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Frequency and time fault diagnosis methods of power transformers

transformers. Electrical Engineering, 99 (3), 1109-1119. [4] Zhang, Y.Y., Wei, H., Liao, R.J., Wang, Y.Y., Yang, L.J., Yan, C.Y. (2017). A new support vector machine model based on improved imperialist competitive algorithm for fault diagnosis of oil-immersed transformers. Journal of Electrical Engineering & Technology, 12 (2), 830-839. [5] Peimankar, A., Weddell, S.J., Jalal, T., Lapthorn, A.C. (2017). Evolutionary multi-objective fault diagnosis of power transformers. Swarm and Evolutionary Computation, 36, 2017, 62-75. [6

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Detection of Gearbox lubrication Using PSO-Based WKNN

, M. (2007). A modified probabilistic neural network for partial volume segmentation in brain MR image. IEEE Transactions on Neural Network , 18 (5), 1424-1432. [22] Chen, B., Liu, H., Chia, J., Bao, Z. (2009). Large margin feature weighting method via linear programming. IEEE Transactions on Knowledge and Data Engineering , 21 (10), 1475-1488. [23] Middlemiss, M.J., Dick, G. (2003). Weighted feature extraction using genetic algorithms for intrusion detection. In The 2003 Congress on Evolutionary Computation (CEC ‘03) , 8

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An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

prediction. In: 2006 International joint conference on neural networks; July, 16–21, 2006, 913–20. [10] R. Ghazali, A.J. Hussain, P. Liatsis, and H. Tawfik, The application of ridge polynomial neural network to multi-step ahead financial time series prediction, Neural Computing & Applications, 17(3), 2008, 311–323. [11] H. Tawfik, and P. Liatsis, Prediction of non-linear time-series using higher-order neural networks, Proceeding IWSSIP’97 Conference, Poznan, Poland, 1977. [12] N. Yong, and D. Wei, A hybrid genetic learning algorithm for Pi– sigma neural

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Sensitivity Jump of Micro Accelerometer Induced by Micro-fabrication Defects of Micro Folded Beams

R eferences [1] Frosio, I., Pedersini, F., Borghese, N.A. (2010). Formulation of stiffness constant and effective mass for a folded beam. Archives of Mechanics, 62, 405-418. [2] Won, S.P., Golnaraghi, F. (2010). A triaxial accelerometer calibration method using a mathematical model. IEEE Transactions on Instrumentation and Measurement, 59, 2144-2153. [3] Sipos, M., Paces, P., Rohac J. & Novacek P. (2012). Analyses of Triaxial Accelerometer Calibration Algorithms. Sensors Journal, IEEE, 12, 1157-1165. [4] Cobb, C.L., Agogino, A

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Hybrid 3D Dynamic Measurement by Particle Swarm Optimization and Photogrammetric Tracking

, 1044-1052. [12] Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. In IEEE International Conference on Neural Networks, November 1995. IEEE, Vol. 4, 1942-1948. [13] Shi, Y., Eberhart, R.C. (1999). Empirical study of particle swarm optimization. In CEC 99 : Congress on Evolutionary Computation, July 1999. IEEE, Vol. 3, 1945-1950. [14] Khosla, A., Kumar, S., Aggarwal, K.K., Singh, J. (2006). Particle swarm for fuzzy models identification.In Nedjah, N., Mourelle, L.M. (eds.) Swarm Intelligent Systems. Studies

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Priority Scheduling in the Planning of Multiple-Structure Construction Projects

. Radziszewska-Zielina, G. Śladowski, “Supporting the Selection of a Variant of the Adaptation of a Historical Building with the Use of Fuzzy Modelling and Structural Analysis”, Journal of Cultural Heritage 26, 53–63, 2017. 20. E. Radziszewska-Zielina, G. Śladowski, M. Sibielak, “Planning the reconstruction of a historical building by using a fuzzy stochastic network”, Automation in Construction 84, 242-257, 2017. 21. M. Rogalska, W. Bożejko, Z. Hejducki, “Time/cost optimization using hybrid evolutionary algorithm in construction project scheduling”, Automation in

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Interactions and Optimizations Analysis between Stiffness and Workspace of 3-UPU Robotic Mechanism

, J., Thomsen, R. (2004). A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In Congress on Evolutionary Computation (CEC2004), June 19-23, 2004. IEEE, vol. 2, 1980-1987. [26] Zhang, D. (2000). Kinetostatic analysis and optimization of parallel and hybrid architectures for machine tools. Ph.D. thesis, Laval University, Quebec, Canada. [27] Talbi, E.G. (2009). Metaheuristics: From Design to Implementation. John Wiley & Sons. [28

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Determination of Radiative Heat Transfer Coefficient at High Temperatures Using a Combined Experimental-Computational Technique

), 539-545. [18] Hrstka, O., Kučerova, A., Lepš, M., Zeman, J. (2003). A competitive comparison of different types of evolutionary algorithms. Computers & Structures, 81 (18-19), 1979-1990. [19] Koči, J., Žumar, J., Pavlik, Z., Černy, R. (2012). Application of genetic algorithm for determination of water vapor diffusion parameters of building materials. Journal of Building Physics, 35 (3), 238-250. [20] Jun, S., Kochan, O. (2014). Investigation of thermocouple drift irregularity impact on error of their inhomogeneity

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Classifiers Accuracy Improvement Based on Missing Data Imputation

, 2005. [6] I. Jordanov, N. Petrov, Intelligent Radar Signal Recognition and Classification. In Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds.) Recent Advances in Computational Intelligence in Defense and Security, 2016, 101-135. [7] I. Jordanov, N. Petrov, A. Petrozziello, Supervised radar signal classification. Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE., 2016, 1464-1471. [8] L. Carro-Calvo, et al., An evolutionary multiclass algorithm for automatic classification of high range resolution radar targets

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Dimensional and Geometrical Errors in Vacuum Thermoforming Products: An Approach to Modeling and Optimization by Multiple Response Optimization

. (2011). Artificial neural networks and evolutionary algorithms in engineering design. Journal of Achievements in Materials and Manufacturing Engineering, 44 (1), 88-95. [19] Martin, P.J., Keaney, T., McCool, R. (2014). Development of a multivariable online monitoring system for the thermoforming process. Polymer Engineering & Science, 54 (12), 2815-2823. [20] Chy, M.M.I., Boulet, B., Haidar, A. (2011). A model predictive controller of plastic sheet temperature for a thermoforming process. In American Control Conference (ACC), San

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