• Title/Summary/Keyword: Weibull Analysis

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Electrical Insulation Characteristics of HTS SMES (고온초전도 SMES의 절연특성)

  • Cheon Hyeon-Gweon;Choi Jae-Hyeong;Kwag Dong-Soon;Kim Hae-Jong;Seong Ki-Chul;Yun Mun-Soo;Kim Sang-Hyun
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
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    • v.19 no.6
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    • pp.574-578
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    • 2006
  • Toward the practical applications, on operation of conduction-cooled HTS SMES at temperatures well below 77 K should be investigated, in order to take advantage of a greater critical current density of HTS and considerably reduce the size and weight of the system. Recently, research and development concerning application of the conduction-cooled HTS SMES that is easily movement are actively progressing in Korea. Electrical insulation under cryogenic temperature is a key and an important element in the application of this apparatus. Using multi wrapped copper by Polyimide film for HTS SMES, the breakdown characteristics of models for turn-to-turn, that is surface contact model, were investigated under ac and impulse voltage at 77 K. A material that is Polyimide film (Kapton) 0.025 mm thickness is used for multi wrapping of the electrode. Statistical analysis of the results using Weibull distribution to examine the wrapping number effects on breakdown voltage under at and impulse voltage in $LN_2$ was carried.

A Study on the Optimal Release Time Decision of a Developed Software by using Logistic Testing Effort Function (로지스틱 테스트 노력함수를 이용한 소프트웨어의 최적인도시기 결정에 관한 연구)

  • Che, Gyu-Shik;Kim, Yong-Kyung
    • Journal of Information Technology Applications and Management
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    • v.12 no.2
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    • pp.1-13
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    • 2005
  • This paper proposes a software-reliability growth model incoporating the amount of testing effort expended during the software testing phase after developing it. The time-dependent behavior of testing effort expenditures is described by a Logistic curve. Assuming that the error detection rate to the amount of testing effort spent during the testing phase is proportional to the current error content, a software-reliability growth model is formulated by a nonhomogeneous Poisson process. Using this model the method of data analysis for software reliability measurement is developed. After defining a software reliability, This paper discusses the relations between testing time and reliability and between duration following failure fixing and reliability are studied. SRGM in several literatures has used the exponential curve, Railleigh curve or Weibull curve as an amount of testing effort during software testing phase. However, it might not be appropriate to represent the consumption curve for testing effort by one of already proposed curves in some software development environments. Therefore, this paper shows that a logistic testing-effort function can be adequately expressed as a software development/testing effort curve and that it gives a good predictive capability based on real failure data.

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Traffic Analysis of a Cognitive Radio Network Based on the Concept of Medium Access Probability

  • Khan, Risala T.;Islam, Md. Imdadul;Amin, M.R.
    • Journal of Information Processing Systems
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    • v.10 no.4
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    • pp.602-617
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    • 2014
  • The performance of a cognitive radio network (CRN) solely depends on how precisely the secondary users can sense the presence or absence of primary users. The incorporation of a spatial false alarm makes deriving the probability of a correct decision a cumbersome task. Previous literature performed this task for the case of a received signal under a Normal probability density function case. In this paper we enhance the previous work, including the impact of carrier frequency, the gain of antennas on both sides, and antenna heights so as to observe the robustness against noise and interference and to make the correct decision of detection. Three small scale fading channels: Rayleigh, Normal, and Weibull were considered to get the real scenario of a CRN in an urban area. The incorporation of a maximal-ratio combining and selection combing with a variation of the number of received antennas have also been studied in order to achieve the correct decision of spectral sensing, so as to serve the cognitive users. Finally, we applied the above concept to a traffic model of the CRN, which we based on a two-dimensional state transition chain.

Cluster and information entropy analysis of acoustic emission during rock failure process

  • Zhang, Zhenghu;Hu, Lihua;Liu, Tiexin;Zheng, Hongchun;Tang, Chun'an
    • Geomechanics and Engineering
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    • v.25 no.2
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    • pp.135-142
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    • 2021
  • This study provided a new research perspective for processing and analyzing AE data to evaluate rock failure. Cluster method and information entropy theory were introduced to investigate temporal and spatial correlation of acoustic emission (AE) events during the rock failure process. Laboratory experiments of granite subjected to compression were carried out, accompanied by real-time acoustic emission monitoring. The cumulative length and dip angle curves of single links were fitted by different distribution models and distribution functions of link length and directionality were determined. Spatial scale and directionality of AE event distribution, which are characterized by two parameters, i.e., spatial correlation length and spatial correlation directionality, were studied with the normalized applied stress. The entropies of link length and link directionality were also discussed. The results show that the distribution of accumulative link length and directionality obeys Weibull distribution. Spatial correlation length shows an upward trend preceding rock failure, while there are no remarkable upward or downward trends in spatial correlation directionality. There are obvious downward trends in entropies of link length and directionality. This research could enrich mathematical methods for processing AE data and facilitate the early-warning of rock failure-related geological disasters.

