• Title/Summary/Keyword: Cumulative Probability Density

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Keyword Network Analysis for Technology Forecasting (기술예측을 위한 특허 키워드 네트워크 분석)

  • Choi, Jin-Ho;Kim, Hee-Su;Im, Nam-Gyu
    • Journal of Intelligence and Information Systems
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    • v.17 no.4
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    • pp.227-240
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    • 2011
  • New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

Reliability Analysis and Fatigue Models of Concrete under Flexural or Split Tensional Cyclic Loadings (휨 또는 쪼갬인장 반복하중을 받는 콘크리트의 신뢰성 해석과 피로모델 제안)

  • Kim Dong-Ho;Sim Do-Sik;Kim Sung-Hwan;Yun Kyong-Ku
    • Journal of the Korea Concrete Institute
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    • v.16 no.5 s.83
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    • pp.581-589
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    • 2004
  • This paper compares the fatigue behaviors of concretes subjected to flexural and split-tensional loadings, and proposes the fatigue reliability models based on experimental results and reliability analysis. The fatigue tests were performed for the specimens of $150 mm{\times}75 mm$ split tensional cylinders and $150 mm{\times}150 mm{\times}550 mm$ flexural beams under constant loadings at three levels (70, 80 and $90\%$) with 0.1 stress ratio, 20 Hz loading speed and sine wave. The reliability analysis on fatigue data was based on Weibull distribution of two-parameters. From fatigue test results, two criteria were proposed to reject the experimental fatigue data because of statistical variation of concrete fatigue data. Two parameters ($\alpha$and u) of Weibull distribution were obtained using graphical method, moment method and maximum likelihood method. The probability density function(P.D.F) and cumulative distribution function(C.D.F) of the Weibull distribution for fatigue life of pavement concrete were derived for various stress levels using parameters, $\alpha$ and u. The goodness-of-fit test by Kolmogorov-Smirnov test was acceptable at $5\%$ level of significance. Based on reliability analysis, a fatigue model for pavement concrete was proposed and compared from existing models.

An attitude survey on the safety of the household utilities with the urban gas (설문에 의한 도시가스 사용가구의 안전의식도 조사)

  • Ko Jae-Sun;Kim Hyo;Lee SuKyoung
    • 한국가스학회:학술대회논문집
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    • 2005.10a
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    • pp.37-43
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    • 2005
  • The questionnaires about the safety of the urban gas have been carried out for the end users. about 8 of 10 persons said that the urban gas Is safe to use, whereas $35\%$ of them said there exists a hazard of an accident in thier residences. There cannot be found the clear evidences that the understandings on the safety of the urban gas have no relations to their ages, sex, and monthly incomes, while the safety is less confidential to the highly educated, the accident-experienced, or the mans who are poor at the safety inspections. Most of the questioned man know the inspection knacks for the gas utilities, but only $60\%$ of them carry out it. They said that they do not feel the necessity of the inspection because they are inspected routinely by the suppliers or the inspection companies. This says that the end user does not concern the safety inspections, and in order to improve the dependency of the user for the self-inspections, the inspection staff should educate the user for the necessity and the knack of inspections to encourage the self-inspection of the gas utilities.

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Optimal Seismic Rehabilitation of Structures Using Probabilistic Seismic Demand Model (확률적 지진요구모델을 이용한 구조물의 최적 내진보강)

  • Park, Joo-Nam;Choi, Eun-Soo
    • Journal of the Earthquake Engineering Society of Korea
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    • v.12 no.3
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    • pp.1-10
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    • 2008
  • The seismic performance of a structure designed without consideration of seismic loading can be effectively enhanced through seismic rehabilitation. The appropriate level of rehabilitation should be determined based on the decision criteria that minimize the anticipated earthquake-related losses. To estimate the anticipated losses, seismic risk analysis should be performed considering the probabilistic characteristics of the hazard and the structural damage. This study presents the decision procedure in which the probabilistic seismic demand model is utilized for the effective estimation and minimization of the total seismic losses through seismic rehabilitation. The probability density function and the cumulative distribution function of the structural damage for a specified time period are established in a closed form, and are combined with the loss functions to derive the expected seismic loss. The procedure presented in this study could be effectively used for making decisions on the seismic rehabilitation of structural systems.