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Survey of Collaborative Data Mining

. 1029–1039. [16] Witten, Ian H., et al. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016. [17] Zhan, Justin. “Privacy-preserving collaborative data mining.” Computational Intelligence Magazine, IEEE 3.2 (2008): 31-41.

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Estimation of Power and Delay in CMOS Circuits using Leakage Control Transistor

coverage of VLSI Implementation technologies, Electronic Design Automation Tools and FPGA Design Black Book”, 2014 Edition. [4] Zhang Zhigang,Wang Jingqin,Wang Li,Wang Meng “Reliability Evaluation of Low-volatage Switchgear Based on Maximum Entropy Principle “, TELKOMNIKA Journal, vol.15, no.1, pp. 101-108, March 2017. [5] Kanan Bala Ray,Sushanta K.Mandal,B.S.Patro”Low Power FGSRAM Cell Using Sleepy and LECTOR Technique “, Indonesian Journal of Electrical Engineering and Computer Science, vol.4, no.2, pp. 333-340, November 2016. [6] NarenderHanchate

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-Mode Compensator, The International Conference on “Computer as a Tool” EUROCON, 2007, Warsaw, 1706-1711. [15] WRÓBEL K., Adaptive fuzzy control based on sets of the second type of the complex drive system operating at low speed, Scientific Papers of the Institute of Electrical Machines, Drives and Measurements of the Wrocław University of Technology, Studies and Research, 2015, 71, 35, 109-117. [1] WAI R., LIN C.M., HSU C.F., Adaptive fuzzy sliding-mode control for electrical servo drive, Fuzzy Sets and Systems, 2004, 143, 2, 295

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Application of Neural Networks to Detect Eccentricity of Induction Motors

., B ellaaj N.M., S ignac L., J elassi K., An effective neural approach for the automatic location of stator interturn faults in induction motor , IEEE Trans. Ind. Electron., 2008, 55(12), 4277–4289. [4] A wadallah M.A., M orcos M.M., Application of AI tools in fault diagnosis of electrical machines and drives. An overview , IEEE Trans. En. Conv., 2003, 18(2), 245–251. [5] N andi S., T oliyat H.A., L i X., Condition monitoring and fault diagnosis of electrical motors. A review , IEEE Trans. En. Conv., 2005, 20(4), 719–729. [6] K owalski

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PMSM Servo-Drive Fed by SiC MOSFETs Based VSI

Controlled Machine Tools. Elektronika ir Elektrotechnika, 20(3), pp. 16-23. Mandra, S., Gałkowski, K., Aschemann, H. and Rauh, A. (2015). Guaranteed cost iterative learning control - an application to control of permanent magnet synchronous motors. In: IEEE 9th International Workshop on Multidimensional (ND) systems (NDS), Portugal. Pajchrowski, T., Urbański, K. and Zawirski, K. (2006). Artificial neural network based robust speed control of permanent magnet synchronous motors. COMPEL, 25(1), pp. 220-234. Preindl, M. and

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An Overview of Design, Control, Power Management, System Stability and Reliability in Electric Ships

cost. Designing inside-out of the hull , Naval Eng. J., 2012, 124(4), 67–80. [8] T hurkins E.J. Jr., Development of an early stage ship design tool for rapid modeling in paramarine , Nav. E. thesis, Dept. Mech. Eng., Massachusetts Inst. Technology, Cambridge, MA, USA, 2012. [9] J urkiewicz D.J., C halfant J., C hryssostomidis C., Modular IPS machinery arrangement in early-stage naval ship design , Proc. 2013 IEEE Electric Ship Technology Symp. (ESTS), Arlington, VA, USA, 2013, 22–24. [10] N estoras K., A tool to create hydrodynamically

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A Review on Big Data Management and Decision-Making in Smart Grid

References Acquisto, G., Domingo-Ferrer, J., Kikiras, P., Torra, V., de Montjoye, Y.A. and Bourka, A. (2015). Privacy by Design in Big Data: An Overview of Privacy Enhancing Technologies in the Era of Big Data Analytics. arXiv preprint arXiv:1512.06000 (2015). Adiba, M., Castrejon-Castillo, J.-C., Espinosa Oviedo, J. A., Vargas-Solar, G. and Zechinelli-Martini, J. L. Netherlands, (2016). Big Data Management Challenges, Approaches, Tools and their Limitations . Networking for Big Data. Afrati, F.N., Borkar, V., Carey, M., Polyzotis, N., Ullman

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