Добірка наукової літератури з теми "Component robustness"
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Статті в журналах з теми "Component robustness":
Blanchard, A., and Y. Zolnierowski. "Dark Energy: Nature and Robustness." International Astronomical Union Colloquium 192 (2005): 573–78. http://dx.doi.org/10.1017/s0252921100009684.
Wang, Wenshan, Qixin Cao, Xiaoxiao Zhu, and Masaru Adachi. "An automatic switching approach of robotic components for improving robot localization reliability in complicated environment." Industrial Robot: An International Journal 41, no. 2 (March 11, 2014): 135–44. http://dx.doi.org/10.1108/ir-04-2013-338.
Herrendörfer, G., A. Tuchscherer, G. Dietl, and M. Tuchscherer. "Die Robustheit von BLUP und EBLUP." Archives Animal Breeding 42, no. 3 (October 10, 1999): 303–18. http://dx.doi.org/10.5194/aab-42-303-1999.
Devitt, S. J., A. G. Fowler, and L. C. L. Hollenberg. "Robustness of Shor's algorithm." Quantum Information and Computation 6, no. 7 (November 2006): 616–29. http://dx.doi.org/10.26421/qic6.7-5.
Sjödahl, Mikael, and Erik Olsson. "Robustness of Laser Speckles as Unique Traceable Markers of Metal Components." Digital 1, no. 1 (February 18, 2021): 54–63. http://dx.doi.org/10.3390/digital1010004.
Hien, Thai Duy, Zensho Nakao, and Yen-Wei Chen. "Intelligent Logo Watermarking Based on Independent Component Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 390–96. http://dx.doi.org/10.20965/jaciii.2004.p0390.
Chakrabarty, Nilanjana, and Dibyojyoti Bhattacharjee. "Assessing Robustness of Asian Countries Ranking." International Journal of Innovation in the Digital Economy 7, no. 1 (January 2016): 24–37. http://dx.doi.org/10.4018/ijide.2016010103.
Illarramendi, M., L. Etxeberria, X. Elkorobarrutia, and G. Sagardui. "Runtime Contracts Checker: Increasing Robustness of Component-Based Software Systems." IOP Conference Series: Materials Science and Engineering 575 (August 13, 2019): 012006. http://dx.doi.org/10.1088/1757-899x/575/1/012006.
Dong, Shangjia, Haizhong Wang, Ali Mostafavi, and Jianxi Gao. "Robust component: a robustness measure that incorporates access to critical facilities under disruptions." Journal of The Royal Society Interface 16, no. 157 (August 2019): 20190149. http://dx.doi.org/10.1098/rsif.2019.0149.
Novacek, Jan, Alexander Viehl, Oliver Bringmann, and Wolfgang Rosenstiel. "Reasoning-Supported Robustness Validation of Automotive E/E Components." International Journal of Semantic Computing 11, no. 04 (December 2017): 473–96. http://dx.doi.org/10.1142/s1793351x17400190.
Дисертації з теми "Component robustness":
Chen, Lu. "Robustness in fire of steel framed structures with realistic connections." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/robustness-in-fire-of-steel-framed-structures-with-realistic-connections(fcf4d94b-56f4-42bc-b20a-e2e497d4c14a).html.
Kolankeh, Arash Kermani, Michael Teichmann, and Fred H. Hamker. "Competition improves robustness against loss of information." Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-166057.
Hissanaga, Hugo Mamoru Aoki. "Previsão da curva de juros com análise de componentes principais utilizando matriz de covariâcia de longo prazo." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/96/96131/tde-27102017-102841/.
Although Principal Component Analysis (PCA) is one of the most common methods to estimate the structure of interest rate volatility, there are strong indications that it is not adequate to estimate interest rate factors when there is temporal dependence and measurement errors. To correct these problems it is necessary to use the longterm covariance matrix, to extract the correct covariance structure present in these processes. In this work, we show that performing the out-of-sample forecasting of the interest rate curve with the Principal Component Analysis (PCA) method based on the long-term covariance matrix (LRCM) seems to be more accurate compared to PCA based on sample covariance matrix.
