• Korean Journal of Health Education and Promotion
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• v.23 no.4
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• pp.125-153
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• 2006

This study was intended to describe women's weight controlling by creating a hypothetic model on the weight adjustment behavior and by examining a cause and effect relationship, and to contribute to countermeasures for practicing their promotion of health and improving the quality of life through creating a predictable model. The subject of study was women who utilize the beauty shop located in Seoul, Busan and Daegu and the study period was 12 weeks from July 10 to September 30 in 2004. Gathered 1093 person's general specialty related with weight adjustment and analyzed covariance to prove the hypothesis using statistics compiled from authentic sources. Also proved coincidence of the hypothetical model. Exogenous variables of the hypothetical model are composed of recognition of her body shape, fatness level, age, stress, and self-respect. Endogenous variables are health- control mind, recognized health state, self-efficacy, intention, and behavior of weight adjustment. There were 5 measured variables for exogenous variable(x). There were 8 measured variable(y) for exogenous variable. And coincidence $x^2=297.38$, standard $x^2(x^2/df)=7.08$, GFI=0.962, AGFI=0.917, NFI=0.875, TLI=0.794, CFI=0.889, RMSEA=0.075. The result of hypothesis had an epoch-making record that 20 out of 27 hypothesis was proved positive way. Generally weight adjustment has been highly seen in housewives, the married and the old age. Health control mind seems to be high as fatness level, age, and self-respect are high and low stress. Recognized health state is high as age and self-respect are high and low stress. However, it is not much related with recognition of her body shape and fatness level. If age, self-respect, health control mind, recognized health state and self-efficacy are high intention of behavior is also high, but intention of behavior has no relation with recognition of her body shape, fatness level and stress. If fatness level, age, self-respect, health control mind, recognized health state and self-efficacy and intention of behavior are high, execution of weight adjustment will be high. However, recognized health state and stress has no influence for weight adjustment. To increase the coincidence of hypothesis and take a simple model I modified a model and then I got the coincidence $x^2=215.62$, standard $x^2(x^2/df)=6.34$, GFI=0.970, AGFI=0.931, NFI=0.902, TLI=0.901, CFI=0.915, RMSEA=0.070. This result is a bit better than original hypothetical model's so that this model might be more suitable. In this modification model, the factors of weight adjustment seems to be high according to this order self-efficacy, recognized health state, age, intention, health control mind, self-respect, fatness level and stress. With this result I suggest ; 1. Enforcement of IR that everybody can be controlled weight adjustment herself and continuous education, which is related with regular habit (food, exercise, restriction of a favorite food and behavior training etc.) is also needed. 2. Because self-efficacy is influenced to execution of weight adjustment specific program which can increase self-efficacy should have to develop and we need to utilize it to take care of herself. 3. To protect fatness and be active weight adjustment the peculiar program including the concept of self-respect, recognized health state, health control mind and intention must be developed and not only women but also all of people should be educated. 4. This hypothetical model is forecasting women's weight adjustment behavior and can be utilized for fundamental data to increase those people's health.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.205-208
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• 2005

본 논문은 특징 가중치 벡터를 적용하여 능동형태 모델(Active Shape Model)기반에서 눈동자의 움직임 추적 속도를 향상시키는 방법을 제안한다. 일반적인 능동형태 모델에서는 객체 추적을 위한 PDM 구성을 위해 각 특징점 구성 벡터의 유클리디안 거리의 최소 값으로 Training Set정렬 과정을 수행한다. 이 과정에서 적절하지 못한 샘플 영상으로 인해 안정된 PDM을 구성하지 못하는 문제점이 발생한다. 이러한 문제점을 해결하기 위하여 본 논문에 서는 형태를 구성하는 특징점마다 가중치를 부여하는 벡터를 작성하고, 최소자승근사법으로 최유사 특징점 벡터를 산출하기 위한 선형방정식을 구상하였다. 이로 인해 안정된 PDM을 구성할 수 있었으며, 눈동자 추적실험을 통해 형태적 움직임을 보정하는 실험을 수행하였다. 실험결과 기존의 능동형태 모델에 비해 반복연산의 횟수가 줄어들고, 다양한 형태로 나타나는 눈동자의 움직임 추적에 보다 안정적인 결과를 얻을 수 있었다.

• International Journal of Contents
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• v.8 no.2
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• pp.7-12
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• 2012

Imbalanced data sets are difficult to be classified since most classifiers are developed based on the assumption that class distributions are well-balanced. In order to improve the error back-propagation algorithm for the classification of imbalanced data sets, a new error function is proposed. The error function controls weight-updating with regards to the classes in which the training samples are. This has the effect that samples in the minority class have a greater chance to be classified but samples in the majority class have a less chance to be classified. The proposed method is compared with the two-phase, threshold-moving, and target node methods through simulations in a mammography data set and the proposed method attains the best results.

• Computers and Concrete
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• v.22 no.4
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.

• Culinary science and hospitality research
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• v.20 no.5
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• pp.16-21
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• 2014

The current study examined college students' overall eating behavior and physical activity, highlighting differences between male and female students attending a public university in the southwestern United States. Research findings indicated that many college students did not eat enough fruits, fruit juices, and green salad. Furthermore, the results of Chi-square analysis showed that there were significant differences in consumption amounts of green salad, hamburgers/hot dogs/sausage, and French fries/potato chips between male and female students. Study findings showed that when students were asked about attributes of food/restaurant choice, female students were more concerned about nutritional aspects when they chose the foods compared with male students. In terms of physical activity levels among college students, male students were more likely to participate in sports activities and weight training. On the other hand, female students were more inclined to walking or bicycling.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1293-1296
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• 1998

This paper presents a new signature verification technique that not only maximally allows variations in signatures of each person, but also discriminates effectively forgeries from true signatures. The signature verification system is designed to detect unstable portions in signatures of same person, and to give large weight on the portion that is difficult to imitate and plays an important role in signature verification. In registration mode, the system extracts subpatterns from training samples and analizes their consistency and singularity by calculating the variance and complexity of this portion. In verification mode, the system verifies a input signature by comparing corresponding subpatterns with the weights of reference subpatterns.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.113-117
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• 1998

We propose a way to show the inherent learning complexity for the multilayer perceptron. We display the solution space and the error surfaces on the input space of a single neuron with two inputs. The evolution of its weights will follow one of the two error surfaces. We observe that when we use the back-propagation(BP) learning algorithm (1), the wight cam not jump to the lower error surface due to the implicit continuity constraint on the changes of weight. The self-relaxation approach is to explicity find out the best combination of all neurons' two error surfaces. The time complexity of training a multilayer perceptron by self-relaxationis exponential to the number of neurons.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.635-644
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• 2019

Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.50-52
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• 2006

This paper Proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.292-293
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• 2008

In this paper, we propose a face hallucination method based on example-learning. The traditional approach based on example-learning requires alignment of face images. In the proposed method, facial images are segmented into patches and the weights are computed to represent input low resolution facial images into weighted sum of low resolution example images. High resolution facial images are hallucinated by combining the weight vectors with the corresponding high resolution patches in the training set. Experimental results show that the proposed method produces more reliable results of face hallucination than the ones by the traditional approach based on example-learning.