Littérature scientifique sur le sujet « Peak prediction »
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Articles de revues sur le sujet "Peak prediction"
Schmitt, Thomas, Tobias Rodemann et Jürgen Adamy. « The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing ». Energies 14, no 9 (29 avril 2021) : 2569. http://dx.doi.org/10.3390/en14092569.
Texte intégralXie, Lianku, Qinglei Yu, Jiandong Liu, Chunping Wu et Guang Zhang. « Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm ». Applied Sciences 14, no 9 (30 avril 2024) : 3839. http://dx.doi.org/10.3390/app14093839.
Texte intégralNakashima, Toshihisa, Takayuki Ohno, Keiichi Koido, Hironobu Hashimoto et Hiroyuki Terakado. « Accuracy of predicting the vancomycin concentration in Japanese cancer patients by the Sawchuk–Zaske method or Bayesian method ». Journal of Oncology Pharmacy Practice 26, no 3 (29 mai 2019) : 543–48. http://dx.doi.org/10.1177/1078155219851834.
Texte intégralGerber, Brandon S., James L. Tangler, Earl P. N. Duque et J. David Kocurek. « Peak and Post-Peak Power Aerodynamics from Phase VI NASA Ames Wind Turbine Data ». Journal of Solar Energy Engineering 127, no 2 (25 avril 2005) : 192–99. http://dx.doi.org/10.1115/1.1862260.
Texte intégralYang, Hyunje, Honggeun Lim, Haewon Moon, Qiwen Li, Sooyoun Nam, Byoungki Choi et Hyung Tae Choi. « Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea ». Forests 14, no 6 (30 mai 2023) : 1131. http://dx.doi.org/10.3390/f14061131.
Texte intégralKeith, David, et Juan Moreno-Cruz. « Pitfalls of coal peak prediction ». Nature 469, no 7331 (janvier 2011) : 472. http://dx.doi.org/10.1038/469472b.
Texte intégralMandoli, Giulia Elena, Federico Landra, Benedetta Chiantini, Carlotta Sciaccaluga, Maria Concetta Pastore, Marta Focardi, Luna Cavigli et al. « Tricuspid Regurgitation Velocity and Mean Pressure Gradient for the Prediction of Pulmonary Hypertension According to the New Hemodynamic Definition ». Diagnostics 13, no 16 (8 août 2023) : 2619. http://dx.doi.org/10.3390/diagnostics13162619.
Texte intégralSoroka, Juliana, Larry Grenkow, Héctor Cárcamo, Scott Meers, Shelley Barkley et John Gavloski. « An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera : Curculionidae) on the Canadian Prairies ». Canadian Entomologist 152, no 1 (21 décembre 2019) : 110–29. http://dx.doi.org/10.4039/tce.2019.71.
Texte intégralLi, Haitao, Guo Yu, Yizhu Fang, Yanru Chen, Chenyu Wang et Dongming Zhang. « Studies on natural gas reserves multi-cycle growth law in Sichuan Basin based on multi-peak identification and peak parameter prediction ». Journal of Petroleum Exploration and Production Technology 11, no 8 (18 juin 2021) : 3239–53. http://dx.doi.org/10.1007/s13202-021-01212-3.
Texte intégralZhang, Yang. « Peak Traffic Prediction Using Nonparametric Approaches ». Advanced Materials Research 378-379 (octobre 2011) : 196–99. http://dx.doi.org/10.4028/www.scientific.net/amr.378-379.196.
Texte intégralThèses sur le sujet "Peak prediction"
Al-Rahamneh, Harran Qoblan Mefleh. « Perceived exertion relationships and prediction of peak oxygen uptake in able-bodied and paraplegic individuals ». Thesis, University of Exeter, 2010. http://hdl.handle.net/10036/3005.
Texte intégralKiuchi, Ryota. « New Ground Motion Prediction Equations for Saudi Arabia and their Application to Probabilistic Seismic Hazard Analysis ». Kyoto University, 2020. http://hdl.handle.net/2433/253095.
Texte intégralThornton, Craig Matthew. « Effects of Land Development on Peak Runoff Rate and its Prediction for Brigalow Catchments in Central Queensland, Australia ». Thesis, Griffith University, 2012. http://hdl.handle.net/10072/365709.
Texte intégralThesis (Masters)
Master of Philosophy (MPhil)
Griffith School of Engineering
Science, Environment, Engineering and Technology
Full Text
Birch, Wiliiam John. « The prediction of peak particle velocity vibration levels in underground structures that arise as the result of surface blasting ». Thesis, University of Leeds, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659028.
