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Статті в журналах з теми "Reliability of model"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

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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3

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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9

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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11

Park, Bumjin. "Improve the Reliability Measures of Bus Arrival Time Estimation Model." Journal of the Korean Society of Civil Engineers 34, no. 2 (2014): 597. http://dx.doi.org/10.12652/ksce.2014.34.2.0597.

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12

Lindén, Julia, Ulf Sellgren, and Anders Söderberg. "Model-based reliability analysis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, no. 3 (July 14, 2016): 277–88. http://dx.doi.org/10.1017/s0890060416000251.

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AbstractThe main function of a heavy truck is to transport goods, with ton-kilometers/year as an example of a major quantitative performance measure. Furthermore, the truck is directly operated by a driver, who has several additional functional requirements, of both ergonomic and communicative characters. Failure of these functions may be a subjective experience, differing between drivers, but the failures are still important. Today's just-in-time delivery systems rely on getting the goods on time, and this requires high availability. Availability is reduced not only by technical failures but also by subjectively experienced failures, because these also require repairs, or downtime. Product reliability is a systems property that cannot be attributed to a single component. It is in many cases related to interaction between components, or to interaction between humans and the technical system, in the case of subjectively experienced failures. Reliability assessments of systems with interactive functions require a system model that includes the interfaces between the technical system and human features that are carriers of interactive functions. This paper proposes a model of system architecture, for the purpose of reliability assessments, that integrates different and complementary representations, such as function–means diagrams and a design structure matrix. The novelty of the presented approach is that it treats and integrates the technical and the human subsystems through the human–technical system interfaces. The proposed systems reliability approach is described and verified with a component analysis case study of an extended truck cab and driver system.

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13

SCHNEIDEWIND, NORMAN. "COMPLEXITY-DRIVEN RELIABILITY MODEL." International Journal of Reliability, Quality and Safety Engineering 15, no. 05 (October 2008): 479–94. http://dx.doi.org/10.1142/s0218539308003179.

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Анотація:

A model of software complexity and reliability is developed that uses an evolutionary process to transition from one software system to the next while complexity metrics are used to predict the reliability for each system. Systems are tested until the software passes defect presence criteria and is released. Testing criteria are based on defect count, defect density, and testing efficiency predictions exceeding specified thresholds. In addition, another type of testing efficiency — a directed graph representing the complexity of the software and defects embedded in the code — is used to evaluate the efficiency of defect detection in NASA satellite system software. Complexity metrics were found to be good predictors of defects and testing efficiency in this evolutionary process.

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14

Khoshgoftaar, Taghi M., and Timothy G. Woodcock. "Software reliability model selection." Quality and Reliability Engineering 8, no. 5 (1992): 457–69. http://dx.doi.org/10.1002/qre.4680080509.

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15

Bierbaum, R. L., T. D. Brown, and T. J. Kerschen. "Model-based reliability analysis." IEEE Transactions on Reliability 51, no. 2 (June 2002): 133–40. http://dx.doi.org/10.1109/tr.2002.1011517.

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16

Stanley, Leanne M., and Michael C. Edwards. "Reliability and Model Fit." Educational and Psychological Measurement 76, no. 6 (July 11, 2016): 976–85. http://dx.doi.org/10.1177/0013164416638900.

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The purpose of this article is to highlight the distinction between the reliability of test scores and the fit of psychometric measurement models, reminding readers why it is important to consider both when evaluating whether test scores are valid for a proposed interpretation and/or use. It is often the case that an investigator judges both the reliability of scores and the fit of a corresponding measurement model to be either acceptable or unacceptable for a given situation, but these are not the only possible outcomes. This article focuses on situations in which model fit is deemed acceptable, but reliability is not. Data were simulated based on the item characteristics of the PROMIS (Patient Reported Outcomes Measurement Information System) anxiety item bank and analyzed using methods from classical test theory, factor analysis, and item response theory. Analytic techniques from different psychometric traditions were used to illustrate that reliability and model fit are distinct, and that disagreement among indices of reliability and model fit may provide important information bearing on a particular validity argument, independent of the data analytic techniques chosen for a particular research application. We conclude by discussing the important information gleaned from the assessment of reliability and model fit.

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17

Koval, D. O., and J. P. Ratusz. "Substation reliability simulation model." IEEE Transactions on Industry Applications 29, no. 5 (1993): 1012–17. http://dx.doi.org/10.1109/28.245727.

