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Добірка наукової літератури з теми "Reliability of model"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Reliability of model"

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Sharma, Dr Rajender Kumar, Dr Archana Kumar, and Sapna Bajaj. "Analysis of Various Software Reliability Models and Proposing a New Model of Software Reliability for Embedded Systems." International Journal of Innovative Research in Computer Science & Technology 5, no. 3 (May 31, 2017): 287–90. http://dx.doi.org/10.21276/ijircst.2017.5.3.6.

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2

Kennedy, Peter, David A. Belsley, and Edwin Kuh. "Model Reliability." Canadian Journal of Economics 19, no. 4 (November 1986): 840. http://dx.doi.org/10.2307/135334.

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Blundell, Richard, David A. Belsley, and Edwin Kuh. "Model Reliability." Economic Journal 97, no. 385 (March 1987): 236. http://dx.doi.org/10.2307/2233339.

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4

Fildes, Robert. "Model reliability." International Journal of Forecasting 4, no. 2 (January 1988): 297–98. http://dx.doi.org/10.1016/0169-2070(88)90089-1.

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5

Rajeevan, A. K., P. V. Shouri, and Usha Nair. "A Reliability Based Model for Wind Turbine Selection." International Journal of Renewable Energy Development 2, no. 2 (June 17, 2013): 69–74. http://dx.doi.org/10.14710/ijred.2.2.69-74.

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A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.
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6

Hai-Liang, Jian, and Wang Chong-Wen. "Web-Oriented Software Reliability Measurement Model and Application." International Journal of Engineering and Technology 4, no. 4 (2012): 358–61. http://dx.doi.org/10.7763/ijet.2012.v4.383.

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7

Szpytko, Janusz. "Human Reliability Model." Journal of Konbin 8, no. 1 (January 1, 2008): 189–200. http://dx.doi.org/10.2478/v10040-008-0112-9.

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Human Reliability ModelThe paper is focusing on reliability model of transport devices' human operator. The presented operator model is base on operation potential approach, with taken into account his features and states helping assure of safety decision-making process. The human reliability model is important for future improvement the human - machine interfaces (HMI).
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8

Muruganantham, B., and K. Vivekanandan. "Multi Perceptional Optimality Matrix Based Web Service Reliability Model." Journal of Software 10, no. 9 (September 2015): 1045–55. http://dx.doi.org/10.17706//jsw.10.9.1045-1055.

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Muruganantham, B., and K. Vivekanandan. "Multi Perceptional Optimality Matrix Based Web Service Reliability Model." Journal of Software 10, no. 9 (2015): 1045–55. http://dx.doi.org/10.17706/jsw.10.9.1045-1055.

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10

Jianjun, Yang, and Li Wenjin. "Reliability Optimization Design of Submarine Free-Running Model Systems." International Journal of Engineering and Technology 8, no. 5 (May 2016): 323–28. http://dx.doi.org/10.7763/ijet.2016.v8.906.

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Дисертації з теми "Reliability of model"

1

Young, Robert Benjamin. "Reliability Transform Method." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33824.

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Анотація:
Since the end of the cold war the United States is the single dominant naval power in the world. The emphasis of the last decade has been to reduce cost while maintaining this status. As the Navyâ s infrastructure decreases, so too does its ability to be an active participant in all aspects of ship operations and design. One way that the navy has achieved large savings is by using the Military Sealift Command to manage day to day operations of the Navyâ s auxiliary and underway replenishment ships. While these ships are an active part of the Navyâ s fighting force, they infrequently are put into harmâ s way. The natural progression in the design of these ships is to have them fully classified under current American Bureau of Shipping (ABS) rules, as they closely resemble commercial ships. The first new design to be fully classed under ABS is the T-AKE. The Navy and ABS consider the T-AKE program a trial to determine if a partnership between the two organizations can extend into the classification of all new naval ships. A major difficulty in this venture is how to translate the knowledge base which led to the development of current military specifications into rules that ABS can use for future ships. The specific task required by the Navy in this project is to predict the inherent availability of the new T-AKE class ship. To accomplish this task, the reliability of T-AKE equipment and machinery must be known. Under normal conditions reliability data would be obtained from past ships with similar mission, equipment and machinery. Due to the unique nature of the T-AKE acquisition, this is not possible. Because of the use of commercial off the shelf (COTS) equipment and machinery, military equipment and machinery reliability data can not be used directly to predict T-AKE availability. This problem is compounded by the fact that existing COTS equipment and machinery reliability data developed in commercial applications may not be applicable to a military application. A method for deriving reliability data for commercial equipment and machinery adapted or used in military applications is required. A Reliability Transform Method is developed that allows the interpolation of reliability data between commercial equipment and machinery operating in a commercial environment, commercial equipment and machinery operating in a military environment, and military equipment and machinery operating in a military environment. The reliability data for T-AKE is created using this Reliability Transform Method and the commercial reliability data. The reliability data is then used to calculate the inherent availability of T-AKE.
Master of Science
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2

Williamson, Jamie D. "Reliability of the Brief Assessment Model." Miami University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=miami1101994832.

