Bibliografie tematiche / Reliability

Letteratura scientifica selezionata sul tema "Reliability"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 7 luglio 2024

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Articoli di riviste sul tema "Reliability"

Frise, Matthew. "The reliability problem for reliabilism". Philosophical Studies 175, n. 4 (28 marzo 2017): 923–45. http://dx.doi.org/10.1007/s11098-017-0899-0.

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Adams, Frederick, e David Kline. "NOMIC RELIABILISM: WEAK RELIABILITY IS NOT ENOUGH". Southern Journal of Philosophy 25, n. 4 (dicembre 1987): 433–43. http://dx.doi.org/10.1111/j.2041-6962.1987.tb01634.x.

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Tantri, B. Roopashri, e N. N. Murulidhar. "An Efficient Estimator of Reliability for Exponential Class Software Reliability Models". Lecture Notes on Software Engineering 2, n. 3 (2014): 201–4. http://dx.doi.org/10.7763/lnse.2014.v2.123.

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SUZUKI, Kazuyuki. "Reliability and Reliability Management". Journal of The Surface Finishing Society of Japan 70, n. 6 (1 giugno 2019): 290–96. http://dx.doi.org/10.4139/sfj.70.290.

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Kazhakov, Stefan. "RELIABILITY TECHNIQUES FOR RFID". Journal Scientific and Applied Research 12, n. 1 (14 novembre 2017): 27–39. http://dx.doi.org/10.46687/jsar.v12i1.224.

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Radio Frequency Identification (RFID) technology has the potential to dramatically improve numerous industrial practices. However, it still faces many challenges, including security and reliability, which may limit its use in many application scenarios. While security has received considerable attention, reliability has escaped much of the research scrutiny. Radio Frequency Identification (RFID) technology is growing rapidly across many different industries. Developers apply the technology not only in traditional applications such as asset or inventory tracking, but also in security services such as electronic passports and RFID-embedded credit cards. However, RFID technology also raises a number of concerns regarding privacy and security.

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Sariyatno, Bintang, e Slamet Suyanto. "Instrumen lembar observasi pengukuran ketercapaian KKNI level 6 pada pengajaran mikro pendidikan biologi". Measurement In Educational Research (Meter) 1, n. 1 (1 maggio 2021): 12. http://dx.doi.org/10.33292/meter.v1i1.107.

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Penelitian pengembangan Instrumen lembar observasi ini bertujuan untuk mengembangkan instrumen lembar observasi pengukuran pengajaran mikro pada program studi pendidikan biologi yang valid dan reliabel, sehingga diperoleh gambaran ketercapaian KKNI level 6. Motode penelitian yang digunakan adalah metode penelitian dan pengembangan (research and development). Validitas isi pada penelitian ini telah memiliki kesesuaian konteks aitem dengan tujuan pengukuran dan aitem-aitem instrumen lembar observasi telah merepresentasikan ciri-ciri atribut yang diukur. Instrumen pengukuran pengajaran mikro ini terdiri atas 4 deskriptor spesifik KKNI level 6; 11 learning outcome, 27 indikator utama, dan 74 aitem.Koefisien reliabilitas instrumen ini berturut-turut, yaitu deskriptor spesifik pertama memiliki koefisien reliabilitas 0,986 yang termasuk reliabilitas tinggi, deskriptor spesifik ke dua memiliki koefisien reliabilitas 0,984 yang termasuk relibilitas tinggi, deskriptor spesifik ke tiga memiliki koefisien reliabilitas 0,841 yang termasuk reliabilitas tinggi, dan deskriptor spesifik ke empat memiliki koefisien reliabilitas 0,964 yang termasuk reliabilitas tinggi. Measuring observation form instruments for level 6 of KKNI achievement on microteaching biology education AbstractThis study aims to find out the content validity and the reliability of observation form instrument to measure the achievement of micro of level 6 KKNI in Biology Education courses.This research is the Research and Development (R & D). The results of the study show that the validity of the content in this study have suitability context items with the purpose of measuring instrument and items instrument observation sheet has represented the characteristics of attributes measured. Observation form of microteaching instrument consists of 4 specific descriptors of KKNI level 6; 11 learning outcomes, 27 main indicators, and 74 items. Instrument reliability of the instrument observation sheet microteaching for each descriptors specific, among other: descriptors specific first coefficient reliability of 0.986 which suggests the reliability is exeellent descriptors specific. The second has a coefficient of reliability 0.984 which suggests the reliability is excellent descriptors specific. The third has a coefficient of reliability 0.841 which suggests reliability excellent descriptors specific. The fourth has a coefficient of reliability 0.964 which suggests reliability is excellent.

