Academic literature on the topic 'Performance metrics'
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Journal articles on the topic "Performance metrics"
Mary, A. Viji Amutha, and Dr T. Jebarajan Dr. T. Jebarajan. "Performance Metrics of Clustering Algorithm." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 165–67. http://dx.doi.org/10.15373/2249555x/august2014/47.
Full textPijpers, Frank P. "Performance metrics." Astronomy & Geophysics 47, no. 6 (December 2006): 6.17–6.18. http://dx.doi.org/10.1111/j.1468-4004.2006.47617.x.
Full textM. Taaffe, Kevin, Robert William Allen, and Lindsey Grigg. "Performance metrics analysis for aircraft maintenance process control." Journal of Quality in Maintenance Engineering 20, no. 2 (May 6, 2014): 122–34. http://dx.doi.org/10.1108/jqme-07-2012-0022.
Full textLiu, Yangguang, Yangming Zhou, Shiting Wen, and Chaogang Tang. "A Strategy on Selecting Performance Metrics for Classifier Evaluation." International Journal of Mobile Computing and Multimedia Communications 6, no. 4 (October 2014): 20–35. http://dx.doi.org/10.4018/ijmcmc.2014100102.
Full textBaker, Noel C., and Patrick C. Taylor. "A Framework for Evaluating Climate Model Performance Metrics." Journal of Climate 29, no. 5 (February 26, 2016): 1773–82. http://dx.doi.org/10.1175/jcli-d-15-0114.1.
Full textLi, Shanshan, and Weiyang Sun. "Image Encryption Performance Evaluation Based on Poker Test." Advances in Multimedia 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6714164.
Full textCzachura, Agnieszka, Jouri Kanters, Niko Gentile, and Maria Wall. "Solar Performance Metrics in Urban Planning: A Review and Taxonomy." Buildings 12, no. 4 (March 23, 2022): 393. http://dx.doi.org/10.3390/buildings12040393.
Full textNochur, A., H. Vedam, and J. Koene. "Alarm Performance Metrics." IFAC Proceedings Volumes 34, no. 27 (June 2001): 203–8. http://dx.doi.org/10.1016/s1474-6670(17)33592-9.
Full textLoo, Jessica, Traci E. Clemons, Emily Y. Chew, Martin Friedlander, Glenn J. Jaffe, and Sina Farsiu. "Beyond Performance Metrics." Ophthalmology 127, no. 6 (June 2020): 793–801. http://dx.doi.org/10.1016/j.ophtha.2019.12.015.
Full textDeLozier, Randall, and Neil Snyder. "ENGINEERING PERFORMANCE METRICS." INCOSE International Symposium 3, no. 1 (July 1993): 599–605. http://dx.doi.org/10.1002/j.2334-5837.1993.tb01632.x.
Full textDissertations / Theses on the topic "Performance metrics"
Sundfors, David. "Performance Metrics for Sustainability Value." Licentiate thesis, KTH, Bygg- och fastighetsekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200315.
Full textQC 20170124
Akcay, Koray. "Performance Metrics For Fundamental Estimation Filters." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606510/index.pdf.
Full textAlpha-Beta Filter, Alpha-Beta-Gamma Filter, Constant Velocity (CV) Kalman Filter, Constant Acceleration (CA) Kalman Filter, Extended Kalman Filter, 2-model Interacting Multiple Model (IMM) Filter and 3-model IMM with respect to their resource requirements and performance. In resource requirement part, fundamental estimation filters are compared according to their CPU usage, memory needs and complexity. The best fundamental estimation filter which needs very low resources is the Alpha-Beta-Filter. In performance evaluation part of this thesis, performance metrics used are: Root-Mean-Square Error (RMSE), Average Euclidean Error (AEE), Geometric Average Error (GAE) and normalized form of these. The normalized form of performance metrics makes measure of error independent of range and the length of trajectory. Fundamental estimation filters and performance metrics are implemented in MATLAB. MONTE CARLO simulation method and 6 different air trajectories are used for testing. Test results show that performance of fundamental estimation filters varies according to trajectory and target dynamics used in constructing the filter. Consequently, filter performance is application-dependent. Therefore, before choosing an estimation filter, most probable target dynamics, hardware resources and acceptable error level should be investigated. An estimation filter which matches these requirements will be &lsquo
the best estimation filter&rsquo
.
Tucker, Christopher John. "Performance metrics for network intrusion systems." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1547.
Full textCirtita, Horatiu. "Performance Metrics in Downstream Supply Chain." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3425091.
