Littérature scientifique sur le sujet « Dynaic prediction »
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Articles de revues sur le sujet "Dynaic prediction"
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Daniele, Mario, et Elisa Raoli. « Early Warning Systems for financial crises prediction in private companies : Evidence from the Italian context ». FINANCIAL REPORTING, no 2 (décembre 2024) : 133–61. https://doi.org/10.3280/fr2024-002006.
Texte intégralRésumé :
Purpose: This study compares models for predicting business financial crises, fo-cusing on which are most effective. In light of the new European Directive on business failure, it highlights a trade-off between predictive accuracy and timeli-ness in static models and offers an alternative approach. Design/methodology/approach: This study examines the Italian early warning system (EWS), testing static alert indicators' predictive ability on a large sample of private companies. It then proposes a dynamic version of the EWS. Findings: The results show a trade-off between predictive ability and timeliness for static models. In contrast, a dynamic system is more accurate in predicting cri-sis events, allowing managers to take corrective actions. Originality: The results highlight the limitations of static prediction models and emphasize the potential of a simple dynamic model that is specifically designed for small- and medium-sized entities (SMEs). Practical implications: This study proposes a dynamic model tailored for SMEs, which are particularly vulnerable to financial crises. This insight can help managers and policymakers balance accurate predictions with timely interventions, especial-ly in European countries implementing crisis prediction models.
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Lin, Huan, Weiye Yu et Zhan Lian. « Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories ». Journal of Marine Science and Engineering 12, no 11 (28 octobre 2024) : 1933. http://dx.doi.org/10.3390/jmse12111933.
Texte intégralRésumé :
Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by historical drifter paths, LSTM achieves better trajectory predictions when trained and tested on relative trajectory intervals rather than the absolute positions of individual trajectory points. In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. The velocity and curvature of ocean currents jointly influence the prediction quality of both methods. In regions characterized by active sub-mesoscale dynamics, such as the fast-flowing and meandering Kuroshio Current and Kuroshio Current Extension, DT predicts more reliable trajectory patterns but lacks precision in detailed position estimates compared to LSTM. However, in areas dominated by the fast but relatively straight North Equatorial Current, the performance of the two methods reverses. The two methods also demonstrate different tolerances for noise and sampling intervals. This study establishes a baseline for selecting machine learning methods for marine drifter prediction and highlights the limitations of AI-based predictions under data-scarce and resource-constrained conditions.
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Stoodley, Catherine J., et Peter T. Tsai. « Adaptive Prediction for Social Contexts : The Cerebellar Contribution to Typical and Atypical Social Behaviors ». Annual Review of Neuroscience 44, no 1 (8 juillet 2021) : 475–93. http://dx.doi.org/10.1146/annurev-neuro-100120-092143.
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Social interactions involve processes ranging from face recognition to understanding others’ intentions. To guide appropriate behavior in a given context, social interactions rely on accurately predicting the outcomes of one's actions and the thoughts of others. Because social interactions are inherently dynamic, these predictions must be continuously adapted. The neural correlates of social processing have largely focused on emotion, mentalizing, and reward networks, without integration of systems involved in prediction. The cerebellum forms predictive models to calibrate movements and adapt them to changing situations, and cerebellar predictive modeling is thought to extend to nonmotor behaviors. Primary cerebellar dysfunction can produce social deficits, and atypical cerebellar structure and function are reported in autism, which is characterized by social communication challenges and atypical predictive processing. We examine the evidence that cerebellar-mediated predictions and adaptation play important roles in social processes and argue that disruptions in these processes contribute to autism.
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Oh, Cheol, Stephen G. Ritchie et Jun-Seok Oh. « Exploring the Relationship between Data Aggregation and Predictability to Provide Better Predictive Traffic Information ». Transportation Research Record : Journal of the Transportation Research Board 1935, no 1 (janvier 2005) : 28–36. http://dx.doi.org/10.1177/0361198105193500104.
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Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature–based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on suggestions for applying prediction techniques effectively.
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Siek, M., et D. P. Solomatine. « Nonlinear chaotic model for predicting storm surges ». Nonlinear Processes in Geophysics 17, no 5 (6 septembre 2010) : 405–20. http://dx.doi.org/10.5194/npg-17-405-2010.
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Abstract. This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.
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Prasanna, Christopher, Jonathan Realmuto, Anthony Anderson, Eric Rombokas et Glenn Klute. « Using Deep Learning Models to Predict Prosthetic Ankle Torque ». Sensors 23, no 18 (6 septembre 2023) : 7712. http://dx.doi.org/10.3390/s23187712.
Texte intégralRésumé :
Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable of accurately estimating and predicting prosthetic ankle torque from inverse dynamics using only six input signals. We performed a hyperparameter optimization protocol that automatically selected the model architectures and learning parameters that resulted in the most accurate predictions. We show that the trained deep neural networks predict ankle torques one sample into the future with an average RMSE of 0.04 ± 0.02 Nm/kg, corresponding to 2.9 ± 1.6% of the ankle torque’s dynamic range. Comparatively, a manually derived analytical regression model predicted ankle torques with a RMSE of 0.35 ± 0.53 Nm/kg, corresponding to 26.6 ± 40.9% of the ankle torque’s dynamic range. In addition, the deep neural networks predicted ankle torque values half a gait cycle into the future with an average decrease in performance of 1.7% of the ankle torque’s dynamic range when compared to the one-sample-ahead prediction. This application of deep learning provides an avenue towards the development of predictive control systems for powered limbs aimed at optimizing prosthetic ankle torque.
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Bisola Oluwafadekemi Adegoke, Tolulope Odugbose et Christiana Adeyemi. « Data analytics for predicting disease outbreaks : A review of models and tools ». International Journal of Life Science Research Updates 2, no 2 (30 avril 2024) : 001–9. http://dx.doi.org/10.53430/ijlsru.2024.2.2.0023.
