• ETRI Journal
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• 제15권2호
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• Steel and Composite Structures
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• 제14권4호
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• pp.335-347
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• 2013

In this paper, the nonlinear cylindrical bending behavior of functionally graded nanocomposite plates reinforced by single-walled carbon nanotubes (SWCNTs) is studied using an efficient and simple refined theory. This theory is based on assumption that the in-plane and transverse displacements consist of bending and shear components in which the bending components do not contribute toward shear forces and, likewise, the shear components do not contribute toward bending moments. The material properties of SWCNTs are assumed to be temperature-dependent and are obtained from molecular dynamics simulations. The material properties of functionally graded carbon nanotube-reinforced composites (FG-CNTCRs) are assumed to be graded in the thickness direction, and are estimated through a micromechanical model. The fundamental equations for functionally graded nanocomposite plates are obtained using the Von-Karman theory for large deflections and the solution is obtained by minimization of the total potential energy. The numerical illustrations concern the nonlinear bending response of FG-CNTRC plates under different sets of thermal environmental conditions, from which results for uniformly distributed CNTRC plates are obtained as comparators.

• 한국산업융합학회 논문집
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• 제11권3호
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• pp.127-134
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• 2008

This study is to develop the structure analysis and optimization algorithm of the strut supported temporary structure for underground constructions. Developed algorithm performs the analysis and the optimization of each strut, wale, and H pile of temporary structures separately. The design variables of nonlinear optimization consist of the cross-sections of temporary structures such as strut, wale, and H pile and the solution of the nonlinear programming is searched using for the method of successive unconstranint minimization technique. The weight of the structure is used for the object function of nonlinear programming. the constraints are derived from the specification of the temporary structures as compressive axial, bending, shear, composite stress and serviceability. The structural analysis is performed based on the elastoplastic beam theory. This developed program can be used to evaluate the applicability, convergence, and effectiveness of the temporary structures.

• Earthquakes and Structures
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• 제22권1호
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• pp.95-107
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• 2022

This paper introduces a novel, rigorous, and efficient probabilistic methodology for the performance-based optimal design (PBOD) of semi-active tuned mass damper (SATMD) for seismically excited nonlinear structures. The proposed methodology is consistent with the modern performance-based earthquake engineering framework and aims to design reliable control systems. To this end, an optimization problem has been defined which considers the parameters of control systems as design variables and minimization of the probability of exceeding a targeted structural performance level during the lifetime as an objective function with a constraint on the failure probability of stroke length damage state associated with mass damper mechanism. The effectiveness of the proposed methodology is illustrated through a numerical example of performance analysis of an eight-story nonlinear shear building frame with hysteretic bilinear behavior. The SATMD with variable stiffness and damping have been designed separately with different mass ratios. Their performance has been compared with that of uncontrolled structure and the structure controlled with passive TMD in terms of probabilistic demand curves, response hazard curves, fragility curves, and exceedance probability of performance levels during the lifetime. Numerical results show the effectiveness, simplicity, and reliability of the proposed PBOD method in designing SATMD with variable stiffness and damping for the nonlinear frames where they have reduced the exceedance probability of the structure up to 49% and 44%, respectively.

• 한국연소학회지
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• 제3권1호
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• pp.21-29
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• 1998

Mathematical modeling of combustion characteristics in HVOF thermal spray processes was carried out on the basis of equilibrium chemistry. The main objective of this work was the development of a computation code which allows to determine chemical composition of combustion products, adiabatic flame temperature, thermodynamic and transport properties. The free energy minimization method was employed with the descent Newton-Raphson technique for numerical solution of systems of nonlinear thermochemical equations. Adiabatic flame temperature was calculated by using a Newton#s iterative method incorporating the computation module of chemical composition. The performance of this code was verified by comparing computational results with data obtained by ChemKin code and in the literature. Comparisons between the calculated and measured flame temperatures showed a deviation less than 2%. It was observed that adiabatic flame temperature augments with increase in combustion pressure; the influence was significant in the region of low pressure but becomes weaker and weaker with increase in pressure. Relationships of adiabatic flame temperature, dissociation ratio and combustion pressure were also analyzed.

• Advances in Energy Research
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• 제4권4호
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• pp.255-274
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• 2016

This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

• 대한기계학회논문집A
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• 제24권3호
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• pp.661-666
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• 2000

Optimization of the elastic joint of train is performed according to the minimization of ten responses which represent driving safety and ride comfort of train and analyzed by using the each response se surface model from stochastic design of experiments. After the each response surface model is constructed, the main effect and sensitivity analyses are successfully performed by 2nd order approximated regression model as described in this paper. We can get the optimal solutions using by nonlinear programming method such as simplex or interval optimization algorithms. The response surface models and the optimization algorithms are used together to obtain the optimal design of the elastic joint of train. the ten 2nd order polynomial response surface models of the three translational stiffness of the elastic joint (design factors) are constructed by using CCD(Central Composite Design) and the multi-objective optimization is also performed by applying min-max and distance minimization techniques of relative target deviation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.859-865
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• 2014

Optimization of an electric machine is mainly a nonlinear multi-modal problem. For the optimization of the multi-modal problem, many function calls are required with much consumption of time. To address this problem, this paper proposes a novel hybrid algorithm in which function calls are less than conventional methods. Specifically, the proposed method uses the kriging metamodel and the fill-blank technique to find an approximated solution in a whole problem region. To increase the convergence speed in local peaks, a parallel gradient assisted simplex method is proposed and combined with the kriging meta-model. The correctness and usefulness of the proposed hybrid algorithm is verified through a mathematical test function and applied into the practical optimization as the cogging torque minimization for an interior permanent magnet synchronous machine.

• 전기학회논문지
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• 제65권8호
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• pp.1347-1354
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• 2016

This paper presents optimal operational scheduling model and economic assessment of Li-ion battery energy storage systems installed in non-residential customers. The operation schedule of a BESS is determined to minimize electric bill, which is composed of demand and energy charges. Dynamic programming is introduced to solve the nonlinear optimization problem. Based on the optimal operation schedule result, the economics of a BESS are evaluated in the investor and the social perspective respectively. Calculated benefits in the investor or customer perspective are the savings of demand charge, energy charge, and related taxes. The social benefits include fuel cost savings of generating units, construction deferral effects of the generation capacity and T&D infra, and incremental CO2 emission cost impacts, etc. Case studies are applied to an large industrial customer that shows similarly repeated load patterns according to days of the week.