• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.51-57
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• 2022

The problem of optimal stochastic GA control of the system with uncertain parameters and unsure noise covariates is studied. First, without knowing the explicit form of the dynamic system, the open-loop determinism problem with path optimization is solved. Next, Gaussian linear quadratic controllers (LQG) are designed for linear systems that depend on the nominal path. A robust genetic neural network (NN) fuzzy controller is synthesized, which consists of a Kalman filter and an optimal controller to assure the asymptotic stability of the discrete control system. A simulation is performed to prove the suitability and performance of the recommended algorithm. The results indicated that the recommended method is a feasible method to improve the performance of active tuned mass damper (ATMD) shear buildings under random earthquake disturbances.

• KDI Journal of Economic Policy
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• v.44 no.1
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• pp.77-98
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• 2022

This paper empirically verifies that the types of capital adjustment costs serve as an important mechanism in relation to investment decision-making after confirming that the investment dispersion of Korean firms is pro-cyclical and can affect business cycles. Specifically, it is found through empirical methods using corporate financial data that capital adjustment costs generally assumed to take a quadratic form in macroeconomics are asymmetric and irreversible in the Korean economy. In particular, capital adjustment costs are empirically proven to cause investment dispersion to expand given that the substitution effect of the marginal value to the marginal cost for one unit of investment in the inter-temporal investment decision is affected by that cost with regard to the resale of owned equipment assets, as opposed to new investments in equipment assets. We ultimately show, albeit indirectly, that investment dispersion can affect business cycles as capital adjustment costs influences investment decisions. What is implied is that the capital adjustment cost is not merely an exogenously deep parameter that fits the dynamics of business cycles in a macroeconomic model but could instead be a policy variable that can be endogenized through government policies.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.277-283
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• 2014

Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.

• Tunnel and Underground Space
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• v.28 no.5
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• pp.426-441
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• 2018

Recently, the generalized Hoek-Brown (GHB) failure criterion has been actively employed in various rock engineering calculations, but the analytical form of the corresponding Mohr failure envelope is not available, making it difficult to extend the application of the GHB criterion. In order to overcome this disadvantage, this study proposes a new method to express the tangential friction angle as an explicit function of normal stress by invoking the polynomial best-fitting to the relationship between normal stress and tangent friction angle implied in the GHB failure function. If this normal stress - tangential friction angle relationship is best-fitted with linear or quadratic polynomial function, it is possible to find the analytical root for tangential friction angle. Subsequently, incorporating the root into the relationship between shear stress and tangential friction angle accomplishes the derivation of the approximate Mohr envelope for the GHB criterion. It is demonstrated that the derived approximate Mohr failure envelopes are very accurate in the entire range of GSI value.

• Journal of Korea Game Society
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• v.9 no.5
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• pp.137-144
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• 2009

This paper predicts 6 facial regions that may have sharply different scattering properties, rendering the face more realistically based on their diffusion profiles. The scattering properties are acquired in the form of high dynamic range by photographing the pattern formed around an unit ray incident on facial skin. The acquired data are fitted to a 'linear combination of Gaussian functions', which well approximates the original diffusion profile of skin and has good characteristics as the filter. During the process, to prevent its solutions from converging into local minima, we take advantage of the genetic algorithm to set up the initial value. Each Gaussian term is applied to the irradiance map as a filter, expressing subsurface scattering effect. In this paper, to efficiently handle the maximum 12 Gaussian filterings, we make use of the parallel capacity of CUDA.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.2
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• pp.21-31
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• 1999

This paper presents a new possibility of calculating optimal control for large scale which consist of non-linear dynamic sub-systems using two level hierarchical structures method. And the proposed method is based on the idea of block pulse transformation to simplify the algorithm and its calculation. This algorithm used an expansion around the equilibrium point of the system to fix the second and higher order terms. These terms are compensated for iteratively at the second level by providing a prediction for the states and controls which form of a part of the higher order terms. In this new approach the quadratic penalty terms are not used in the cost function. This allows convergence over a longer time horizon and also provides faster convergence. And the method is applied to the problem of optimization of the synchronous machine. Results show that the new approach is superior to conventional numerical method or other previous algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.45-52
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• 2006

In this study, a nonlinear control by feedback linearization method, one of nonlinear control schemes based on the nonlinear model, is proposed to suppress the flutter of a supersonic composite panel using piezoelectric materials. Most of the previous panel flutter controllers are the LQR(Linear Quadratic Regulator) which is based on the linear model. A nonlinear feedback linearizing controller proposed in this study considers the nonlinear characteristics of the system model. We use the actuator implemented by piezoceramic PZT. Using the principle of virtual displacements and a finite element discretization with the conforming four-node rectangular element, we first derive the discretized dynamic equations of motion, which are transformed into a nonlinear coupled-modal equations of motion of state space form. The effectiveness of the proposed method is also compared with the LQR based on the linear model through numerical simulations in the time domain using the Newmark method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.27-35
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• 2019

There is a growing interest in various microgrid solutions that supply electricity 24 hours a day to off-grid areas where are not connected with the main grid, and Korea has many positive effects by constructing overseas microgrids as a country operating the emission trading scheme. Since it is not clear how to obtain load curves that is one of the inputs of the HOMER used to design a microgrid optimization plan, or it is necessary to examine whether electricity is supplied to the peak load level of the areas where have not received the electricity benefits from the viewpoint of the demand management, a methodology should be developed to know the load composition ratio and the shape of the daily load curve. In this paper, the relative coefficient and average load information for each load group obtained from the survey are used besides peak load and total average load. A mathematical model is proposed to derive the load composition ratio in the form of a Quadratic Programming and the load forecasting is performed using simple linear regression with future indicators. The effectiveness of the proposed method is confirmed for the Philippine island region supported by Korea Energy Agency and the Asian Development Bank.