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

• Wind and Structures
• /
• 제9권6호
• /
• pp.441-455
• /
• 2006

A more applicable optimization model for extracting flutter derivatives of bridge decks is presented, which is suitable for time-varying weights for fitting errors and different lengths of vertical bending and torsional free vibration data. A stochastic search technique for searching the optimal solution of optimization problem is developed, which is more convenient in understanding and programming than the alternate iteration technique, and testified to be a valid and efficient method using two numerical examples. On the basis of the section model test of Sutong Bridge deck, the flutter derivatives are extracted by the stochastic search technique, and compared with the identification results using the modified least-square method. The Empirical Mode Decomposition method is employed to eliminate noise, trends and zero excursion of the collected free vibration data of vertical bending and torsional motion, by which the identification precision of flutter derivatives is improved.

• KSBB Journal
• /
• 제7권2호
• /
• pp.112-117
• /
• 1992

Streptococcus zooepidemicus에 의한 hyaluronic acid 생성의 최적 배지 조성은 batch cultre 조건에서 0.1% 쇠고기 추출물, 0.1% 효모 추출물, 3.0% 포도당 2.0% peptone, 0.1% 식염 및 0.5% $CaCO_3$이였으며, 배지의 포기 pH는 7.5로서 $37^{\circ}C$에서 36시간 진탕배양하는 것이 양호했다. 특히 공시균의 생육에 수반되어 hyaluronic acid가 생성되므로 배지의 pH를 중화하기위해 $CaCO_3$의 첨가는 필수적이었다.

• 한국지능시스템학회논문지
• /
• 제25권1호
• /
• pp.97-104
• /
• 2015

An 'associative cube', a class of auto-associative memories, is revisited here, in which training data and hidden orthogonal basis functions such as wavelet packets or Fourier kernels, are combined in the weight cube. This weight cube has hidden units in its depth, represented by a three dimensional cubic structure. We develop an unsupervised incremental learning mechanism based upon the adaptive least squares method. Training data are mapped into orthogonal basis vectors in a least-squares sense by updating the weights which minimize an energy function. Therefore, a prescribed orthogonal kernel is incrementally assigned to an incoming data. Next, we show how a decoding procedure finds the closest one with a competitive network in the hidden layer. As noisy test data are applied to an associative cube, the nearest one among the original training data are restored in an optimal sense. The simulation results confirm robustness of associative cubes even if test data are heavily distorted by various types of noise.

• KSBB Journal
• /
• 제22권1호
• /
• pp.53-57
• /
• 2007

강낭콩 유식물로부터 PBS에 의한 추출, $(NH_4)_2SO_4$ 침전, Sepadex G-100 column chromatography에 의해 lectin을 분리한 다음, 이들의 생화학적 특성으로서 분자량, 적혈구 응집반응, 열 안정성, 최적 온도 및 최적 pH를 연구하였다. 이 과정에 토끼 혈액의 적혈구를 이용하여 활성을 측정하였다. 이 lectin의 분자량은 46 kDa와 44 kDa로서, 각각 2개의 subunit를 갖는 tetramer이다. 정제된 이 lectin의 최적 반응 온도는 30$^{\circ}C$이며, $40\sim80^{\circ}C$에서 열 안정성을 보였다. 또한 이 lectin의 최적 pH는 pH 8.2이다.

• 산업경영시스템학회지
• /
• 제33권4호
• /
• pp.69-78
• /
• 2010

Multi-criteria ABC inventory classification is one of the most widely employed techniques for efficient inventory control, and it considers more than one criterion for categorizing inventory items into groups of different importance. Recently, Ramanathan (2006) proposed a weighted linear optimization (WLO) model for the problem of multi-criteria ABC inventory classification. The WLO model generates a set of criteria weights for each item and assigns a normalized score to each item for ABC analysis. Although the WLO model is considered to have many advantages, it has a limitation that many items can share the same optimal efficiency score. This limitation can hinder a precise classification of inventory items. To overcome this deficiency, we propose a context-dependent DEA based method for multi-criteria ABC inventory classification problems. In the proposed model, items are first stratified into several efficiency levels, and then the relative attractiveness of each item is measured with respect to less efficient ones. Based on this attractiveness measure, items can be further discriminated in terms of their importance. By a comparative study between the proposed model and the WLO model, we argue that the proposed model can provide a more reasonable and accurate classification of inventory items.

