• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.173-185
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• 2006

In this paper, we propose two methods for setting initial weights in the PLS algorithm which is employed to measure the customer satisfaction in SEM. Using data from the survey of the students conducted with the questionnaire of the ACSI survey, we evaluate the education service in terms of the satisfaction level of the students and compare our proposed methods with the previous method.

• Journal of Mechanical Science and Technology
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• 제15권2호
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• pp.192-198
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• 2001

We consider an interpolation problem with nonuniform rational B-spline curves given ordered data points. The existing approaches assume that weight for each point is available. But, it is not the case in practical applications. Schneider suggested a method which interpolates data points by automatically determining the weight of each control point. However, a drawback of Schneiders approach is that there is no guarantee of avoiding undesired poles; avoiding negative weights. Based on a quadratic programming technique, we use the weights of the control points for interpolating additional data. The weights are restricted to appropriate intervals; this guarantees the regularity of the interpolating curve. In a addition, a knot placement is proposed for pleasing interpolation. In comparison with integral B-spline interpolation, the proposed scheme leads to B-spline curves with fewer control points.

• 한국전자통신학회논문지
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• 제6권4호
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• pp.581-587
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• 2011

무선센서네트워크는 높은 수준의 상황정보를 추론할 수 있기 위해 이질적인 다중센서로 이루어질 필요가 있다. 다중센서에 의해 수집된 데이터를 상황 정보추론에 활용할 때 다중센서 데이터 융합이 필요하다. 본 논문에서는 Dempster-Shafer의 증거이론에 입각하여 무선센서네트워크에서의 데이터 융합할 때 센서별 가중치를 부여하는 방안을 제안하였다. 센서별 이벤트 발생빈도수를 기준으로 하였는데, 센서별 이벤트 발생 빈도수는 해당 센서가 입수한 상황데이터의 가중치를 계산하는데 반영해야 할 요소이다. 센서별 이벤트 발생빈도에 기초하여 가중치를 계산하였으며 이 가중치를 부여하여 다중센서 데이터 융합하였을 때 신뢰도가 더욱 뚜렷한 격차를 보이게 함으로써 상황정보를 추론함에 있어서 용이할 수 있었다.

• 한국의류학회지
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• 제7권1호
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• pp.37-43
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• 1983

The objective of the study is to obtain the basic data to establish the standard value of proper clothing weights in the change of thermal environment. For the purpose of this research, clothing weight and thermal sensation have been collected from 160 college student in Seoul and Kwangju area in April, July, October. Results are as follows : 1. Subjects were in Comfortable condition, particularly in Spring and Autumn. But in summer they were in warm condition and the case were reversed in winter when they were under cool condition. 2. The frequency of comfortable thermal sensation were low below 16.5 degree, above 27.5 degree, and were high between 16.5 degree and 23 degree on room temperature. 3. Generally, the positive correlation were found between clothing weights and thermal sensation. 4. Clothing weights and thermal comfort were as follows. Season : Spring, Autumn, Room Temperature(${\circ}C$) : 16.3$\~$23, Clothing Weights($g/m^2$) : 589.9$\~$750.6, Season : Summer Room Temperature(${\circ}C$) : 27$\~$32, Clothing Weights($g/m^2$) : 362.4$\~$432.5, Season : Winter, Room Temperature(${\circ}C$) : 12.5$\~$19.3, Clothing Weights($g/m^2$) : 913.7$\~$1206.2

• 대한가정학회지
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• 제35권4호
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• pp.139-148
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• 1997

The rates of subcutaneos fat on the system of clothing weights including clothing microclimate subjective sensations were measured to get basic data to develop guideline for healthy clothing life. for this study skinfold thickness the rate of subcutaneos fot clothing microclimate subjective sensations and clothing weights were measured from 85 male and 105 female colligians. The results were as follows: 1. The rate of subcutaneos fat showed negative correlation with the temperature inside clothing in chest but not with the temperatures in back and thigh. The correlation was not significant between the rate of subcutaneos fat and humidity inside clothing 2. The correlation between the rate of subcutaneos fat and thermal sensations was positively significant at 5% level. However no correlation was found between the rate of subcutaneos fat and humid sensations. 3. There was significant correlation between the rate of subcutaneos fat and under clothing weights and total clothing weights.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.404-409
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• 1998

In this paper, we propose a learning algorithm of fuzzy neural networks with trapezoidal fuzzy weights. This fuzzy neural networks can use fuzzy numbers as well as real numbers, and represent linguistic information better than standard neural networks. We construct trapezodal fuzzy weights by the composition of two triangles, and devise a learning algorithm using the two triangular membership functions, The results of computer simulations on numerical data show that the fuzzy neural networks have high fitting ability for target output.

• 한국경영과학회지
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• 제34권4호
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• pp.153-163
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• 2009

Unlike the mean-variance approach, the stochastic dominance approach is to form a portfolio that stochastically dominates a predetermined benchmark portfolio such as KOSPI. This study is to search a set of portfolio weights for the first-order stochastic dominance with maximum expected return by managing the constraint set and the objective function separately. A nonlinear programming algorithm was developed and tested with promising results against Korean stock market data sets.

• 지능정보연구
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• 제22권2호
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• pp.1-11
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• 2016

Entani and Tanaka(2007)는 불확실한 데이터를 처리하기에 적합한 구간 평가결과를 얻는 새로운 방법을 제시하였다. 무엇보다 그들의 방법은 구간 데이터를 정규화하여 redundant 데이터를 제거하는데 특징이 있다. 더 나가 정규화된 구간데이터를 활용하여 계층분석과정(AHP)에서 최종 구간 우선순위벡터를 도출한다. 이 논문에서는 구간 데이터의 정규화 목적을 달성하기 위해 구간 데이터의 꼭지점을 구하는 쉽고 간편한 휴리스틱 방법을 제시한다. 한편 간단한 검사법을 활용하여 정규화된 데이터를 활용하여 최종 구간 우선순위벡터를 도출하는 방법을 제시하고자 한다. 아울러 Entani and Tanaka(2007)가 제시한 대안간 지배관계 규명 방법을 확장한 지배관계 규명 방법을 제시하고자 한다.

• Toxicological Research
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• 제17권2호
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• pp.83-90
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• 2001

The background control data were compiled from rat developmental toxicity studies con-ducted at Toxicology Research Center, KRICT during the 1993-1999 period. These data were assembled in order to provide background in formation for the maternal and fetal data collected in 13 developmental toxicity studies using Sprague-Dawley rats. A total of 325 mated females were used in these studies during the seven-year period and overall pregnancy rate of these females was 93.8%. The present background control data included body weights, food consumption, hematological values, and organ weights of pregnant females, caesarean section data, and fetal examination data. These data can be used not only as a historical database for the meaningful interpretation of data from reproductive and developmental toxicity studies, but also as a contribution to biological characterization oj Sprague-Dawley rats.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1295-1304
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• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.