Literatura científica selecionada sobre o tema "Peak prediction"
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Artigos de revistas sobre o assunto "Peak prediction"
Schmitt, Thomas, Tobias Rodemann e Jürgen Adamy. "The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing". Energies 14, n.º 9 (29 de abril de 2021): 2569. http://dx.doi.org/10.3390/en14092569.
Texto completo da fonteXie, Lianku, Qinglei Yu, Jiandong Liu, Chunping Wu e Guang Zhang. "Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm". Applied Sciences 14, n.º 9 (30 de abril de 2024): 3839. http://dx.doi.org/10.3390/app14093839.
Texto completo da fonteNakashima, Toshihisa, Takayuki Ohno, Keiichi Koido, Hironobu Hashimoto e 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, n.º 3 (29 de maio de 2019): 543–48. http://dx.doi.org/10.1177/1078155219851834.
Texto completo da fonteGerber, Brandon S., James L. Tangler, Earl P. N. Duque e J. David Kocurek. "Peak and Post-Peak Power Aerodynamics from Phase VI NASA Ames Wind Turbine Data". Journal of Solar Energy Engineering 127, n.º 2 (25 de abril de 2005): 192–99. http://dx.doi.org/10.1115/1.1862260.
Texto completo da fonteYang, Hyunje, Honggeun Lim, Haewon Moon, Qiwen Li, Sooyoun Nam, Byoungki Choi e Hyung Tae Choi. "Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea". Forests 14, n.º 6 (30 de maio de 2023): 1131. http://dx.doi.org/10.3390/f14061131.
Texto completo da fonteKeith, David, e Juan Moreno-Cruz. "Pitfalls of coal peak prediction". Nature 469, n.º 7331 (janeiro de 2011): 472. http://dx.doi.org/10.1038/469472b.
Texto completo da fonteMandoli, 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, n.º 16 (8 de agosto de 2023): 2619. http://dx.doi.org/10.3390/diagnostics13162619.
Texto completo da fonteSoroka, Juliana, Larry Grenkow, Héctor Cárcamo, Scott Meers, Shelley Barkley e John Gavloski. "An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies". Canadian Entomologist 152, n.º 1 (21 de dezembro de 2019): 110–29. http://dx.doi.org/10.4039/tce.2019.71.
Texto completo da fonteLi, Haitao, Guo Yu, Yizhu Fang, Yanru Chen, Chenyu Wang e 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, n.º 8 (18 de junho de 2021): 3239–53. http://dx.doi.org/10.1007/s13202-021-01212-3.
Texto completo da fonteZhang, Yang. "Peak Traffic Prediction Using Nonparametric Approaches". Advanced Materials Research 378-379 (outubro de 2011): 196–99. http://dx.doi.org/10.4028/www.scientific.net/amr.378-379.196.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteKiuchi, 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.
Texto completo da fonteThornton, 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.
Texto completo da fonteThesis (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.
Texto completo da fonteWang, 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.
Texto completo da fonteMaster 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.
Texto completo da fontebilek 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.
Texto completo da fonteChen, Yuyao. "Contribution of machine learning to the prediction of building energy consumption". Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0119.
Texto completo da fonteThe 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.
Texto completo da fontePreisler, Frederik. "Predicting peak flows for urbanising catchments". Thesis, Queensland University of Technology, 1992.
Encontre o texto completo da fonteLivros sobre o assunto "Peak prediction"
Campeau, Gail Annette. Prediction of shotcrete damage through the analysis of peak particle velocity. Sudbury, Ont: Laurentian University, School of Engineering, 1999.
Encontre o texto completo da fonteFuture scenarios: How communities can adapt to peak oil and climate change. Totnes: Green, 2009.
Encontre o texto completo da fonteS, Rohatgi Upendra, e U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Systems Research., eds. 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.
Encontre o texto completo da fonteHolmgren, David. Future Scenarios: How Communities Can Adapt to Peak Oil and Climate Change. Chelsea Green Publishing, 2012.
