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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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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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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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Measuring the Moment and the Magnitude of the Abrupt Change of the Gaussian Process Bandwidth

Abstract

The maximum likelihood algorithm is introduced for measuring the unknown moment of abrupt change and bandwidth jump of a fast-fluctuating Gaussian random process. This algorithm can be technically implemented much simpler than the ones obtained by means of common approaches. The technique for calculating the characteristics of the synthesized measurer is presented and the closed analytical expressions for the conditional biases and variances of the resulting estimates are found using the additive local Markov approximation of the decision statistics. By statistical simulation methods, it is confirmed that the presented measurer is operable, while the theoretical formulas describing its performance well approximate the corresponding experimental data in a wide range of the parameter values of the analyzed random process.

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Effects of Quartz Glass Insulation on Platinum Gold Thermocouples

Abstract

Au/Pt thermocouples are considered as an alternative to High Temperature Platinum Resistance Thermometers and are one of the prime candidates to replace them as the interpolating instrument of the International Temperature Scale of 1990 (ITS-90) in the temperature range between about 660 °C and 962 °C. This work presents the results of investigation of two Au/Pt thermocouples that used exclusively quartz glass (SiO2) as insulation material. Measurements in fixed points of Zn, Al, and Ag were realized on these thermocouples as well with interchanged inner insulation made of high purity aluminium oxide (Al2O3). The conducted experiments tested the performance of Au/Pt thermocouples with the use of different insulation materials. The measured electromotive forces were found to be sensitive to the replacement of the quartz glass by aluminium oxide as an insulation material of the Au/Pt thermocouples. This change of insulation has resulted in a temperature increase up to about 0.5 K measured at the freezing point of silver. The decreasing insulation resistance of quartz glass at higher temperatures is believed to be the source of thermoelectric instability.

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Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

UKSim European Symposium on Computer Modeling and Simulation, pages 60–65, Athens, Greece, 2009. IEEE. [20] Millie Pant, Radha Thangaraj, and Ajith Abraham. Particle swarm optimization: performance tuning and empirical analysis. Foundations of Computational Intelligence, 3:101–128, 2009. [21] P. Raja and S. Pugazhenthi. Path Planning for Mobile Robots in Dynamic Environments Using Particle Swarm Optimization. In 2009 International Conference on Advances in Recent Technologies in Communication and Computing, pages 401–405, Kottayam, Kerala, India, 2009

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Decision-Making Enhancement in a Big Data Environment: Application of the K-Means Algorithm to Mixed Data

. [11] Gal Engelberg, Oded Koren, and Nir Perel. Big data performance evaluation analysis using apache pig. International Journal of Software Engineering and Its Applications, 10(11):429–440, 2016. [12] Johann Füller, Katja Hutter, Julia Hautz, and Kurt Matzler. User roles and contributions in innovation-contest communities. Journal of Management Information Systems, 31(1):273–308, 2014. [13] Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The google file system. In Proceedings of the 19th ACM Symposium on Operating Systems Principles, pages 20

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