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The burgeoning field of data analytics has emerged as a pivotal force in the realm of public health, particularly in the context of predicting and mitigating disease outbreaks. This comprehensive review delves into the diverse landscape of models and tools employed in data analytics for disease outbreak prediction. With a focus on synthesizing existing knowledge, the paper aims to provide a nuanced understanding of the strengths, limitations, and future directions within this dynamic field. The review begins with an exploration of various models utilized for disease outbreak prediction, ranging from statistical approaches to machine learning models and epidemiological frameworks. Each model category is scrutinized for its efficacy in capturing the complexities inherent in infectious disease dynamics. Simultaneously, the paper investigates the array of tools and technologies leveraged in disease outbreak prediction, encompassing Geographic Information Systems (GIS), data visualization tools, and big data analytics platforms. A critical aspect of the review lies in the examination of diverse data sources contributing to predictive analytics. Epidemiological data, environmental factors, and the burgeoning influence of social media and web data are dissected for their roles in enhancing the accuracy and timeliness of outbreak predictions. Amidst the promises of data analytics, the paper navigates the challenges inherent in predicting disease outbreaks. Issues of data quality and availability, model complexity, interpretability, and ethical considerations are dissected, providing a holistic view of the hurdles that practitioners encounter. Drawing upon case studies and real-world applications, the review showcases instances where data analytics has proven successful in predicting disease outbreaks, shedding light on both triumphs and setbacks. The implications for public health, lessons learned from challenges, and the evolving role of data analytics in shaping global health preparedness are thoroughly discussed. As the paper unfolds, it illuminates future trends and innovations in the field, foreseeing the integration of advanced technologies, global collaboration for information sharing, and the adaptation of predictive analytics for emerging diseases. The review culminates in a comprehensive conclusion, summarizing key findings and emphasizing the potential transformative impact of data analytics on the landscape of disease outbreak prediction.
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Zhang, Xiaopeng. « Paris House Rental Price Index Prediction-A Classical Statistical Model Approach ». Highlights in Science, Engineering and Technology 88 (29 mars 2024) : 294–99. http://dx.doi.org/10.54097/q6kz2d72.
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The study focuses on predicting rental prices in Paris and aims to contribute to urban economics and data analytics. It analyzes a wide range of data sources, including historical rental prices, economic indicators, demographics, and regulations. The goal is to compare classical statistical models' prediction accuracy of these three models: ARIMA, dynamic regression, and random forest. The results reveal that the ARIMA model performs best, offering more accurate predictions. ARIMA relies on time series analysis, capturing complex patterns in rental prices, making it well-suited for dynamic real estate markets. The study also examines the impact of external factors like rent reference indices, house price indices, and unemployment rates on prediction accuracy. While these factors seem promising, the further analysis suggests they can introduce noise into predictions if not chosen carefully. In conclusion, this research contributes valuable insights for rental market stakeholders. The ARIMA model proves effective for rental price forecasting in Paris, emphasizing the importance of understanding intrinsic time series patterns. This study can guide decision-making for renters, landlords, and investors in Paris and similar urban areas, providing a better understanding of predictive modeling in housing markets.
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Nik Nurul Hafzan, Mat Yaacob, Deris Safaai, Mat Asiah, Mohamad Mohd Saberi et Safaai Siti Syuhaida. « Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment ». MATEC Web of Conferences 255 (2019) : 03002. http://dx.doi.org/10.1051/matecconf/201925503002.
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Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events. Higher education institutions nowadays are under increasing pressure to respond to national and global economic, political and social changes such as the growing need to increase the proportion of students in certain disciplines, embedding workplace graduate attributes and ensuring that the quality of learning programs are both nationally and globally relevant. However, in higher education institution, there are significant numbers of students that stop their studies before graduation, especially for undergraduate students. Problem related to stopping out student and late or not graduating student can be improved by applying analytics. Using analytics, administrators, instructors and student can predict what will happen in future. Administrator and instructors can decide suitable intervention programs for at-risk students and before students decide to leave their study. Many different machine learning techniques have been implemented for predictive modelling in the past including decision tree, k-nearest neighbour, random forest, neural network, support vector machine, naïve Bayesian and a few others. A few attempts have been made to use Bayesian network and dynamic Bayesian network as modelling techniques for predicting at- risk student but a few challenges need to be resolved. The motivation for using dynamic Bayesian network is that it is robust to incomplete data and it provides opportunities for handling changing and dynamic environment. The trends and directions of research on prediction and identifying at-risk student are developing prediction model that can provide as early as possible alert to administrators, predictive model that handle dynamic and changing environment and the model that provide real-time prediction.
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Kim, Jeonghun, et Ohbyung Kwon. « A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data ». Sustainability 13, no 6 (11 mars 2021) : 3099. http://dx.doi.org/10.3390/su13063099.
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The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these predictions may not be accurate due to the simplicity of the models, so a prediction model with more diverse input features is needed. However, it is difficult to propose a universal predictive model globally because there are differences in data availability by country and region. Moreover, the training data for predicting confirmed patients is typically an imbalanced dataset consisting mostly of normal data; this imbalance negatively affects the accuracy of prediction. Hence, the purposes of this study are to extract rules for selecting appropriate prediction algorithms and data imbalance resolution methods according to the characteristics of the datasets available for each country or region, and to predict the number of COVID-19 patients based on these algorithms. To this end, a decision tree-type rule was extracted to identify 13 data characteristics and a discrimination algorithm was selected based on those characteristics. With this system, we predicted the COVID-19 situation in four regions: Africa, China, Korea, and the United States. The proposed method has higher prediction accuracy than the random selection method, the ensemble method, or the greedy method of discriminant analysis, and prediction takes very little time.
Thèses sur le sujet "Dynaic prediction"
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Chabeau, Lucas. « Développement et validation d’un outil multivarié de prédiction dynamique d’un échec de greffe rénale ». Electronic Thesis or Diss., Nantes Université, 2024. http://www.theses.fr/2024NANU1033.
Texte intégralRésumé :
Pour de nombreuses pathologies chroniques, la prédiction dynamique d’un événement clinique d’intérêt peut être utile dans une démarche de médecine personnalisée. Dans un tel contexte, les pronostics peuvent être mis à jour tout au long du suivi du patient, à chaque nouvelle information relevée. Ce travail de thèse CIFRE en collaboration avec Sêmeia, consiste à développer et valider un outil de prédiction dynamique de l’échec de greffe rénale. L’outil proposé prédit l’échec de greffe rénale, en compétition avec le décès avec greffon fonctionnel à un horizon de cinq ans. La prédiction est estimée à partir d’informations disponibles à l’inclusion du patient et de trois marqueurs biologiques collectés au cours de son suivi (créatininémie, protéinurie et anticorps antidonneur de type II) permettant d’actualiser le pronostic. Cet outil a été validé pour des temps de prédictions compris entre 1 an et 6 ans posttransplantation. Cette thèse a fait l’objet de trois travaux originaux. Un premier travail, a consisté à développer une procédure d’inférence pour estimer un modèle conjoint pour données longitudinales et données de survie compatible avec l’outil de prédiction. Nous avons mené dans un second travail, une réflexion autours de l’hétérogénéité de la définition de l’horizon de prédiction dans la littérature relative aux prédictions dynamiques. Enfin, nous présentons la construction et la validation du modèle de prédiction dynamique de l’échec de greffe rénale. Le modèle a présenté de bonnes capacités de discrimination et de calibrationFor many chronic diseases, dynamic prediction of a clinical event of interest can be useful in personalised medicine. In this context, prognoses can be updated throughout the patient's follow-up, as new information becomes available. This CIFRE doctoral thesis, in collaboration with Sêmeia, involves developing and validating a dynamic prediction tool for kidney graft failure. The proposed tool predicts kidney graft failure, in competition with death with a functional graft, over a five-year time horizon. The prediction is based on information available at patient inclusion and three biological markers collected during follow-up (serum creatinine, proteinuria and type II donor-specific antibodies). allowing to update the prognosis. This tool has been validated for prediction times of between 1 and 6 years post-transplant. This doctoral thesis was the subject of three original projects. The first involved developing an inference procedure to estimate a joint model for longitudinal and survival data compatible with the prediction tool. Secondly, we examined the heterogeneity in the definition of prediction horizon in the dynamic prediction literature. Finally, we present the construction and validation of a dynamic prediction model for renal transplant failure. The model showed good discrimination and calibration
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Greco, Antonino. « The role of task relevance in the modulation of brain dynamics during sensory predictions ». Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.
