• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.2
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• pp.304-313
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• 2012

Uncertainty increases as a result of environment change and change of individual decision-making in extreme weather. This study consider individual decision-making which has been not covered until now. The purpose of this study is making Agent-Based Model to predict it more accurate that how much change travel demand in heavy rain and heavy snow. Through this model, it can be utilized to forecast travel demand, changes in travel behavior and traffic patterns. It will be also possible to predict discomfort index and risk of accidents.

• Journal of the Korean Magnetics Society
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• v.23 no.2
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• pp.62-67
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• 2013

This paper introduces an effective methodology for reliability-based design optimization of electromagnetic products, where a performance measure approach is adopted to accurately assess probabilistic constrains. Two design problems consisting of a loudspeaker and a superconducting magnetic energy storage system are considered. The efficiency of the proposed method in evaluating the failure probability of performances during the optimization process are compared with the existing method based on the reliability index approach. Moreover, in term of the accuracy of probability failure values, optimized design results are examined with reference values obtained from the Monte Carlo simulation.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.185-195
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• 2020

This study examines the association between governance quality at country level and stock market performance. Specifically, the study investigates the influence of control of corruption, government effectiveness, political stability and absence of violence, rule of law, regulatory quality, and voice and accountability on all-share index of the stock markets of the six Gulf Cooperation Council (GCC) countries. This study is anchored on two theories - the Efficient Market Hypothesis (EMH) and Institutional Theory. The study employs panel data spanning from 2006 to 2017. The findings show that political stability and absence of violence and rule of law exhibit a significant positive impact on stock market performance, while regulatory quality and voice and accountability have a significant, but negative relationship with stock market performance. The results imply that quality of governance in terms of rule of law and political stability devoid of violence have strong impact on stock market returns. Similarly, improved stock market returns are largely dependent on the efficiency of the institutional environment of market as investors are always wary of the inherent risks associated with the uncertainty of the market. This study has crucial policy implications for the government of the GCC countries and stock market participants.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Convergence Security Journal
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• v.8 no.4
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• pp.97-104
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• 2008

Recently, the wireless positioning techniques and mobile computing techniques have rapidly developed to use location data of moving objects. The more the number of moving objects is numerous and the more periodical sampling of locations is frequent, the more location data of moving objects become very large. Hence the system should be able to efficiently manage mass location data, support various spatio-temporal queries for LBS, and solve the uncertainty problem of moving objects. Therefore, in this paper, innovating the location data of moving object effectively, we propose Rend 3DR-tree method to decrease the dead space and complement the overlapping of nodes by utilizing 3DR-tree with the indexing structure to support indexing of current data and history data.

• Structural Engineering and Mechanics
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• v.55 no.5
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• pp.979-999
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• 2015

This paper proposes a novel reliability analysis method which computes reliability index, most probable point and probability of failure of uncertain systems more efficiently and accurately with compared to Monte Carlo, first-order reliability and response surface methods. It consists of Initial and Simulation steps. In Initial step, a number of space-filling designs are selected throughout the variables space, and then in Simulation step, performances of most of samples are estimated via interpolation using the space-filling designs, and only for a small number of the samples actual performance function is used for evaluation. In better words, doing so, we use a simple interpolation function called "reduced" function instead of the actual expensive-to-evaluate performance function of the system to evaluate most of samples. By using such a reduced function, total number of evaluations of actual performance is significantly reduced; hence, the method can be called Reduced Function Evaluations method. Reliabilities of six examples including series and parallel systems with multiple failure modes with truncated and/or non-truncated random variables are analyzed to demonstrate efficiency, accuracy and robustness of proposed method. In addition, a reliability-based design optimization algorithm is proposed and an example is solved to show its good performance.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.4
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• pp.217-224
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• 2003

The objective of this paper is to suggest the technique of program to perform structural optimization design after reliability analysis to consider the uncertainties of structural reponses. AFOSM method is used for reliability analysis then, structural optimization design is developed for 10-bar truss and 3 span 10 stories planar frame model is subject to reliability indices and probability of failure by reliability analysis. SQP method is used for optimization design method, this method has many attractions. As a result of analyzing with having and not having constraints and uncertainty, the minimum weight of truss and planar frame increased respectively 20.92% and average 8.08%.

• Structural Monitoring and Maintenance
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• v.2 no.2
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• pp.115-132
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• 2015

This paper presents a new method for seismic vulnerability assessment of buildings with reference to their operational limit state. The importance of this kind of evaluation arises from the civil protection necessity that some buildings, considered strategic for seismic emergency management, should retain their functionality also after a destructive earthquake. The method is based on the identification of experimental modal parameters from ambient vibrations measurements. The knowledge of the experimental modes allows to perform a linear spectral analysis computing the maximum structural drifts of the building caused by an assigned earthquake. Operational condition is then evaluated by comparing the maximum building drifts with the reference value assigned by the Italian Technical Code for the operational limit state. The uncertainty about the actual building seismic frequencies, typically significantly lower than the ambient ones, is explicitly taken into account through a probabilistic approach that allows to define for the building the Operational Index together with the Operational Probability Curve. The method is validated with experimental seismic data from a permanently monitored public building: by comparing the probabilistic prediction and the building experimental drifts, resulting from three weak earthquakes, the reliability of the method is confirmed. Finally an application of the method to a strategic building in Italy is presented: all the procedure, from ambient vibrations measurement, to seismic input definition, up to the computation of the Operational Probability Curve is illustrated.