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18

de Bustamante, Amalio Saiz, and Barbara Saiz de Bustamante. "Multinomial-exponential reliability function: a software reliability model." Reliability Engineering & System Safety 79, no. 3 (March 2003): 281–88. http://dx.doi.org/10.1016/s0951-8320(02)00160-6.

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19

Horn, R., and S. J. Korn. "Model selection: reliability and bias." Biophysical Journal 55, no. 2 (February 1989): 379–81. http://dx.doi.org/10.1016/s0006-3495(89)82816-4.

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20

Suresh, Nalina, A. N. V. Rao, and A. J. G. Babu. "A software reliability growth model." International Journal of Quality & Reliability Management 13, no. 3 (April 1996): 84–94. http://dx.doi.org/10.1108/02656719610116108.

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21

Alsherri, A., and K. P. George. "Reliability Model for Pavement Performance." Journal of Transportation Engineering 114, no. 3 (May 1988): 294–306. http://dx.doi.org/10.1061/(asce)0733-947x(1988)114:3(294).

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22

Goulter, Ian C., and Francois Bouchart. "Reliability‐Constrained Pipe Network Model." Journal of Hydraulic Engineering 116, no. 2 (February 1990): 211–29. http://dx.doi.org/10.1061/(asce)0733-9429(1990)116:2(211).

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23

., Edward E. Osakue. "A LOGNORMAL RELIABILITY DESIGN MODEL." International Journal of Research in Engineering and Technology 05, no. 08 (August 25, 2016): 245–59. http://dx.doi.org/10.15623/ijret.2016.0508043.

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24

Kwon, Hyuk Jae. "RELIABILITY MODEL FOR STORM SEWER." Environmental Engineering and Management Journal 13, no. 1 (2014): 181–90. http://dx.doi.org/10.30638/eemj.2014.022.

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25

Zhou, Ying, and Joseph Davis. "Open source software reliability model." ACM SIGSOFT Software Engineering Notes 30, no. 4 (July 2005): 1–6. http://dx.doi.org/10.1145/1082983.1083273.

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26

Ejembi, Emmanuel, and Shawulu Hunira Nggada. "Reliability Evaluation under Shock Model." International Journal of Control and Automation 11, no. 8 (August 31, 2018): 127–38. http://dx.doi.org/10.14257/ijca.2018.11.8.12.

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27

Sahinoglu, M. "Compound-Poisson software reliability model." IEEE Transactions on Software Engineering 18, no. 7 (July 1992): 624–30. http://dx.doi.org/10.1109/32.148480.

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28

Lisenker, Boris, and Yosef Nevo. "CMOS VLSI reliability test model." Microelectronics Reliability 37, no. 1 (January 1997): 115–20. http://dx.doi.org/10.1016/0026-2714(96)00243-0.

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29

Mir, Khurshid Ahmad. "A Software Reliability Growth Model." Journal of Modern Mathematics and Statistics 5, no. 1 (January 1, 2011): 13–16. http://dx.doi.org/10.3923/jmmstat.2011.13.16.

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30

Cao, Shan-shan, and Jun-qing Lei. "Hybrid reliability model for fatigue reliability analysis of steel bridges." Journal of Central South University 23, no. 2 (February 2016): 449–60. http://dx.doi.org/10.1007/s11771-016-3090-4.

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31

Sameera, Mekhala Sridevi, Gangadhara Rao Kancharla, and R. Satya Prasad. "Software Reliability Measurement Using Combined Goel OKUMOTO and ANOM Perfect Debugging Model." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (February 20, 2019): 780–87. http://dx.doi.org/10.5373/jardcs/v11sp11/20193096.

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32

Hu, Tao, and Chun Hui Yang. "Study on Reliability Model of Multistate Phased Mission System Based on BDD." International Journal of Engineering and Technology 8, no. 5 (May 2016): 334–37. http://dx.doi.org/10.7763/ijet.2016.v8.908.

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33

Radej, Blaz, and Gaber Beges. "An Enhanced Model for Reliability Prediction of a Supercapacitor's Lifetime: Developing an Improved Reliability Model." IEEE Industrial Electronics Magazine 13, no. 3 (September 2019): 26–34. http://dx.doi.org/10.1109/mie.2019.2922417.

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34

Řehák, Jan. "Quality of Data I. Classical Model of Measuring Reliability and its Practical Application." Czech Sociological Review 34, no. 1 (February 1, 1998): 51–60. http://dx.doi.org/10.13060/00380288.1998.34.1.07.