Full textDietrich, Nathan S. "Performance metrics for correlation and tracking algorithms." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA391959.
Full textWanderman-Milne, Skye A. "Virtualized application performance prediction using system metrics." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77450.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 79-80).
Virtualized datacenter administrators would like to consolidate virtual machines (VMs) onto as few physical hosts as possible in order to decrease costs, but must leave enough physical resources for each VM to meet application service level objectives (SLOs). The threshold between good and bad performance in terms of resource settings, however, is hard to determine and rarely static due to changing workloads and resource usage. Thus, in order to avoid SLO violations, system administrators must err on the side of caution by running fewer VMs per host than necessary or setting reservations, which prevents resources from being shared. To ameliorate this situation, we are working to design and implement a system that automatically monitors VM-level metrics to predict impending application SLO violations, and takes appropriate action to prevent the SLO violation from occurring. So far we have implemented the performance prediction, which is detailed in this document, while the preventative actions are left as future work. We created a three-stage pipeline in order to achieve scalable performance prediction. The three stages are prediction, which predicts future VM ESX performance counter values based on current time-series data; aggregation, which aggregates the predicted VM metrics into a single set of global metrics; and finally classification, which for each VM classifies its performance as good or bad based on the predicted VM counters and the predicted global state. Prediction of each counter is performed by a least-squares linear fit, aggregation is performed simply by summing each counter across all VMs, and classification is performed using a support vector machine (SVM) for each application. In addition, we created an experimental system running a MongoDB instance in order to test and evaluate our pipeline implementation. Our results on this experimental system are promising, but further research will be necessary before applying these techniques to a production system.
by Skye A. Wanderman-Milne.
M.Eng.
Burdett, Yan Liu. "Correlation of Software Quality Metrics and Performance." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/109.
Full textJackson, Kenneth J. "Forecast error metrics for Navy inventory management performance." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5756.
Full textThis research establishes metrics for determining overall Navy secondary inventory forecasting accuracy when compared to actual demands at the Naval Inventory Control Point (NAVICP). Specifically, two performance metrics are introduced: the average performance index (API) and the median absolute deviation performance index (MPI). API measures forecasting accuracy of secondary inventory when compared against demand or forecast performance over a four-quarter period. MPI measures the quarterly variability of forecast errors over the same period. The API and MPI metrics allow for the identification of poorly forecasted NAVICP secondary inventory items. The metrics can be applied to entire inventories or subsets of items based on type, demand, or cost. In addition, the API metric can be used to show overall inventory performance, providing NAVICP with a graphical means to assess forecasting performance improvements (or degradations) over time. The new forecasting accuracy methods developed in this research will allow the Navy to continually gauge the overall health of their inventory management practices and provide a method for improving forecasting accuracy. Additionally, they will assist NAVICP in complying with DoD directives that require NAVICP to monitor and continually develop improvements to inventory management practices.
Forman, Rachel Emily. "Objective performance metrics for improved space telerobotics training." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68408.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 47-50).
NASA astronauts undergo many hours of formal training and self-study to gain proficiency in space teleoperation tasks. After each lesson, instructors score an astronaut's performance in several broad skill categories, including 'General Situational Awareness', 'Maneuvers/Task Performance', and 'Hand- Controller Techniques'. A plus, check, or minus indicates that the student is ahead of, at, or behind the expected skill level. The scoring of the final evaluation for a robotics training course is also largely subjective, with the instructor designating an integer score for the student between 1 (Unsatisfactory) and 5 (Strong) in the same skill categories. This thesis research project was designed to: (1) consider the variety of quantitative metrics that could be embedded into a space robotics training simulation, and (2) investigate at what point and by what means it is most constructive for performance assessment to be revealed to an operator-in-training. We reviewed the current largely qualitative space robotics performance metrics, as well as new quantitative kinematic metrics of manual control skills-including those explored thus far only in laboratory experiments-and additional measures of executive function and supervisory control performance. Kinematic metrics include quantitative measures such as rate of change of linear and rotational acceleration. Potential measures of executive function and supervisory control include camera selection and clearance monitoring. To instantiate our ideas, we chose a specific "fly-to" space telerobotics task taught in the early phases of NASA Generic Robotics Training (GRT) and developed a pilot training experiment (n=16) using our virtual robotics training workstation. Our goal was to evaluate potential performance metrics designed to encourage use of multi-axis control, and to compare real-time ("live") performance feedback alternatives (live visual vs. live aural vs. none). Movement time decreased and multi-axis and bimanual control use gradually increased across trials. All subjects had the opportunity to view post-trial performance feedback including these metrics. Although our subjects overwhelmingly preferred the live, visual feedback condition, no reliable additional effects of live feedback condition were found, except perhaps among the more experienced subjects. However, the experiment demonstrated that embedded performance metrics potentially could quantify and improve some important aspects of GRT evaluations.