Devamitta, Perera Muditha Virangika. "Robustness of normal theory inference when random effects are not normally distributed." Kansas State University, 2011. http://hdl.handle.net/2097/8786.
Department of Statistics
Paul I. Nelson
The variance of a response in a one-way random effects model can be expressed as the sum of the variability among and within treatment levels. Conventional methods of statistical analysis for these models are based on the assumption of normality of both sources of variation. Since this assumption is not always satisfied and can be difficult to check, it is important to explore the performance of normal based inference when normality does not hold. This report uses simulation to explore and assess the robustness of the F-test for the presence of an among treatment variance component and the normal theory confidence interval for the intra-class correlation coefficient under several non-normal distributions. It was found that the power function of the F-test is robust for moderately heavy-tailed random error distributions. But, for very heavy tailed random error distributions, power is relatively low, even for a large number of treatments. Coverage rates of the confidence interval for the intra-class correlation coefficient are far from nominal for very heavy tailed, non-normal random effect distributions.
Meng, Li. "Robust estimation of the number of components for mixtures of linear regression." Kansas State University, 2014. http://hdl.handle.net/2097/17856.
Department of Statistics
Weixin Yao
In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion is robust and not sensitive to outliers. The superiority of the trimmed methods in comparison with the traditional information criterion methods is illustrated through a simulation study. A real data application is also used to illustrate the effectiveness of the trimmed model selection methods.
Loguercio, Salvatore. "Reductionist and Integrative approaches to explore the H.pylori genome." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3425099.
Hawwari, Yasmine. "Developement of some signal processing tools for vibro-acoustic based diagnosis of aeronautic machines." Electronic Thesis or Diss., Lyon, INSA, 2022. https://theses.insa-lyon.fr/publication/2022ISAL0131/these.pdf.
Pre-processing vibration signals in harsh conditions such as the aeronautic conditions seems a complicated task. The operating conditions are nonstationary and the motor exhibits at least two harmonic non-linear families related to low and high pressure shaft. Furthermore, the design constraints impose a reduced number of accelerometers (generally two) which is unfortunately insufficient to detect all the shaft related phenomena. The acoustic signals are not subjected to the latter constraint. However, they are very noisy in comparison to vibration signals and may not detect low energy problems and very low frequency phenomena. Besides, the obtained signals depend strongly on the microphone position and its directivity in addition to the problem of clipping with medium to high acoustic pressure values. Thus, the PhD objective is to propose methods robust to mainly (i) the interference between different linear and non-linearly related phenomena, (ii) the nonstationary operating conditions and (iii) the broadband noise phenomena. These scientific difficulties are considered through (1) a blind detection of spectral peaks, (2) the estimation of the instantaneous speed and (3) the estimation of the deterministic/tonal component
Rioual, Pascal. "Modélisation et commande d'un redresseur à MLI alimenté par un réseau déséquilibré." Cachan, Ecole normale supérieure, 1993. http://www.theses.fr/1993DENS0008.
Huang, He. "Développement de modèles prédictifs pour la robustesse électromagnétique des composants électroniques." Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0036/document.