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35

Заневський, І. П., and Л. Г. Заневська. "model of the reliability of the test, alternative models inside class correlation." Teorìâ ta Metodika Fìzičnogo Vihovannâ, no. 1 (March 31, 2014): 43–52. http://dx.doi.org/10.17309/tmfv.2014.1.1044.

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Анотація:

The aim was to create a model of the reliability of the test, the application of which would in the practical work with the tests to receive the value of the coefficient of reliability in the theoretical framework from zero to one, as is customary in classical theory of reliability tests. The basic idea of solving this problem was in determining the true variance of the test results, the value of which is equal to the arithmetical average of the results of the repeated measurements. Methodology of the research was based on provisions of the classical theory of sports tests, mathematical theory of reliability of motor tests, on the model of intraclass correlation, ANOVA, using the method of Shapiro-Wilk. Calculations were carried out using Excel and Mathematica computer programs. It was shown that the intraclass correlation model is incomplete with the line of the classical theory of reliability tests. The proposed model of retest reliability does not have this shortage, and the corresponding coefficient is defined in terms of the classical theory of reliability (0 ÷ 1). In the range of almost important quantities of the proposed model and the intraclass model correlations lead to different estimates of the reliability of the test not only quantitatively (≈ 20%), but also qualitatively, i.e., with significant excess width of the ranges of test estimation of reliability coefficient of the corresponding size.

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36

Tao, Ran, and Chi-Ming Tam. "System reliability optimization model for construction projects via system reliability theory." Automation in Construction 22 (March 2012): 340–47. http://dx.doi.org/10.1016/j.autcon.2011.09.012.

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37

Khan, Maroof A., and H. M. Islam. "Reliability Computation and Bayesian Analysis of System Reliability with Lomax Model." Safety and Reliability 29, no. 1 (March 2009): 5–14. http://dx.doi.org/10.1080/09617353.2009.11690870.

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38

Sen, Ananda. "Estimation of Current Reliability in a Duane-Based Reliability Growth Model." Technometrics 40, no. 4 (November 1998): 334–44. http://dx.doi.org/10.1080/00401706.1998.10485562.

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39

Gunartha, I. Wayan, Badrun Kartowagiran, and Siti Partini Suardiman. "PENGEMBANGAN MODEL EVALUASI PROGRAM LAYANAN PENDIDIKAN ANAK USIA DINI (PAUD)." Jurnal Penelitian dan Evaluasi Pendidikan 18, no. 1 (June 1, 2014): 30–43. http://dx.doi.org/10.21831/pep.v18i1.2122.

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Анотація:

Penelitian ini bertujuan: (1) mengembangkan model evaluasi program layanan PAUD (TK kelompok B) dan (2) menilai keefektifan model yang dikembangkan. Penelitian ini merupakan research and development. Subjek coba adalah guru dan siswa TK kelompok B. Instrumen pengumpul data adalah kuesioner dan lembar observasi. Uji coba dilakukan 3 tahap dengan jumlah subjek meningkat setiap tahapnya. Analisis validitas kuesioner menggunakan exploratory factor analysis dan cofirmatory factor analysis (CFA). Estimasi reliabilitas kuesioner tingkat perkembangan anak dan life skills menggunakan CFA dengan menghitung composite reliability. Estimasi reliabilitas kuesioner kompetensi guru dan fasilitas menggunakan teknik Alpha. Estimasi reliabilitas lembar pengamatan dengan dua orang pengamat menggunakan teknik Kappa. Hasil penelitian berupa model evaluasi program layanan PAUD (Model IPPO), yang terdiri atas prosedur, panduan evaluasi, dan instrumen-instrumennya yang cukup baik. Berdasarkan uji coba, semua instrumen memiliki kecocokan model yang baik (fit), validitas konstruk, dan reliabilitas yang memenuhi persyaratan akademik. Menurut penilaian para praktisi dan pengguna model, model IPPO sangat efektif untuk diterapkan.Kata kunci: pengembangan, model evaluasi, pendidikan anak usia dini______________________________________________________________DEVELOPING A PROGRAM EVALUATION MODEL OF EARLY CHILDHOOD EDUCATION (ECE) SERVICEAbstract The study was to: (1) develop an evaluation model of early childhood education (Group-B Kindergarten); and (2) evaluate the effectiveness of the model being developed. The study is a research and development one. The subject of the research was the teachers and the students of Group-B Kindergarten. The data gathering instruments were questionnaires and observational sheet. The experiment was performed in 3 stages with increasing number of participants for each stage. To analyze the questionnaire’s validity, the researchers employed exploratory factor analysis and confirmatory factor analysis (CFA).To estimate the reliability of the questionnaire of the children’s developmental stage and life skills, the researchers employed CFA by calculating the composite reliability. To estimate the reliability of the questionnaire’s of the teacher competence and the facility, the researchers employed Alpha technique. For the estimation of reliability toward the observational sheet with two observers the researchers employed Kappa technique. The result of the research was a model of evaluation of the early childhood education (IPPO Program), consisting of procedures, evaluation guideline and the well-designed instruments. Based on the experiment, all of the instruments have had a goodness of fit (fit), construct validity and reliability that meet all of the academic requirements. According to the evaluation from the practitioners and the model users, the IPPO model is very effective to be implemented.Keyword: development, evaluation model, early childhood education.

