• 제목/요약/키워드: Probability measure

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Structural safety factor for small unmanned aircraft (소형 무인기 구조 안전계수)

  • Kim, Sung-Joon;Lee, Seung-gyu;Kim, Tae-Uk
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.25 no.2
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    • pp.12-17
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    • 2017
  • Manned aircraft structural design is based on structural safety factor of 1.5, and this safety factor is equivalent to a probability of failure of between 10-2 and 10-3. The target failure probability of FARs is between 10-6 and 10-9 per flight according to aircraft type. NATO released STANAG 4703 to established the airworthiness requirements for small UAV which is less than 150kg. STANAG 4703 requires the Target Level of Safety according to MTOW. The requirements of failure probability for small UAV is between 10-4 and 10-5. In this paper, requirements of airworthiness certification for small UAV were investigated and the relationship of safety factors to the probability of structural failure is analyzed to reduce measure of safety factor and structural weight of unmanned aircraft.

Model based Fault Detection and Diagnosis of Induction Motors using Probability Density Estimation (확률분포추정기법을 이용한 유도전동기의 모델기반 고장진단 알고리즘 개발)

  • Kim, Kwang-Su;Lee, Young-Jin;Song, Xian-Hui;Lee, Kwon-Soon
    • Proceedings of the KIEE Conference
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    • 2008.04b
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    • pp.171-173
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    • 2008
  • This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

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Model based Fault Detection and Diagnosis of Induction Motors using Online Probability Density Estimation (온라인 확률추정기법을 이용한 모델기반 유도전동기의 고장진단 알고리즘 연구)

  • Kim, Kwang-Su;Lee, Young-Jin;Lee, Kwon-Soon
    • Proceedings of the KIEE Conference
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    • 2008.07a
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    • pp.1503-1504
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    • 2008
  • This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

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Balanced Accuracy and Confidence Probability of Interval Estimates

  • Liu, Yi-Hsin;Stan Lipovetsky;Betty L. Hickman
    • International Journal of Reliability and Applications
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    • v.3 no.1
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    • pp.37-50
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    • 2002
  • Simultaneous estimation of accuracy and probability corresponding to a prediction interval is considered in this study. Traditional application of confidence interval forecasting consists in evaluation of interval limits for a given significance level. The wider is this interval, the higher is probability and the lower is the forecast precision. In this paper a measure of stochastic forecast accuracy is introduced, and a procedure for balanced estimation of both the predicting accuracy and confidence probability is elaborated. Solution can be obtained in an optimizing approach. Suggested method is applied to constructing confidence intervals for parameters estimated by normal and t distributions

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LARGE DEVIATION PRINCIPLE FOR SOLUTIONS TO SDE DRIVEN BY MARTINGALE MEASURE

  • Cho, Nhan-Sook
    • Communications of the Korean Mathematical Society
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    • v.21 no.3
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    • pp.543-558
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    • 2006
  • We consider a type of large deviation Principle(LDP) using Freidlin-Wentzell exponential estimates for the solutions to perturbed stochastic differential equations(SDEs) driven by Martingale measure(Gaussian noise). We are using exponential tail estimates and exit probability of a diffusion process. Referring to Freidlin-Wentzell inequality, we want to show another approach to get LDP for the solutions to SDEs.

THE ARCSINE LAW IN THE GENERALIZED ANALOGUE OF WIENER SPACE

  • Ryu, Kun Sik
    • Journal of the Chungcheong Mathematical Society
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    • v.30 no.1
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    • pp.67-76
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    • 2017
  • In this note, we prove the theorems in the generalized analogue of Wiener space corresponding to the second and the third arcsine laws in either concrete or analogue of Wiener space [1, 2, 7] and we show that our results are exactly same to either the concrete or the analogue of Wiener case when the initial condition gives either the Dirac measure at the origin or the probability Borel measure.

Performance Analysis of Economic VaR Estimation using Risk Neutral Probability Distributions

  • Heo, Se-Jeong;Yeo, Sung-Chil;Kang, Tae-Hun
    • The Korean Journal of Applied Statistics
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    • v.25 no.5
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    • pp.757-773
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    • 2012
  • Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.

No-Arbitrage Interest Rate Models Under the Fractional Brownian Motion (Fractional Brownian Motion을 이용한 이자율모형)

  • Rhee, Joon-Hee
    • The Korean Journal of Financial Management
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    • v.25 no.1
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    • pp.85-108
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    • 2008
  • In this paper, the fBm interest rate theory is investigated by using Wick integral. The well-known Affine, Quadratic and HJM are derived from fBm framework, respectively. We obtain new theoretical results, and zero coupon bond pricing formula from newly obtained probability measure.

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RELATIONS BETWEEN THE ITO PROCESSES

  • Choi, Won
    • Communications of the Korean Mathematical Society
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    • v.10 no.1
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    • pp.207-213
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    • 1995
  • Let $(\Omega, F, P)$ be a probability space with F a $\sigma$-algebra of subsets of the measure space $\Omega$ and P a probability measure on $\Omega$. Suppose $a > 0$ and let $(F_t)_{t \in [0,a]}$ be an increasing family of sub-$\sigma$-algebras of F. If $r > 0$, let $J = [-r,0]$ and $C(J, R^n)$ the Banach space of all continuous paths $\gamma : J \to R^n$ with the sup-norm $\Vert \gamma \Vert = sup_{s \in J}$\mid$\gamma(s)$\mid$$ where $$\mid$\cdot$\mid$$ denotes the Euclidean norm on $R^n$. Let E,F be separable real Banach spaces and L(E,F) be the Banach space of all continuous linear maps $T : E \to F$.

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Microprocessor Control of a Prosthetic Arm by EMG Pattern Recognition (EMG 패턴인식을 이용한 인공팔의 마이크로프로세서 제어)

  • Hong, Suk-Kyo
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.33 no.10
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    • pp.381-386
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    • 1984
  • This paper deals with the microcomputer realization of EMG pattern recognition system which provides identification of motion commands from the EMG signals for the on-line control of a prosthetic arm. A probabilistic model of pattern is formulated in the feature space of integral absolute value(IAV) to describe the relation between a motion command and the location of corresponding pattern. This model enables the derivation of sample density function of a command in the feature space of IAV. Classification is caried out through the multiclass sequential decision process, where the decision rule and the stopping rule of the process are designed by using the simple mathematical formulas defined as the likelihood probability and the decision measure, respectively. Some floating point algorithms such as addition, multiplication, division, square root and exponential function are developed for calculating the probability density functions and the decision measure. Only six primitive motions and one no motion are incorporated in this paper.

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