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Wan, Bo. "Improved Usage Model for Web Application Reliability Testing." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23135.

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Testing the reliability of an application usually requires a good usage model that accurately captures the likely sequences of inputs that the application will receive from the environment. The models being used in the literature are mostly based on Markov chains. They are used to generate test cases that are statistically close to what the applica-tion is expected to receive when in production. In this thesis, we propose a model for reli-ability testing that is created directly from the log file of a web application. Our proposed model is also based on Markov chains and has two components: one component, based on a modified tree, captures the most frequent behaviors, while the other component is another Markov chain that captures infrequent behaviors. The result is a statistically cor-rect model that shows clearly what most users do on the site. The thesis also presents an evaluation method for estimating the accuracy of vari-ous reliability-testing usage models. The method is based on comparison between ob-served users’ traces and traces inferred from the usage model. Our method gauges the accuracy of the reliability-testing usage model by calculating the sum of goodness-of-fit values of each traces and scaling the result between 0 and 1. Finally, we present an experimental study on the log of a real web site and discuss the way to use proposed usage model to generate test sequences, as well as strength and weakness of the model for reliability testing.
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Stockwell, Kathryn S. "Automatic phased mission system reliability model generation." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/13583.

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There are many methods for modelling the reliability of systems based on component failure data. This task becomes more complex as systems increase in size, or undertake missions that comprise multiple discrete modes of operation, or phases. Existing techniques require certain levels of expertise in the model generation and calculation processes, meaning that risk and reliability assessments of systems can often be expensive and time-consuming. This is exacerbated as system complexity increases. This thesis presents a novel method which generates reliability models for phasedmission systems, based on Petri nets, from simple input files. The process has been automated with a piece of software designed for engineers with little or no experience in the field of risk and reliability. The software can generate models for both repairable and non-repairable systems, allowing redundant components and maintenance cycles to be included in the model. Further, the software includes a simulator for the generated models. This allows a user with simple input files to perform automatic model generation and simulation with a single piece of software, yielding detailed failure data on components, phases, missions and the overall system. A system can also be simulated across multiple consecutive missions. To assess performance, the software is compared with an analytical approach and found to match within 5% in both the repairable and non-repairable cases. The software documented in this thesis could serve as an aid to engineers designing new systems to validate the reliability of the system. This would not require specialist consultants or additional software, ensuring that the analysis provides results in a timely and cost-effective manner.
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Kallan, Michael A. "Characterizing reliability for a Faculty Climate Survey: Estimation model dependencies and reliability generalization." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280288.

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Methods. Four reliability estimation models were employed to obtain estimates for faculty appointment and gender group measures derived from four questionnaire scales of a Faculty Climate Survey. Faculty responses were analyzed via (a) coefficient alpha, (b) IRT-Rasch, (c) IRT-Unfolding, and (d) CFA methods. Estimates and their components were compared across-groups within scale and within-group across scales to determine differences among estimation models and to uniquely characterize those differences. Scale dimensionality was assessed per-scale per-group using CFA. Secondary analyses included: (a) independent and dependent-group tests to determine the statistical significance of coefficient alpha differences; (b) bootstrapping simulation to determine the effect of sample size on estimates; and (c) analysis of variance to determine whether attitudinal differences existed between appointment, gender, or appointment-by-gender groups. Results. (1) Reliability estimation models identified important differences between appointment and gender group estimates for scale measures and among scale estimates for each group's set of scale measures. (2) Models were not equally sensitive to detecting differences, either between groups or among scales per group. (3) Alpha and CFA estimates did not always function as lower- and upper-bounds of an expected estimate range: 30% of alpha-CFA range "endpoints" were underestimates of observed ranges. (4) IRT-based estimates were generally located between alpha and CFA estimates, closer to alpha than to CFA estimates. (5) IRT-Unfolding estimates were frequently but not always greater than IRT-Rasch estimates: 30% were less. (6) Alpha and CFA estimation components did not provide comparable item-level information; thus, alpha and CFA plans for characterizing and improving scales differed. (7) IRT-Rasch and IRT-Unfolding estimation components did not provide comparable person-measure information, thereby informing observed differences in IRT-based estimates. (8) Sample size had an effect on CFA estimation: samples of N = 50 achieved highest estimates; samples of N = 500 best reproduced original estimates and components. (9) Modeling error via CFA made meaningful contributions to understanding scale functioning. (10) ANOVA findings were potentially modifiable (e.g., effect sizes), considering obtained reliability estimates. Conclusion. Reliability estimates have group, measure, and model-dependencies that influence the size and nature of obtained estimates and must be accounted for when estimates are interpreted.
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Askvid, Per. "A Model-Based Approach for Reliability Prediction." Thesis, Linköping University, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54323.

