• Title/Summary/Keyword: Probabilistic modeling

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Probabilistic modeling of geopolymer concrete using response surface methodology

  • Kathirvel, Parthiban;Kaliyaperumal, Saravana Raja Mohan
    • Computers and Concrete
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    • v.19 no.6
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    • pp.737-744
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    • 2017
  • Geopolymer Concrete is typically proportioned with activator solution leading to moderately high material cost. Such cost can be enduring in high value added applications especially when cost savings can be recognized in terms of reduction in size of the members. Proper material selection and mix proportioning can diminish the material cost. In the present investigation, a total of 27 mixes were arrived considering the mix parameters as liquid-binder ratio, slag content and sodium hydroxide concentration to study the mechanical properties of geopolymer concrete (GPC) mixes such as compressive strength, split tensile strength and flexural strength. The derived statistical Response Surface Methodology is beleaguered to develop cost effective GPC mixes. The estimated responses are not likely to contrast in linear mode with selected variables; a plan was selected to enable the model of any response in a quadratic manner. The results reveals that a fair correlation between the experimental and the predicted strengths.

Intelligent Service Modeling based Probabilistic Approach (확률기반 지능형 서비스 모델링)

  • Go Young-Cheol;Sohn Joo-Chan
    • Proceedings of the Korea Information Processing Society Conference
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    • 2006.05a
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    • pp.565-568
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    • 2006
  • 주변상황 정보를 이용하여 지능형 서비스를 제공하려는 많은 연구들이 진행되고 있다. 본 논문은 이러한 연구중의 하나로서, 베이지안 네트워크를 이용하여 서비스를 모델링하고 이를 기반으로 서비스의 능동적(proactive) 제공에 대한 방법을 제시한다. 상황인지는 유비쿼터스 환경에서 다양한 응용영역에서 이용되고 있으며, 이와 관련된 많은 연구들이 진행되고 있다. 그러나, 관측 대상의 상황정보의 부재 혹은 불확실한 상황에서는 상황정보만으로 사용자에게 지능적인 서비스를 제공하기에는 한계가 있다. 본 연구에서는 베이지안 네트워크를 이용하여 확률기반으로 서비스를 모델링 한다. 모델링 결과를 이용하여 상황에 따라 적절한 서비스 제공을 예시한다.

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A Study on the Probabilistic Production Cost Simulation by the Mixture of Cumulants Approximation (Mixture of Cumulants Approximaton 법에 의한 발전 시물레이션에 관한 연구)

  • 송길영;김용하
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.40 no.1
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    • pp.1-9
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    • 1991
  • This paper describes a new method of calculating expected energy generation and loss of load probability (L.O.L.P) for electric power system operation and expansion planning. The method represents an equivalent load duration curve (E.L.D.C) as a mixture of cumulants approximation (M.O.N.A). By regarding a load distribution as many normal distributions-rather than one normal distribution-and representing each of them in terms of Gram-Charlier expansion, we could improve the accuracy of results. We developed an algorithm which automatically determines the number of distribution and demarcation points. In modeling of a supply system, we made subsets of generators according to the number of generator outage: since the calculation of each subset's moment needs to be processed rapidly, we further developed specific recursive formulae. The method is applied to the test systems and the results are compared with those of cumulant, M.O.N.A. and Booth-Baleriaux method. It is verified that the M.O.C.A. method is faster and more accure than any other method.

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Probabilistic Modeling for Evaluation of Information Security Investment Portfolios (확률모형을 이용한 정보보호 투자 포트폴리오 분석)

  • Yang, Won-Seok;Kim, Tae-Sung;Park, Hyun-Min
    • Journal of the Korean Operations Research and Management Science Society
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    • v.34 no.3
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    • pp.155-163
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    • 2009
  • We develop a probability model to evaluate information security investment portfolios. We assume that organizations install portfolios of information security countermeasures to mitigate the damage such as loss of the transaction being processed, damage of hardware and data, etc. A queueing model and Its expected value analysis are used to derive the lost cost of transactions being processed, the replacement cost of hardwares, and the recovery cost of data. The net present value for each portfolio is derived and organizations can select the optimal information security investment portfolio by comparing portfolios.

Aircraft Arrival Time Prediction via Modeling Vectored Area Navigation Arrivals (관제패턴 모델링을 통한 도착예정시간 예측기법 연구)

  • Hong, Sungkwon;Lee, Keumjin
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.22 no.2
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    • pp.1-8
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    • 2014
  • This paper introduces a new framework of predicting the arrival time of an aircraft by incorporating the probabilistic information of what type of trajectory pattern will be applied by human air traffic controllers. The proposed method is based on identifying the major patterns of vectored trajectories and finding the statistical relationship of those patterns with various traffic complexity factors. The proposed method is applied to the traffic scenarios in real operations to demonstrate its performances.

