Relevant bibliographies by topics / Real time prediction

Academic literature on the topic 'Real time prediction'

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Published: 4 June 2021
Last updated: 26 January 2023

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Journal articles on the topic "Real time prediction"

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Fridovich-Keil, David, Andrea Bajcsy, Jaime F. Fisac, Sylvia L. Herbert, Steven Wang, Anca D. Dragan, and Claire J. Tomlin. "Confidence-aware motion prediction for real-time collision avoidance1." International Journal of Robotics Research 39, no. 2-3 (June 24, 2019): 250–65. http://dx.doi.org/10.1177/0278364919859436.

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One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about where other agents are going to move. Though there has been much recent work in building predictive models, no model is ever perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability. In such cases, the robot may plan trajectories that appear safe but, in fact, lead to collision. Rather than trust a model’s predictions blindly, we propose that the robot should use the model’s current predictive accuracy to inform the degree of confidence in its future predictions. This model confidence inference allows us to generate probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly. We accomplish this by maintaining a Bayesian belief over a single parameter that governs the variance of our human motion model. We couple this prediction algorithm with a recently proposed robust motion planner and controller to guide the construction of robot trajectories that are, to a good approximation, collision-free with a high, user-specified probability. We provide extensive analysis of the combined approach and its overall safety properties by establishing a connection to reachability analysis, and conclude with a hardware demonstration in which a small quadcopter operates safely in the same space as a human pedestrian.
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Dissanayake, Vipula, Sachini Herath, Sanka Rasnayaka, Sachith Seneviratne, Rajith Vidanaarachchi, and Chandana Gamage. "Real-Time Gesture Prediction Using Mobile Sensor Data for VR Applications." International Journal of Machine Learning and Computing 6, no. 3 (June 2016): 215–19. http://dx.doi.org/10.18178/ijmlc.2016.6.3.600.

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Huitian Lu, W. J. Kolarik, and S. S. Lu. "Real-time performance reliability prediction." IEEE Transactions on Reliability 50, no. 4 (2001): 353–57. http://dx.doi.org/10.1109/24.983393.

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Georgakakos, Konstantine P. "Real-time flash flood prediction." Journal of Geophysical Research 92, no. D8 (1987): 9615. http://dx.doi.org/10.1029/jd092id08p09615.

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Zissis, Dimitrios, Elias K. Xidias, and Dimitrios Lekkas. "Real-time vessel behavior prediction." Evolving Systems 7, no. 1 (March 24, 2015): 29–40. http://dx.doi.org/10.1007/s12530-015-9133-5.

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Maber, J., P. Dewar, J. P. Praat, and A. J. Hewitt. "REAL TIME SPRAY DRIFT PREDICTION." Acta Horticulturae, no. 566 (December 2001): 493–98. http://dx.doi.org/10.17660/actahortic.2001.566.64.

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AHALPARA, DILIP P., and JITENDRA C. PARIKH. "MODELING TIME SERIES DATA OF REAL SYSTEMS." International Journal of Modern Physics C 18, no. 02 (February 2007): 235–52. http://dx.doi.org/10.1142/s0129183107010474.

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Dynamics of complex systems is studied by first considering a chaotic time series generated by Lorenz equations and adding noise to it. The trend (smooth behavior) is separated from fluctuations at different scales using wavelet analysis and a prediction method proposed by Lorenz is applied to make out of sample predictions at different regions of the time series. The prediction capability of this method is studied by considering several improvements over this method. We then apply this approach to a real financial time series. The smooth time series is modeled using techniques of non linear dynamics. Our results for predictions suggest that the modified Lorenz method gives better predictions compared to those from the original Lorenz method. Fluctuations are analyzed using probabilistic considerations.
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Březková, L., M. Starý, and P. Doležal. "The real-time stochastic flow forecast." Soil and Water Research 5, No. 2 (May 24, 2010): 49–57. http://dx.doi.org/10.17221/13/2009-swr.

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In the Czech Republic, deterministic flow forecasts with the lead time of 48 hours, calculated by rainfall-runoff models for basins of a size of several hundreds to thousands square kilometers, are nowadays a common part of the operational hydrological service. The Czech Hydrometeorological Institute (CHMI) issues daily the discharge forecast for more than one hundred river profiles. However, the causal rainfall is a random process more than a deterministic one, therefore the deterministic discharge forecast based on one precipitation prediction is a significant simplification of the reality. Since important decisions must be done during the floods, it is necessary to take into account the indeterminity of the input meteorological data and to express the uncertainty of the resulting discharge forecast. In the paper, a solution of this problem is proposed. The time series of the input precipitation prediction data have been generated repeatedly (by the Monte Carlo method) and, subsequently, the set of discharge forecasts based on the repeated hydrological model simulations has been obtained and statistically evaluated. The resulting output can be, for example, the range of predicted peak discharges, the peak discharge exceeding curve or the outflow volume exceeding curve. The properties of the proposed generator have been tested with acceptable results on several flood events which occurred over the last years in the upper part of the Dyje catchment (Podhradí closing profile). The rainfall-runoff model HYDROG, which has been in operation in CHMI since 2003, was used for hydrological simulation.
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Brown, Andrew, and Toby Gifford. "Prediction and Proactivity in Real-Time Interactive Music Systems." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 5 (June 30, 2021): 35–39. http://dx.doi.org/10.1609/aiide.v9i5.12644.