One important objective of the electromagnetic compatibility (EMC) studies is to make the products compliant with the EMC requirement of the customers or the standards. However, all the EMC compliance verifications are applied before the delivery of final products. So we might have some new questions about the EMC performance during their lifetime. Will the product still be EMC compliant in several years? Can a product keep the same EMC performance during its whole lifetime? If not, how long the EMC compliance can be maintained? The study of the long-term EMC level, which is called “electromagnetic robustness”, appeared in the recent years. Past works showed that the degradation caused by aging could induce failures of electronic system, including a harmful evolution of electromagnetic compatibility. In this study, the long-term evolution of the EMC levels of two electronic component groups has been studied. The first electronic component type is the integrated circuit. The high-frequency currents and voltages during the switching activities of ICs are responsible for unintentional emissions or coupling. Besides, ICs are also very often the victim of electromagnetic interference. Another group of components is the passive component. In an electronic system, the IC components usually work together with the passive components at PCB level. The functions of passive components in an electronic system, such as filtering and decoupling, also have an important influence on the EMC levels.In order to analyze the long-term evolution of the EMC level of the electronic components, the study in this thesis tends to propose general predictive methods for the electromagnetic compatibility levels of electronic components which evolve with time
Lin, ChengJu, and 林正茹. "Sensitivity analysis and robustness in principal component analysis." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/32368985442194082708.
東海大學
統計學系
91
Lemma 2.1 of Sibson (1979) is generalized to the second order perturbation of a symmetric matrix. Thus, the second order theoretical influence functions for the eigenvalues and eigenvectors can be developed to detect the masked influential observations in principal component analysis. In addition, a robust principal component can thus be developed by downweighting the identified influential observations. Numerical example illustrates the techniques.
Частини книг з теми "Component robustness":
Zhang, Jiejie, Ying Chen, Stefan Klotz, and Kian Guan Lim. "International Yield Curve Prediction with Common Functional Principal Component Analysis." In Robustness in Econometrics, 287–304. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50742-2_17.
Simons, Kevin, and Judith Stafford. "CMEH: Container Managed Exception Handling for Increased Assembly Robustness." In Component-Based Software Engineering, 122–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24774-6_12.
Chen, Aiyou, and Peter J. Bickel. "Robustness of Prewhitening Against Heavy-Tailed Sources." In Independent Component Analysis and Blind Signal Separation, 225–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_29.
Bozga, Marius, Lucas Bueri, and Radu Iosif. "Decision Problems in a Logic for Reasoning About Reconfigurable Distributed Systems." In Automated Reasoning, 691–711. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_40.
Creß, Christian, Lukas Rabe, and Alois Knoll. "Enhancing Robustness Against Component Failures in Intelligent Transportation Systems Through Self-diagnosis Functionality." In Proceedings of the 12th International Scientific Conference on Mobility and Transport, 21–35. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8361-0_3.
Murayama, Toru, and Lorenzo Sabattini. "Preservation of Giant Component Size After Robot Failure for Robustness of Multi-robot Network." In Distributed Autonomous Robotic Systems, 415–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92790-5_32.
Tan, Zheng, Weihui Zhong, and Yingzhao Qiu. "Research on robustness of space steel frames against progressive collapse based on component method." In Civil Engineering and Energy-Environment Vol 1, 162–68. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003433644-19.
Noller, Yannic. "Hybrid Differential Software Testing." In Ernst Denert Award for Software Engineering 2020, 167–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83128-8_9.
Pignocchino, Gianmarco, Alessandro Pezzoli, and Angelo Besana. "Satellite Data and Epidemic Cartography: A Study of the Relationship Between the Concentration of NO2 and the COVID-19 Epidemic." In Communications in Computer and Information Science, 55–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94426-1_5.
Papastavrou, Y., D. Cash, D. Hawkes, and B. Hutton. "A Multi-component similarity measure for improved robustness of non-rigid registration of combined FDG PET-CT head and neck images." In IFMBE Proceedings, 433–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89208-3_102.
Тези доповідей конференцій з теми "Component robustness":
Chen, Mei, and Yan Liu. "Informative Component Extraction with Robustness Consideration." In 2008 Chinese Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/ccpr.2008.18.
Wang, Xiaoping, Yunhao Liu, Zheng Yang, Junliang Liu, and Jun Luo. "ETOC: Obtaining robustness in component-based localization." In 2010 18th IEEE International Conference on Network Protocols (ICNP). IEEE, 2010. http://dx.doi.org/10.1109/icnp.2010.5762755.
