Relevant bibliographies by topics / Prediction of RUL

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Prediction of RUL'

Author: Grafiati
Published: 28 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Prediction of RUL"

1

Peng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, and Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion." Sensors 21, no. 2 (2021): 418. http://dx.doi.org/10.3390/s21020418.

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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LST
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Peng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, and Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion." Sensors 21, no. 2 (2021): 418. http://dx.doi.org/10.3390/s21020418.

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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LST
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Gómez-Pau, Álvaro, Jordi-Roger Riba, and Manuel Moreno-Eguilaz. "Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans." Applied Sciences 10, no. 24 (2020): 9041. http://dx.doi.org/10.3390/app10249041.

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The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since
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Liu, Haiping, Jianjun Wu, Xiang Ye, Taijian Liao, and Minlin Chen. "A method based on Dempster-Shafer theory and support vector regression-particle filter for remaining useful life prediction of crusher roller sleeve." Mechanics & Industry 20, no. 1 (2019): 106. http://dx.doi.org/10.1051/meca/2018038.

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In order to solve the problem of accurately predicting the remaining useful life (RUL) of crusher roller sleeve under the partially observable and nonlinear nonstationary running state, a new method of RUL prediction based on Dempster-Shafer (D-S) data fusion and support vector regression-particle filter (SVR-PF) is proposed. First, it adopts the correlation analysis to select the features of temperature and vibration signal, and subsequently utilize wavelet to denoising the features. Lastly, comparing the prediction performance of the proposed method integrates temperature and vibration signa
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Lu, Cun, Zheng Jian Gu, and Yuan Yan. "RUL Prediction of Lithium Ion Battery Based on ARIMA Time Series Algorithm." Materials Science Forum 999 (June 2020): 117–28. http://dx.doi.org/10.4028/www.scientific.net/msf.999.117.

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Lithium ion battery is a key component of energy storage system. Accurate and scientific prediction of its Remaining Useful Life (RUL) is an important factor to check the operation of energy storage system is whether reliable. ARIMA is an effective time series prediction processing method, which can be used to calculate battery RUL and its confidence interval. And the more predicted samples, the higher the prediction accuracy. Compared with the empirical model and support vector machine algorithm, the analysis results show that the support vector machine is over-fitting. For two sets of the ex
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Pang, Xiaoqiong, Rui Huang, Jie Wen, Yuanhao Shi, Jianfang Jia, and Jianchao Zeng. "A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon." Energies 12, no. 12 (2019): 2247. http://dx.doi.org/10.3390/en12122247.

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Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decompositi
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Qin, Aisong, Qinghua Zhang, Qin Hu, Guoxi Sun, Jun He, and Shuiquan Lin. "Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator." Shock and Vibration 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6754968.

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Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predi
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Hao, Xiuhong, Shuqiang Wang, Mengfan Chen, and Deng Pan. "Remaining Useful Life Prediction of High-Frequency Swing Self-Lubricating Liner." Shock and Vibration 2021 (January 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/8843374.

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The remaining useful life (RUL) prediction of self-lubricating spherical plain bearings is essential for replacement decision-making and the reliability of high-end equipment. The high-frequency swing self-lubricating liner (HSLL) is the key component of self-lubricating spherical plain bearings under high-frequency oscillation conditions. In this study, a RUL prediction method was proposed based on the Wiener process and grey system theory. First, the predictive processing of the wear depth was carried out using the grey model GM(1,1) to reduce the randomness and enhance the inherent regulari
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Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (2021): 932. http://dx.doi.org/10.3390/s21030932.

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipme
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Mu, Zongyi, Yan Ran, Genbao Zhang, Hongwei Wang, and Xin Yang. "Remaining useful life prediction method for machine tools based on meta-action theory." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, no. 4 (2021): 580–90. http://dx.doi.org/10.1177/1748006x211002544.

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Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based
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Dissertations / Theses on the topic "Prediction of RUL"

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Martello, Rosanna. "Cloud storage and processing of automotive Lithium-ion batteries data for RUL prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Lithium-ion batteries are the ideal choice for electric and hybrid vehicles, but the high cost and the relatively short life represent an open issue for automotive industries. For this reason, the estimation of battery Remaining Useful Life (RUL) and the State of Health (SoH) are primary goals in the automotive sector. Cloud computing provides all the resources necessary to store, process and analyze all sensor data coming from connected vehicles in order to develop Predictive Maintenance tasks. This project describes the work done during my internship at FEV Italia s.r.l. The aims were desig
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Popara, Nikola. "Využití umělé inteligence k monitorování stavu obráběcího stroje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444960.