Supported by the National Space Biomedical Research Institute through NASA NCC9-58
by Rachel Emily Forman.
S.M.
Wolbert, Daniel (Daniel Joseph). "Utilization of visual metrics to drive intended performance." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39689.
Full textIncludes bibliographical references.
In recent years the American industrial landscape has undergone tremendous change as companies have worked to adopt Lean practices. This transformation has been difficult, but necessary, as American companies work to remain competitive with foreign competitors. A key enabler of these Lean transformations has been the use of visual metrics to communicate how a process has performed as well as to set goals for future performance. The challenge is to first identify what data is available and then create metrics that encourage and reward Lean behaviors. This thesis explores the introduction of visual metrics for a part inspection process at the Raytheon Company. Prior to the introduction of these metrics, there was limited ability to determine how the process was performing. As a result, downstream customers were able to track when a part entered the inspection process but were unable to predict when the inspection would be completed. This introduced a risk to the process and created a sense of frustration through the facility. The visual metrics for the inspection area were created on a series of visual dashboards that display common Lean metrics, such as cycle time and backlog (or work-in-process). Through these dashboards the area will be able to understand how it is performing and initiate continuous improvement projects to improve performance.
by Daniel Wolbert.
S.M.
M.B.A.
Books on the topic "Performance metrics"
Boyd, William J., Ann Brockhaus, Matthew R. Chini, Paul A. Esposito, Kul B. Garg, Jonathan M. Haas, Judy L. Jarrell, et al., eds. Industrial Hygiene Performance Metrics. 2700 Prosperity Ave., Suite 250 Fairfax, VA 22031: American Industrial Hygiene Association, 2001. http://dx.doi.org/10.3320/978-1-931504-12-6.
Full textAmerican Industrial Hygiene Association. Metrics Subcommittee., ed. Industrial hygiene performance metrics. Fairfax, VA: American Industrial Hygiene Association, 2001.
Find full textLehmann, Donald R. Marketing metrics and financial performance. Cambridge, Mass: Marketing Science Institute, 2006.
Find full textAlbert, Sylvie, and Manish Pandey. Performance Metrics for Sustainable Cities. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003096566.
Full textSamur, Evren. Performance Metrics for Haptic Interfaces. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4225-6.
Full textLehmann, Donald R. Marketing metrics and financial performance. Cambridge, Mass: Marketing Science Institute, 2006.
Find full textLehmann, Donald R. Marketing metrics and financial performance. Cambridge, Mass: Marketing Science Institute, 2006.
Find full textservice), SpringerLink (Online, ed. Performance Metrics for Haptic Interfaces. London: Springer London, 2012.
Find full textTouran, Northeastern University Ali. Performance Metrics for Publicâ€"Private Partnerships. Washington, D.C.: Transportation Research Board, 2021. http://dx.doi.org/10.17226/26171.
Full textKounev, Samuel, Ian Gorton, and Kai Sachs, eds. Performance Evaluation: Metrics, Models and Benchmarks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69814-2.
Full textBook chapters on the topic "Performance metrics"
Goldshtein, Sasha, Dima Zurbalev, and Ido Flatow. "Performance Metrics." In Pro .NET Performance, 1–6. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4459-2_1.
Full textTokhi, M. O., M. A. Hossain, and M. H. Shaheed. "Performance Metrics." In Parallel Computing for Real-time Signal Processing and Control, 81–110. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0087-4_4.
Full textCarstens, Deborah Sater, and Gary L. Richardson. "Performance Metrics." In Project Management Tools and Techniques, 233–44. Second Edition. | Boca Raton : CRC Press, 2019. | Revised edition of Project management tools and techniques, [2013]: CRC Press, 2019. http://dx.doi.org/10.1201/9780429263163-17.
Full textSproull, ByBob. "Performance Metrics." In The Focus and Leverage Improvement Book, 191–206. 1 Edition. | New York, NY : Taylor & Francis, [2019]: Productivity Press, 2018. http://dx.doi.org/10.4324/9780429444456-10.