Texte intégralWang, Zijian. « DM EMI Noise Analysis for Single Channel and Interleaved Boost PFC in Critical Conduction Mode ». Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/32719.
Texte intégralMaster of Science
Akeil, Salah. « Comparative Study On Ground Vibrations Prediction By Statistical And Neural Networks Approaches At Tuncbilek Coal Mine, Panel Byh ». Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605058/index.pdf.
Texte intégralbilek Coal Mine, Panel BYH, were measured to find out the site-specific attenuation and to assess the structural damage risk. A statistical approach is applied to the collected data, and from the data analysis an attenuation relationship is established to be used in predicting the peak particle velocity as well as to calculate the maximum allowable charge per delay. The values of frequencies are also analyzed to investigate the damage potential to the structures of Tunç
bilek Township. A new approach to predict the peak particle velocity is also proposed in this research study. A neural network technique from the branch of the artificial intelligence is put forward as an alternative approach to the statistical technique. Findings of this study indicate, according to USBM (1980) criteria, that there is no damage risk to the structures in Tunç
bilek Township induced by bench blasting performed at Tunç
bilek coal mine, Panel BYH. Therefore, it is concluded that the damage claims put forward by the inhabitants of Tunç
bilek township had no scientific bases. It is also concluded that the empirical statistical technique is not the only acceptable approach that can be taken into account in predicting the peak particle velocity. An alternative and interesting neural network approach can also give a satisfactory accuracy in predicting peak particle velocity when compared to a set of additional recorded data of PPV.
Goutham, Mithun. « Machine learning based user activity prediction for smart homes ». The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.
Texte intégralChen, Yuyao. « Contribution of machine learning to the prediction of building energy consumption ». Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0119.
Texte intégralThe ongoing energy transition, pivotal to mitigate global warming, could significantly benefit from advances in building energy consumption prediction. With the advent of big data, data-driven models are increasingly effective in forecasting tasks and machine learning is probably the most efficient method to build such predictive models nowadays. In this work, we provide a comprehensive review of machine learning techniques for forecasting, regarding preprocessing as well as state-of-the-art models such as deep neural networks. Despite the achievements of state-of-art models, accurately predicting high-fluctuation electricity consumption still remains a challenge. To tackle this challenge, we propose to explore two paths: the utilization of soft-DTW loss functions and the inclusion of exogenous variables. By applying the soft-DTW loss function with a residual LSTM neural network on a real dataset, we observed significant improvements in capturing the patterns of high-fluctuation load series, especially in peak prediction. However, conventional error metrics prove insufficient in adequately measuring this ability. We therefore introduce confusion matrix analysis and two new error metrics: peak position error and peak load error based on the DTW algorithm. Our findings reveal that soft-DTW outperforms MSE and MAE loss functions with lower peak position and peak load error. We also incorporate soft-DTW loss function with MSE, MAE, and Time Distortion Index. The results show that combining the MSE loss function performs the best and helps alleviate the problem of overestimated and sharp peaks problems occured. By adding exogenous variables with soft-DTW loss functions, the inclusion of calendar variables generally enhances the model’s performance, particularly when these variables exhibit higher Pearson’s correlation coefficients with the target variable. However, when the correlation between the calendar variables and the historical load patterns is relatively low, their inclusion has a negative impact on the model’s performance. A similar relationship is observed with weather variables
Hiesböcková, Tereza. « Předpovídání povodňových průtoků v měrných profilech Borovnice - Dalečín ». Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2012. http://www.nusl.cz/ntk/nusl-225458.
Texte intégralPreisler, Frederik. « Predicting peak flows for urbanising catchments ». Thesis, Queensland University of Technology, 1992.
Trouver le texte intégralLivres sur le sujet "Peak prediction"
Campeau, Gail Annette. Prediction of shotcrete damage through the analysis of peak particle velocity. Sudbury, Ont : Laurentian University, School of Engineering, 1999.
Trouver le texte intégralFuture scenarios : How communities can adapt to peak oil and climate change. Totnes : Green, 2009.
Trouver le texte intégralS, Rohatgi Upendra, et U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Systems Research., dir. Bias in peak clad temperature predictions due to uncertainties in modeling of ECC bypass and dissolved non-condensable gas phenomena. Washington, DC : Division of Systems Research, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1990.
Trouver le texte intégralHolmgren, David. Future Scenarios : How Communities Can Adapt to Peak Oil and Climate Change. Chelsea Green Publishing, 2012.
Trouver le texte intégralPrediction of peak VO ́values from 9-minute run distances in young males, 9-14 years. 1985.