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Taylor, John M. "Reliability". Journal of Nursing Education 60, n. 2 (febbraio 2021): 65–66. http://dx.doi.org/10.3928/01484834-20210120-02.

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Jackson, A. S., G. Atkinson e W. G. Hopkins. "RELIABILITY". Medicine & Science in Sports & Exercise 33, n. 5 (maggio 2001): S173. http://dx.doi.org/10.1097/00005768-200105001-00983.

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Flowers, Kenneth R. "Reliability". Journal of Hand Therapy 14, n. 1 (gennaio 2001): 1. http://dx.doi.org/10.1016/s0894-1130(01)80017-x.

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Keller-McNulty, Sallie, Alyson Wilson e Christine Anderson-Cook. "Reliability". Statistical Science 21, n. 4 (novembre 2006): 427. http://dx.doi.org/10.1214/088342306000000664.

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Tesi sul tema "Reliability"

Mafu, Masakheke. "Reliability analysis: assessment of hardware and human reliability". Thesis, Rhodes University, 2017. http://hdl.handle.net/10962/6280.

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Most reliability analyses involve the analysis of binary data. Practitioners in the field of reliability place great emphasis on analysing the time periods over which items or systems function (failure time analyses), which make use of different statistical models. This study intends to introduce, review and investigate four statistical models for modeling failure times of non-repairable items, and to utilise a Bayesian methodology to achieve this. The exponential, Rayleigh, gamma and Weibull distributions will be considered. The performance of the two non-informative priors will be investigated. An application of two failure time distributions will be carried out. To meet these objectives, the failure rate and the reliability functions of failure time distributions are calculated. Two non-informative priors, the Jeffreys prior and the general divergence prior, and the corresponding posteriors are derived for each distribution. Simulation studies for each distribution are carried out, where the coverage rates and credible intervals lengths are calculated and the results of these are discussed. The gamma distribution and the Weibull distribution are applied to failure time data.The Jeffreys prior is found to have better coverage rate than the general divergence prior. The general divergence shows undercoverage when used with the Rayleigh distribution. The Jeffreys prior produces coverage rates that are conservative when used with the exponential distribution. These priors give, on average, the same average interval lengths and increase as the value of the parameter increases. Both priors perform similar when used with the gamma distribution and the Weibull distribution. A thorough discussion and review of human reliability analysis (HRA) techniques will be considered. Twenty human reliability analysis (HRA) techniques are discussed; providing a background, description and advantages and disadvantages for each. Case studies in the nuclear industry, railway industry, and aviation industry are presented to show the importance and applications of HRA. Human error has been shown to be the major contributor to system failure.

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Chua, See Ju. "On the reliability of Type II censored reliability analyses". Thesis, Swansea University, 2009. https://cronfa.swan.ac.uk/Record/cronfa42621.

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This thesis considers the analysis of reliability data subject to censoring, and, in particular, the extent to which an interim analysis - here, using information based on Type II censoring - provides a guide to the final analysis. Under a Type II censored sampling, a random sample of n units is put on test simultaneously, and the test is terminated as soon as r (1 ≤ r ≤ n, although we are usually interested in r < n) failures are observed. In the case where all test units were observed to fail (r = n), the sample is complete. From a statistical perspective, the analysis of the complete sample is to be preferred, but, in practice, censoring is often necessary; such sampling plan can save money and time, since it could take a very long time for all units to fail in some instances. From a practical perspective, an experimenter may be interested to know the smallest number of failures at which the experiment can be reasonably or safely terminated with the interim analysis still providing a close and reliable guide to the analysis of the final, complete data. In this thesis, we aim to gain more insight into the roles of censoring number r and sample size n under this sampling plan. Our approach requires a method to measure the precision of a Type II censored estimate, calculated at censoring level r, in estimating the complete estimate, and hence the study of the relationship between interim and final estimates. For simplicity, we assume that the lifetimes follow the exponential distribution, and then adopt the methods to the two- parameter Weibull and Burr Type XII distributions, both are widely used in reliability modelling. We start by presenting some mathematical and computational methodology for estimating model parameters and percentile functions, by the method of maximum likelihood. Expressions for the asymptotic variances and covariances of the estimators are given. In practice, some indication of the likely accuracy of these estimates is often desired; the theory of asymptotic Normality of maximum likelihood estimator is convenient, however, we consider the use of relative likelihood contour plots to obtain approximate confidence regions of parameters in relatively small samples. Finally, we provide formulae of the correlations between the interim and final maximum likelihood estimators of model parameters and a particular percentile function, and discuss some practical implications of our work, based on the results obtained from published data and simulation experiments.