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We advocate for the use of predictive techniques in interactive computer music systems. We suggest that the inclusion of prediction can assist in the design of proactive rather than reactive computational performance partners. We summarize the significant role prediction plays in human musical decisions, and the only modest use of prediction in interactive music systems to date. After describing how we are working toward employing predictive processes in our own metacreation software we reflect on future extensions to these approaches.
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Aboudina, Aya, Ehab Diab, and Amer Shalaby. "Predictive Analytics of Streetcar Bunching Occurrence Time for Real-Time Applications." Transportation Research Record: Journal of the Transportation Research Board 2675, no. 6 (January 29, 2021): 441–52. http://dx.doi.org/10.1177/0361198121990698.

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Bunching occurs when transit vehicles are unable to maintain their scheduled headways, resulting in two or more vehicles arriving at a stop in close succession and following each other too closely thereafter. Very few studies have explored the prediction of bunching in real-time, particularly for streetcar services. Predicting the time to bunching in real-time allows transit agencies to take more preventive actions to avoid the occurrence of bunching or to minimize its effects. In this study, we present a comprehensive literature review of the recent research conducted in bunching and real-time prediction models. Based on the findings from the literature review, we propose a model for real-time prediction of streetcar bunching. The Kalman filtering model predicts the travel time to bunching incidents and is tested and analyzed using an automatic vehicle location data feed for one streetcar route (Route 506 Carlton), obtained from the Toronto Transit Commission’s next bus system. The results show that: (1) the model provides good predication quality given that it relies only on the real-time GPS feed of streetcars, which makes it practical for use in real-time prediction applications; (2) the model prediction accuracy improves as the transit vehicle travels away from the terminal; and (3) increasing the number of past days involved in the calculations beyond 6 days or increasing the number of leading trips considered in the same day beyond 7 or 10 trips might increase the prediction error.
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Dissertations / Theses on the topic "Real time prediction"

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Neikter, Carl-Fredrik. "Cache Prediction and Execution Time Analysis on Real-Time MPSoC." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15394.

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Real-time systems do not only require that the logical operations are correct. Equally important is that the specified time constraints always are complied. This has successfully been studied before for mono-processor systems. However, as the hardware in the systems gets more complex, the previous approaches become invalidated. For example, multi-processor systems-on-chip (MPSoC) get more and more common every day, and together with a shared memory, the bus access time is unpredictable in nature. This has recently been resolved, but a safe and not too pessimistic cache analysis approach for MPSoC has not been investigated before. This thesis has resulted in designed and implemented algorithms for cache analysis on real-time MPSoC with a shared communication infrastructure. An additional advantage is that the algorithms include improvements compared to previous approaches for mono-processor systems. The verification of these algorithms has been performed with the help of data flow analysis theory. Furthermore, it is not known how different types of cache miss characteristic of a task influence the worst case execution time on MPSoC. Therefore, a program that generates randomized tasks, according to different parameters, has been constructed. The parameters can, for example, influence the complexity of the control flow graph and average distance between the cache misses.

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Chen, Hao. "Real-time Traffic State Prediction: Modeling and Applications." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64292.

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Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future.
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Gross, Hans-Gerhard. "Measuring evolutionary testability of real-time software." Thesis, University of South Wales, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365087.

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Brune, Sascha. "Landslide generated tsunamis : numerical modeling and real-time prediction." Phd thesis, Universität Potsdam, 2009. http://opus.kobv.de/ubp/volltexte/2009/3298/.