Trouver le texte intégralPrediction of peak VO2ș values from 9-minute run distances in young males, 9-14 years. 1985.
Trouver le texte intégralLynch, Michael C. The “Peak Oil” Scare and the Coming Oil Flood. ABC-CLIO, LLC, 2016. http://dx.doi.org/10.5040/9798400605017.
Texte intégralComparison of a prediction of maximal oxygen consumption by the YMCA Submaximal Bicycle Ergometer Test to a measurement of peak oxygen consumption. 1987.
Trouver le texte intégralComparison of a prediction of maximal oxygen consumption by the YMCA Submaximal Bicycle Ergometer Test to a measurement of peak oxygen consumption. 1985.
Trouver le texte intégralPeak oxygen deficit as a predictor of sprint and middle-distance track performance. 1992.
Trouver le texte intégralChapitres de livres sur le sujet "Peak prediction"
Wu, Wenjie, Heping Jin, Gan Wang, Yihan Li, Wanru Zeng, Feng Liu, Huiheng Luo et Tao Liang. « Research on Wind Power Peak Prediction Method ». Dans Lecture Notes in Electrical Engineering, 643–51. Singapore : Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1068-3_66.
Texte intégralScherbart, Alexandra, Wiebke Timm, Sebastian Böcker et Tim W. Nattkemper. « Improved Mass Spectrometry Peak Intensity Prediction by Adaptive Feature Weighting ». Dans Advances in Neuro-Information Processing, 513–20. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_63.
Texte intégralGoodwin, Morten, et Anis Yazidi. « A Pattern Recognition Approach for Peak Prediction of Electrical Consumption ». Dans Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 265–75. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44654-6_26.
Texte intégralXue, Weixian, et Liangmin Wang. « Prediction of Carbon Peak in Shaanxi Province and Its Cities ». Dans Atlantis Highlights in Intelligent Systems, 938–45. Dordrecht : Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-200-2_97.
Texte intégralKanwar, Neeraj, Divay Bargoti et Vinay Kumar Jadoun. « Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning ». Dans Lecture Notes in Electrical Engineering, 215–21. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1476-7_21.
Texte intégralLi, Yajing, Jieren Cheng, Yuqing Kou, Dongwan Xia et Victor S. Sheng. « Prediction of Passenger Flow During Peak Hours Based on Deep Learning ». Dans Smart Innovation, Systems and Technologies, 213–28. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7161-9_17.
Texte intégralSun, Yanli, Di Zhang et Qiang Liu. « Prediction of peak carbon emission in Liaoning Province based on energy consumption ». Dans Advances in Urban Engineering and Management Science Volume 2, 435–41. London : CRC Press, 2022. http://dx.doi.org/10.1201/9781003345329-57.
Texte intégralMahmud, Khizir, Weilun Peng, Sayidul Morsalin et Jayashri Ravishankar. « A Day-Ahead Power Demand Prediction for Distribution-Side Peak Load Management ». Dans Proceedings of International Joint Conference on Computational Intelligence, 305–15. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7564-4_27.
Texte intégralPeteuil, Christophe, Simon Carladous et Nicolle Mathys. « Peak Discharge Prediction in Torrential Catchments of the French Pyrenees : The ANETO Method ». Dans Management of Mountain Watersheds, 93–110. Dordrecht : Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2476-1_8.
Texte intégralRovelli, Antonio. « Strong Ground Motions in Italy : Accelerogram Spectral Properties and Prediction of Peak Values ». Dans Strong Ground Motion Seismology, 333–54. Dordrecht : Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-017-3095-2_11.
Texte intégralActes de conférences sur le sujet "Peak prediction"
Singh, Rayman Preet, Peter Xiang Gao et Daniel J. Lizotte. « On hourly home peak load prediction ». Dans 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2012. http://dx.doi.org/10.1109/smartgridcomm.2012.6485977.
Texte intégralKhafaf, Nameer Al, et Ayman H. El-Hag. « Prediction of leakage current peak value ». Dans 2018 11th International Symposium on Mechatronics and its Applications (ISMA). IEEE, 2018. http://dx.doi.org/10.1109/isma.2018.8330118.
Texte intégralWeiss, M. A., A. Masarie et R. Beard. « Peak Deviation from Prediction in Atomic Clocks ». Dans 2007 IEEE International Frequency Control Symposium Joint with the 21st European Frequency and Time Forum. IEEE, 2007. http://dx.doi.org/10.1109/freq.2007.4319231.
