• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.5
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• pp.11-24
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• 2011

A real-time hybrid test of a 3 story-3 bay reinforced concrete frame which is divided into numerical and physical substructure models under uniaxial earthquake excitation was run using a fixed iteration implicit HHT time integration method. The first story inner non-ductile column was selected as the physical substructure model, and uniaxial earthquake excitation was applied to the numerical model until the specimen failed due to severe damage. A finite-element analysis program, Mercury, was newly developed and optimized for a real-time hybrid test. The drift ratio based on the top horizontal displacement of the physical substructure model was compared with the result of a numerical simulation by OpenSees and the result of a shaking table test. The experiment in this paper is one of the most complex real-time hybrid tests, and the description of the hardware, algorithm and models is presented in detail. If there is an improvement in the numerical model, the evaluation of the tangent stiffness matrix of the physical substructure model in the finite element analysis program and better software to reduce the computational time of the element state determination for the force-based beam-column element, then the comparison with the results of the real-time hybrid test and the shaking table test deserves to make a recommendation. In addition, for the goal of a "Numerical simulation of the complex structures under dynamic loading", the real time hybrid test has enough merit as an alternative to dynamic experiments of large and complex structures.

• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• ETRI Journal
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• v.43 no.2
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• pp.357-370
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• 2021

The evolution of blockchain-based systems has enabled researchers to develop nextgeneration e-voting systems. However, the classical consensus method of blockchain, that is, Proof-of-Work, as implemented in Bitcoin, has a significant impact on energy consumption and compromises the scalability, efficiency, and latency of the system. In this paper, we propose a hybrid consensus model (PSC-Bchain) composed of Proof of Credibility and Proof of Stake that work mutually to address the aforementioned problems to secure e-voting systems. Smart contracts are used to provide a trustworthy public bulletin board and a secure computing environment to ensure the accuracy of the ballot outcome. We combine a sharding mechanism with the PSC-Bchain hybrid approach to emphasize security, thus enhancing the scalability and performance of the blockchain-based e-voting system. Furthermore, we compare and discuss the execution of attacks on the classical blockchain and our proposed hybrid blockchain, and analyze the security. Our experiments yielded new observations on the overall security, performance, and scalability of blockchain-based e-voting systems.

• Steel and Composite Structures
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• v.34 no.5
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• pp.715-732
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• 2020

High-strength steel composite Y-eccentrically braced frame (Y-HSS-EBF) is a novel structural system. In this study, the spatial substructure hybrid simulation test (SHST) method is used to further study the seismic performance of Y-HSS-EBF. Firstly, based on the cyclic loading tests of two single-story single-span Y-HSS-EBF planar specimens, a finite element model in OpenSees was verified to provide a reference for the numerical substructure analysis model for the later SHST. Then, the SHST was carried out on the OpenFresco test platform. A three-story spatial Y-HSS-EBF model was taken as the prototype, the top story was taken as the experimental substructure, and the remaining two stories were taken as the numerical substructure to be simulated in OpenSees. According to the test results, the validity of the SHST was verified, and the main seismic performance indexes of the SHST model were analyzed. The results show that, the SHST based on the OpenFresco platform has good stability and accuracy, and the results of the SHST agree well with the global numerical model of the structure. Under strong seismic action, the plastic deformation of Y-HSS-EBF mainly occurs in the shear link, and the beam, beam-columns and braces can basically remain in the elastic state, which is conducive to post-earthquake repair.

• Wind and Structures
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• v.24 no.4
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• pp.385-403
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• 2017

In recent years, the wind energy has played an increasingly important role in national energy sector of many countries. To harvest more electric power, the wind turbine (WT) tower structure becomes physically larger, which may cause more risks during long-term operation. Associated with the great development of WT projects, the number of accidents related to large-scaled WT has also been increased. Therefore, a structural health monitoring (SHM) system for WT structures is needed to ensure their safety and serviceability during operational time. The objective of this study is to develop a hybrid damage detection method for WT tower structures by measuring vibration and impedance responses. To achieve the objective, the following approaches are implemented. Firstly, a hybrid damage detection scheme which combines vibration-based and impedance-based methods is proposed as a sequential process in three stages. Secondly, a series of vibration and impedance tests are conducted on a lab-scaled model of the WT structure in which a set of bolt-loosening cases is simulated for the segmental joints. Finally, the feasibility of the proposed hybrid damage detection method is experimentally evaluated via its performance during the damage detection process in the tested model.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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• pp.299-312
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• 2016

The main objective of this paper is to develop computer simulation program for performance evaluation and cost estimation of a reverse osmosis (RO) and pressure-retarded osmosis (PRO) hybrid process to propose guidelines for its economic competitiveness use in the field. A solution-diffusion model modified with film theory and a simple cost model was applied to the simulation program. Using the simulation program, the effects of various factors, including the Operating conditions, membrane properties, and cost parameters on the RO and RO-PRO hybrid process performance and cost were examined. The simulation results showed that the RO-PRO hybrid process can be economically competitive with the RO process when electricity cost is more than 0.2 $/kWh, the PRO membrane cost is same as RO membrane cost, the power density is more than $8W/m^2$ and PRO recovery is same as 1/(1-RO recovery).

• Smart Structures and Systems
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• v.31 no.5
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• pp.437-454
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• 2023

Real-time hybrid simulation (RTHS), which has the advantages of a substructure pseudo-dynamic test, is widely used to investigate the rate-dependent mechanical response of structures under earthquake excitation. However, time delay in RTHS can cause inaccurate results and experimental instabilities. Thus, this study proposes a model-based adaptive control strategy using a Kalman filter (KF) to minimize the time delay and improve RTHS stability and accuracy. In this method, the adaptive control strategy consists of three parts-a feedforward controller based on the discrete inverse model of a servohydraulic actuator and physical specimen, a parameter estimator using the KF, and a feedback controller. The KF with the feedforward controller can significantly reduce the variable time delay due to its fast convergence and high sensitivity to the error between the desired displacement and the measured one. The feedback control can remedy the residual time delay and minimize the method's dependence on the inverse model, thereby improving the robustness of the proposed control method. The tracking performance and parametric studies are conducted using the benchmark problem in RTHS. The results reveal that better tracking performance can be obtained, and the KF's initial settings have limited influence on the proposed strategy. Virtual RTHSs are conducted with linear and nonlinear physical substructures, respectively, and the results indicate brilliant tracking performance and superb robustness of the proposed method.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.1
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• pp.9-15
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• 2012

An unsteady internal ballistic performance model was proposed to take account for the variance of local regression rate along the grain port of a hybrid rocket combustor. The characteristic parameters of hybrid rocket motor was investigated. The performance model of concern in the study was fairly comparable with the test result. The combustion coefficients and local burning characteristics of a hybrid rocket motor were evaluated. The local variation of the oxidizer mass flow rate results in the changes of local regression rate, pressure, temperature, and gas velocity to flow direction, which was analyzed quantitatively.