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When developing products, reliability is an important factor that has to be considered. For safety critical systems it is important to know the probability that an item will perform a required function without failure under stated conditions for a stated period of time. The main goal of a reliability prediction analysis is to predict the rate at which the product of a system will fail. To perform this prediction there are a number of methodologies available.

This Master Thesis proposes a model-based approach for reliability prediction calculations based on the physics of failure and supported by analysis of test-data field returns and physical models provided by the FIDES methodology. FIDES based reliability models have been integrated into a model-based diagnosis environment for seamless integration with other safety assessment analysis.

The model-based diagnosis environment used in this thesis is model-based reasoner RODON developed by Uptime Solutions AB. Components that uses the FIDES methodology have been developed in RODON, where components can be combined to systems by drag and drop method. Usage profiles that are defined according to the FIDES methodology in RODON are not system specific, which makes them reusable in other systems. The developed library of components and usage profiles makes it easy to model complex systems and perform reliability predictions according to the FIDES methodology.

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Jiang, Zhihua. "A reliability model for systems undergoing remanufacture." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0005/MQ45429.pdf.

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Yerneni, Ashok. "A reliability model incorporating software quality metrics." Thesis, Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/50098.

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Анотація:
Large scale software development efforts in the past decade have posed a problem in terms of the reliability of the software. The size and complexity of software that is being developed is growing rapidly and integrating diverse pieces of software in the operational environment also poses severe reliability issues, resulting in increased development and operational costs. A number of reliability models have been defined in the literature to deal with problems of this kind. However, most of these models treat the system as a "black box" and do not consider the complexity of the software in its reliability predictions. Also, reliability is predicted after the system had been completely developed leaving little scope for any major design changes to improve system reliability. This thesis reports on an effort to develop a reliability model based on complexity metrics which characterize a software system and runtime metrics which reflect the degree of testing of the system. A complete development of the reliability model is presented here. The model is simple and reflects on our intuition of the software development process and our understanding of the significance of the complexity metrics. Credibility analysis is done on the model by simulating a number of systems and applying the model. Data collected from three FORTRAN coded systems developed for NASA Goddard was used as representative of the actual software systems. An analysis of the results is finally presented.
Master of Science
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Rodrigues, Genaina Nunes. "A model driven approach for software reliability prediction." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1446004/.

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Software reliability, one of the major software quality attributes, quantitatively expresses the continuity of correct service delivery. In current practice, reliability models are typically measurement-based models, and mostly employed in isolation at the later stage of the soft ware development process, after architectural decisions have been made that cannot easily be reversed early software reliability prediction models are often insufficiently formal to be ana- lyzable and not usually connected to the target system. We postulate it is possible to overcome these issues by supporting software reliability engineering from requirements to deployment using scenario specifications. We contribute a novel reliability prediction technique that takes into account the component structure exhibited in the scenarios and the concurrent nature of component-based systems by extending scenario specifications to model (1) the probability of component failure, and (2) scenario transition probabilities. Those scenarios are subsequently transformed into enhanced behaviour models to compute the system reliability. Additionally we enable the integration between reliability and development models through profiles that extend the core Unified Modelling Language (UML). By means of a reli ability profile, the architecture of a component-based system can express both method invoca tions and deployment relationships between the application components in one environment. To facilitate reliability prediction, and determine the impact of concurrency on systems reliability, we have extended the Label Transition System Analyser Tool (LTSA), implementing a plugin for reliability analysis. Finally, we evaluate our analysis technique with a case study focusing on Condor, a dis tributed job scheduler and resource management system. The purpose of the case study is to evaluate the efficacy of our analysis technique and to compare it with other reliability tech niques.
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10

Kim, Injoong. "Development of a knowledge model for the computer-aided design for reliability of electronic packaging systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/22708.

