• Interaction and multiscale mechanics
• /
• 제5권3호
• /
• pp.211-228
• /
• 2012

This paper reports the development of a generalized inverse analysis formulation for the parameter estimation of four-parameter Burger model. The analysis is carried out by formulating the problem as a mathematical programming formulation in terms of identification of the design vector, the objective function and the design constraints. Thereafter, the formulated constrained nonlinear multivariable problem is solved with the aid of fmincon: an in-built constrained optimization solver module available in MatLab. In order to gain experience, a synthetic case-study is considered wherein key issues such as the determination and setting up of variable bounds, global optimality of the solution and minimum number of data-points required for prediction of parameters is addressed. The results reveal that the developed technique is quite efficient in predicting the model parameters. The best result is obtained when the design variables are subjected to a lower bound without any upper bound. Global optimality of the solution is achieved using the developed technique. A minimum of 4-5 randomly selected data-points are required to achieve the optimal solution. The above technique has also been adopted for real-time settlement of four oil refineries with encouraging results.

• Proceedings of the KIEE Conference
• /
• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
• /
• pp.2891-2893
• /
• 2005

In this paper, we introduce a new category of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. The conventional SOFPNN algorithm leads to a tendency to produce overly complex networks as well as a repetitive computation load by the trial and error method and/or the a repetitive parameter adjustment by designer. In order to generate a structurally and parametrically optimized network, such parameters need to be optimal. In this study, in solving the problems with the conventional SOFPNN, we introduce a new design approach of evolutionary optimized SOFPNN. Optimal parameters design available within FPN (viz. the no. of input variables, the order of the polynomial, input variables, and the no. of membership function) lead to structurally and parametrically optimized network which is more flexible as well as simpler architecture than the conventional SOFPNN. In addition, we determine the initial apexes of membership functions by genetic algorithm.

• Transactions on Control, Automation and Systems Engineering
• /
• 제4권1호
• /
• pp.62-74
• /
• 2002

The tracking of a reference temperature trajectory in a polymerization batch reactor is a common problem and has critical importance because the quality control of a batch reactor is usually achieved by implementing the trajectory precisely. In this study, only energy balances around a reactor are considered as a design model for control synthesis, and material balances describing concentration variations of involved components are treated as unknown disturbances, of which the effects appear as time-varying parameters in the design model. For the synthesis of a tracking controller, a method combining the input-output linearization of a time-variant system with the parameter estimation is proposed. The parameter estimation method provides parameter estimates such that the estimated outputs asymptotically follow the measured outputs in a specified way. Since other unknown external disturbances or uncertainties can be lumped into existing parameters or considered as another separate parameters, the method is useful in practices exposed to diverse uncertainties and disturbances, and the designed controller becomes robust. And the design procedure and setting of tuning parameters are simple and clear due to the resulted linear design equations. The performances and the effectiveness of the proposed method are demonstrated via simulation studies.

• The Korean Journal of Applied Statistics
• /
• 제27권7호
• /
• pp.1151-1161
• /
• 2014

Despite the fact that the optimal design for nonlinear model depends on the unknown quantity of parameter to estimate basically, its popularity is growing in bio and engineering statistics area since all those models in the area are virtually nonlinear. In this paper we have dealt with the case when the researcher has multiple objectives in experimentation, decision among the competing models, protection against the departure from the assumed model, and the con icting interests among design criteria. To tackle these issues we attempted several new approaches which are taking advantage of the easiness of constrained optimal design. Several nonlinear models were tested.

• Journal of Korean Society for Quality Management
• /
• 제25권2호
• /
• pp.140-153
• /
• 1997

As product quality is maily determined in the product design and process design step, systematic design should be performed through parameter design and tolerance design. Therefore, we introduced analysis of variance and regression analysis as a statistical method which determine optimal levels of affective design factors to product characteristics, then we compared that process and result. In analysis of variance, variation of quality characteristics arises from noise factors, so the optimal levels of design factors are selected to minimize the effect of noise factors. In regression analysis, variation of quality characteristics aries from variation of each own design factors. As a method to reduce variation of these quality characteristics, sensitivity analysis was performed about each design factors. Through this sensitivity analysis, we represented process to calculate the interaction term of the factors.

• Journal of the Korean Institute of Telematics and Electronics
• /
• 제12권5호
• /
• pp.19-24
• /
• 1975

The problem of realizing a driving-Point impedance, the argument, $\theta$o, of which is as constant as possible over a given frequency reange was considered. An optimal design technique was applied by varying systematically the shape of the distributed element and the parameter values of the lumped elements. As a result it was possible to make the argument over two decades of frequencies within-2.5$^{\circ}$ for $\theta$o=- 30$^{\circ}$ and -60$^{\circ}$ and very flat above a certain frequency for $\theta$o=-45$^{\circ}$.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 한국건설관리학회 2008년도 정기학술발표대회 논문집
• /
• pp.638-641
• /
• 2008

Design is an iterative, generative, and multidisciplinary process by its nature. Iteration is frequent in most of the engineering design and development projects including construction. Design iterations cause rework, and extra efforts are required to get the optimal sequence and to manage the projects. Contrary to simple design, isolation of the generative iterations in complex design systems is very difficult, but reduction in overall iterations is possible. Design depends upon the information flow within domain and also among various design disciplines and organizations. Therefore, it is suggested that managers should be aware about the crucial iterations causing rework and optimal sequence as well. In this way, managers can handle design parameters related to such iterations proactively. Numbers of techniques are available to reduce iterations for various kinds of engineering designs. In this paper, parameter based Design Structure Matrix (DSM) is chosen. To create this DSM, a survey was performed and then partitioned using a model. This paper provides an easy approach to those companies involved in or intend to be involved in "design and build projects."

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
• /
• pp.629-634
• /
• 2000

Generally, the accuracy of motion control systems is strongly influenced by both the mechanical characteristics and servo characteristics of feed drive systems. In the fed drive systems of machine tools that consist of mechanical parts and electrical parts, a torsional vibration is often generated because of its elastic elements in torque transmission. Especially, a torsional vibration caused by the elasticity of mechanical elements might deteriorate the quick movement of system and lead to shorten the life time of the mechanical transmission elements. So it is necessary to analyze the electromechanical system mathematically to optimize the dynamic characteristics of the feed drive system. In this paper, based on the simplifies feed drive system model, radius errors due to position gain mismatch and servo response characteristic have been developed and an optimal criterion for tuning the gain of speed controller is discussed. The proportional and integral parameter gain of the feed drive controller are optimal design variables for the gain tuning of PI speed controller. Through the optimization problem formulation, both proportional and integral parameter are optimally tuned so as to compensate the radius errors by using the genetic algorithm. As a result, higher performance on circular profile tests has been achieved than the one with standard parameters.

• Advances in materials Research
• /
• 제13권1호
• /
• pp.1-18
• /
• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

• The KSFM Journal of Fluid Machinery
• /
• 제1권1호
• /
• pp.41-48
• /
• 1998

This study was carried out to investigate the effect of design parameters on the volume flow-rate and the noise level and to finally find the optimal design variables. Eighteen cross-flow fans were designed by the method of orthogonal array, and the flow-rate and the noise level were measured. These data were analyzed by the neural network system. The effects of eight design variables(scroll exit angle, scroll arc length et al.) on the fan performance and the noise level were valuated and discussed. This experiment shows that the design solutions suggested by neural network system may increase its volume flow-rate and reduce noise simultaneously.