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40

Ajenikoko, Ganiyu A., and Olawale O. Olaluwoye. "A Generalized Model for Electrical Power Distribution Feeders’ Contributions to System Reliability Indices." International Journal of Engineering Research 3, no. 11 (November 1, 2014): 640–44. http://dx.doi.org/10.17950/ijer/v3s11/1104.

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41

ZHOU, Na-qin. "Reliability model of component-based software." Journal of Computer Applications 28, no. 6 (June 20, 2008): 1630–31. http://dx.doi.org/10.3724/sp.j.1087.2008.01630.

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42

Seehase, H. "A reliability model for connector contacts." IEEE Transactions on Reliability 40, no. 5 (1991): 513–23. http://dx.doi.org/10.1109/24.106766.

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43

VaraPrasadRao, B., K. Gangadhara Rao, and B. Srinivasa Rao. "Inverse Rayleigh Software Reliability Growth Model." International Journal of Computer Applications 75, no. 6 (August 23, 2013): 1–5. http://dx.doi.org/10.5120/13112-0470.

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44

Yadav, Nikita, V. B. Singh, and Madhu Kumari. "Generalized Reliability Model for Cloud Computing." International Journal of Computer Applications 88, no. 14 (February 14, 2014): 13–16. http://dx.doi.org/10.5120/15419-3888.

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45

Chowdhury, R. N., W. H. Tang, and I. Sidi. "Reliability model of progressive slope failure." Géotechnique 37, no. 4 (December 1987): 467–81. http://dx.doi.org/10.1680/geot.1987.37.4.467.

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46

Benaicha, H., and A. Chaker. "Weibull Mixture Model for Reliability Analysis." International Review of Electrical Engineering (IREE) 9, no. 5 (October 31, 2014): 986. http://dx.doi.org/10.15866/iree.v9i5.4021.

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47

HeeCheul KIM, and HyoungKeun Park. "Exponentiated Exponential Software Reliability Growth Model." International Journal of Advancements in Computing Technology 1, no. 2 (December 31, 2009): 56–64. http://dx.doi.org/10.4156/ijact.vol1.issue2.7.

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48

Pałasz, Przemysław, Józef Żurek, and Radosław Przysowa. "Markov Reliability Model for Heat Meters." Journal of KONBiN 45, no. 1 (March 1, 2018): 83–96. http://dx.doi.org/10.2478/jok-2018-0005.

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Abstract The reliability of thermal energy meters is analysed using the Markov model which describes the operation of these meters in a large number of apartments and offices by a media accounting company. The data has been extracted from a relational database storing information on the operation, installation and exchange of these measures from the last 10 years. The built Markov model turned out to be ergodic, which allowed determining its limiting distribution. In addition, the probability distributions for the cumulated consumption were determined in the work - separately for all meters and meters’ failures.

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49

Zhang, Ruoxue, and Sankaran Mahadevan. "Bayesian methodology for reliability model acceptance." Reliability Engineering & System Safety 80, no. 1 (April 2003): 95–103. http://dx.doi.org/10.1016/s0951-8320(02)00269-7.

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50

Smidts, C., D. Sova, and G. K. Mandela. "Architectural model for software reliability quantification." Computer Standards & Interfaces 21, no. 2 (June 1999): 168. http://dx.doi.org/10.1016/s0920-5489(99)92186-4.

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