Texte intégralLiu, Jinxiang, et Laura E. Brown. « Prediction of Hour of Coincident Daily Peak Load ». Dans 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2019. http://dx.doi.org/10.1109/isgt.2019.8791587.
Texte intégralAi, Songpu, Antorweep Chakravorty et Chunming Rong. « Evolutionary Ensemble LSTM based Household Peak Demand Prediction ». Dans 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2019. http://dx.doi.org/10.1109/icaiic.2019.8668971.
Texte intégralLiu, Jianlei, et Kurt J. Marfurt. « Thin bed thickness prediction using peak instantaneous frequency ». Dans SEG Technical Program Expanded Abstracts 2006. Society of Exploration Geophysicists, 2006. http://dx.doi.org/10.1190/1.2370418.
Texte intégralMa, Yingwen, et Li Zhou. « Real-time flutter boundary prediction using peak-hold method ». Dans Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII, sous la direction de Peter J. Shull. SPIE, 2018. http://dx.doi.org/10.1117/12.2301241.
Texte intégralTieleman, H. W., M. A. K. Elsayed et M. R. Hajj. « Prediction of Peak Wind Loads on Low Rise Structures ». Dans Solutions to Coastal Disasters Conference 2005. Reston, VA : American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40774(176)49.
Texte intégralChiodo, E., et D. Lauria. « Probabilistic description and prediction of electric peak power demand ». Dans 2012 Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS). IEEE, 2012. http://dx.doi.org/10.1109/esars.2012.6387418.
Texte intégralIslam, Shafiqul, James Leech, Charles C. Y. Lin et Lukas Chrostowski. « Peak Blood Glucose Prediction Algorithm Following a Meal Intake ». Dans 2007 Canadian Conference on Electrical and Computer Engineering. IEEE, 2007. http://dx.doi.org/10.1109/ccece.2007.149.
Texte intégralRapports d'organisations sur le sujet "Peak prediction"
Ronstadt, Jackie A. Post-Wildfire Peak Discharge Prediction Methods in Northern New Mexico. Office of Scientific and Technical Information (OSTI), décembre 2017. http://dx.doi.org/10.2172/1414163.
Texte intégralSi, Hongjun, Saburoh Midorikawa et Tadahiro Kishida. Development of NGA-Sub Ground-Motion Model of 5%-Damped Pseudo-Spectral Acceleration Based on Database for Subduction Earthquakes in Japan. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, décembre 2020. http://dx.doi.org/10.55461/lien3652.
Texte intégralArhin, Stephen, Babin Manandhar, Hamdiat Baba Adam et Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, avril 2021. http://dx.doi.org/10.31979/mti.2021.1943.
Texte intégralWells, Beric, Scott Cooley et Joseph Meacham. Prediction of Peak Hydrogen Concentrations for Deep Sludge Retrieval in Tanks AN-101 and AN-106 from Historical Data of Spontaneous Gas Release Events. Office of Scientific and Technical Information (OSTI), octobre 2013. http://dx.doi.org/10.2172/1148634.
Texte intégralLinker, Taylor, et Timothy Jacobs. PR-457-18204-R01 Variable Fuel Effects on Legacy Compressor Engines Phase IV - Predictive NOx Modeling. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), mai 2019. http://dx.doi.org/10.55274/r0011584.
Texte intégralHuang, Tao, et Venkatesh Merwade. Developing Customized NRCS Unit Hydrographs (Finley UHs) for Ungauged Watersheds in Indiana. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317644.
Texte intégralStewart, Charles W., Stacey A. Hartley, Perry A. Meyer et Beric E. Wells. Predicting Peak Hydrogen Concentrations from Spontaneous Gas Releases in Hanford Waste Tanks. Office of Scientific and Technical Information (OSTI), juillet 2005. http://dx.doi.org/10.2172/15016741.
Texte intégralDerdzinski, Pat, Dale Thoreson et Larry Tranel. NE Iowa’s Experience with Predictive Equation for Alfalfa Quality (PEAQ). Ames (Iowa) : Iowa State University, janvier 2008. http://dx.doi.org/10.31274/ans_air-180814-887.
Texte intégralDinovitzer. L52243 Modeling of Delayed Hydrogen Cracking for In-Service Welds. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), mars 2005. http://dx.doi.org/10.55274/r0010916.
Texte intégralGlover, Austin, et Dusty Brooks. Comparison of Side-on Peak Overpressure Predictions and Measurements for Type IV Composite Overwrapped Pressure Vessel Catastrophic Failure. Office of Scientific and Technical Information (OSTI), janvier 2023. http://dx.doi.org/10.2172/2004890.
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