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This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used
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Mohammadisohrabi, Ali. "Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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A key idea underlying many Predictive Maintenance solutions is Remaining Useful Life (RUL) of machine parts, and it simply involves a prediction on the time remaining before a machine part is likely to require repair or replacement. Nowadays, with respect to fact that the systems are getting more complex, the innovative Machine Learning and Deep Learning algorithms can be deployed to study the more sophisticated correlations in complex systems. The exponential increase in both data accumulation and processing power make the Deep Learning algorithms more desirable that before. In this paper a L
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Jin, Wenjing. "Modeling of Machine Life Using Accelerated Prognostics and Health Management (APHM) and Enhanced Deep Learning Methodology." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479821186023747.

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Daher, Alaa. "Diagnostic et pronostic des défauts pour la maintenance préventive et prédictive. Application à une colonne de distillation." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMR090/document.

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Le procédé de distillation est largement utilisé dans de nombreuses applications telles que la production pétrochimique, le traitement du gaz naturel, les raffineries de pétrole, etc. Généralement, la maintenance des réacteurs chimiques est très coûteuse et perturbe la production pendant de longues périodes. Tous ces facteurs démontrent réellement la nécessité de stratégies efficaces de diagnostic et de pronostic des défauts pour pouvoir réduire et éviter le plus grand nombre de ces problèmes catastrophiques. La première partie de nos travaux vise à proposer une méthode de diagnostic fiable po
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Sanzani, Matteo. "La costruzione di un indicatore di salute per la manutenzione predittiva attraverso la programmazione genetica mono-obiettivo." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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La manutenzione predittiva, o Prognostic Health Management (PHM), è l’innovativa politica manutentiva basata monitoraggio continuo dello stato di salute dei componenti meccanici, grazie all’acquisizione dei dati tramite sensori applicati sui componenti stessi. Questi dati non sono facilmente analizzabili direttamente: è difatti necessaria un’attività di processing, volta ad estrarre delle caratteristiche significative e sintetiche del segnale, chiamate in letteratura features. Tipicamente, alla fase di estrazione delle features, segue una fase di selezione delle features e/o costruzione di un
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Sowan, Bilal I. "Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stag
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Sowan, Bilal Ibrahim. "Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stage
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Lowy, Elliott. "The evolution of the golden rule /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9017.

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Balla, Chaitanya Kumar. "Prediction of Remaining Service Life of Pavements." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1279316853.

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Books on the topic "Prediction of RUL"

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Moṅʻ, Moṅʻ. Mranʻ māʹ rui r̋ā ʼāyu canʻ b̋edaṅʻ paññā. Tuiṅʻ L̋aṅʻ C̋ā pe, 2001.

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Gavrilov, O. A. Strategii͡a︡ pravotvorchestva i sot͡s︡ialʹnoe prognozirovanie. In-t gosudarstva i prava Rossiĭskoĭ akademii nauk, 1993.

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V, Kehiaian H., Renon H, and International Symposium on Critical Evaluation and Prediction of Phase Equilibria in Multicomponent Systems (2nd : 1985 : Paris, France), eds. Measurement, evaluation, and prediction of phase equilibria: A collection of selected papers from the Second International IUPAC Workshop on Vapor-Liquid Equilibria in 1-Alkanol +n-Alkane Mixtures, Paris, France, 5-7 September 1985 and the Second International Symposium on Critical Evaluation and Prediction of Phase Equilibria in Multicomponent Systems, Paris France, 11-13 September 1985. Elsevier, 1986.

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Nōrin Suisan Gijutsu Kaigi. Jimukyoku. Kankyō hendō ni tomonau kaiyō seibutsu daihassei no yosoku, seigyo gijutsu no kaihatsu: Kurage-rui no daihassei yosoku, seigyo gijutsu no kaihatsu = Study for the prediction and control of the population outbreak of the marine life in relation to environmental change : studies of prediction and control of jellyfish outbreaks (STOPJELLY). Nōrin Suisanshō Nōrin Suisan Gijutsu Kaigi Jimukyoku, 2014.

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Birch, Jonathan. The Rule under Attack. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198733058.003.0003.

