeasures of climate change in the mid-watershed over the Korean Peninsula.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.95-100
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• 1995

In this study, we have evaluated the effect of amplitude and frequency perturbation of EGG signal for single vowels associated with laryngeal pathology. The normal EGG signal was properly characterized by an autoregressive model which has an optimal order of ninth using the parametric method. This can be analyzed by determining the transfer function. Perturbations in the fundamental pitch and in the peak amplitude of EGG signal measured with a four-electrode system using the modulation/demodulation techniques were investigated for the purpose of developing a decision criteria for the laryngeal function analysis. The abnormal EGG signal has nonperiodic and unstable characteristics. It can be discriminated by the calculation of opening and closing time of glottis using the EGG signal. In case of normal and abnormal subjects, m$\pm$0.5*sd was discriminating line for frequency perturbation and m$\pm$2*sd for normal amplitude perturbations, respectively. Also, The normal and abnormal cases of the subjects can be discriminated effectively using the pattern of attractor derived with Hilbert transform of EGG signal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.575-594
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• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.33-40
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• 2023

With the development of supercomputing technology and hardware technology, numerical computation methods are also being advanced. Accordingly, improved weather prediction becomes possible. In this paper, we propose to apply the LAPACK(Linear Algebra PACKage) BLAS(Basic Linear Algebra Subprograms) library to the linear algebraic numerical computation part within the source code to improve the performance of the cumulative parametric code, Unicon(A Unified Convection Scheme), which is included in SCAM(Single-Columns Atmospheric Model, simplified version of CESM(Community Earth System Model)) and performs standby operations. In order to analyze this, an overall execution structure diagram of SCAM was presented and a test was conducted in the relevant execution environment. Compared to the existing source code, the SCOPY function achieved 0.4053% performance improvement, the DSCAL function 0.7812%, and the DDOT function 0.0469%, and all of them showed a 0.8537% performance improvement. This means that the LAPACK BLAS application method, a library for high-density linear algebra operations proposed in this paper, can improve performance without additional hardware intervention in the same CPU environment.

• Coupled systems mechanics
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• v.13 no.4
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• pp.293-310
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• 2024

This paper investigates the dynamic response of structures during earthquakes and provides a clear understanding of soil-structure interaction phenomena. It analyses various parameters, comprising ground shear wave velocity and structure properties. The effect of soil impedance function form on the structural response of the system through the use of springs and dashpots with two frequency cases: independent and dependent frequencies. The superstructure and the ground were modeled linearly. Using the substructure method, two different approaches are used in this study. The first is an analytical formulation based on the dynamic equilibrium of the soil-structure system modeled by an analog model with three degrees of freedom. The second is a numerical analysis generated with 2D finite element modeling using ABAQUS software. The superstructure is represented as a SDOF system in all the SSI models assessed. This analysis establishes the key parameters affecting the soil-structure interaction and their effects. The different results obtained from the analysis are compared for each studied case (frequency-independent and frequency-dependent impedance functions). The achieved results confirm the sensitivity of buildings to soil-structure interaction and highlight the various factors and effects, such as soil and structure properties, specifically the shear wave velocity, the height and mass of the structure. Excitation frequency, and the foundation anchoring height, also has a significant impact on the fundamental parameters and the response of the coupled system at the same time. On the other hand, it have been demonstrated that the impedance function forms play a critical role in the accurate evaluation of structural behavior during seismic excitation. As a result, the evaluation of SSI effects on structural response must take into account the dynamic properties of the structure and soil accordingly.