Texte intégralRésumé :
Associative learning is a fundamental ability biological systems possess in order to adapt to a nonstationary environment. One of the core aspects of associative learning theoretical frameworks is that surprising events drive learning by signalling the need to update the system’s beliefs about the probability structure governing stimuli associations. Specifically, the central neural system generates internal predictions to anticipate the causes of its perceptual experience and compute a prediction error to update its generative model of the environment, an idea generally known as the predictive coding framework. However, it is not clear whether the brain generates these predictions only for goal-oriented behavior or they are more a general characteristic of the brain function. In this thesis, I explored the role of task relevance in modulating brain activity when exposed to sensory associative learning task. In the first study, participants were asked to perform a perceptual detection task while audio-visual stimuli were presented as distractors. These distractors possessed a probability structure that made some of them more paired than others. Results showed that occipital activity triggered by the conditioned stimulus was elicited just before the arrival of the unconditioned visual stimulus. Moreover, occipital activity after the onset of the unconditioned stimulus followed a pattern of precision-weighted prediction errors. In the second study, two more sessions were added to the task in the previous study in which the probability structure for all stimuli associations was identical and the whole experiment was spanned in six days across two weeks. Results showed a difference in the modulation of the beta band induced by the presentation of the unconditioned stimulus preceded by the predictive and unpredictive conditioned auditory stimuli by comparing the pre and post sessions activity. In the third study, participants were exposed to a similar task with respect to the second study with the modification that there was a condition in which the conditioned-unconditioned stimulus association was task-relevant, thus allowing to directly compare task-relevant and task-irrelevant associations. Results showed that both types of associations had similar patterns in terms of activity and functional connectivity when comparing the brain responses to the onset of the unconditioned visual stimulus. Taken together, these findings demonstrate irrelevant associations rely on the same neural mechanisms of relevant ones. Thus, even if task relevance plays a modulatory role on the strength of the neural effects of associative learning, predictive processes take place in sensory associative learning regardless of task relevance.
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Greco, Antonino. « The role of task relevance in the modulation of brain dynamics during sensory predictions ». Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.
Texte intégralRésumé :
Associative learning is a fundamental ability biological systems possess in order to adapt to a nonstationary environment. One of the core aspects of associative learning theoretical frameworks is that surprising events drive learning by signalling the need to update the system’s beliefs about the probability structure governing stimuli associations. Specifically, the central neural system generates internal predictions to anticipate the causes of its perceptual experience and compute a prediction error to update its generative model of the environment, an idea generally known as the predictive coding framework. However, it is not clear whether the brain generates these predictions only for goal-oriented behavior or they are more a general characteristic of the brain function. In this thesis, I explored the role of task relevance in modulating brain activity when exposed to sensory associative learning task. In the first study, participants were asked to perform a perceptual detection task while audio-visual stimuli were presented as distractors. These distractors possessed a probability structure that made some of them more paired than others. Results showed that occipital activity triggered by the conditioned stimulus was elicited just before the arrival of the unconditioned visual stimulus. Moreover, occipital activity after the onset of the unconditioned stimulus followed a pattern of precision-weighted prediction errors. In the second study, two more sessions were added to the task in the previous study in which the probability structure for all stimuli associations was identical and the whole experiment was spanned in six days across two weeks. Results showed a difference in the modulation of the beta band induced by the presentation of the unconditioned stimulus preceded by the predictive and unpredictive conditioned auditory stimuli by comparing the pre and post sessions activity. In the third study, participants were exposed to a similar task with respect to the second study with the modification that there was a condition in which the conditioned-unconditioned stimulus association was task-relevant, thus allowing to directly compare task-relevant and task-irrelevant associations. Results showed that both types of associations had similar patterns in terms of activity and functional connectivity when comparing the brain responses to the onset of the unconditioned visual stimulus. Taken together, these findings demonstrate irrelevant associations rely on the same neural mechanisms of relevant ones. Thus, even if task relevance plays a modulatory role on the strength of the neural effects of associative learning, predictive processes take place in sensory associative learning regardless of task relevance.
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Currier, Patrick Norman. « A Method for Modeling and Prediction of Ground Vehicle Dynamics and Stability in Autonomous Systems ». Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/27632.
Texte intégralRésumé :
A future limitation of autonomous ground vehicle technology is the inability of current algorithmic techniques to successfully predict the allowable dynamic operating ranges of unmanned ground vehicles. A further difficulty presented by real vehicles is that the payloads may and probably will change with unpredictably time as will the terrain on which it is expected to operate. To address this limitation, a methodology has been developed to generate real-time estimations of a vehicleâ s instantaneous Maneuvering Manifold. This approach uses force-moment method techniques to create an adaptive, parameterized vehicle model. A technique is developed for estimation of vehicle load state using internal sensors combined with low-magnitude maneuvers. An unscented Kalman filter based estimator is then used to estimate tire forces for use in determining the ground/tire coefficient of friction. Probabilistic techniques are then combined with a combined-slip pneumatic trail based estimator to estimate the coefficient of friction in real-time. This data is then combined to map out the instantaneous maneuvering manifold while applying techniques to account for dynamic rollover and stability limitations. The algorithms are implemented in MATLAB, simulated against TruckSim models, and results are shown to demonstrate the validity of the techniques. The developed methodology is shown to be a novel approach that is capable of addressing the problem of successfully estimating the available maneuvering manifold for autonomous ground vehicles.Ph. D.
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Chen, Yutao. « Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon ». Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421957.
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Fast implementations of NMPC are important when addressing real-time control of systems exhibiting features like fast dynamics, large dimension, and long prediction horizon, as in such situations the computational burden of the NMPC may limit the achievable control bandwidth.
For that purpose, this thesis addresses both algorithms and applications.