Encontre o texto completo da fontePrediction of peak VO ́values from 9-minute run distances in young males, 9-14 years. 1985.
Encontre o texto completo da fontePrediction of peak VO2ș values from 9-minute run distances in young males, 9-14 years. 1985.
Encontre o texto completo da fonteLynch, Michael C. The “Peak Oil” Scare and the Coming Oil Flood. ABC-CLIO, LLC, 2016. http://dx.doi.org/10.5040/9798400605017.
Texto completo da fonteComparison of a prediction of maximal oxygen consumption by the YMCA Submaximal Bicycle Ergometer Test to a measurement of peak oxygen consumption. 1987.
Encontre o texto completo da fonteComparison of a prediction of maximal oxygen consumption by the YMCA Submaximal Bicycle Ergometer Test to a measurement of peak oxygen consumption. 1985.
Encontre o texto completo da fontePeak oxygen deficit as a predictor of sprint and middle-distance track performance. 1992.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Peak prediction"
Wu, Wenjie, Heping Jin, Gan Wang, Yihan Li, Wanru Zeng, Feng Liu, Huiheng Luo e Tao Liang. "Research on Wind Power Peak Prediction Method". In Lecture Notes in Electrical Engineering, 643–51. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1068-3_66.
Texto completo da fonteScherbart, Alexandra, Wiebke Timm, Sebastian Böcker e Tim W. Nattkemper. "Improved Mass Spectrometry Peak Intensity Prediction by Adaptive Feature Weighting". In 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.
Texto completo da fonteGoodwin, Morten, e Anis Yazidi. "A Pattern Recognition Approach for Peak Prediction of Electrical Consumption". In 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.
Texto completo da fonteXue, Weixian, e Liangmin Wang. "Prediction of Carbon Peak in Shaanxi Province and Its Cities". In 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.
Texto completo da fonteKanwar, Neeraj, Divay Bargoti e Vinay Kumar Jadoun. "Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning". In Lecture Notes in Electrical Engineering, 215–21. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1476-7_21.
Texto completo da fonteLi, Yajing, Jieren Cheng, Yuqing Kou, Dongwan Xia e Victor S. Sheng. "Prediction of Passenger Flow During Peak Hours Based on Deep Learning". In Smart Innovation, Systems and Technologies, 213–28. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7161-9_17.
Texto completo da fonteSun, Yanli, Di Zhang e Qiang Liu. "Prediction of peak carbon emission in Liaoning Province based on energy consumption". In Advances in Urban Engineering and Management Science Volume 2, 435–41. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003345329-57.
Texto completo da fonteMahmud, Khizir, Weilun Peng, Sayidul Morsalin e Jayashri Ravishankar. "A Day-Ahead Power Demand Prediction for Distribution-Side Peak Load Management". In 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.
Texto completo da fontePeteuil, Christophe, Simon Carladous e Nicolle Mathys. "Peak Discharge Prediction in Torrential Catchments of the French Pyrenees: The ANETO Method". In Management of Mountain Watersheds, 93–110. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2476-1_8.
Texto completo da fonteRovelli, Antonio. "Strong Ground Motions in Italy: Accelerogram Spectral Properties and Prediction of Peak Values". In Strong Ground Motion Seismology, 333–54. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-017-3095-2_11.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Peak prediction"
Singh, Rayman Preet, Peter Xiang Gao e Daniel J. Lizotte. "On hourly home peak load prediction". In 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2012. http://dx.doi.org/10.1109/smartgridcomm.2012.6485977.
Texto completo da fonteKhafaf, Nameer Al, e Ayman H. El-Hag. "Prediction of leakage current peak value". In 2018 11th International Symposium on Mechatronics and its Applications (ISMA). IEEE, 2018. http://dx.doi.org/10.1109/isma.2018.8330118.
Texto completo da fonteWeiss, M. A., A. Masarie e R. Beard. "Peak Deviation from Prediction in Atomic Clocks". In 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.
