Relevant bibliographies by topics / Computational intelligence – Reliability

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Computational intelligence – Reliability'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Computational intelligence – Reliability"

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Levitin, Gregory, and Kai-Yuan Cai. "Guest Editorial: Computational Intelligence in Reliability Engineering." IEEE Transactions on Reliability 60, no. 2 (2011): 368. http://dx.doi.org/10.1109/tr.2011.2136570.

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Bharathi R and Selvarani R. "Software Reliability Assessment of Safety Critical System Using Computational Intelligence." International Journal of Software Science and Computational Intelligence 11, no. 3 (2019): 1–25. http://dx.doi.org/10.4018/ijssci.2019070101.

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In the recent past, automotive industries are concentrating on software controlled automatic functions for its safety operations. The automotive safety and reliability lie in its design, construction, and software implementation. To assess the software reliability, the hidden design errors are classified and quantified. The temporal characteristic of numerical error is analyzed and its probabilistic behavior is explored using a novel framework called software failure estimation with numerical error (SFENE). Here, a model is devised to assess the probability of occurrence of the numerical error
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Bhuyan, Manmath Kumar, Durga Prasad Mohapatra, and Srinivas Sethi. "A survey of computational intelligence approaches for software reliability prediction." ACM SIGSOFT Software Engineering Notes 39, no. 2 (2014): 1–10. http://dx.doi.org/10.1145/2579281.2579293.

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Yadav, Shivani. "Component-Based Software System using Computational Intelligence Technique for Reliability Prediction." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (2020): 3708–21. http://dx.doi.org/10.30534/ijatcse/2020/184932020.

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Kotthoff, Lars. "Reliability of computational experiments on virtualised hardware." Journal of Experimental & Theoretical Artificial Intelligence 26, no. 1 (2013): 33–49. http://dx.doi.org/10.1080/0952813x.2013.784812.

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Kingston, Greer B., Mohammadreza Rajabalinejad, Ben P. Gouldby, and Pieter H. A. J. M. Van Gelder. "Computational intelligence methods for the efficient reliability analysis of complex flood defence structures." Structural Safety 33, no. 1 (2011): 64–73. http://dx.doi.org/10.1016/j.strusafe.2010.08.002.

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Hines, J. W., and R. E. Uhrig. "Trends in computational intelligence in nuclear engineering." Progress in Nuclear Energy 46, no. 3-4 (2005): 167–75. http://dx.doi.org/10.1016/j.pnucene.2005.03.002.

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López, E., J. Campos, C. Tardon, F. Salgado, J. Tardon, and R. López. "Solution to the Reliability Problem of Radial Electric Distribution Systems Through Artificial Intelligence." International Journal of Computers Communications & Control 1, no. 2 (2006): 61. http://dx.doi.org/10.15837/ijccc.2006.2.2286.

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In this paper the distribution electric system reliability is recognized like an artificial intelligence problem. How this idea is applied in evaluation of reliability is detailed. Concepts as Intelligence Matrix and Inter-feeder Route are defined. From the last one a reliability prediction strategy for medium voltage networks is proposed and tested.
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Sahilahudin, Muhamad Fazrizal Faiz, Ismail Musirin, Shahrizal Jelani, and Mohd Helmi Mansor. "Computational Intelligence Based Technique for Multi-DG Installation in Transmission System." E3S Web of Conferences 152 (2020): 03001. http://dx.doi.org/10.1051/e3sconf/202015203001.

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The increasing electricity demand in transmission system has caused the power transmission system experiencing stress condition. This phenomenon has forced the system to need urgent additional supply to maintain system adequacy, in particular the de-regulated power system environment. Distributed generation (DG) has been identified as one of the possible solutions to address this issue. DG installation has the capability to reduce transmission loss and improve the voltage profile. This paper presents evolutionary programming (EP) technique for optimizing the sizing and locations in DG installa
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Reidsma, Dennis, and Jean Carletta. "Reliability Measurement without Limits." Computational Linguistics 34, no. 3 (2008): 319–26. http://dx.doi.org/10.1162/coli.2008.34.3.319.