First, fast NMPC algorithms for controlling continuous-time dynamic systems using a long prediction horizon have been developed.
A bridge between linear and nonlinear MPC is built using partial linearizations or sensitivity update. In order to update the sensitivities only when necessary, a Curvature-like measure of nonlinearity (CMoN) for dynamic systems has been introduced and applied to existing NMPC algorithms. Based on CMoN, intuitive and advanced updating logic have been developed for different numerical and control performance. Thus, the CMoN, together with the updating logic, formulates a partial sensitivity updating scheme for fast NMPC, named CMoN-RTI. Simulation examples are used to demonstrate the effectiveness and efficiency of CMoN-RTI. In addition, a rigorous analysis on the optimality and local convergence of CMoN-RTI is given and illustrated using numerical examples.
Partial condensing algorithms have been developed when using the proposed partial sensitivity update scheme. The computational complexity has been reduced since part of the condensing information are exploited from previous sampling instants. A sensitivity updating logic together with partial condensing is proposed with a complexity linear in prediction length, leading to a speed up by a factor of ten.
Partial matrix factorization algorithms are also proposed to exploit partial sensitivity update. By applying splitting methods to multi-stage problems, only part of the resulting KKT system need to be updated, which is computationally dominant in on-line optimization. Significant improvement has been proved by giving floating point operations (flops).
Second, efficient implementations of NMPC have been achieved by developing a Matlab based package named MATMPC. MATMPC has two working modes: the one completely relies on Matlab and the other employs the MATLAB C language API. The advantages of MATMPC are that algorithms are easy to develop and debug thanks to Matlab, and libraries and toolboxes from Matlab can be directly used. When working in the second mode, the computational efficiency of MATMPC is comparable with those software using optimized code generation. Real-time implementations are achieved for a nine degree of freedom dynamic driving simulator and for multi-sensory motion cueing with active seat.Implementazioni rapide di NMPC sono importanti quando si affronta il controllo in tempo reale di sistemi che presentano caratteristiche come dinamica veloce, ampie dimensioni e orizzonte di predizione lungo, poiché in tali situazioni il carico di calcolo dell'MNPC può limitare la larghezza di banda di controllo ottenibile. A tale scopo, questa tesi riguarda sia gli algoritmi che le applicazioni. In primo luogo, sono stati sviluppati algoritmi veloci NMPC per il controllo di sistemi dinamici a tempo continuo che utilizzano un orizzonte di previsione lungo. Un ponte tra MPC lineare e non lineare viene costruito utilizzando linearizzazioni parziali o aggiornamento della sensibilità. Al fine di aggiornare la sensibilità solo quando necessario, è stata introdotta una misura simile alla curva di non linearità (CMoN) per i sistemi dinamici e applicata agli algoritmi NMPC esistenti. Basato su CMoN, sono state sviluppate logiche di aggiornamento intuitive e avanzate per diverse prestazioni numeriche e di controllo. Pertanto, il CMoN, insieme alla logica di aggiornamento, formula uno schema di aggiornamento della sensibilità parziale per NMPC veloce, denominato CMoN-RTI. Gli esempi di simulazione sono utilizzati per dimostrare l'efficacia e l'efficienza di CMoN-RTI. Inoltre, un'analisi rigorosa sull'ottimalità e sulla convergenza locale di CMoN-RTI viene fornita ed illustrata utilizzando esempi numerici. Algoritmi di condensazione parziale sono stati sviluppati quando si utilizza lo schema di aggiornamento della sensibilità parziale proposto. La complessità computazionale è stata ridotta poiché parte delle informazioni di condensazione sono sfruttate da precedenti istanti di campionamento. Una logica di aggiornamento della sensibilità insieme alla condensazione parziale viene proposta con una complessità lineare nella lunghezza della previsione, che porta a una velocità di un fattore dieci. Sono anche proposti algoritmi di fattorizzazione parziale della matrice per sfruttare l'aggiornamento della sensibilità parziale. Applicando metodi di suddivisione a problemi a più stadi, è necessario aggiornare solo parte del sistema KKT risultante, che è computazionalmente dominante nell'ottimizzazione online. Un miglioramento significativo è stato dimostrato dando operazioni in virgola mobile (flop). In secondo luogo, sono state realizzate implementazioni efficienti di NMPC sviluppando un pacchetto basato su Matlab chiamato MATMPC. MATMPC ha due modalità operative: quella si basa completamente su Matlab e l'altra utilizza l'API del linguaggio MATLAB C. I vantaggi di MATMPC sono che gli algoritmi sono facili da sviluppare e eseguire il debug grazie a Matlab e le librerie e le toolbox di Matlab possono essere utilizzate direttamente. Quando si lavora nella seconda modalità, l'efficienza computazionale di MATMPC è paragonabile a quella del software che utilizza la generazione di codice ottimizzata. Le realizzazioni in tempo reale sono ottenute per un simulatore di guida dinamica di nove gradi di libertà e per il movimento multisensoriale con sedile attivo.
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Garside, Simon. « Dynamic prediction of road traffic networks ». Thesis, Lancaster University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387431.
Texte intégral
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Choudhury, Nazim Ahmed. « Mining Time-aware Actor-level Evolution Similarity for Link Prediction in Dynamic Network ». Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18640.
Texte intégralRésumé :
Topological evolution over time in a dynamic network triggers both the addition and deletion of actors and the links among them. A dynamic network can be represented as a time series of network snapshots where each snapshot represents the state of the network over an interval of time (for example, a minute, hour or day). The duration of each snapshot denotes the temporal scale/sliding window of the dynamic network and all the links within the duration of the window are aggregated together irrespective of their order in time. The inherent trade-off in selecting the timescale in analysing dynamic networks is that choosing a short temporal window may lead to chaotic changes in network topology and measures (for example, the actors’ centrality measures and the average path length); however, choosing a long window may compromise the study and the investigation of network dynamics. Therefore, to facilitate the analysis and understand different patterns of actor-oriented evolutionary aspects, it is necessary to define an optimal window length (temporal duration) with which to sample a dynamic network. In addition to determining the optical temporal duration, another key task for understanding the dynamics of evolving networks is being able to predict the likelihood of future links among pairs of actors given the existing states of link structure at present time. This phenomenon is known as the link prediction problem in network science. Instead of considering a static state of a network where the associated topology does not change, dynamic link prediction attempts to predict emerging links by considering different types of historical/temporal information, for example the different types of temporal evolutions experienced by the actors in a dynamic network due to the topological evolution over time, known as actor dynamicities. Although there has been some success in developing various methodologies and metrics for the purpose of dynamic link prediction, mining actor-oriented evolutions to address this problem has received little attention from the research community. In addition to this, the existing methodologies were developed without considering the sampling window size of the dynamic network, even though the sampling duration has a large impact on mining the network dynamics of an evolutionary network. Therefore, although the principal focus of this thesis is link prediction in dynamic networks, the optimal sampling window determination was also considered.