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Brunelle, Russell Dedric. "Customer-centered reliability measures for flexible multistate reliability models /". Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/10691.

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Saini, Gagandeep Singh. "Reliability-based design with system reliability and design improvement". Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Saini_09007dcc8070d586.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2009.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 23, 2009) Includes bibliographical references (p. 66-68).

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Unlusoy, Ozlem. "Reliability Analysis Process And Reliabilty Improvement Of An Inertial Measurement Unit (imu)". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612387/index.pdf.

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Reliability is one of the most critical performance measures of guided missile systems. It is directly related to missile mission success. In order to have a high reliability value, reliability analysis should be carried out at all phases of the system design. Carrying out reliability analysis at all the phases of system design helps the designer to make reliability related design decisions in time and update the system design. In this study, reliability analysis process performed during the conceptual design phase of a Medium Range Anti-Tank Missile System Inertial Measurement Unit (IMU) was introduced. From the reliability requirement desired for the system, an expected IMU reliability value was derived by using reliability allocation methods. Then, reliability prediction for the IMU was calculated by using Relex Software. After that, allocated and predicted reliability values of the IMU were compared. It was seen that the predicted reliability value of the IMU did not meet the required reliability value. Therefore, reliability improvement analysis was carried out.

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ROBINSON, DAVID GERALD. "MODELING RELIABILITY IMPROVEMENT DURING DESIGN (RELIABILITY GROWTH, BAYES, NON PARAMETRIC)". Diss., The University of Arizona, 1986. http://hdl.handle.net/10150/183971.

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Past research into the phenomenon of reliability growth has emphasised modeling a major reliability characteristic in terms of a specific parametric function. In addition, the time-to-failure distribution of the system was generally assumed to be exponential. The result was that in most cases the improvement was modeled as a nonhomogeneous Poisson process with intensity λ(t). Major differences among models centered on the particular functional form of the intensity function. The popular Duane model, for example, assumes that λ(t) = β(1 – α)t ⁻ᵅ. The inability of any one family of distributions or parametric form to describe the growth process resulted in a multitude of models, each directed toward answering problems encountered with a particular test situation. This thesis proposes two new growth models, neither requiring the assumption of a specific function to describe the intensity λ(t). Further, the first of the models only requires that the time-to-failure distribution be unimodal and that the reliability become no worse as development progresses. The second model, while requiring the assumption of an exponential failure distribution, remains significantly more flexible than past models. Major points of this Bayesian model include: (1) the ability to encorporate data from a number of test sources (e.g. engineering judgement, CERT testing, etc.), (2) the assumption that the failure intensity is stochastically decreasing, and (3) accountability of changes that are incorporated into the design after testing is completed. These models were compared to a number of existing growth models and found to be consistently superior in terms of relative error and mean-square error. An extension to the second model is also proposed that allows system level growth analysis to be accomplished based on subsystem development data. This is particularly significant, in that, as systems become larger and more complex, development efforts concentrate on subsystem levels of design. No analysis technique currently exists that has this capability. The methodology is applied to data sets from two actual test situations.

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Kaya, Deniz. "Software Reliability Assessment". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606466/index.pdf.

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In spite of the fact that software reliability studies have attracted great deal of attention from different disciplines in 1970s, applications of the subject have rarely been involved in the software industry. With the rise of technological advances especially in the military electronics field, reliability of software systems gained importance. In this study, a company in the defense industries is inspected for their abilities and needs regarding software reliability, and an improvement proposal with metrics measurement system is formed. A computer tool is developed for the evaluation of the performance of the improvement proposal. Results obtained via this tool indicate improved abilities in the development of reliable software products.

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Chan, Pee Yuaw. "Software reliability prediction". Thesis, City, University of London, 1986. http://openaccess.city.ac.uk/18127/.

