• 한국CDE학회논문집
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• 제12권3호
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• pp.163-170
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• 2007

A torque converter, connected to a transmission/transaxle input shaft, connects, multiplies and interrupts the flow of engine torque into the transmission. Damper springs are usually equipped in a torque converter to convert stably the torque power supplied from engine. Damper Springs generally have the most flexible design variables among vehicle transmission parts, so that they could be effective design factors to improve the entire vehicle's performance. Damper spring, however, has geometric complexity after it equipped in a torque converter. For that reason, modeling a damper spring requires expert's knowledge to determine many design parameters and satisfy the functional requirements at the same time. In this paper, we introduce an optimum design method applied in detailed-design stage to reduce design process and financial loss caused by adequate design. Many design variables have to be classified and structuralized for Optimization. This also could make designer concentrate on functional requirements of damper spring, not on design possibility. In addition, modeling an assembled spring has technical restriction with primitives of the current major CAD solutions because of complexity of assembled spring shape. Thus, one of modeling solution presented in this paper since detailed and exact modeling is important for CAE or DMU.

• 한국태양에너지학회 논문집
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• 제23권3호
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• pp.7-14
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• 2003

Absorption potential of desiccant solution significantly decreases after absorbing moisture from humid air, and a regeneration process requires a great amount of energy to recover absorption potential of desiccant solution. In an effort to develop an energy efficient regenerator, this study examines a regeneration process using hot air heated by solar radiation to recover absorption potential by evaporating moisture in liquid desiccant. More specifically, this study is aimed at finding the optimum operating condition of the regenerator by utilizing a well-established statistical tool, so-called response surface methodology(RSM), which may provide a functional relationship between independent and dependent variables. It is demonstrated that an optimization model to find the optimum operating condition can be obtained using the functional relationship between regeneration rate and affecting factors which is approximated on the basis experimental results.

• 전자공학회논문지A
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• 제32A권6호
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• pp.104-110
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• 1995

In this paper, an effective method for the minimization of two-level Reed-muller expressions by cube selection whcih considers functional complexity is presented. In contrast to the previous methods which use Xlinking operations to join two cubes for minimizatio, the cube selection method tries to select cubes one at a time until they cover the ON-set of the given function. This method works for most benchmark circuits, but for parity-type functions it shows power performance. To solve this problem, a cost function which computes the functional complexity instead of only the size of ON-set of the function is used. Therefore the optimization is performed considering how the trun minterms are grouped together so that they can be realized by only a small number of cubes. In other words, it considers how the function is changed and how the change affects the next optimization step. Experimental results shows better performance in many cases including parity-type functions compared to pervious results.

• Journal of Communications and Networks
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• 제12권3호
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• pp.253-265
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• 2010

The problem of pricing for a telecommunication network is investigated with respect to the users' sensitivity to the pricing structure. A functional optimization problem is formulated, in order to compute price reallocations as functions of data collected in real time during the network evolution. No a-priori knowledge about the users' utility functions and the traffic demands is required, since adaptive reactions to the network conditions are sought in real time. To this aim, a neural approximation technique is studied to exploit an optimal pricing control law, able to counteract traffic changes with a small on-line computational effort. Owing to the generality of the mathematical framework under investigation, our control methodology can be generalized for other decision variables and cost functionals.

• Natural Product Sciences
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• 제21권2호
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• pp.141-145
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• 2015

Hesperidin, which is the most abundant flavonoid of Citrus unshiu (Rutaceae), has been reported to possess diverse activities and widely used as functional foods and cosmetics. For the development of functional products, extraction procedure is indispensable. Extraction conditions affect the composition of extract as well as its biological activity. Therefore, we tried to optimize extraction conditions such as extraction solvent, extraction time and extraction temperature for maximum yield of hesperidin using response surface methodology with threelevel-three-factor Box-Behnken design (BBD). Regression analysis showed a good fit of the experimental data and the optimal condition was obtained as ethanol concentration, 59.0%; temperature $71.5^{\circ}C$ and extraction time, 12.4 h. The hesperidin yield under the optimal condition was found to be $287.8{\mu}g$ per 5 mg extract, which was well matched with the predicted value of 290.5 μg. These results provides optimized extraction condition for hesperidin and might be useful for the development of hesperidin as functional products like health supplements, cosmetics and medicinal products.

• 한국세라믹학회:학술대회논문집
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• 한국세라믹학회 2004년도 춘계총회 및 연구발표회 초록집
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• pp.27.3-27.3
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• 2004

• 전기학회논문지
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• 제57권11호
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• pp.2108-2116
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• 2008

The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1763-1768
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• 2003

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. Based on the independence axiom of axiomatic design theory that illustrates the relationship between desired specifications and design parameters, the designs can be classified into three types: uncoupled, decoupled and coupled. To best approach the target performance with the maximum robustness is one of the main functional requirements of a mechanical system. Most engineering designs are pertaining to either coupled or decoupled ones, but these designs cannot currently accomplish a real robustness thus a trade-off between performance and robustness has to be made. In this research, the game theory will be applied to optimize the trade-off.

• 전기학회논문지
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• 제67권6호
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• pp.767-772
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• 2018

Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

• 대한기계학회논문집A
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• 제36권8호
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• pp.897-904
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• 2012

강선설계문제는 실수형 설계변수인 형상변수와 정수형 설계변수인 강선의 개수로 이루어져 있다. 또한, 탄이 강선의 통과하는 거동을 표현하기 위하여 비선형 유한요소 해석을 사용하므로 많은 해석시간이 요구된다. 따라서, 본 연구에서는 실험계획법 기반의 효율적인 강선설계 방법을 제안한다. 첫 번째로, 3 개의 형상변수와 1 개의 정수형 변수를 포함하는 4 개의 설계변수에 대해서 보스의 직교배열표를 사용하여 25 개의 실험점을 생성한 후 각 실험점에 대해서 비선형 유한 요소 해석을 수행한다. 다음으로는 포열에서 탄이 탈출할 때의 탄의 속도와 각속도를 만족시키는 동시에 탄의 저항력을 최소화 하기 위해서 가상설계개념을 수행한다. 제안하는 가상설계개념은 설계 목적과 제약조건 그리고 효과분석을 포함하는 범함수로 생성된다. 마지막으로 가상설계개념으로부터 주어지는 새로운 설계는 초기 설계보다 나은 결과를 보여주고 있다.