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Piccinini, Federico. « Dynamic load balancing based on latency prediction ». Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143333.
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Spotify is a music streaming service that offers access to a vast music catalogue; it counts more than 24 million active users in 28 different countries. Spotify's backend is made up by a constellation of independent loosely-coupled services; each service consists of a set of replicas, running on a set of servers in multiple data centers: each request to a service needs to be routed to an appropriate replica. Balancing the load across replicas is crucial to exploit available resources in the best possible way, and to provide optimal performances to clients. The main aim of this project is exploring the possibility of developing a load balancing algorithm that exploits request-reply latencies as its only load index. There are two reasons why latency is an appealing load index: in the first place it has a significant impact on the experience of Spotify users; in the second place, identifying a good load index in a distributed system presents significant challenges due to phenomena that might arise from the interaction of the different system components such as multi-bottlenecks. The use of latency as load index is even more attractive under this light, because it allows for a simple black box model where it is not necessary to model resource usage patterns and bottlenecks of every single service individually: modeling each system would be an impractical task, due both to the number of services and to the speed at which these services evolve. In this work, we justify the choice of request-reply latency as a load indicator, by presenting empirical evidence that it correlates well with known reliable load index obtained through a white box approach. In order to assess the correlation between latency and a known load index obtained through a white box approach, we present measurements from the production environment and from an ad-hoc test environment. We present the design of a novel load balancing algorithm based on a modified ' accrual failure detector that exploits request-reply latency as an indirect measure of the load on individual backends; we analyze the algorithm in detail, providing an overview of potential pitfalls and caveats; we also provide an empirical evaluation of our algorithm, compare its performances to a pure round-robin scheduling discipline and discuss which parameters can be tuned and how they affect the overall behavior of the load balancer.
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Alkindi, Ahmed Bin Masoud Bin Ali. « Performance optimisation through modelling and dynamic prediction ». Thesis, University of Warwick, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399475.
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Somoye, Adesina Eniari. « A computer prediction of robot dynamic performance ». Thesis, University of Surrey, 1985. http://epubs.surrey.ac.uk/848049/.
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A survey of existing literature on robot research indicated that little work had been done in the area of general dynamic simulation of Industrial robots which possessed the facility for quick evaluation of different robot structures and drive configurations. The Robot Arm Dynamic Simulation Package ( RADSP ) presented in this thesis has been developed with these features in mind to provide rapid assessment of Industrial robots using modular computation. The simulation package is written in FORTRAN 77 and can be executed on a PRIME or other suitable microcomputers. The freebody method is employed for the generation of the kinematic and dynamic motion algorithms because of its simplicity and short procedures which are found to be compatible with the modular concept. The freebody method is employed in the modelling of serial structural robots on the assumption that the Coriolis and Gyroscopic torque terms are insignificant in the robot torque equations at robot velocities. The RADSP consists of simulation program modules which represent the four main parts of an Industrial robot; these are the controller, the power actuator, the joint and the link. Also, the RADSP includes the inverse kinematic, trajectory planning, result, geometrical modelling and graph plotting package modules. The validation of the RADSP was undertaken on the Asea IRb6 robot for the three upper links and on a robot belt - drive. The results of the validation works were found to be acceptable.
Livres sur le sujet "Dynaic prediction"
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Building and Fire Research Laboratory (U.S.) et Factory Mutual Research Corporation, dir. Prediction of fire dynamics. Gaithersburg, MD : The Institute, 1997.
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Hein, Putter, dir. Dynamic prediction in clinical survival analysis. Boca Raton : CRC Press, 2012.
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Lalanne, M. Rotordynamics prediction in engineering. Chichester : Wiley, 1990.
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A, Ladd J., Yuhas A. J et United States. National Aeronautics and Space Administration., dir. Dynamic inlet distortion prediction with a combined computational fluid dynamics and distortion synthesis approach. [Washington, DC : National Aeronautics and Space Administration, 1996.
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Hiermaier, Stefan, dir. Predictive Modeling of Dynamic Processes. Boston, MA : Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0727-1.
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Lughofer, Edwin, et Moamar Sayed-Mouchaweh, dir. Predictive Maintenance in Dynamic Systems. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2.
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Sreeramesh, Kalluri, Bonacuse Peter J. 1960- et Symposium on Multiaxial Fatigue and Deformation : Testing and Prediction (1999 : Seattle, Wash.), dir. Multiaxial fatigue and deformation : Testing and prediction. W. Conshohocken, PA : ASTM, 2000.
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Sebastian, James W. Parametric prediction of the transverse dynamic stability of ships. Monterey, Calif : Naval Postgraduate School, 1998.
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Phillips, Norman A. Dispersion processes in large-scale weather prediction. Geneva, Switzerland : Secretariat of the World Meteorological Organization, 1990.
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Phillips, Norman A. Dispersion processes in large scale weather prediction. [Geneva] : World Meteorological Organization, 1990.
Trouver le texte intégralChapitres de livres sur le sujet "Dynaic prediction"
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Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser et Wil M. P. van der Aalst. « Remaining Time Prediction for Processes with Inter-case Dynamics ». Dans Lecture Notes in Business Information Processing, 140–53. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.
Texte intégralRésumé :
AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
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Shao, Yaping, Gongbing Peng et Lance M. Leslie. « The Environmental Dynamic System ». Dans Environmental Modelling and Prediction, 21–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04868-9_2.
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Garcia, Angel E. « Molecular Dynamics Simulations of Protein Folding ». Dans Protein Structure Prediction, 315–30. Totowa, NJ : Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_12.
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Principe, Jose C., Ludong Wang et Jyh-Ming Kuo. « Non-Linear Dynamic Modelling with Neural Networks ». Dans Signal Analysis and Prediction, 275–90. Boston, MA : Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_20.
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Dodla, Venkata Bhaskar Rao. « Weather Prediction — Lorenz Chaos Theory — Nonlinear Dynamics, Ensemble Prediction ». Dans Numerical Weather Prediction, 189–205. London : CRC Press, 2022. http://dx.doi.org/10.1201/9781003354017-6.
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Dietrich, William E., Dino G. Bellugi, Leonard S. Sklar, Jonathan D. Stock, Arjun M. Heimsath et Joshua J. Roering. « Geomorphic Transport Laws for Predicting Landscape form and Dynamics ». Dans Prediction in Geomorphology, 103–32. Washington, D. C : American Geophysical Union, 2013. http://dx.doi.org/10.1029/135gm09.
