• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.94-103
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• 2019

The optimal grasping point of the object varies depending on the shape of the object, such as the weight, the material, the grasping contact with the robot hand, and the grasping force. In order to derive the optimal grasping points for each object by a three fingered robot hand, optimal point and posture have been derived based on the geometry of the object and the hand using the artificial neural network. The optimal grasping cost function has been derived by constructing the cost function based on the probability density function of the normal distribution. Considering the characteristics of the object and the robot hand, the optimum height and width have been set to grasp the object by the robot hand. The resultant force between the contact area of the robot finger and the object has been estimated from the grasping force of the robot finger and the gravitational force of the object. In addition to these, the geometrical and gravitational center points of the object have been considered in obtaining the optimum grasping position of the robot finger and the object using the artificial neural network. To show the effectiveness of the proposed algorithm, the friction cone for the stable grasping operation has been modeled through the grasping experiments.

• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1541-1547
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• 2006

An efficient procedure to obtain the optimal stacking sequence and the minimum weight of stiffened laminated composite curved panels under several loading conditions and stiffener layouts has been developed based on the finite element method and the genetic algorithm that is powerful for the problem with integer variables. Often, designing composite laminates ends up with a stacking sequence optimization that may be formulated as an integer programming problem. This procedure is applied for a problem to find the stacking sequence having a maximum critical buckling load factor and the minimum weight. The object function in this case is the weight of a stiffened laminated composite shell. Three different types of stiffener layouts with different loading conditions are investigated to see how these parameters influence on the stacking sequence optimization of the panel and the stiffeners. It is noticed from the results that the optimal stacking sequence and lay-up angles vary depending on the types. of loading and stiffener spacing.

• Journal of the Chungcheong Mathematical Society
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• v.29 no.4
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• pp.663-676
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• 2016

In this paper we propose a new family of eighth order optimal methods for solving nonlinear equations by using weight function methods. The methods of the family require three function and one derivative evaluations per step and has order of convergence eight, and so they are optimal in the sense of Kung-Traub hypothesis. Precise analysis of convergence is given. Some members of the family are compared with several existing methods to show their performance and as a result to confirm that our methods are as competitive as compared to them.

• International Journal of Contents
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• v.7 no.3
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• pp.1-5
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• 2011

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.5
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• pp.27-37
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• 2020

Structural optimization problems with discrete design variables require more function calculations (or finite element analyses) than those in the continuous design space. In this study, a method to find an optimal solution in the discrete design of the truss structure is presented, reducing the number of function calculations. Because a continuous optimal solution is the Karush-Kuhn-Tucker point that satisfies the optimality condition, it is assumed that the discrete optimal solution is around the continuous optimum. Then, response values such as weight, displacement, and stress are predicted using approximate models-referred to as hybrid metamodels-within specified design ranges. The discrete design method using the hybrid metamodels is used as a post-process of the continuous optimization process. Standard truss design problems of 10-bar, 25-bar, 15-bar, and 52-bar are solved to show the usefulness of this method. The results are compared with those of existing methods.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of Ocean Engineering and Technology
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• v.17 no.4
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• pp.66-72
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• 2003

This paper proposes an optimum, problematic design for structural and control systems, taking a 3-D truss structure as an example. The structure is subjected to initial static loads and time-varying disturbances. The structure is controlled by a state feedback H$_{\infty}$ controller which suppress the effects of disturbances. The design variables are the cross sectional areas of truss members. The structural objective function is the structural weight. For the control objective, we consider two types of performance indices, The first function represents the effect of the initial loads. The second function is the norm of the feedback gain, These objective functions are in conflict with each other but are transformed into one control objective by the weighting method. The structural objectives is treated as the constraint, By introducing the second control objective which considers the magnitude of the feedback gain, we can create a design to model errors.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.2
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• pp.151-156
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• 2008

It is necessary to perform the research works on the general structural designs and optimum structural designs of double hull tankers and bulk carriers due to the newly built Common Structural Rules(CSR). In this study, an optimum structural design of a mid-ship part of double hull oil tanker was carried out by using the CSR. An optimum structural design program was developed by using the Pareto optimal based multi-objective function method. The hull weight and fabrication cost obtained by the single and multi-objective function methods were compared with existing ship by the consideration of CSR and material cost which is recently increasing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7377-7384
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• 2015

Agricultural tractors must have a function of ROPS to protect drivers under roll-over accident. In this study, finite element analyses and an optimal design were performed to reduce the cost and the production period of the cabin frame of a tractor to pass the ROPS strength test. To confirm the pass of ROPS strength test of an initial design model, the results of deformation and principal strain from the analyses were evaluated. To reduce the weight of the cabin frame, design of experiment of Taguchi was implemented, and an optimal design was obtained. The weight of the optimal design model was reduced by 7% comparing with the initial design model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.141-156
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• 2011

In this paper we develop a link cost function for data delivery in sensor network. Usually most conventional methods determine the optimal coefficients in the cost function without considering the surrounding environment of the node such as the wireless propagation environment or the topological environment. Due to this reason, there are limitations to improve the quality of data delivery such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of Partially Connected Neural Network (PCNN) which is modeled according to the input types whether inputs are correlated or uncorrelated. The correlated inputs are connected to the hidden layer of the PCNN in a coupled fashion but the uncoupled inputs are in an uncoupled fashion. We also propose the training technique for finding an optimal weight vector in the link cost function. The link cost function is trained to the direction that the packet transmission success ratio of each node maximizes. In the experimental section, we show that our method outperforms other conventional methods in terms of the quality of data delivery and the energy efficiency.