• 한국멀티미디어학회논문지
• /
• 제2권2호
• /
• pp.129-136
• /
• 1999

특정 이미지에서의 선형적 특정은 이미지를 분석하고 이해하는데 충분한 정보를 제공하기도 한다. 본고에 서는 이미지에서 선형적 특징을 추출하기 위한 신뢰성 있는 방법을 제시한다. 일반적으로 후프 변형 방법은 이러한 선형적 특정을 추출하는 최적의 방법 중의 하나로 인식되어 왔다. 대부분의 후프 기반 방법들은 특정 edge 모델올 선택하고, 인식된 edge 픽셀의 속성을 반영하는 변형식을 활용하여 파라미터 공간에 그 발생빈도 를 기록하는 과정을 거치게 된다. 주로 edge 픽셀의 gradient 크기와 방향이 선형적 특정을 결정하는데 사용되 지만, 본고에서는 그 값틀이 퍼지변수로 활용될 수 있음을 보이고 파라미터 공간에 누적값을 계산하는데 활용한다- 이 방법을 기존의 방법과 비교하기 위하여 에러 측정 방식을 제안하고, 실험을 한 결과, 기존의 방법과 비교하여 우수한 성능을 보인다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
• /
• pp.171-174
• /
• 2003

In this paper, a Learning module for a reconfigurable neural network processor(ERNIE) was proposed for an On-chip learning. The existing reconfigurable neural network processor(ERNIE) has a much better performance than the software program but it doesn't support On-chip learning function. A learning module which is based on Back Propagation algorithm was designed for a help of this weak point. A pipeline structure let the learning module be able to update the weights rapidly and continuously. It was tested with five types of alphabet font to evaluate learning module. It compared with C programed neural network model on PC in calculation speed and correctness of recognition. As a result of this experiment, it can be found that the neural network processor(ERNIE) with learning module decrease the neural network training time efficiently at the same recognition rate compared with software computing based neural network model. This On-chip learning module showed that the reconfigurable neural network processor(ERNIE) could be a evolvable neural network processor which can fine the optimal configuration of network by itself.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2007년도 춘계학술대회A
• /
• pp.96-101
• /
• 2007

A new metallic sandwich panel with a quasi-Kagome truss core subjected to bending load has been analyzed. First, equations of the failure loads corresponding to the eight failure modes are presented. Then, non-dimensional forms of the equations are derived as functions of three geometric variables, one material parameter (yield strain), one load index and one weight index. Failure maps are presented for a given weight index. By using the dimensionless forms of equations as the design constraints, two kinds of optimization are performed. One is based on the weight, that is, the objective function, namely, the dimensionless load is to be maximized for a given weight. Another is based on the load, that is, the dimensionless weight is to be minimized for a given load. The results of the two optimization processes are found to agree each other. The optimized geometric variables are derived as a function of given weights or failure loads. The performance of the quasi-Kagome truss as the core of a sandwich panel is evaluated by comparison with those of honeycomb cored and octet truss cored panels

• Smart Structures and Systems
• /
• 제21권1호
• /
• pp.123-137
• /
• 2018

Structural monitoring is the most important part of the construction and operation of the embankment dams. Appropriate instruments selection for dams is vital, as inappropriate selection causes irreparable loss in critical condition. Due to the lack of a systematic approach to determine adequate instruments, a framework based on three comparable Multi-Attribute Decision Making (MADM) methods, which are VIKOR, technique of order preference by similarity to ideal solution (TOPSIS) and Preference ranking organization method for enrichment evaluation (PROMETHEE), has been developed. MADM techniques have been widely used for optimizing priorities and determination of the most suitable alternatives. However, the results of the different methods of MADM have indicated inconsistency in ranking alternatives due to closeness of judgements from decision makers. In this study, 9 criteria and 42 geotechnical instruments have been applied. A new method has been developed to determine the decision makers' importance weights and an aggregation method has been introduced to optimally select the most suitable instruments. Consequently, the outcomes of the aggregation ranking correlate about 94% with TOPSIS and VIKOR, and 83% with PROMETHEE methods' results providing remarkably appropriate prioritisation of instruments for embankment dams.