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Hirsch, Markus, Klaus Oppenauer et Luigi del Re. « Dynamic Engine Emission Models ». Dans Automotive Model Predictive Control, 73–87. London : Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_5.
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Sao, Ashutosh, Simon Gottschalk, Nicolas Tempelmeier et Elena Demidova. « MetaCitta : Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks ». Dans Advances in Knowledge Discovery and Data Mining, 70–82. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33383-5_6.
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AbstractAccurate spatio-temporal prediction is essential for capturing city dynamics and planning mobility services. State-of-the-art deep spatio-temporal predictive models depend on rich and representative training data for target regions and tasks. However, the availability of such data is typically limited. Furthermore, existing predictive models fail to utilize cross-correlations across tasks and cities. In this paper, we propose MetaCitta, a novel deep meta-learning approach that addresses the critical challenges of data scarcity and model generalization. MetaCitta adopts the data from different cities and tasks in a generalizable spatio-temporal deep neural network. We propose a novel meta-learning algorithm that minimizes the discrepancy between spatio-temporal representations across tasks and cities. Our experiments with real-world data demonstrate that the proposed MetaCitta approach outperforms state-of-the-art prediction methods for zero-shot learning and pre-training plus fine-tuning. Furthermore, MetaCitta is computationally more efficient than the existing meta-learning approaches.
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O’Neil, Patrick. « Dynamic, Covert Network Simulation ». Dans Social Computing, Behavioral - Cultural Modeling and Prediction, 239–47. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29047-3_29.
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Ang, Zhendong, et Umang Mathur. « Predictive Monitoring with Strong Trace Prefixes ». Dans Computer Aided Verification, 182–204. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65630-9_9.
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AbstractRuntime predictive analyses enhance coverage of traditional dynamic analyses based bug detection techniques by identifying a space of feasible reorderings of the observed execution and determining if any reordering in this space witnesses the violation of some desired safety property. The most popular approach for modelling the space of feasible reorderings is through Mazurkiewicz’s trace equivalence. The simplicity of the framework also gives rise to efficient predictive analyses, and has been the de facto means for obtaining space and time efficient algorithms for monitoring concurrent programs.In this work, we investigate how to enhance the predictive power of trace-based reasoning, while still retaining the algorithmic benefits it offers. Towards this, we extend trace theory by naturally embedding a class of prefixes, which we call strong trace prefixes. We formally characterize strong trace prefixes using an enhanced dependence relation, study its predictive power and establish a tight connection to the previously proposed notion of synchronization-preserving correct reorderings developed in the context of data race and deadlock prediction. We then show that despite the enhanced predictive power, strong trace prefixes continue to enjoy the algorithmic benefits of Mazurkiewicz traces in the context of prediction against co-safety properties, and derive new algorithms for synchronization-preserving data races and deadlocks with better asymptotic space and time usage. We also show that strong trace prefixes can capture more violations of pattern languages. We implement our proposed algorithms and our evaluation confirms the practical utility of reasoning based on strong prefix traces.
Actes de conférences sur le sujet "Dynaic prediction"
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Abras, Jennifer, et Nathan Hariharan. « A Dual-Step Deep Learning-Based Surrogate Model for Dynamic Stall Predictions ». Dans Vertical Flight Society 80th Annual Forum & Technology Display, 1–10. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1325.
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In the field of aerodynamics, there is a growing need for rapid load prediction in engineering applications. Surrogate modeling offers a promising solution, providing faster results compared to high-fidelity computational models. This study focuses on a Machine Learning (ML) framework tailored for surrogate modeling, specifically for integrated aerodynamic load predictions in aircraft design. Central to this framework is a Deep Neural Network (DNN) component capable of handling both steady-state and fluctuating aerodynamics. A key challenge for surrogate models lies in maintaining prediction accuracy, especially in scenarios involving nonlinear flow phenomena like flow separation and transonic shifts. To address these challenges, we introduce a two-step physics-state predictor that integrates an intermediate Convolutional Neural Network (CNN) component. This approach enhances the surrogate model's capability to accurately represent dynamic separated flows and other nonlinear patterns without relying on unrealistic user inputs. Results are presented for NACA0015 dynamic stall predictions for two different physics-state inputs.
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Madenci, Erdogan, Nam Phan et Atila Barut. « Multi-Body Peridynamics for Failure Prediction in Rotating Thick Composites ». Dans Vertical Flight Society 71st Annual Forum & Technology Display, 1–7. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10266.
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This study presents the integration of the nonlocal peridynamic theory with the well-established rigid multibody dynamics algorithms to predict material and structural failure modes simultaneously in flexible multibody dynamic systems. Although the existing structural dynamics analysis methods provide accurate dynamic simulation and deformation predictions, they lack the capability to predict material and structural failure initiation and failure modes simultaneously necessary to ensure durability and reliability. This analysis accounts for large elastic deformations coupled with dynamic motion loads without the need of post-processing for failure prediction. The accuracy of this approach is verified against the solutions already available in the literature. It specifically demonstrates damage initiation and growth in rotating composite blades.
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Jackson, Ryan, Michael Jump et Peter Green. « Towards Gaussian Process Models of Complex Rotorcraft Dynamics ». Dans Vertical Flight Society 74th Annual Forum & Technology Display, 1–11. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12828.
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Physical law based models (also known as white box models) are widely applied in the aerospace industry, providing models for dynamic systems such as helicopter flight simulators. To meet the criteria of real-time simulation, simplifications to the underlying physics sometimes have to be applied, leading to errors in the model's predictions. Grey-box models use both physics-based and data-based models. They have potential to reduce the difference between a simulator's and real rotorcraft's response. In the current work, a preliminary step to the grey-box approach, a machine learnt data-based, i.e 'black box' model is applied to the dynamic response of a helicopter. The machine learning methods used are probabilistic and can capture uncertainties associated with the model's prediction. In the current paper, machine learning is used to create a Gaussian Process (GP) non-linear autoregressive (NARX) model that predicts pitch, roll and yaw rate. The predictions are compared to a physical law based model created using FLIGHTLAB software. The GP outperforms the FLIGHTLAB model in terms of root mean squared error, when predicting the pitch, roll and yaw rate of a Bo105 helicopter.
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Gennaretti, Massimo, Roberto Celi, Claudio Pasquali, Felice Cardito, Jacopo Serafini et Giovanni Bernardini. « Dynamic Wake Inflow Modeling in Ground Effect for Flight Dynamics Applications ». Dans Vertical Flight Society 73rd Annual Forum & Technology Display, 1–12. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12059.