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In computational linguistics, a reliability measurement of 0.8 on some statistic such as κ is widely thought to guarantee that hand-coded data is fit for purpose, with 0.67 to 0.8 tolerable, and lower values suspect. We demonstrate that the main use of such data, machine learning, can tolerate data with low reliability as long as any disagreement among human coders looks like random noise. When the disagreement introduces patterns, however, the machine learner can pick these up just like it picks up the real patterns in the data, making the performance figures look better than they really are.
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Dissertations / Theses on the topic "Computational intelligence – Reliability"

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Green, Robert C. II. "Novel Computational Methods for the Reliability Evaluation of Composite Power Systems using Computational Intelligence and High Performance Computing Techniques." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1338894641.

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Markovic, Milan. "Utilising provenance to enhance social computation." Thesis, University of Aberdeen, 2016. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=228546.

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Many online platforms employ networks of human workers to perform computational tasks that can be difficult for a machine to perform (e.g. recognising an object from an image). This approach can be referred to as social computation. However, systems that utilise social computation often suffer from a lack of transparency, which results in difficulties in the decision-making process (e.g. assessing reliability of outputs). This thesis investigates how the lack of transparency can be addressed by recording provenance, which includes descriptions of social computation workflows and their executio
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Wang, Lingfeng. "Integration of renewable energy sources: reliability-constrained power system planning and operations using computational intelligence." 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2343.

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Renewable sources of energy such as wind turbine generators and solar panels have attracted much attention because they are environmentally friendly, do not consume fossil fuels, and can enhance a nation’s energy security. As a result, recently more significant amounts of renewable energy are being integrated into conventional power grids. The research reported in this dissertation primarily investigates the reliability-constrained planning and operations of electric power systems including renewable sources of energy by accounting for uncertainty. The major sources of uncertainty in these sys
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Rangwala, Mohammed M. "Secure Digital Provenance: Challenges and a New Design." Thesis, 2014. http://hdl.handle.net/1805/6051.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Derived from the field of art curation, digital provenance is an unforgeable record of a digital object's chain of successive custody and sequence of operations performed on the object. It plays an important role in accessing the trustworthiness of the object, verifying its reliability and conducting audit trails of its lineage. Digital provenance forms an immutable directed acyclic graph (DAG) structure. Since history of an object cannot be changed, once a provenance chain has been created it must be protected in order to guarantee
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Books on the topic "Computational intelligence – Reliability"

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Levitin, Gregory, ed. Computational Intelligence in Reliability Engineering. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37368-1.

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van Gulijk, Coen, and Elena Zaitseva, eds. Reliability Engineering and Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74556-1.

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Marwala, Tshilidzi. Condition Monitoring Using Computational Intelligence Methods: Applications in Mechanical and Electrical Systems. Springer London, 2012.

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Marwala, Tshilidzi. Condition Monitoring Using Computational Intelligence Methods: Applications in Mechanical and Electrical Systems. Springer London, 2012.

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Chan, Hing Kai. Fuzzy Hierarchical Model for Risk Assessment: Principles, Concepts, and Practical Applications. Springer London, 2013.

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Levitin, Gregory. Computational Intelligence in Reliability Engineering (Studies in Computational Intelligence). Springer, 2006.

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Levitin, Gregory. Computational Intelligence in Reliability Engineering (Studies in Computational Intelligence). Springer, 2006.

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Gregory, Levitin, ed. Computational intelligence in reliability engineering. Springer, 2007.

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Wang, Xiaojun, and Hing Kai Chan. Fuzzy Hierarchical Model for Risk Assessment: Principles, Concepts, and Practical Applications. Springer, 2015.

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Book chapters on the topic "Computational intelligence – Reliability"

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Chinnam, Ratna Babu, and Bharatendra Rai. "Computation Intelligence in Online Reliability Monitoring." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37372-8_9.