Full textLeasure, Bruce, David J. Kuck, Sergei Gorlatch, Murray Cole, Gregory R. Watson, Alain Darte, David Padua, et al. "Performance Metrics." In Encyclopedia of Parallel Computing, 1522–25. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2182.
Full textKurtz, David. "Performance Metrics." In PeopleSoft for the Oracle DBA, 213–67. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-3708-2_9.
Full textEeckhout, Lieven. "Performance Metrics." In Computer Architecture Performance Evaluation Methods, 5–14. Cham: Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01727-8_2.
Full textSproull, Bob. "Performance Metrics." In Learning from the Past, Present, and Future to Drive Profits to New Levels, 265–86. New York: Productivity Press, 2023. http://dx.doi.org/10.4324/9781003462385-19.
Full textLink, Albert N., and John T. Scott. "Performance Evaluation Metrics." In Public Accountability, 17–21. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5639-8_4.
Full textKasilingam, Raja G. "Logistics performance metrics." In Logistics and Transportation, 214–34. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5277-2_9.
Full textConference papers on the topic "Performance metrics"
Mackey, Jamie. "UDOT Signal Performance Metrics: New and Upcoming Metrics." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316023.
Full textTalbot, R. G., Chris R. Benn, and Rene G. M. Rutten. "Telescope performance metrics." In Astronomical Telescopes and Instrumentation, edited by Peter J. Quinn. SPIE, 2002. http://dx.doi.org/10.1117/12.460734.
Full textStevenson, Scott. "Maintaining Signal Performance Metrics." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316040.
Full textHuang, Hui-Min, and Seungbin Moon. "Session details: Measures & metrics." In PerMIS '10: Performance Metrics for Intelligent Systems. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/3260126.
Full textFarnsworth, Grant. "UDOT Automated Freeway Performance Metrics." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316026.
Full textMackey, Jamie. "UDOT Signal Performance Metrics: Configuration." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316039.
Full textKonell, Jeremiah P., Jack Van Schenck, Joseph P. Bratton, and Steven J. Polasik. "Practical IMP Performance Metrics." In 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64528.
Full textBrunnert, Andreas. "Green Software Metrics." In ICPE '24: 15th ACM/SPEC International Conference on Performance Engineering. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3629527.3652883.
Full textBalakirsky, Stephen, Thomas Kramer, and Frederick Proctor. "Metrics for mixed pallet stacking." In the 10th Performance Metrics for Intelligent Systems Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/2377576.2377587.
Full textBalakirsky, Stephen, and Henrik Christensen. "Session details: Performance metrics for mixed palletizing operations." In PerMIS '10: Performance Metrics for Intelligent Systems. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/3260127.
Full textReports on the topic "Performance metrics"
Stephan, E. IP Performance Metrics (IPPM) Metrics Registry. RFC Editor, August 2005. http://dx.doi.org/10.17487/rfc4148.
Full textDuffield, N., A. Morton, and J. Sommers. Loss Episode Metrics for IP Performance Metrics (IPPM). RFC Editor, May 2012. http://dx.doi.org/10.17487/rfc6534.
Full textDietz, R., and R. Cole. Transport Performance Metrics MIB. RFC Editor, August 2005. http://dx.doi.org/10.17487/rfc4150.
Full textBagnulo, M., B. Claise, P. Eardley, A. Morton, and A. Akhter. Registry for Performance Metrics. RFC Editor, November 2021. http://dx.doi.org/10.17487/rfc8911.
Full textDemichelis, C., and P. Chimento. IP Packet Delay Variation Metric for IP Performance Metrics (IPPM). RFC Editor, November 2002. http://dx.doi.org/10.17487/rfc3393.
Full textAlmes, G., S. Kalidindi, and M. Zekauskas. A One-Way Delay Metric for IP Performance Metrics (IPPM). Edited by A. Morton. RFC Editor, January 2016. http://dx.doi.org/10.17487/rfc7679.
Full textAlmes, G., S. Kalidindi, and M. Zekauskas. A One-Way Loss Metric for IP Performance Metrics (IPPM). Edited by A. Morton. RFC Editor, January 2016. http://dx.doi.org/10.17487/rfc7680.
Full textKoles, G., R. Hitchcock, and M. Sherman. Metrics for building performance assurance. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/374167.
Full textPaxson, V., G. Almes, J. Mahdavi, and M. Mathis. Framework for IP Performance Metrics. RFC Editor, May 1998. http://dx.doi.org/10.17487/rfc2330.
Full textPressel, D. M. Performance Metrics for Parallel Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada373437.
Full text