Maghami, Peiman. "Robustness of flexible systems with component-level uncertainties." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-4149.
Li, Ying, Fuwei Li, Lifeng Lai, and Jun Wu. "On The Adversarial Robustness of Principal Component Analysis." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413607.
Sueaseenak, Direk, Theerasak Chanwimalueang, Manas Sangworasil, and Chuchart Pintavirooj. "An investigation of robustness in independent component analysis EMG." In 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2009. http://dx.doi.org/10.1109/ecticon.2009.5137237.
Dumitrescu, Ana-Maria, and Mihaela Albu. "Robustness against measurement component for controllers in LV active filters." In 2012 IEEE International Workshop on Applied Measurements for Power Systems (AMPS). IEEE, 2012. http://dx.doi.org/10.1109/amps.2012.6344014.
Drukker, K., N. P. Gruszauskas, and M. L. Giger. "Principal component analysis, classifier complexity, and robustness of sonographic breast lesion classification." In SPIE Medical Imaging, edited by Nico Karssemeijer and Maryellen L. Giger. SPIE, 2009. http://dx.doi.org/10.1117/12.811341.
Maji, Amiya K., Fahad A. Arshad, Saurabh Bagchi, and Jan S. Rellermeyer. "An empirical study of the robustness of Inter-component Communication in Android." In 2012 42nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 2012. http://dx.doi.org/10.1109/dsn.2012.6263963.
De la Torre, Fernando, Alvaro Collet, Manuel Quero, Jeffrey F. Cohn, and Takeo Kanade. "Filtered Component Analysis to Increase Robustness to Local Minima in Appearance Models." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383056.
Wang, Qianqian, Quanxue Gao, Xinbo Gao, and Feiping Nie. "Angle Principal Component Analysis." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/409.
Звіти організацій з теми "Component robustness":
Ardanaz, Martín, Eduardo A. Cavallo, Alejandro Izquierdo, and Jorge Puig. Output Effects of Fiscal Consolidations: Does Spending Composition Matter? Inter-American Development Bank, December 2021. http://dx.doi.org/10.18235/0003881.
McEntee, Alice, Sonia Hines, Joshua Trigg, Kate Fairweather, Ashleigh Guillaumier, Jane Fischer, Billie Bonevski, James A. Smith, Carlene Wilson, and Jacqueline Bowden. Tobacco cessation in CALD communities. The Sax Institute, June 2022. http://dx.doi.org/10.57022/sneg4189.
Mitra, Sudeshna, Amlanjyoti Goswami, Deepika Jha, Sahil Sasidharan, Kaye Lushington, and Tsomo Wangchuk. Land Records Modernisation in India: Himachal Pradesh. Indian Institute for Human Settlements, 2021. http://dx.doi.org/10.24943/9788195648504.
Martin, Ciaran. Five tests for risk-based approaches to national cybersecurity in resource-constrained environments. Digital Pathways at Oxford, April 2022. http://dx.doi.org/10.35489/bsg-dp-wp_2022/05.
Cook, Joshua, Laura Ray, and James Lever. Dynamics modeling and robotic-assist, leader-follower control of tractor convoys. Engineer Research and Development Center (U.S.), February 2022. http://dx.doi.org/10.21079/11681/43202.
Irudayaraj, Joseph, Ze'ev Schmilovitch, Amos Mizrach, Giora Kritzman, and Chitrita DebRoy. Rapid detection of food borne pathogens and non-pathogens in fresh produce using FT-IRS and raman spectroscopy. United States Department of Agriculture, October 2004. http://dx.doi.org/10.32747/2004.7587221.bard.
Scientists Confirm Robustness of Key Component in Ultra-High-Efficiency Solar Cell (Fact Sheet). Office of Scientific and Technical Information (OSTI), May 2011. http://dx.doi.org/10.2172/1015889.