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Rotor dynamic wake inflow modeling is one of the main issues in the development of efficient and reliable tools for design and flight dynamic simulation of rotorcraft. In general, it is affected by the operating condition (hovering, advancing, steady or maneuvering flight), but the interference with external obstacles might play a crucial role, especially during landing or close-to-ground procedures. The aim of this paper is the presentation of a LTI, finite-state model for the prediction of dynamic wake inflow of helicopter rotors in ground effect. First, two high-fidelity boundary-element aerodynamic formulations capable of taking into account ground effects on wake dynamics are applied, compared and correlated with experimental data. Then, finite-state dynamic inflow models are extracted from a set of responses to kinematic perturbations provided by the high-fidelity aerodynamic tool. The capability of the proposed low-order modeling to capture with good accuracy the time evolution of dynamic wake inflow induced by arbitrary rotor commands perturbations is tested by comparison with the predictions directly provided by the time-marching high-fidelity solver.
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Thornburgh, Robert, Andrew Kreshock et Matthew Wilbur. « A Dynamic Calibration Method for Experimental and Analytical Hub Load Comparison ». Dans Vertical Flight Society 71st Annual Forum & Technology Display, 1–14. The Vertical Flight Society, 2015. http://dx.doi.org/10.4050/f-0071-2015-10271.
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This paper presents the results from an ongoing effort to produce improved correlation between analytical hub force and moment prediction and those measured during wind-tunnel testing on the Aeroelastic Rotor Experimental System (ARES), a conventional rotor testbed commonly used at the Langley Transonic Dynamics Tunnel (TDT). A frequency-dependent transformation between loads at the rotor hub and outputs of the testbed balance is produced from frequency response functions measured during vibration testing of the system. The resulting transformation is used as a dynamic calibration of the balance to transform hub loads predicted by comprehensive analysis into predicted balance outputs. In addition to detailing the transformation process, this paper also presents a set of wind-tunnel test cases, with comparisons between the measured balance outputs and transformed predictions from the comprehensive analysis code CAMRAD II. The modal response of the testbed is discussed and compared to a detailed finite-element model. Results reveal that the modal response of the testbed exhibits a number of characteristics that make accurate dynamic balance predictions challenging, even with the use of the balance transformation.
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Hyeon, Eunjeong, Youngki Kim, Niket Prakash et Anna G. Stefanopoulou. « Influence of Speed Forecasting on the Performance of Ecological Adaptive Cruise Control ». Dans ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9046.
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Abstract In congested urban conditions, the fuel economy of a vehicle can be highly affected by traffic flow and particularly, the immediately preceding (lead) vehicle. Thus, estimating the future trajectories of the lead vehicle is essential to optimize the following vehicle’s maneuvers for its fuel economy. This paper investigates the influence of speed forecasting on the performance of an ecological adaptive cruise control (eco-ACC) strategy for connected autonomous vehicles. The real-time speed predictor proposed in [1] is applied to forecast the future speed profiles of the lead vehicle over a short prediction horizon. Under the assumption that vehicle-to-vehicle (V2V) communications are available, V2V information from multiple lead vehicles is utilized in the prediction process. Eco-ACC is formulated in a model predictive control (MPC) framework to control the connected autonomous vehicle. The influence of the state prediction to the performance of eco-ACC in terms of fuel economy and acceleration is evaluated with different number of connected vehicles.
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Kemmerer, Julian, et Baris Taskin. « Range-based Dynamic Routing of Hierarchical On Chip Network Traffic ». Dans SLIP (System Level Interconnect Prediction). New York, New York, USA : ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633953.
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Cameron, Fraser, et Gu¨nter Niemeyer. « Predicting Blood Glucose Levels Around Meals for Patients With Type I Diabetes ». Dans ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4060.
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Insulin pumps and continuous glucose monitors enable automatic control of blood glucose (BG) levels for patients with type 1 diabetes. Such controllers should carefully assess the likely future BG levels before injecting insulin, since the effects of insulin are prolonged, potentially deadly, and irreversible. Meals pose a strong challenge to this assessment as they create large, fast disturbances. Fortunately, meals have consistent and predictable effects, if their size and start time are known. We present a predictive algorithm that embeds meal detection and estimation into BG prediction. It uses a multiple hypothesis fault detector to identify meal occurrences, and linear Kalman filters to estimate meal sizes. It extrapolates and combines the state and state covariance estimates to form a prediction of BG values and uncertainties. These inputs enable controllers to assess and trade off the acute risks of low and chronic risks of high BG levels. We evaluate the predictor on simulated and clinical data.
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Hendricks, Terry J., Chendong Huang et Joseph C. Giglio. « Experimental Correlation of a Thermal / Fluid Dynamic / Electrical Performance Model of a Multiple-Tube, Vapor-Anode AMTEC Cell ». Dans ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-1007.
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Abstract An AMTEC (Alkali-Metal Thermal-to-Electric Conversion) cell performance analysis model described by Hendricks et al. (1998) has been correlated with experimental data by comparing its predictions for Beta” alumina solid electrolyte (BASE) and evaporator temperatures, voltage, power output, and conversion efficiency with experimental measurements on two versions of a PX-6 AMTEC cell. The critical features of this AMTEC cell model, the PX-6 experimental set up and testing, and the model correlation PX-6 test data are discussed in this work. Model prediction/test data comparisons are presented across a wide range of current-voltage conditions and hot side temperatures for two significantly different cell lengths. Cell model predictions demonstrate good agreement with experimental PX-6 test data in simultaneously predicting BASE tube and evaporator temperatures, the onset of sodium (Na) condensation in the BASE tubes, current-voltage characteristics, and power output in high current ranges (i.e., > 1.5 A). The model also has demonstrated good capability to predict cell conversion efficiency at high currents when Na is not condensing in the BASE tubes. The good model prediction/test data comparisons have demonstrated the progress in developing this cell performance model and increased confidence in its technical foundations, algorithm implementation, and capability to predict AMTEC cell performance. The AMTEC cell model’s capability to simultaneously predict many critical cell performance parameters across a wide range of hot side temperatures, at high current conditions, and different cell lengths demonstrates the progress that has been made in its development. It has demonstrated good predictive capability, utility, and flexibility as a performance design and analysis tool for sophisticated AMTEC cell design. Testing limitations prevented testing at low current levels (i.e., < 1.5 A), so future experimental validation studies should focus on correlating model predictions at low currents.
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Leu, Jessica, et Masayoshi Tomizuka. « Motion Planning for Industrial Mobile Robots With Closed-Loop Stability Enhanced Prediction ». Dans ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9208.