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HRG has been criticized for being an ‘empty statement’ or tautology, for failing to yield predictions, and for failing to yield causal explanations of change. There is some justification for these charges, yet they do not undermine the value of HRG as an organizing framework. In response to the ‘tautology’ complaint, we should admit that HRG is tautology-like, in that it avoids detailed dynamical assumptions. But this is an advantage in an organizing framework, because it ensures its compatibility with a wide range of more detailed models. In response to the ‘prediction’ complaint, we should c
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United States. Army Aviation Research and Technology Activity. and Langley Research Center, eds. A comparison of fatigue life prediction methodologies for rotor craft. National Aeronautics and Space Administration, Langley Research Center, 1990.

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Lee, Christoph I. Rule Out Subarachnoid Hemorrhage for Headache. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0003.

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This chapter, found in the headache section of the book, provides a succinct synopsis of a key study examining the use of computed tomography (CT) to rule out a head bleed or subarachnoid hemorrhage among patients with acute headaches. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. Researchers reported that the criteria had high sensitivity and high negative predictive value for identifying subarachnoid hemorrhage among patients presenting to the emergency department with
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Die Vermessung der Utopie: Mythen des Kapitalismus und die kommende Gesellschaft, Raul Zelik im Gespräch mit Elmar Altvater. Blumenbar, 2009.

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Hough, Catherine L. Chronic critical illness. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0377.

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Chronic critical illness (CCI) is common and describes a state of prolonged critical illness, in which patients have persisting organ failures requiring treatment in an intensive care setting. There are many different definitions of CCI, with most including prolonged (> 96 hours) mechanical ventilation. Advanced age, higher severity of illness, and poor functional status prior to critical illness are all important risk factors, but prediction of CCI is imperfect. Although requirement for mechanical ventilation is the hallmark, CCI encompasses much more than the respiratory system, with effe
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van der Meer, Tom. Dissecting the Causal Chain from Quality of Government to Political Support. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793717.003.0008.

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This chapter investigates to what extent cross-national differences in political support can be explained by the quality of government. The quality of government perspective implies that the executive ought to be bound by its own rules: impartiality and rule of law. The chapter formulates and tests hypotheses about the effects of governmental impartiality, rule of law, bureaucratic professionalism, and corruption on citizens’ political support using data from the ESS 2012. Of these indicators, it is the impartiality of policy implementation by the national bureaucracy that stands out as a cons
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Book chapters on the topic "Prediction of RUL"

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Yan, Dong, and Xiukun Wei. "RUL Prediction for Bearings Based on Fault Diagnosis." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7986-3_102.

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Wu, Qianhui, Yu Feng, and Biqing Huang. "RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description." In Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2666-9_13.

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Wu, Qianhui, Yu Feng, and Biqing Huang. "RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description." In Challenges and Opportunity with Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61994-1_14.

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Fürnkranz, Johannes. "Prediction Rule." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_837.

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Stemberger, Joseph P. "Rule ordering in Child phonology." In Principles and Prediction. John Benjamins Publishing Company, 1993. http://dx.doi.org/10.1075/cilt.98.25ste.

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Iverson, Gregory K. "Lexical versus postlexical rule application in Catalan." In Principles and Prediction. John Benjamins Publishing Company, 1993. http://dx.doi.org/10.1075/cilt.98.27ive.

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Kuhn, Max, and Kjell Johnson. "Classification Trees and Rule-Based Models." In Applied Predictive Modeling. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6849-3_14.

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Kuhn, Max, and Kjell Johnson. "Regression Trees and Rule-Based Models." In Applied Predictive Modeling. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6849-3_8.

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Tsukimoto, Hiroshi. "Rule Extraction from Prediction Models." In Methodologies for Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48912-6_6.

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Wanhill, Russell, Simon Barter, and Loris Molent. "Cubic Rule Life Prediction Examples." In SpringerBriefs in Applied Sciences and Technology. Springer Netherlands, 2019. http://dx.doi.org/10.1007/978-94-024-1675-6_8.

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Conference papers on the topic "Prediction of RUL"

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Hu, Chao, Byeng D. Youn, and Taejin Kim. "Semi-Supervised Learning With Co-Training for Data-Driven Prognostics." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48302.

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Traditional data-driven prognostics often requires a large amount of failure data for the offline training in order to achieve good accuracy for the online prediction. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while suspension data are readily available. In such cases, it becomes essentially critical to utilize suspension data, which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic
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Zhang, Yuxuan, Yuanxiang Li, Lei Jia, Xian Wei, and Yi Lu Murphey. "Sequential Information Bottleneck Network for RUL Prediction." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002732.

