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Pulse Shape Discrimination of Neutrons and Gamma Rays Using Kohonen Artificial Neural Networks

References [1] R. T. Kouzes, The 3He Supply Problem, Report 413 No. PNNL-18388, Pacific 414 Northwest National Laboratory, Richland, WA, 2009 [2] A. Enqvist, placeI. Pzsit, S. Avdic, Sample characterization using both neutron and gamma multiplicities, Nuclear Instruments & Methods A, vol. 615, pp. 62-69, 2010 [3] V. L. Romodanov, V. K. Sakharov, A. G. Belevitin, V. V. Afanas’ev, I. V. Mukhamad’varov, D. N. Chernikov, Computational-experimental studies of a facility for detecting fissile materials in airports

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Validation test of a cam mover based micrometric pre-alignment system for future accelerator components

References [1] Artoos, K., Capatina, O., Collette, C., Guinchard, M., Hauviller, C., Lackner, F., Pfingstner, J., Schmickler, H., Sylte, M. (2009). Study of the stabilization to the nanometer level of mechanical vibrations of the CLIC main beam quadrupoles. In Proceedings of the 23rd Particle Accelerator Conference Canada’s National Laboratory for Particle and Nuclear Physics. ratory lear Physics [2] Mainaud Durand, H., Touzé, T., Griffet, S., Kemppinen, J., Lackner, F. (2010). CLIC active pre-alignment system: Proposal for

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Web–Based Framework For Breast Cancer Classification

, and Rached Tourki. Automated breast cancer diagnosis based on gvf-snake segmentation, wavelet features extraction and fuzzy classification. Journal of Signal Processing Systems , 55(1-3):49–66, 2009. [24] O.L. Mangasarian, R. Setiono, and W.H. Wolberg. Pattern Recognition via Linear Programming: Theory and Application to Medical Diagnosis. Large-Scale Num. Opt., Philadelphia: SIAM , pages 22–31, 1990. [25] A. Marcano-Cedeño, J. Quintanilla-Domínguez, and D. Andina. WBCD breast cancer database classification applying artificial metaplasticity neural

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Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

for speech recognition, The Lincoln Laboratory Journal, 1:107–124, 1988 [24] C. Mencar, C. Castiello, R. Cannone, and A. M. Fanelli, Interpretability assessment of fuzzy knowledge bases: A cointension based approach, International Journal of Approximate Reasoning, 52(4):501–518, 2011 [25] J. R. Quinlan, Induction of decision trees, Machine Learning, 1(1):81–106, 1986 [26] J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993 [27] Y. Ren, L. Zhang, and P. N. Suganthan, Ensemble

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Risk Assessment For Industrial Control Systems Quantifying Availability Using Mean Failure Cost (MFC)

’s nuclear-fuel enrichment program,” IEEE Spectrum, 2013. [4] D. P. Fidler, ”Was Stuxnet an Act of War? Decoding a Cyberattack,” IEEE Security & Privacy, vol. 9, pp. 56-59, 2011. [5] ”Sector Risk Snapshot,” DHS Office of Cyber and Infrastructure Analysis (OCIA) ed. Washington, DC, 2014, p. 52. [6] ”Inventory of Risk Management/Risk Assessment Methods,” in Risk Management/Risk Assessment Methods and Tools, ENISA European Network and Information Security Agency ed. Heraklion, Greece, 2014. [7] ”Comparison of Risk Management Methods and Tools,” in Risk

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The Cost-Benefit Relations of the Future Environmental Related Developments Strategies in the Hungarian Energy Sector

Growthinthe U.S.: An Empirical Note. Energy Sources, Part B: Economics, Planning, and Policy, Volume 5 (2010), Issue 3, pp. 301-307. DOI: 10.1080/1556724080253395 [8] Birol F., Argiri M. World energy prospects to 2020. Elsevier, Energy, Volume 24 (1999), Issue 11, November 1999. pp. 905-918, DOI:10.1016/S0360-5442(99)00045-6 [9] Jewell J. Ready for nuclear energy?: An assessment of capacities and motivations for launching new national nuclear power programs. Elsevier, Energy Policy, Volume 39 (2011), Issue 3, pp. 1041-1055. DOI: 10.1016/j

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