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Two methods are proposed to find the maximum likelihood parameter estimates of a number of software reliability models. On the basis of the results from analysing 7 sets of real data, these methods are found to be both efficient and reliable. The simple approach of adapting software reliability predictions by Keiller and Littlewood (1984) can produce improved predictions, but at the same time, introduces a lot of internal noise into the adapted predictions. This is due to the fact that the adaptor is a joined-up function. An alternative adaptive procedure, which involves the parametric spline adaptor, can produce at least as good adapted predictions without the predictions being contaminated by internal noise as in the simple approach. Miller and Sofer (1986a) proposed a method for estimating the failure rate of a program non-parametrically. Here, these non-parametric rates are used to produce reliability predictions and their quality is analysed and compared with the parametric predictions.

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Bhusal, Prabodh. "Distribution reliability analysis". Thesis, Wichita State University, 2007. http://hdl.handle.net/10057/1532.

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This thesis presents an example for optimization of distribution maintenance scheduling of a recloser. It applies a risk reduction technique associated with maintenance of the equipment. Furthermore, this thesis examines how various distribution system designs, including distributed energy resources (DER), affect distribution reliability indices, System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI).
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering
"December 2007."

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Wright, David R. "Software reliability prediction". Thesis, City University London, 2001. http://openaccess.city.ac.uk/8387/.

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This thesis presents some extensions to existing methods of software reliability estimation and prediction. Firstly, we examine a technique called 'recalibration' by means of which many existing software reliability prediction algorithms assess past predictive performance in order to improve the accuracy of current reliability predictions. This existing technique for forecasting future failure times of software is already quite general. Indeed, whenever your predictions are produced in the form of time-to-failure distributions, successively as more actual failure times are observed, you can apply recalibration irrespective both of which probabilistic software reliability model and of which statistical inference technique you are using. In the current work we further generalise the recalibration method to those situations where empirical failure data take the form of failure-counts rather than precise inter-failure times. We then briefly explore how the reasoning we have used, in this extension of recalibration to the prediction of failure-count sequences, might further extend to recalibration of other representations of predicted reliability. Secondly, the thesis contains a theoretical discussion of some modelling possibilities for improving software reliability predictions by the incorporation of disparate sources of data. There are well established techniques for forecasting the reliability of a particular software product using as data only the past failure behaviour of that software under statistically representative operational testing. However, there may sometimes be reasons for seeking improved predictive accuracy by using data of other kinds too, rather than relying on this single source of empirical evidence. Notable among these is the economic impracticability, in many cases, of obtaining sufficient, representative software failure vs. time data (from execution of the particular product in question) to determine, by inference applied to software reliability growth models, whether or not a high reliability requirement has been achieved in a particular case, prior to extensive operational use of the software in question. For example, this problem arises in particular for safety-critical systems, whose required reliability is often extremely high. An accurate reliability assessment is often required in advance of a decision whether to release the software for actual use in the field. Another argument for attempting to determine other usable data sources for software reliability prediction is the value that would attach to rigorous empirical confirmation or refutation of any of the many existing theories and claims about what are the factors of software reliability, and how these factors may interact, in some given context. In those cases, such as some safety-critical systems, in which assessment of a high reliability level is required at an early stage, the necessary assessment is in practice often currently carried out rather informally, and often does claim to take account of many different types of evidence experience of previous, similar systems; evidence of the efficacy of the development process; expert judgement, etc-to supplement the limited available data on past failure vs. time behaviour which emanates from testing of the software within a realistic usage environment. Ideally, we would like this assessment to allow all such evidence to be combined into a final numerical measure of reliability in a scientifically more rigorous way. To address these problems, we first examine some candidate general statistical regression models used in other fields such as medicine and insurance and discuss how these might be applied to prediction of software reliability. We have here termed these models explanatory variables regression models. The goal here would be to investigate statistically how to explain differences in software failure behaviour in terms of differences in other measured characteristics of a number of different statistical 'individuals', or 'experimental units': We discuss the interpretation, within the software reliability context, of this statistical concept of an 'individual', with our favoured interpretation being such that a single statistical reliability regression model would be used to model simultaneously a family of parallel series of inter-failure times emanating from measurably different software products or from measurably different installations of a single software product. In statistical regression terms here, each one of these distinct failure vs. time histories would be the 'response variable' corresponding to one of these 'individuals'. The other measurable differences between these individuals would be captured in the model as explanatory variable values which would differ from one individual to another. Following this discussion, we then leave general regression models to examine a slightly different theoretical approach-to essentially the same question of how to incorporate diverse data within our predictions-through an examination of models for 'unexplained' differences between individuals' failure behaviours. Here, rather than assuming the availability of putative 'explanatory variables' to distinguish our statistical individuals and 'explain' the way that their reliabilities differ, we instead use randomness alone to model their differences in reliability. We have termed the class of models produced by this approach similar products models, meaning models in which we regard the individuals' different likely failure vs. time behaviours as initially (i. e. a priori) indistinguishable to us: Here, either we cannot (or we choose not to attempt with a formal model to) explain the differences between individuals' reliabilities in terms of other metrics applied to our individuals, but we do still expect that the 'similar products" (i. e. the individuals') reliabilities will be different from each other: We postulate the existence of a single probability distribution from which we may assume our individuals' true, unknown reliabilities to have all been drawn independently in a random fashion. We present some mathematical consequences, showing how, within such a modelling framework, prior belief about the distribution of reliabilities assumes great importance for model consequences. We also present some illustrative numerical results that seem to suggest that experience from previous products or environments, so represented within the model-even where very high operational dependability has been achieved in such previous cases-can only modestly improve our confidence in the reliability of a new product, or of an existing product when transferred to a new environment.