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Utkin, Lev V., and Frank P. A. Coolen. "Imprecise reliability: An introductory overview." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37372-8_10.

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Tian, Liang, and Afzel Noore. "Computational Intelligence Methods in Software Reliability Prediction." In Computational Intelligence in Reliability Engineering. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37368-1_12.

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Huang, Hong-Zhong, Xin Tong, and Ming J. Zuo. "Posbist Reliability Theory for Coherent Systems." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37372-8_11.

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Levitin, Gregory, Xiaohui Hu, and Yuan-Shun Dai. "Particle Swarm Optimization in Reliability Engineering." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37372-8_4.

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Ning, Xiaozhou, Yunju Yan, Kangkang Qu, and Zhilao Li. "Reliability Analysis on Wing Structures under the Gust Load." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16527-6_5.

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Albeanu, Grigore, Henrik Madsen, and Florin Popențiu-Vlădicescu. "Computational Intelligence Approaches for Software Quality Improvement." In Springer Series in Reliability Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43412-0_18.

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Hryniewicz, Olgierd. "Fuzzy Sets in the Evaluation of Reliability." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37372-8_13.

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Zuo, Ming J., Zhigang Tian, and Hong-Zhong Huang. "Neural Networks for Reliability-Based Optimal Design." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37372-8_7.

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Hu, Q. P., M. Xie, and S. H. Ng. "Software Reliability Predictions using Artificial Neural Networks." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37372-8_8.

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Conference papers on the topic "Computational intelligence – Reliability"

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Salgado, Marcia F. P., Walmir M. Caminhas, and Benjamim R. Menezes. "Computational intelligence in reliability and maintainability engineering." In 2009 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2009. http://dx.doi.org/10.1109/rams.2009.4914693.

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Tamanna and Om Prakash Sangwan. "Computational intelligence based approaches to software reliability." In 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence (Confluence). IEEE, 2017. http://dx.doi.org/10.1109/confluence.2017.7943144.

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Kulamala, Vinod Kumar, A. Sarath Chandra Teja, Abha Maru, Yogesh Singla, and Durga Prasad Mohapatra. "Predicting Software Reliability using Computational Intelligence Techniques: A Review." In 2018 International Conference on Information Technology (ICIT). IEEE, 2018. http://dx.doi.org/10.1109/icit.2018.00033.

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Bo Xing, Wen-Jing Gao, and Thilidzi Marwla. "The applications of computational intelligence in system reliability optimization." In 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES). IEEE, 2013. http://dx.doi.org/10.1109/cies.2013.6611722.

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Liu, Fangyao, Yong Shi, and Zhengxin Chen. "Smart City Ranking Reliability Analysis." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00109.

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Dixit, Saumya, and Pradeep Tomar. "Automated test data generation using computational intelligence." In 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions). IEEE, 2015. http://dx.doi.org/10.1109/icrito.2015.7359319.

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Fulep, Timea, Pal Michelberger, and Laszlo Nadai. "Applicability of Qualitative Reliability Analysis for Redundant Systems." In 2007 International Symposium on Computational Intelligence and Intelligent Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isciii.2007.367383.

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Ibrahim, Wan Rohaizad Wan, and Shaharuddin Salleh. "Weighted-Minimum Cost Reliability Ratio Path Problem." In Modelling, Identification and Control / 827: Computational Intelligence. ACTAPRESS, 2015. http://dx.doi.org/10.2316/p.2015.826-044.

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Li, Jian-Ping, Felician Campean, and Alastair Wood. "Reliability-inspired species conservation for multimodal functions." In 2010 UK Workshop on Computational Intelligence (UKCI). IEEE, 2010. http://dx.doi.org/10.1109/ukci.2010.5625606.

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Mohammadian, Masoud. "Computational intelligence for risk analysis in software testing." In 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2017. http://dx.doi.org/10.1109/icrito.2017.8342400.

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