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Galar, Diego, Uday Kumar, and Yuan Fuqing. "RUL prediction using moving trajectories between SVM hyper planes." In 2012 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2012. http://dx.doi.org/10.1109/rams.2012.6175481.

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Tang, Ting, Hui-Mei Yuan, and Jun Zhu. "RUL prediction of lithium batteries based on DLUKF algorithm." In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea48937.2020.9248133.

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Gao, Zehai, Cunbao Ma, and Yige Luo. "RUL prediction for IMA based on deep regression method." In 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 2017. http://dx.doi.org/10.1109/iwcia.2017.8203556.

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Yan, Dong, Xiukun Wei, and Guorui Zhai. "RUL prediction for railway vehicle bearings based on fault diagnosis." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978862.

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Jia, Chao, and Hanwen Zhang. "RUL Prediction: Reducing Statistical Model Uncertainty Via Bayesian Model Aggregation." In 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). IEEE, 2019. http://dx.doi.org/10.1109/safeprocess45799.2019.9213433.

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Li, Huaxin, and Yanxue Wang. "A Sparse Coding Approach to RUL Prediction in Rolling Bearing." In 2017 International Conference on Sensing, Diagnostics, Prognostics and Control (SDPC). IEEE, 2017. http://dx.doi.org/10.1109/sdpc.2017.41.

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Jiang, Yuanyuan, Wenwen Zeng, Li Chen, and Yuanfang Xin. "Lithium-Ion Battery RUL Indirect Prediction Based on GAAA-ELM." In 2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC). IEEE, 2018. http://dx.doi.org/10.1109/sdpc.2018.8664829.

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Khelif, Racha, Simon Malinowski, Brigitte Chebel-Morello, and Noureddine Zerhouni. "RUL prediction based on a new similarity-instance based approach." In 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE). IEEE, 2014. http://dx.doi.org/10.1109/isie.2014.6865006.

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Reports on the topic "Prediction of RUL"

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Mueller, Ulrich, and Mark Watson. Measuring Uncertainty about Long-Run Prediction. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w18870.

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Campshure, David A., and Eugene H. Drucker. Predicting First-Run Gunnery Performance on Tank Table VIII. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada228201.

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Blanchflower, David, and Alex Bryson. The Sahm Rule and Predicting the Great Recession Across OECD Countries. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29300.

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Townsend, Richard L., P. Westhagen, D. Yasuda, and J. R. Skalski. Evaluation of the 1994 Predictions of the Run-Timing of Wild Migrant Yearling Chinook in the Snake River Basin. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/239306.

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Townsend, Richard L., Peter Westhagen, and Dean Yasuda. Evaluation of the 1995 Predictions of the Run-Timing of Wild Migrant Yearling Chinook in the Snake River Basin Using Program RealTime. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/418436.

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Beer, W. Nicholas, Susannah Iltis, and James J. Anderson. Evaluation of the 2008 Predictions of Run-Timing and Survival of Wild Migrant Yearling Chinook and Steelhead on the Columbia and Snake Rivers. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/947611.

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Townsend, Richard L., Dean Yasuda, and J. R. Skalski. Evaluation of the 1996 Predictions of the Run-Timing of Wild Migrant Spring/Summer Yearling Chinook in the Snake River Basin Using Program RealTime. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/650231.

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Nishimura, Masatsugu, Yoshitaka Tezuka, Enrico Picotti, Mattia Bruschetta, Francesco Ambrogi, and Toru Yoshii. Study of Rider Model for Motorcycle Racing Simulation. SAE International, 2020. http://dx.doi.org/10.4271/2019-32-0572.

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Various rider models have been proposed that provide control inputs for the simulation of motorcycle dynamics. However, those models are mostly used to simulate production motorcycles, so they assume that all motions are in the linear region such as those in a constant radius turn. As such, their performance is insufficient for simulating racing motorcycles that experience quick acceleration and braking. Therefore, this study proposes a new rider model for racing simulation that incorporates Nonlinear Model Predictive Control. In developing this model, it was built on the premise that it can c
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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. W
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Beer, W. Nicholas, Joshua A. Hayes, and Pamela Shaw. Evaluation of the 1998 Predictions of the Run-Timing of Wild Migrant Yearling Chinook and Water Quality at Multiple Locations on the Snake and Columbia Rivers using CRiSP/RealTime, 1998 Technical Report. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/14088.

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