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Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2008.
Committee Co-Chair: Peak, Russell; Committee Co-Chair: Sitaraman, Suresh; Committee Member: Paredis, Christiaan; Committee Member: Pucha, Raghuram; Committee Member: Wong, C.
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Книги з теми "Reliability of model"

1

Veatch, Michael H. Mission reliability model users guide. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1986.

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Medina, Joseph M. Mission reliability model programmers guide. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1986.

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3

Jiang, Zhihua. A reliability model for systems undergoing remanufacture. Ottawa: National Library of Canada, 1999.

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4

Srinivasan, Raghavan, Bo Lan, Caroline Mozingo, James Bonneson, Craig Lyon, Bhagwant Persaud, and Geni Bahar. Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results. Washington, D.C.: Transportation Research Board, 2021. http://dx.doi.org/10.17226/26517.

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5

Dennison, Thomas E. Fitting and prediction uncertainty for a software reliability model. Monterey, Calif: Naval Postgraduate School, 1992.

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6

Duffy, Stephen F. Extension of a noninteractive reliability model for ceramic matrix composites. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1990.

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7

Optimizing reservoir resources: Including a new model for reservoir reliability. New York: Wiley, 1999.

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8

Bund, Robert E. J. Evaluation of a discrete modification of the continuous AMSAA reliability growth model. Monterey, California: Naval Postgraduate School, 1989.

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9

Portinale, Luigi. Modeling and analysis of dependable systems: A probabilistic graphical model perspective. New Jersey: World Scientific, 2015.

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10

Thalieb, Rio M. An accuracy analysis of Army Material System Analysis Activity discrete reliability growth model. Monterey, California: Naval Postgraduate School, 1988.

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Частини книг з теми "Reliability of model"

1

Weik, Martin H. "reliability model." In Computer Science and Communications Dictionary, 1463. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15998.

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Kumar, Vikas, and R. Vidhyalakshmi. "Reliability Model." In Reliability Aspect of Cloud Computing Environment, 111–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3023-0_5.

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Benedini, Marcello, and George Tsakiris. "Model Reliability." In Water Quality Modelling for Rivers and Streams, 245–63. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5509-3_20.

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4

Bieńkowska, Agnieszka, Katarzyna Tworek, and Anna Zabłocka-Kluczka. "Development of the Organizational Reliability Model." In Organizational Reliability, 42–180. 1 Edition. | New York : Routledge, 2020. | Series: Routledge studies in management, organizations and society: Routledge, 2020. http://dx.doi.org/10.4324/9781003047995-3.

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Fazlollahtabar, Hamed, and Mohammad Saidi-Mehrabad. "Reliability Model for AGV." In Studies in Systems, Decision and Control, 41–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14747-5_4.

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Kelly, Dana, and Curtis Smith. "Bayesian Model Checking." In Springer Series in Reliability Engineering, 39–50. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-187-5_4.

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Kolesnichenko, Anna, Anne Remke, Pieter-Tjerk de Boer, and Boudewijn R. Haverkort. "Model Checking Two Layers of Mean-Field Models." In Springer Series in Reliability Engineering, 341–69. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30599-8_13.

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Savchuk, Vladimir, and Chris P. Tsokos. "Theoretical Model for Engineering Reliability." In Bayesian Theory and Methods with Applications, 219–46. Paris: Atlantis Press, 2011. http://dx.doi.org/10.2991/978-94-91216-14-5_8.

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Krishna, G. Sri, and Rajib Mall. "Model-Based Software Reliability Prediction." In Information Systems, Technology and Management, 145–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12035-0_15.

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Christodoulakis, D., and G. Pantziou. "Another Software Reliability Prediction Model." In Fehlertolerierende Rechensysteme / Fault-Tolerant Computing Systems, 298–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-45628-2_26.

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Тези доповідей конференцій з теми "Reliability of model"

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Kim, Injoong, Suresh K. Sitaraman, and Russell S. Peak. "Reliability Objects Model: A Knowledge Model of System Design for Reliability." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79934.

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Анотація:
Designing complex systems that satisfy a target reliability is difficult because of complex assembly structures and logical connections, numerous components and associated failure modes, limited reliability data or prediction models, and multi-disciplinary nature. To overcome these difficulties and to design complex systems in a systematic way, this research aims to develop a knowledge model of system design for reliability, called Reliability Object Model. This knowledge model contains a) a new failure analysis structure, b) reliability metrics that represent random failures and wearout failures, c) algorithms that allocate, predict, and assess reliability using the failure analysis structure, d) rules for design changes. The use of Reliability Object Model is demonstrated by prototype reliability tools and simplified electronic systems.
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SCHABOWSKY, R., M. LUNIEWICZ, and M. RADLAUER. "Automated reliability model construction." In Astrodynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1986. http://dx.doi.org/10.2514/6.1986-2091.