Parametric survival model based on the Lévy distribution

  • Valencia-Orozco, Andrea;Tovar-Cuevas, Jose R.
    • Communications for Statistical Applications and Methods
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    • v.26 no.5
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    • pp.445-461
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    • 2019
  • It is possible that data are not always fitted with sufficient precision by the existing distributions; therefore this article presents a methodology that enables the use of families of asymmetric distributions as alternative probabilistic models for survival analysis, with censorship on the right, different from those usually studied (the Exponential, Gamma, Weibull, and Lognormal distributions). We use a more flexible parametric model in terms of density behavior, assuming that data can be fit by a distribution of stable distribution families considered unconventional in the analyses of survival data that are appropriate when extreme values occur, with small probabilities that should not be ignored. In the methodology, the determination of the analytical expression of the risk function h(t) of the $L{\acute{e}}vy$ distribution is included, as it is not usually reported in the literature. A simulation was conducted to evaluate the performance of the candidate distribution when modeling survival times, including the estimation of parameters via the maximum likelihood method, survival function ${\hat{S}}$(t) and Kaplan-Meier estimator. The obtained estimates did not exhibit significant changes for different sample sizes and censorship fractions in the sample. To illustrate the usefulness of the proposed methodology, an application with real data, regarding the survival times of patients with colon cancer, was considered.

Bayesian and maximum likelihood estimations from exponentiated log-logistic distribution based on progressive type-II censoring under balanced loss functions

  • Chung, Younshik;Oh, Yeongju
    • Communications for Statistical Applications and Methods
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    • v.28 no.5
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    • pp.425-445
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    • 2021
  • A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.

Analytical Approximation Algorithm for the Inverse of the Power of the Incomplete Gamma Function Based on Extreme Value Theory

  • Wu, Shanshan;Hu, Guobing;Yang, Li;Gu, Bin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.12
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    • pp.4567-4583
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    • 2021
  • This study proposes an analytical approximation algorithm based on extreme value theory (EVT) for the inverse of the power of the incomplete Gamma function. First, the Gumbel function is used to approximate the power of the incomplete Gamma function, and the corresponding inverse problem is transformed into the inversion of an exponential function. Then, using the tail equivalence theorem, the normalized coefficient of the general Weibull distribution function is employed to replace the normalized coefficient of the random variable following a Gamma distribution, and the approximate closed form solution is obtained. The effects of equation parameters on the algorithm performance are evaluated through simulation analysis under various conditions, and the performance of this algorithm is compared to those of the Newton iterative algorithm and other existing approximate analytical algorithms. The proposed algorithm exhibits good approximation performance under appropriate parameter settings. Finally, the performance of this method is evaluated by calculating the thresholds of space-time block coding and space-frequency block coding pattern recognition in multiple-input and multiple-output orthogonal frequency division multiplexing. The analytical approximation method can be applied to other related situations involving the maximum statistics of independent and identically distributed random variables following Gamma distributions.

Generation of Synthetic Time Series Wind Speed Data using Second-Order Markov Chain Model (2차 마르코프 사슬 모델을 이용한 시계열 인공 풍속 자료의 생성)

  • Ki-Wahn Ryu
    • Journal of Wind Energy
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    • v.14 no.1
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    • pp.37-43
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    • 2023
  • In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.

Analysis of Wind Energy Potential on the West Coast of South Korea Using Public Data from the Korea Meteorological Administration (기상청 공공데이터를 활용한 대한민국 서해안 일대의 바람자원 분석)

  • Sangkyun Kang;Sung-Ho Yu;Sina Hadadi;Dae-Won Seo;Jungkeun Oh;Jang-Ho Lee
    • Journal of Wind Energy
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    • v.14 no.3
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    • pp.14-24
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    • 2023
  • The significance of renewable energy has been on the rise, as evidenced by the 3020 renewable energy plan and the 2050 carbon neutrality strategy, which seek to advance a low-carbon economy by implementing a power supply strategy centered around renewable energy sources. This study examines the wind resources on the west coast of South Korea and confirms the potential for wind power generation in the area. Wind speed data was collected from 22 automatic weather system stations and four light house automatic weather system stations provided by the Korea Meteorological Administration to evaluate potential sites for wind farms. Weibull distribution was used to analyze the wind data and calculate wind power density. Annual energy production and capacity factors were estimated for 15-20 MW-class large wind turbines through the height correction of observed wind speeds. These findings offer valuable information for selecting wind power generation sites, predicting economic feasibility, and determining optimal equipment capacity for future wind power generation sites in the region.

Failure Data Analysis of J79 Engine Transfer Gearbox for Aircraft Maintenance Planning (항공기 정비계획을 위한 J79 엔진 Transfer Gearbox의 고장데이터 분석)

  • Choi, Jae-Man;Yang, Seung-Hyo;Hwang, Young-Ha;Son, Ik-Sang;On, Yong-Sub;Kim, Young-Jin
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.34 no.6
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    • pp.781-787
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    • 2010
  • Forecasting possible failure characteristics is very important in maintenance planning because it helps in predicting any future failures and determining the optimum replacement interval. This paper examines the time.to-failure distribution of the transfer gearbox of a J79 engine by using a probability plotting technique which is one of the most convenient techniques for reliability analysis. Various probability distributions are evaluated for determining the suitable probability distribution of the failure data of the transfer gearbox, and the resulting correlation coefficient indicates that failure data have a lognormal distribution. The expected number of unscheduled maintenance actions and the optimum replacement interval for various values of cost ratios are determined.