• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 추계학술대회
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• pp.653-655
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• 2011

EPCglobal proposed a Q-algorithm, which is used for selecting a slot-count in the next query round. However, it is impossible to allocate an optimized slot-count because the original Q-algorithm did not define an optimized weight C value. In this paper, we propose an adaptive Q-algorithm, in which we differentiate the weight values with respect to collision and empty slots. The weight values are defined with the identification time as well as the collision probability.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2002년도 추계학술대회
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• pp.289-312
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• 2002

This study suggests the Link weight analysis approach to choose input variables and an integrated model to make more accurate bankruptcy prediction model. the Link weight analysis approach is a method to choose input variables to analyze each input node's link weight which is the absolute value of link weight between an input nodes and a hidden layer. There are the weak-linked neurons elimination method, the strong-linked neurons selection method in the link weight analysis approach. The Integrated Model is a combined type adapting Bagging method that uses the average value of the four models, the optimal weak-linked-neurons elimination method, optimal strong-linked neurons selection method, decision-making tree model, and MDA. As a result, the methods suggested in this study - the optimal strong-linked neurons selection method, the optimal weak-linked neurons elimination method, and the integrated model - show much higher accuracy than MDA and decision making tree model. Especially the integrated model shows much higher accuracy than MDA and decision making tree model and shows slightly higher accuracy than the optimal weak-linked neurons elimination method and the optimal strong-linked neurons selection method.

• Journal of information and communication convergence engineering
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• 제9권2호
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• pp.172-176
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• 2011

In the Gen-2 Q-algorithm, the values of weight C, which is the parameter for incrementing or decrementing the slot-count size, are not optimized in the standard. However, the standard suggests that the reader uses small values of C when the slot-count is large and larger values of C when the slot-count is small. In this case, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to select the weight based on the slot-count size of current query round. Through various computer simulations, it is demonstrated that the proposed scheme achieves more stable performances than Gen-2 Q-algorithm.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.228-232
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• 2002

This paper is reporting the whole process of developing a weight measuring scaler of truck and trailer system for static and dynamic condition. The sensors attached on the top of springs each wheels. Acquisition and data processing performs accurate data extraction from noise environment, filtering and estimation. Weight information was highly distorted with noise and perturbation. Hence the perturbation was classified several categories and evaluated for accurate signal extract. The final products supply accurate and easy readable data of load weight for truck. It supplies total weight as well as loading condition of each axle. It is expected that it give the information to the truck operator of proper amount loading and safe condition to drive with it.

• 디지털산업정보학회논문지
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• 제10권4호
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• pp.53-61
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• 2014

In this paper, we proposed covariance weight matrix using SPT matrix in order to accurate target estimation. We have estimated a target using modified covariance matrix and beam steering error method. We have minimized beam steering error in order to estimation desired a target. This method obtain optimum covariance weight using modified SPT matrix. This paper of proposal method is showed good performance than general method. We updated a weight of covariance matrix using modified SPT matrix. We obtain optimum covariance matrix weight to application beam steering error method in order to beam steering toward desired target. Through simulation, we showed that compare proposal method with general method. It have improved resolution of estimation target to good performance more proposed method than general method.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.285-295
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• 2024

Driven by the vague assessment big data, a product service system (PSS) evaluation method is developed based on a hybrid model of multi-weight combination and improved TOPSIS by relative entropy. The index values of PSS alternatives are solved by the integration of the stakeholders' vague assessment comments presented in the form of trapezoidal fuzzy numbers. Multi-weight combination method is proposed for index weight solving of PSS evaluation decision-making. An improved TOPSIS by relative entropy (RE) is presented to overcome the shortcomings of traditional TOPSIS and related modified TOPSIS and then PSS alternatives are evaluated. A PSS evaluation case in a printer company is given to test and verify the proposed model. The RE closeness of seven PSS alternatives are 0.3940, 0.5147, 0.7913, 0.3719, 0.2403, 0.4959, and 0.6332 and the one with the highest RE closeness is selected as the best alternative. The results of comparison examples show that the presented model can compensate for the shortcomings of existing traditional methods.

