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Multi-objective optimization based on an improved cross-entropy method. A case study of a micro-scale manufacturing process.

, , и . Inf. Sci., (2016)

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Rodolfo Stecher

Rodolfo Mazzi

Rodolfo Ferrini

Rodolfo Deppe

Rodolfo Valentino

 

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Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System., , , и . Sensors, 17 (9): 2109 (2017)Embedded fuzzy-control system for machining processes: Results of a case study., , , , и . Comput. Ind., 50 (3): 353-366 (2003)Embedded Fuzzy Control System in an Open Computerized Numerical Control; A Technology Transfer Case-Study., , , , и . IFSA, том 2715 из Lecture Notes in Computer Science, стр. 442-449. Springer, (2003)A two-step machine learning approach for dynamic model selection: A case study on a micro milling process., , , , , и . Comput. Ind., (2022)A Distributed Intelligent Monitoring System Applied to a Micro-scale Turning Process., , и . IWANN (2), том 5518 из Lecture Notes in Computer Science, стр. 338-345. Springer, (2009)Optimal Fuzzy Control for a Time-Delay System Using Simulated Annealing: An Application to High-Performance Drilling., , и . ICNC (5), стр. 639-643. IEEE Computer Society, (2007)Transductive-Weighted Neuro-Fuzzy Inference System for Tool Wear Prediction in a Turning Process., , , и . HAIS, том 5572 из Lecture Notes in Computer Science, стр. 113-120. Springer, (2009)Conductance sensor for micromachining. A case study on monitoring tool-workpiece contact., , , и . ICIT, стр. 1422-1426. IEEE, (2015)1st International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS 2015)., , , и . ICSE (2), стр. 1009-1010. IEEE Computer Society, (2015)ISBN 978-1-4799-1934-5 (Vol. I + II ???).Dynamic Model of the Machining Process on the Basis of Neural Networks: From Simulation to Real Time Application., , , , и . International Conference on Computational Science (3), том 2331 из Lecture Notes in Computer Science, стр. 574-583. Springer, (2002)