• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1507-1516
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• 2001

An analytical method is presented for evaluation of the steady state periodic behavior of nonlinear structural systems. This method is based on the substructure synthesis formulation and a harmonic balance procedure, which is applied to the analysis of nonlinear responses. A complex nonlinear system is divided into substructures, of which equations are approximately transformed to modal coordinates including nonlinear term under the reasonable procedure. Then, the equations are synthesized into the overall system and the nonlinear solution for the system is obtained. Based on the harmonic balance method, the proposed procedure reduces the size of large degrees-of-freedom problem in the solving nonlinear equations. Feasibility and advantages of the proposed method are illustrated using the study of the nonlinear rotating machine system as a large mechanical structure system. Results obtained are reported to be an efficient approach with respect to nonlinear response prediction when compared with other conventional methods.

• Journal of KSNVE
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• v.10 no.1
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• pp.144-152
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• 2000

The identification of a nonlinear vibration system based on the time domain parametric model has been widely studied in recent years. In most of the studies, the NARMAX model has been used for the identification of a nonlinear system. However, the computational load for the identification with this model is quite heavy. In this paper, a new modeling procedure for nonlinear system identification in discrete time domain is proposed. The proposed model has less initial nonlinear terms than NARMAX model, and the terms in the proposed model are derived from physically meaningful way. The performance of the proposed method is evaluated through the simulation, and the result shows that the proposed model can identify the nonlinear characteristics of the vibration system very will less computational effort.

• International Journal of Highway Engineering
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• v.19 no.4
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

• Earthquakes and Structures
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• v.9 no.2
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• pp.375-390
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• 2015

Staircase is a vertical transportation element commonly used in every multistoried structure. Inclined flights of staircase are usually casted monolithically with RC frame. The structural configuration of stairs generally introduces discontinuities into the typical regular reinforced concrete frame composed of beams and columns. Inclined position of flight transfers both vertical as well as horizontal forces in the frame. Under lateral loading, staircase in a multistory RC frame building develops truss action creating a local stiffening effect. In case of seismic event the stiff area around staircase attracts larger force. Therefore, special attention is required while modeling and analyzing the building with staircase. However, in general design practice, designers usually ignore the staircase while modeling either due to ignorance or to avoid complexity. A numerical study has been conducted to examine the effect of ignoring staircase in modeling and design of RC frame buildings while they are really present in structure, may be at different locations. Linear dynamic analysis is performed on nine separate building models to evaluate influence of staircase on dynamic characteristics of building, followed by nonlinear static analysis on the same models to access their seismic performance. It is observed that effect of ignoring staircase in modeling is severe and leads to unsafe structure. Effect of location and orientation of staircase is also important in determining seismic performance of RC frame buildings.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.41-55
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• 1995

In this paper, an optimal idenfication method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together wlth optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzzy-neural networks(FNNs) are tuned automatically using improved modified complex method and modified learning algorithm. For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activateti sluge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.791-793
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• 2000

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantage of fuzzy approach over traditional ones lies on the fact that fuzzy system does not require a detail mathematical description of the system while modeling. As modeling method. the Group Method of Data Handling(GMDH) is introduced by A.G. Ivakhnenko GMDH is an analysis technique for identifying nonlinear relationships between system's inputs and output. We study a Novel Neuro-Fuzzy Network (NNFN) in this paper. NNFN is a network resulting from the combination of a fuzzy inference system and polynomial neural network(PNN) (7) which is advanced structure of GMDH. Simulation involve a series of synthetic as well as experimental data used across various neurofuzzy systems.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.243-246
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• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.473-473
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• 2011

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily reference evapotranspiration (ETo) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it consists of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily ETo data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as ETo modeling can be generalized using GMDH-NNM.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1642-1651
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• 2008

The dynamic behavior of power systems is affected by the interactions between linear and nonlinear components. To analyze those complicated power systems, the linear approaches have been widely used so far. Especially, a synchronous generator has been designed by using linear models and traditional techniques. However, due to its wide operating range, complex dynamics, transient performances, and its nonlinearities, it cannot be accurately modeled as linear methods based on small-signal analysis. This paper describes an application of the Takaki-Sugeno (T-S) fuzzy method to model the synchronous generator in a single-machine infinite bus (SMIB) system. The T-S fuzzy model can provide a highly nonlinear functional relation with a comparatively small number of fuzzy rules. The simulation results show that the proposed T-S fuzzy modeling captures all dynamic characteristics for the synchronous generator, which are exactly same as those by the conventional modeling method.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05a
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• pp.159-162
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• 2005

To overcome the drawbacks of conventional load-control method and displacement-control method, the so-called volume-control method was developed by utilizing a pressure node added into a layered shell element. The pressure node has an increment of pressure as an additional degree of freedom of the shell element. In this study, the hollow RC columns are discretized with multi-layered shell elements and a modeling technique utilizing the volume-control analysis for various hollow RC column structures is introduced. The results of the nonlinear analysis using the modeling for hollow RC columns subjected to lateral reversed cyclic loading as well as lateral loading under compression are shown. Validity of the modeling technique is also verified by comparing the analysis results with experimental results and other analysis data.