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Libri sul tema "Reliability"

Blischke, Wallace R., e D. N. Prabhakar Murthy. Reliability. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/9781118150481.

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Reliability. Oxford: Oxford University Press, 2010.

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Petrucci, L. G. D. Reliability. Solihull: L & A Press, 1985.

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Amar, A. D. Objective assessment of operational reliability: 1989 results of passenger car breakdown in N.Y.-N.J. area. South Orange, N.J: W. Paul Stillman School of Business Administration, Seton Hall University, 1989.

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Wang, Cao. Structural Reliability and Time-Dependent Reliability. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62505-4.

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1951-, Thompson Bruce, a cura di. Score reliability: Contemporary thinking on reliability issues. Thousand Oaks, Calif: SAGE Publications, 2003.

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1951-, Thompson Bruce, a cura di. Score reliability: Contemporary thinking on reliability issues. Thousand Oaks, Calif: SAGE Publications, 2003.

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T, O'Connor Patrick D., e Arab School on Science and Technology., a cura di. Reliability engineering. Washington: Hemisphere Pub. Co., 1988.

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Nash, Franklin R. Reliability Assessments. Boca Raton : Taylor & Francis, 2016. | “A CRC title.”: CRC Press, 2017. http://dx.doi.org/10.1201/9781315371009.

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Bradley, Edgar. Reliability Engineering. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor &: CRC Press, 2016. http://dx.doi.org/10.1201/9781315367422.

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Capitoli di libri sul tema "Reliability"

Lutz, Josef, Heinrich Schlangenotto, Uwe Scheuermann e Rik De Doncker. "Reliability and Reliability Testing". In Semiconductor Power Devices, 489–581. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70917-8_12.

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Shrout, Patrick E. "Reliability". In Textbook of Psychiatric Epidemiology, 73–85. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470976739.ch5.

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Kerre, Etienne, Takehisa Onisawa, Bart Cappelle e Igor Gazdik. "Reliability". In The Handbooks of Fuzzy Sets Series, 391–420. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5645-9_12.

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Lister, A. M., e R. D. Eager. "Reliability". In Fundamentals of Operating Systems, 153–65. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4757-2252-9_10.

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Blischke, Wallace R., M. Rezaul Karim e D. N. Prabhakar Murthy. "Reliability". In Springer Series in Reliability Engineering, 35–57. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-647-4_3.

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Sijtsma, Klaas, e L. Andries van der Ark. "Reliability". In Encyclopedia of Personality and Individual Differences, 4385–402. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1348.

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Levesque, Roger J. R. "Reliability". In Encyclopedia of Adolescence, 2339. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1695-2_593.

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Bellamy, Nicholas. "Reliability". In Musculoskeletal Clinical Metrology, 11–24. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2218-4_3.

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Kenny, Peter. "Reliability". In Better Business Decisions from Data, 211–18. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0184-8_21.

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Nahler, Gerhard. "reliability". In Dictionary of Pharmaceutical Medicine, 159. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1217.

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Atti di convegni sul tema "Reliability"

"Reliability". In 2016 International Conference on Field-Programmable Technology (FPT). IEEE, 2016. http://dx.doi.org/10.1109/fpt.2016.7929432.

