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The Use of Focus-Variation Microscopy for the Assessment of Active Surfaces of a New Generation of Coated Abrasive Tools

References [1] Wang, L., Gao, R.X. (Eds.) (2006). Condition Monitoring and Control for Intelligent Manufacturing. Springer. [2] Wegener, K., Hoffmeister, H.W., Karpuschewski, B., Kuster, F., Hahmann, W.C., Rabiey, M. (2011). Conditioning and monitoring of grinding wheels. CIRP Annals-Manufacturing Technology, 60 (2), 757-777. [3] Darafon, A., Warkentin, A., Bauer, R. (2013). Characterization of grinding wheel topography using a white chromatic sensor. International Journal of Machine Tools and Manufacture, 70

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Development and Implementation of a Simplified Tool Measuring System

References Schulz, H., Hock, S. (1995). High-speed milling of die and molds - cutting conditions and technology. CIRP Ann. - Manuf. Technol. , 44, 35-38. Dobrzanski, L. A., Golombek, K., Mikula, J., Pakula, D. (2006). Cutting ability improvement of coated tool materials. J. Achiev. Mater. Manuf. Eng. , 17, 41-44. Niranjan Prasad, K., Ramamoorthy, B. (2001). Tool wear evaluation by stereo vision and prediction by artificial neural network. J. Mater. Process. Technol. , 112, 43

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Soft Computing Tools for Virtual Drug Discovery

Three-Dimensional Structures, J. Med. Chem, 47, 2004, 2977-2980. [4] Stefano Forli, Raccoon—AutoDock VS: an automated tool for preparing AutoDock virtual screenings, http://autodock.scripps.edu/resources/raccoon , Accessed: 2016-01-10. [5] G. M. Morris and R. Huey and W. Lindstrom and M. F. Sanner and R. K. Belew and D. S. Goodsell and A. J. Olson, Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility, J. Computational Chemistry, 16, 2009, 2785-2791. [6] P. G. Polishchuk and T. I. Madzhidov and A. Varnek, Estimation of

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Precision Measurement of Cylinder Surface Profile on an Ultra-Precision Machine Tool

References Tanaka, K. et al. (2007). Development of an ultraprecision machine tool. Journal of Japan Society for Abrasive Technology , 51, 553-558. Gao, W. (2005). Precision nanometrology and its appications to precision nanosystems. International Journal of Precision Engineering and Manufacturing , 6, 14-20. Kiyono, S., Gao, W. (1994). Profile measurement of machined surface with a new dirrerential method. Precision Engineering , 16, 212-218. Jywe, W

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Setup for Triggering Force Testing of Touch Probes for CNC Machine Tools and CMMs

of Mechanical Sciences and Engineering , 25 (2). [4] Cauchick-Miguel, P.A., King, T.G. (1998). Factors which influence CMM touch trigger probe performance. International Journal of Machine Tools & Manufacture , 38 (4), 363-374. [5] Dobosz, M., Woźniak, A. (2005). CMM touch trigger probes testing using a reference axis. Precision Engineering , 29 (3), 281-289. [6] Salah, H.R.A. (2010). Probing system characteristics in coordinate metrology. Measurement Science Review , 10 (4), 120-129. [7] Cho, M

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

Perel, and Dror Bendet. Enhancement of the k-means algorithm for mixed data in big data platforms. In Proceedings of SAI Intelligent Systems Conference, pages 1025–1040. Springer, 2018. [24] Sara Landset, Taghi M Khoshgoftaar, Aaron N Richter, and Tawfiq Hasanin. A survey of open source tools for machine learning with big data in the hadoop ecosystem. Journal of Big Data, 2(1):24, 2015. [25] James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela H Byers. Big data: The next frontier for innovation, competition

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

: Beijing University of Posts and Telecommunications Press, 283–313. [13] Otin, R., Verpoorte, J., Schippers, H. (2011). Finite element model for the computation of the transfer impedance of cable shields. IEEE Transactions on Elec- tromagnetic Compatibility , 53 (4), 950–958. [14] Otin, R., Verpoorte, J., Schippers, H., Isanta, R. (2015). A finite element tool for the electromagnetic analysis of braided cable shields. Computer Physics Communications , (191), 209–220. [15] International Electrotechnical Commission. (2013). Metallic communication cable

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

Machine Learning Research, vol. 18, no. 1, pp. 559–563, 2017. [38] C. Huertas and R. Juárez-Ramirez, Filter feature selection performance comparison in high-dimensional data: A theoretical and empirical analysis of most popular algorithms, in 17th International Conference on Information Fusion (FUSION), 2014, pp. 1–8. [39] M. A. Hall, Feature Selection for Discrete and Numeric Class Machine Learning, 1999. [40] J. Strickland, Data Analytics Using Open-Source Tools. Lulu.com, 2016. [41] P. Kashyap, Machine Learning for Decision Makers: Cognitive

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QUEST QUALITY MANAGEMENT TOOL FOR SUSTAINABLE URBAN MOBILITY

References [1] „QUALITY MANAGEMENT TOOL FOR URBAN ENERGY EFFICIENT SUSTAINABLE TRANSPORT” - INFORMATION LEAFLET ABOUT THE QUEST-PROJECT, 2013. [2] „QUEST-REPORT AND ACTION PLAN” FOR THE CITY OF BÉKÉSCSABA, MOBIL CITY CONSULTANCY, 2013. [3] WWW.QUEST-PROJECT.EU [4] WWW.MOBILITYPLANS.EU

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Improved Performance of 50 kN Dead Weight Force Machine using Automation as a Tool

Improved Performance of 50 kN Dead Weight Force Machine using Automation as a Tool

Continuously growing technologies and increasing quality requirements have exerted thrust to the metrological institutes to maintain a high level of calibration and measurement capabilities. Force, being very vital in various engineering and non - engineering applications, is measured by force transducers. Deviations in the values observed and mentioned in the calibration certificate for force transducers may primarily be due to the creep, time loading effect and temperature effect if not properly compensated. Beside these factors, machine interaction, parasitic components etc. pertaining to the quality of the force standard machine used for calibration also contribute to the deviations. An attempt has been made by National Physical Laboratory, India (NPLI) to automate the 50 kN dead weight force machine to minimize the influence of factors other than the factors related to the machine itself. The calibration of force transducers is carried out as per the standard calibration procedure based on standard ISO 376-2004 using the automated 50 kN dead weight force machine (cmc ± 0.003% (k=2)) under similar conditions both in manual mode and automatic mode. The metrological characterization shows improved metrological results for the force transducers when the 50 kN dead weight force machine is used in automatic mode as compared to the manual mode.

Open access