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Abstract Real-time, safe, and stable motion planning in co-robot systems involving dynamic human robot interaction (HRI) remains challenging due to the time varying nature of the problem. One of the biggest challenges is to guarantee closed-loop stability of the planning algorithm in dynamic environments. Typically, this can be addressed if there exists a perfect predictor that precisely predicts the future motions of the obstacles. Unfortunately, a perfect predictor is not possible to achieve. In HRI environments in this paper, human workers and other robots are the obstacles to the ego robot. We discuss necessary conditions for the closed-loop stability of a planning problem using the framework of model predictive control (MPC). It is concluded that the predictor needs to be able to detect the obstacles’ movement mode change within a time delay allowance and the MPC needs to have a sufficient prediction horizon and a proper cost function. These allow MPC to have an uncertainty tolerance for closed-loop stability, and still avoid collision when the obstacles’ movement is not within the tolerance. Also, the closed-loop performance is investigated using a notion of M-convergence, which guarantees finite local convergence (at least M steps ahead) of the open-loop trajectories toward the closed-loop trajectory. With this notion, we verify the performance of the proposed MPC with stability enhanced prediction through simulations and experiments. With the proposed method, the robot can better deal with dynamic environments and the closed-loop cost is reduced.
Rapports d'organisations sur le sujet "Dynaic prediction"
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Tuniki, Himanshu Patel, Gabriel Bekö et Andrius Jurelionis. Using Adaptive Behaviour Patterns of Open Plan Office Occupants in Energy Consumption Predictions. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541563857.
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One of the factors that affects energy consumption in buildings is the level of control that occupants have over their environment, as well as their adaptive behaviour. The aim of this study was to focus on the adaptive clothing behaviour pattern, and to analyse its impact on energy consumption when integrated into a dynamic energy prediction tool. A questionnaire survey was conducted in an office building to collect the occupant behaviour data. The occupant clothing levels and the window opening behaviour were integrated into the dynamic energy performance prediction software, IDA ICE. The results of the simulations showed that the impact of adaptive clothing behaviour on energy consumption is relatively small, but it can meaningfully improve thermal comfort. Including adaptive behaviour in energy simulations can help in improving the accuracy of the energy performance and comfort predictions.
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Gallagher, B., et T. Eliassi-Rad. API Requirements for Dynamic Graph Prediction. Office of Scientific and Technical Information (OSTI), octobre 2006. http://dx.doi.org/10.2172/1036864.
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Pfeffer, Richard L. Nonlinear Dynamics Underlying Atmospheric Prediction. Fort Belvoir, VA : Defense Technical Information Center, septembre 1995. http://dx.doi.org/10.21236/ada305478.
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Соловйов, В. М., et В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.
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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a characteristic way in time periods that characterize the different phases of the behavior and state of the stock market. This fact encouraged to use monitoring and prediction of critical and crisis states in socio-economic systems.
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Davis, Steven J., et Pawel M. Krolikowski. Reservation Wages Revisited : Empirics with the Canonical Model. Federal Reserve Bank of Cleveland, octobre 2024. http://dx.doi.org/10.26509/frbc-wp-202423.
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Using innovative longitudinal data from a survey of unemployment insurance (UI) recipients, we test several implications of a canonical job search model for reservation wages during unemployment spells. First, consistent with the model, we find that reservation wages fall faster when UI benefit durations are shorter. However, workers set their initial reservation wages higher, and adjust them slower, relative to model predictions. Second, workers' expectations—elicited at the beginning of their unemployment spell—about how their reservation wage will evolve if they remain unemployed are largely congruent with reservation wage realizations, as assumed in the canonical model. Third, our data on expectations and realizations suggest that dynamic selection over the unemployment spell is inconsequential for our results. Fourth, higher wages on workers' lost jobs, relative to predictions from a Mincerian wage regression, hasten the expected and realized declines in reservation wages over the unemployment spell. Finally, reservation wages are a more powerful predictor of re-employment wages than wages on the previous job.
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Vas, Dragos, Elizabeth Corriveau, Lindsay Gaimaro et Robyn Barbato. Challenges and limitations of using autonomous instrumentation for measuring in situ soil respiration in a subarctic boreal forest in Alaska, USA. Engineer Research and Development Center (U.S.), décembre 2023. http://dx.doi.org/10.21079/11681/48018.
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Subarctic and Arctic environments are sensitive to warming temperatures due to climate change. As soils warm, soil microorganisms break down carbon and release greenhouse gases such as methane (CH₄) and carbon dioxide (CO₂). Recent studies examining CO₂ efflux note heterogeneity of microbial activity across the landscape. To better understand carbon dynamics, our team developed a predictive model, Dynamic Representation of Terrestrial Soil Predictions of Organisms’ Response to the Environment (DRTSPORE), to estimate CO₂ efflux based on soil temperature and moisture estimates. The goal of this work was to acquire respiration rates from a boreal forest located near the town of Fairbanks, Alaska, and to provide in situ measurements for the future validation effort of the DRTSPORE model estimates of CO₂ efflux in cold climates. Results show that soil temperature and seasonal soil thaw depth had the greatest impact on soil respiration. However, the instrumentation deployed significantly altered the soil temperature, moisture, and seasonal thaw depth at the survey site and very likely the soil respiration rates. These findings are important to better understand the challenges and limitations associated with the in situ data collection used for carbon efflux modeling and for estimating soil microbial activity in cold environments.
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Trott, Kevin C. Simulation Development for Dynamic Situation Awareness and Prediction. Fort Belvoir, VA : Defense Technical Information Center, septembre 2005. http://dx.doi.org/10.21236/ada440082.
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Trott, Kevin. Simulation for Dynamic Situation Awareness and Prediction III. Fort Belvoir, VA : Defense Technical Information Center, mars 2010. http://dx.doi.org/10.21236/ada516768.
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Kimagai, Toru, et Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. Warrendale, PA : SAE International, mai 2005. http://dx.doi.org/10.4271/2005-08-0305.
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Perdigão, Rui A. P. Earth System Dynamic Intelligence with Quantum Technologies : Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World. Meteoceanics, octobre 2021. http://dx.doi.org/10.46337/211028.
Texte intégralRésumé :
We hereby embark on a frontier journey articulating two of our flagship programs – “Earth System Dynamic Intelligence” and “Quantum Information Technologies in the Earth Sciences” – to take the pulse of our planet and discern its manifold complexity in a critically changing world. Going beyond the traditional stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid approaches to information and complexity, the underlying fundamental science ignites disruptive developments empowering complex problem solving across frontier natural, social and technical geosciences. Taking aim at complex multiscale planetary problems, the roles of our flagships are put into evidence in different contexts, ranging from I) Interdisciplinary analytics, model design and dynamic prediction of hydro-climatic and broader geophysical criticalities and extremes across multiple spatiotemporal scales; to II) Sensing the pulse of our planet and detecting early warning signs of geophysical phenomena from Space with our Meteoceanics QITES Constellation, at the interface between our latest developments in non-linear dynamics and emerging quantum technologies.