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Djambazova, Edita. "Achieving System Reliability Using Reliability Adjustment". In CompSysTech '22: International Conference on Computer Systems and Technologies 2022. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3546118.3546129.

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Cai, Jinchen, e Chunhong Chen. "Circuit Reliability Analysis Using Signal Reliability Correlations". In 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2017. http://dx.doi.org/10.1109/qrs-c.2017.34.

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Ai, Jun, Hanyu Pei e Liang Yan. "Software Reliability Virtual Testing for Reliability Assessment". In 2014 IEEE 8th International Conference on Software Security and Reliability-Companion (SERE-C). IEEE, 2014. http://dx.doi.org/10.1109/sere-c.2014.24.

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O'HARA, K. "Liquid propulsion system reliability - 'Design for reliability'". In 25th Joint Propulsion Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-2628.

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Pey, Kin Leong, e Takeshi Furusawa. "CMOS & Interconnect Reliability - Advanced Dielectric Reliability". In 2007 IEEE International Electron Devices Meeting. IEEE, 2007. http://dx.doi.org/10.1109/iedm.2007.4418981.

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Eagan, Brendan, Jais Brohinsky, Jingyi Wang e David Williamson Shaffer. "Testing the reliability of inter-rater reliability". In LAK '20: 10th International Conference on Learning Analytics and Knowledge. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375462.3375508.

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Caers, J. F. J. M., X. J. Zhao, J. Mooren, L. Stulens e E. Eggink. "Design for reliability - a Reliability Engineering Framework". In High Density Packaging (ICEPT-HDP). IEEE, 2010. http://dx.doi.org/10.1109/icept.2010.5582735.

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Linick, Bruce. "Peeling the Reliability Onion: Telecommunications Services Reliability". In 2008 Conference on Optical Fiber Communication - OFC 2008 Collocated National Fiber Optic Engineers. IEEE, 2008. http://dx.doi.org/10.1109/ofc.2008.4528315.

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Zhan, Suoyue, Khawja Sikander e Chunhong Chen. "Allocating Gate Reliability for Circuit Reliability Optimization". In 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2019. http://dx.doi.org/10.1109/mwscas.2019.8884830.

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Rapporti di organizzazioni sul tema "Reliability"

Mislevy, Robert J. Can There Be Reliability Without Reliability?".". Fort Belvoir, VA: Defense Technical Information Center, ottobre 1994. http://dx.doi.org/10.21236/ada289480.

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Wayne, Martin. Reliability Growth Planning with Reliability Assurance Testing. Aberdeen Proving Ground, MD: DEVCOM Analysis Center, marzo 2023. http://dx.doi.org/10.21236/ad1196219.

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Barlow, Richard E., William S. Jewell e Sheldon M. Ross. Reliability Analysis. Fort Belvoir, VA: Defense Technical Information Center, agosto 1986. http://dx.doi.org/10.21236/ada187220.

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Samaniego, Francisco J. Studies in Reliability Economics and Reliability Modeling and Inference. Fort Belvoir, VA: Defense Technical Information Center, agosto 2008. http://dx.doi.org/10.21236/ada500005.

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Ashwill, Thomas D., Alistair B. Ogilvie e Joshua A. Paquette. Blade reliability collaborative :. Office of Scientific and Technical Information (OSTI), aprile 2013. http://dx.doi.org/10.2172/1095939.

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Woloshun, Keith Albert. Target Window Reliability. Office of Scientific and Technical Information (OSTI), febbraio 2016. http://dx.doi.org/10.2172/1238127.

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VARIAN ASSOCIATES INC SANTA CLARA CA. MODFET Reliability Study. Fort Belvoir, VA: Defense Technical Information Center, maggio 1988. http://dx.doi.org/10.21236/ada204711.

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Ellison, Robert J. Assuring Software Reliability. Fort Belvoir, VA: Defense Technical Information Center, agosto 2014. http://dx.doi.org/10.21236/ada609884.

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Hu, Chenming. VLSI Reliability Research. Fort Belvoir, VA: Defense Technical Information Center, agosto 1992. http://dx.doi.org/10.21236/ada254883.

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Gates, R. K., G. J. Gibson e K. K. McLain. Reliability Growth Prediction. Fort Belvoir, VA: Defense Technical Information Center, settembre 1986. http://dx.doi.org/10.21236/ada176128.

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