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Krajcuskova, Zuzana. "Unconventional Reliability Growth Model." In 2008 18th International Conference Radioelektronika. IEEE, 2008. http://dx.doi.org/10.1109/radioelek.2008.4542727.

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Krajcuskova, Zuzana. "New reliability growth model." In 2009 19th International Conference Radioelektronika (RADIOELEKTRONIKA). IEEE, 2009. http://dx.doi.org/10.1109/radioelek.2009.5158740.

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Schneidewind, Norm, and Mike Hinchey. "A Complexity Reliability Model." In 2009 20th International Symposium on Software Reliability Engineering (ISSRE 2009). IEEE, 2009. http://dx.doi.org/10.1109/issre.2009.10.

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6

Amstel, M. F. van, M. G. J. van den Brand, and L. J. P. Engelen. "Using a DSL and Fine-Grained Model Transformations to Explore the Boundaries of Model Verification." In Reliability Improvement Companion. IEEE, 2011. http://dx.doi.org/10.1109/ssiri-c.2011.26.

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7

Schneidewind, Norm, and Mike Hinchey. "A Reliability Model for Complex Systems." In Reliability Improvement Companion. IEEE, 2011. http://dx.doi.org/10.1109/ssiri-c.2011.23.

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8

Alonso, Pablo David, Angel Adrian Moreno, and Franco Luis Palmieri. "Petroleum Facilities Reliability Assessment Model." In Latin American & Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/107012-ms.

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9

Krajcuskova, Zuzana. "New unconventional reliability growth model." In 2010 20th International Conference Radioelektronika (RADIOELEKTRONIKA 2010). IEEE, 2010. http://dx.doi.org/10.1109/radioelek.2010.5478580.

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10

Yongqiang Zhang and Jingjie Yin. "Software reliability model by AGP." In 2008 IEEE International Conference on Industrial Technology - (ICIT). IEEE, 2008. http://dx.doi.org/10.1109/icit.2008.4608638.

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Звіти організацій з теми "Reliability of model"

1

Schneidewind, Norman F. MCTSSA Software Reliability Handbook. Volume IV: Schneidewind Software Reliability and Metrics Model Tool List. Fort Belvoir, VA: Defense Technical Information Center, May 1997. http://dx.doi.org/10.21236/ada329130.

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2

Schneidewind, Norman F., and Julie Heineman. MCTSSA Software Reliability Handbook. Volume IV: Schneidewind Software Reliability and Metrics Model Tool List. Fort Belvoir, VA: Defense Technical Information Center, May 1997. http://dx.doi.org/10.21236/ada329131.

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3

Schneidewind, Norman F. MCTSSA Software Reliability Handbook. Volume III: Schneidewind Software Reliability and Metrics Model Tool List. Fort Belvoir, VA: Defense Technical Information Center, June 1997. http://dx.doi.org/10.21236/ada329132.

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4

Guler Yigitoglu, Askin, Thomas Harrison, and Michael Scott Greenwood. Nuclear Hybrid Energy System Reliability Model Progress Report. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1615830.

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5

Ferraiolo, Hildegard. A Credential Reliability and Revocation Model for Federated Identities. Gaithersburg, MD: National Institute of Standards and Technology, November 2012. http://dx.doi.org/10.6028/nist.ir.7817.

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6

Logan, R., and C. Nitta. Solution Verification Linked to Model Validation, Reliability, and Confidence. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/923117.

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7

Rabiti, Cristian, Andrea Alfonsi, Dongli Huang, Frederick Gleicher, Bei Wang, Hany S. Adbel-Khalik, Valerio Pascucci, and Curtis L. Smith. System Reliability Analysis Capability and Surrogate Model Application in RAVEN. Office of Scientific and Technical Information (OSTI), November 2015. http://dx.doi.org/10.2172/1376079.

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8

Choi, Kyung K. System Reliability-Based Design Optimization Under Input and Model Uncertainties. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada605988.

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9

Fuentes, Rolando, Jorge Blazquez, and Iqbal Adjali. Reorganizing Power Markets: A Reliability Insurance Business Model for Utilities. King Abdullah Petroleum Studies and Research Center, November 2018. http://dx.doi.org/10.30573/ks--2018-dp45.

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10

Davis, J. Lynn. Final Report: System Reliability Model for Solid-State Lighting (SSL) Luminaires. Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1360770.

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