Texto completo da fonteLiu, Jinxiang, e Laura E. Brown. "Prediction of Hour of Coincident Daily Peak Load". In 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2019. http://dx.doi.org/10.1109/isgt.2019.8791587.
Texto completo da fonteAi, Songpu, Antorweep Chakravorty e Chunming Rong. "Evolutionary Ensemble LSTM based Household Peak Demand Prediction". In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2019. http://dx.doi.org/10.1109/icaiic.2019.8668971.
Texto completo da fonteLiu, Jianlei, e Kurt J. Marfurt. "Thin bed thickness prediction using peak instantaneous frequency". In SEG Technical Program Expanded Abstracts 2006. Society of Exploration Geophysicists, 2006. http://dx.doi.org/10.1190/1.2370418.
Texto completo da fonteMa, Yingwen, e Li Zhou. "Real-time flutter boundary prediction using peak-hold method". In Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII, editado por Peter J. Shull. SPIE, 2018. http://dx.doi.org/10.1117/12.2301241.
Texto completo da fonteTieleman, H. W., M. A. K. Elsayed e M. R. Hajj. "Prediction of Peak Wind Loads on Low Rise Structures". In Solutions to Coastal Disasters Conference 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40774(176)49.
Texto completo da fonteChiodo, E., e D. Lauria. "Probabilistic description and prediction of electric peak power demand". In 2012 Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS). IEEE, 2012. http://dx.doi.org/10.1109/esars.2012.6387418.
Texto completo da fonteIslam, Shafiqul, James Leech, Charles C. Y. Lin e Lukas Chrostowski. "Peak Blood Glucose Prediction Algorithm Following a Meal Intake". In 2007 Canadian Conference on Electrical and Computer Engineering. IEEE, 2007. http://dx.doi.org/10.1109/ccece.2007.149.
Texto completo da fonteRelatórios de organizações sobre o assunto "Peak prediction"
Ronstadt, Jackie A. Post-Wildfire Peak Discharge Prediction Methods in Northern New Mexico. Office of Scientific and Technical Information (OSTI), dezembro de 2017. http://dx.doi.org/10.2172/1414163.
Texto completo da fonteSi, Hongjun, Saburoh Midorikawa e 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, dezembro de 2020. http://dx.doi.org/10.55461/lien3652.
Texto completo da fonteArhin, Stephen, Babin Manandhar, Hamdiat Baba Adam e Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, abril de 2021. http://dx.doi.org/10.31979/mti.2021.1943.
Texto completo da fonteWells, Beric, Scott Cooley e 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), outubro de 2013. http://dx.doi.org/10.2172/1148634.
Texto completo da fonteLinker, Taylor, e 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), maio de 2019. http://dx.doi.org/10.55274/r0011584.
Texto completo da fonteHuang, Tao, e Venkatesh Merwade. Developing Customized NRCS Unit Hydrographs (Finley UHs) for Ungauged Watersheds in Indiana. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317644.
Texto completo da fonteStewart, Charles W., Stacey A. Hartley, Perry A. Meyer e Beric E. Wells. Predicting Peak Hydrogen Concentrations from Spontaneous Gas Releases in Hanford Waste Tanks. Office of Scientific and Technical Information (OSTI), julho de 2005. http://dx.doi.org/10.2172/15016741.
Texto completo da fonteDerdzinski, Pat, Dale Thoreson e Larry Tranel. NE Iowa’s Experience with Predictive Equation for Alfalfa Quality (PEAQ). Ames (Iowa): Iowa State University, janeiro de 2008. http://dx.doi.org/10.31274/ans_air-180814-887.
Texto completo da fonteDinovitzer. L52243 Modeling of Delayed Hydrogen Cracking for In-Service Welds. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), março de 2005. http://dx.doi.org/10.55274/r0010916.
Texto completo da fonteGlover, Austin, e 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), janeiro de 2023. http://dx.doi.org/10.2172/2004890.
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