• 한국지역사회생활과학회지
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• 제25권2호
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• pp.175-192
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• 2014

This study explores the effects of various factors on the use of nutrition information on food away from home (FAFH). Consumer groups were classified into groups according to their perception of being overweight and obese (correctly perceiving, underestimating and overestimating weight). For this, frequency analysis, a chi-square test, and an ANOVA were conducted to determine any differences between factors, and a logit analysis (SPSS 18.0) was conducted to identify those factors influencing the use of information. Information recognition, intentions to use information and the use on FAFH showed significant differences across the groups. In addition, health inspection, the perception of the intrinsic quality of restaurants, labeling, FAFH expenditure, and some individual characteristics showed significant differences between groups. The information recognition of FAFH had a significant positive effect on information use in all groups. In all groups, labeling had a significant positive effect on information use, and family health concerns had a significant positive effect on information use in the group correctly perceiving weight. The price of domestic food items, household head and household income had significant positive effects on information use in the group overestimating weight.

• 동아시아식생활학회지
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• 제10권6호
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• pp.522-529
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• 2000

This study was carried out to find the factors which are related to the weight gain during pregnancy of women and infant birth weight. The information of the general characteristics and pregnancy outcomes of the 506 women who had a delivery during Jan to Dec, 1997 in a hospital at Taegu area were collected from the medical records. The results are as follows. The mean age of the subjects was 29 years old and the average prepregnancy weight was 52.75kg. They gained 13.51kg of weight during the pregnancy. The weight gain during pregnancy was higher in prepregnancy BMI<20kg/m$^2$, the infant weight was heavier in groups that had over 14kg of weight gain during the pregnancy than other groups. The prepregnancy BMI was negatively correlated to weight gain during pregnancy(r=0.2825), and positively correlated to number of pregnancy(r=0.2146), number of living delivery(r=0.1409), and infant weight(r=0.1250). The baby weight was Positively correlated to weight gain during pregnancy(r=0.1392) and Apgar score(r=0.1627). The results showed that the prepregnancy BMI and weight gain during pregnancy may be the influential factors on the infant weight, thus we need to develop the specific nutritional management program according to the status of prepregnancy weight.

• 한방비만학회지
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• 제19권1호
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• pp.12-23
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• 2019

Objectives: Obesity is associated with a high mortality risk and impairment in health-related quality of life (HRQOL). The aim of this article is to examine the impact of weight loss on HRQOL and which questionnaires sensitively reflect weight loss effects on HRQOL. Methods: PubMed, Scopus, Research Information Sharing Service, and Korean Studies Information Service System were searched for the studies related to weight loss and HRQOL, published from 2009 to 2018. A total of 28 studies were eligible for inclusion. HRQOL results after weight loss from selected studies were classified and reported according to questionnaires. Results: Twenty-two studies reported statistically significant HRQOL improvements after weight loss and especially, all of studies with weight loss of more than 5% reported HRQOL improvements. HRQOL questionnaires were classified as generic, obesity-related and depression questionnaires. The most commonly used questionnaires were Short-Form health survey 36 (SF-36), Impact of Weight on Quality Life-Lite (IWQOL-Lite) and Beck Depression Inventory (BDI) respectively. SF-36 had a tendency to reflect physical health. IWQOL-Lite score was tended to be changed sensitively according to weight change. Depression questionnaires including BDI reported improvement of depression while mental aspects of SF-36 not changed in same studies. Conclusions: Improvements of HRQOL were noted in studies with weight loss of more than 5%. The main questionnaires for evaluating HRQOL were SF-36, IWQOL-Lite and BDI. It is suggested to use these questionnaires together for evaluating multiple aspects of impact of weight loss on HRQOL.

• Journal of the Korean Data and Information Science Society
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• 제12권2호
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• pp.1-10
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• 2001

신경망은 점차 분류 및 함수추정을 위한 현대 통계적 방법론으로 부각되고 있다. 신경망은 특히 선형 회귀함수를 일반화시키는 유연한(flexible) 방법을 제공하며 일반적 비선형 함수를 모수화하는 방법으로 간주된다. 본 논문에서는 함수추정을 위한 신경망을 생각한다. 신경망이 훈련자료를 과대적합하는 것을 피할 수 있도록 하는 간단한 방법은 정칙화(regularization)이다. 신경망에서는 정칙화를 위해 주로 가중치 감소법(weight decay method)을 사용한다. 함수추정을 위해 가중치감소 신경망을 사용할 때 은닉노드수, 가중치모수, 학습률 및 학습반복회수가 중요한 모수이다. 본 논문에서는 유전자 알고리즘을 사용하여 가중치감소 신경망의 중요한 모수들을 자동으로 최적화하는 방법을 제안하고 결과적으로 가중치감소 신경망을 자동학습하는 방법을 설명한다. 그리고 다른 함수추정방법들과 자동학습된 가중치감소 신경망을 비교분석한다.