Intelligent Update of Environment Model in Dynamic Environments through Generalized Stochastic Petri Net (추계적 페트리넷을 통한 동적 환경에서의 지능적인 환경정보의 갱신)

  • Park, Joong-Tae;Lee, Yong-Ju;Song, Jae-Bok
    • Proceedings of the KIEE Conference
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    • 2006.10c
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    • pp.181-183
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    • 2006
  • This paper proposes an intelligent decision framework for update of the environment model using GSPN(generalized stochastic petri nets). The GSPN has several advantages over direct use of the Markov Process. The modeling, analysis, and performance evaluation are conducted on the mathematical basis. By adopting the probabilistic approach, our decision framework helps the robot to decide the time to update the map. The robot navigates autonomously for a long time in dynamic environments. Experimental results show that the proposed scheme is useful for service robots which work semi-permanently and improves dependability of navigation in dynamic environments.

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Model-based inverse regression for mixture data

  • Choi, Changhwan;Park, Chongsun
    • Communications for Statistical Applications and Methods
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    • v.24 no.1
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    • pp.97-113
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    • 2017
  • This paper proposes a method for sufficient dimension reduction (SDR) of mixture data. We consider mixture data containing more than one component that have distinct central subspaces. We adopt an approach of a model-based sliced inverse regression (MSIR) to the mixture data in a simple and intuitive manner. We employed mixture probabilistic principal component analysis (MPPCA) to estimate each central subspaces and cluster the data points. The results from simulation studies and a real data set show that our method is satisfactory to catch appropriate central spaces and is also robust regardless of the number of slices chosen. Discussions about root selection, estimation accuracy, and classification with initial value issues of MPPCA and its related simulation results are also provided.

Modeling of Remediation Design in Theoretically Heterogeneous Domain

  • Ko, Nak-Youl;Lee, Kang-Kun
    • Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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    • 2004.09a
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    • pp.302-306
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    • 2004
  • Probabilistic approaches are applied to the problem of groundwater remediation design to consider the risk of design and heterogeneity of real condition. Hydraulic conductivity fields are generated by two methods. First, the homogeneous domains which have the hydraulic conductivity with log-normal distribution are constructed by using Latin Hypercube method. Second, random fields with a certain spatial correlation are also generated. The optimal solutions represented by cumulative distribution function (CDF) of relative cost are calculated by three different manners. The one uses the homogeneous domains with the optimal design of base condition. It shows that ver)'wide range of cost and the influences of different penalty values. The other one uses the random field with same design and shows narrow range of cost. These CDF can reflect on the risk of optimal solution in a simple exampie condition and be effective in estimating the cost of groundwater remediation.

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Spectrum Sensing Under Uncertain Channel Modeling

  • Biglieri, Ezio
    • Journal of Communications and Networks
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    • v.14 no.3
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    • pp.225-229
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    • 2012
  • We examine spectrum sensing in a situation of uncertain channel model. In particular, we assume that, besides additive noise, the observed signal contains an interference term whose probability distribution is unknown, and only its range and maximum power are known. We discuss the evaluation of the detector performance and its design in this situation. Although this paper specifically deals with the design of spectrum sensors, its scope is wider, as the applicability of its results extends to a general class of problems that may arise in the design of receivers whenever there is uncertainty about how to model the environment in which one is expected to operate. The theory expounded here allows one to determine the performance of a receiver, by combining the available (objective) probabilistic information with (subjective) information describing the designer's attitude.

Resistive Hts-Fcl Emtdc Modeling By Using Probabilistic Design Methodology

  • Yoon, Jae-Young;Kim, Jong-Yul;Lee, Seung-Ryul
    • KIEE International Transactions on Power Engineering
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    • v.4A no.2
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    • pp.69-72
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    • 2004
  • Nowadays, one of the serious problems in the KEPCO system is a much higher fault current than the SCC (Short Circuit Capacity) of the circuit breaker. Since superconductivity technology has become more developed, the HTS-FCL (High Temperature Superconductor-Fault Current Limiter) may become an attractive alternative to solving the fault current problem. In order to achieve the best performance, the parameters of HTS-FCL should be designed optimally. Under this setting, this paper presents the optimal design method of parameters for resistive type HTS-FCL using the Monte Carlo technique.