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Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

rolling element bearings. Tribology International , 32 (8), 469-480. [4] Gebraeel, N., Lawley, M., Liu, R., Parmeshwaran, V. (2004). Residual life predictions from vibration-based degradation signals: A Neural Network approach. IEEE Transactions on Industrial Electronics , 51 (3), 694-700. [5] Qiu, H., Lee, J., Lin, J., Yu, G. (2003). Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Advanced Engineering Informatics , 17 (3), 127-140. [6] Huang, R.Q., Xi, L.F., Li, X.L., Liu, C.R., Qiu, H., Lee, J

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The Parallel Bayesian Toolbox for High-performance Bayesian Filtering in Metrology

-3744. [17] Garcia, E., Zschiegner, N., Hausotte, T. (2013).Parallel high-performance computing of Bayes estimation for signal processing and metrology. In Computing, Management and Telecommunications (ComManTel), 21-24 January 2013. IEEE, 212-218. [18] Welch, G., Bishop, G. The Kalman Filter homepage. http://www.cs.unc.edu/~welch/kalman. [19] Identification and Decision Making Research Group, University of West Bohemia. Nonlinear Estimation Framework homepage. http://nft.kky.zcu.cz/nef. [20] Cambridge University. Sequential

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Measurement Techniques for Electromagnetic Shielding Behavior of Braided-Shield Power Cables: An Overview and Comparative Study

Commission. (2006). Metallic communication cables test methods - Part 4-5: Electromagnetic compatibility (EMC) - Coupling or screening attenuation - Absorbing clamp method. IEC 62153-4-5:2006. Geneva. [19] European Committee for Electrotechnical Standardization. (2002). Communication cables. Specifications for test methods. Electrical test methods. Electromagnetic performance. EN 50289-1-6:2002. Brussels. [20] International Electrotechnical Commission. (2015). Metallic communication cable test methods - Part 4-4: Electromagnetic compatibility (EMC) - Test

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Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU

. [37] Speech recognition performance, https://en.wikipedia.org/wiki/Speech_recognition#Performance , last retrieved July 2017. [38] Levenshtein distance, https://en.wikipedia.org/wiki/Levenshtein_distance , last retrieved July 2017. [39] A. C. Morris, V. Maier, P. Green, From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition. Eighth International Conference on Spoken Language Processing, 2004. [40] Word error rate, https://en.wikipedia.org/wiki/Word_error_rate , last retrieved July 2017. [41] A

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Effect of the Volume of Magneto-rheological Fluid on Shear Performance

Effect of the Volume of Magneto-rheological Fluid on Shear Performance

As a kind of smart material, MR (magneto-rheological) fluid is dramatically influenced by the external magnetic field and can change from the liquid state to semi-solid state in several milliseconds. In this paper, the effect of different volume of MRF on its shear performance is proposed. A set of testing systems, including the plate-on-plate MRF shearing test rig, is built up to measure the relationship between the produced shear torque and the added volume of MRF in different current. The variation of magnetic flux density in the shear gap is measured by teslameter and simulated before and after MRF is added. The results validate the effect of volume on the shear torque experimentally.

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An Atlas Based Performance Evaluation of Inhomogeneity Correcting Effects

] Boyes, R. G., Gunter, J. L., Frost, C., Janke, A. L., Yeatman, T., Hill, D. L., and Fox, N. C. “Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils”. Neuroimage, vol. 39, no. 4, pp. 1752-1762, 2008. [5] Chua, Z. Y., Zheng, W., Chee, M. W., and Zagorodnov, V. “Evaluation of performance metrics for bias field correction in MR brain images”. Journal of Magnetic Resonance Imaging, vol. 29, no. 6, pp. 1271-1279, 2009. [6] Guillemaud, R., and Brady, M. “Estimating the bias field of MR images

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Performance of Magnetorheological Fluids Flowing Through Metal Foams

, 46, 6-17. Liu, X.H., Wong, P.L., Wang, W., Bullough, W.A. (2010). Feasibility study on storage of MR Fluid using metal foams. Journal of Intelligent Material Systems and Structures , 21, 1193-1200. Liu, X.H. (2010). Shear performance of novel disk-type porous foam metal magneto-rheological (MR) fluid actuator. Optoelectronics and Advanced Materials - Rapid Communications , 9, 1346-1349. LORD Corporation. Hydrocarbon-Based MR Fluid MRF-132AD. Retrieved from http

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Fault Prognosis of Hydraulic Pump Based on Bispectrum Entropy and Deep Belief Network

fusion of sensitive components. Chinese Journal of Scientific Instrument , 37 (6), 1290-1298. [24] Huang, B., Feng, G., Tang, X., Gu, J.X., Xu, G., Cattley, R., Gu, F., Ball, A.D. (2019). A performance evaluation of two bispectrum analysis methods applied to electrical current signals for monitoring induction motor-driven systems. Energies , 12 (8), 1438. [25] Zhou, Y.B., Liu, Y.B., Li, H., Teng, W., Li, Z. (2013). Fault feature extraction for gear crack based on bispecral entropy. China Mechanical Engineering , 24 (2), 190-194. [26] Tamilselvan, P

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Detecting Driver’s Fatigue, Distraction and Activity Using a Non-Intrusive Ai-Based Monitoring System

Driving Systems, SAE International, Tech. Rep., 2016. [7] M. Cunningham and M. Regan, Autonomous Vehicles: Human Factors Issues and Future Research, in Proceedings of the 2015 Australasian Road Safety Conference, Gold Coast, 2015. [8] N. Merat, A. H. Jamson, F. C. H. Lai, and O. Carsten, Highly Automated Driving, Secondary Task Performance, and Driver State, Human Factors, vol. 54, no. 5, pp. 762–771, 2012. [9] J. Radlmayr, C. Gold, L. Lorenz, M. Farid, and K. Bengler, How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in

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Finger or Stylus: Their Impact on the Performance of On-line Signature Verification Systems

and L. Z. Szabo, “On-line verification of finger drawn signatures,” in 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI) , 2016, pp. 419–424. [14] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki, “The DET Curve in Assessment of Detection Task Performance,” Proc. Eurospeech ’97 , pp. 1895–1898, 1997.

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