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Submarine landslides can generate local tsunamis posing a hazard to human lives and coastal facilities. Two major related problems are: (i) quantitative estimation of tsunami hazard and (ii) early detection of the most dangerous landslides. This thesis focuses on both those issues by providing numerical modeling of landslide-induced tsunamis and by suggesting and justifying a new method for fast detection of tsunamigenic landslides by means of tiltmeters. Due to the proximity to the Sunda subduction zone, Indonesian coasts are prone to earthquake, but also landslide tsunamis. The aim of the GITEWS-project (German-Indonesian Tsunami Early Warning System) is to provide fast and reliable tsunami warnings, but also to deepen the knowledge about tsunami hazards. New bathymetric data at the Sunda Arc provide the opportunity to evaluate the hazard potential of landslide tsunamis for the adjacent Indonesian islands. I present nine large mass movements in proximity to Sumatra, Java, Sumbawa and Sumba, whereof the largest event displaced 20 km³ of sediments. Using numerical modeling, I compute the generated tsunami of each event, its propagation and runup at the coast. Moreover, I investigate the age of the largest slope failures by relating them to the Great 1977 Sumba earthquake. Continental slopes off northwest Europe are well known for their history of huge underwater landslides. The current geological situation west of Spitsbergen is comparable to the continental margin off Norway after the last glaciation, when the large tsunamigenic Storegga slide took place. The influence of Arctic warming on the stability of the Svalbard glacial margin is discussed. Based on new geophysical data, I present four possible landslide scenarios and compute the generated tsunamis. Waves of 6 m height would be capable of reaching northwest Europe threatening coastal areas. I present a novel technique to detect large submarine landslides using an array of tiltmeters, as a possible tool in future tsunami early warning systems. The dislocation of a large amount of sediment during a landslide produces a permanent elastic response of the earth. I analyze this response with a mathematical model and calculate the theoretical tilt signal. Applications to the hypothetical Spitsbergen event and the historical Storegga slide show tilt signals exceeding 1000 nrad. The amplitude of landslide tsunamis is controlled by the product of slide volume and maximal velocity (slide tsunamigenic potential). I introduce an inversion routine that provides slide location and tsunamigenic potential, based on tiltmeter measurements. The accuracy of the inversion and of the estimated tsunami height near the coast depends on the noise level of tiltmeter measurements, the distance of tiltmeters from the slide, and the slide tsunamigenic potential. Finally, I estimate the applicability scope of this method by employing it to known landslide events worldwide.
Submarine Erdrutsche können lokale Tsunamis auslösen und stellen somit eine Gefahr für Siedlungen an der Küste und deren Einwohner dar. Zwei Hauptprobleme sind (i) die quantitative Abschätzung der Gefahr, die von einem Tsunami ausgeht und (ii) das schnelle Erkennen von gefährlichen Rutschungsereignissen. In dieser Doktorarbeit beschäftige ich mich mit beiden Problemen, indem ich Erdrutschtsunamis numerisch modelliere und eine neue Methode vorstelle, in der submarine Erdrutsche mit Hilfe von Tiltmetern detektiert werden. Die Küstengebiete Indonesiens sind wegen der Nähe zur Sunda-Subduktionszone besonders durch Tsunamis gefährdet. Das Ziel des GITEWS-Projektes (Deutsch- Indonesisches Tsunami-Frühwarnsystem) ist es, schnell und verlässlich vor Tsunamis zu warnen, aber auch das Wissen über Tsunamis und ihre Anregung zu vertiefen. Neue bathymetrische Daten am Sundabogen bieten die Möglichkeit, das Gefahrenpotential von Erdrutschtsunamis für die anliegenden indonesischen Inseln zu studieren. Ich präsentiere neun große Rutschungereignisse nahe Sumatra, Java, Sumbawa und Sumba, wobei das größte von ihnen 20 km³ Sediment bewegte. Ich modelliere die Ausbreitung und die Überschwemmung der bei diesen Rutschungen angeregten Tsunamis. Weiterhin untersuche ich das Alter der größten Hanginstabilitäten, indem ich sie zu dem Sumba Erdbeben von 1977 in Beziehung setze. Die Kontinentalhänge im Nordwesten Europa sind für Ihre immensen unterseeischen Rutschungen bekannt. Die gegenwärtige geologische Situation westlich von Spitzbergen ist vergleichbar mit derjenigen des norwegischen Kontinentalhangs nach der letzten Vergletscherung, als der große Tsunamianregende Storegga-Erdrutsch stattfand. Der Einfluss der arktischen Erwärmung auf die Hangstabilität vor Spitzbergen wird untersucht. Basierend auf neuen geophysikalischen Messungen, konstruiere ich vier mögliche Rutschungsszenarien und berechne die entsprechenden Tsunamis. Wellen von 6 Metern Höhe könnten dabei Nordwesteuropa erreichen. Ich stelle eine neue Methode vor, mit der große submarine Erdrutsche mit Hilfe eines Netzes aus Tiltmetern erkannt werden können. Diese Methode könnte in einem Tsunami-Frühwarnsystem angewendet werden. Sie basiert darauf, dass die Bewegung von großen Sedimentmassen während einer Rutschung eine dauerhafte Verformung der Erdoberfläche auslöst. Ich berechne diese Verformung und das einhergehende Tiltsignal. Im Falle der hypothetischen Spitzbergen-Rutschung sowie für das Storegga-Ereignis erhalte ich Amplituden von mehr als 1000 nrad. Die Wellenhöhe von Erdrutschtsunamis wird in erster Linie von dem Produkt aus Volumen und maximaler Rutschungsgeschwindigkeit (dem Tsunamipotential einer Rutschung) bestimmt. Ich führe eine Inversionsroutine vor, die unter Verwendung von Tiltdaten den Ort und das Tsunamipotential einer Rutschung bestimmt. Die Genauigkeit dieser Inversion und damit der vorhergesagten Wellenhöhe an der Küste hängt von dem Fehler der Tiltdaten, der Entfernung zwischen Tiltmeter und Rutschung sowie vom Tsunamipotential ab. Letztlich bestimme ich die Anwendbarkeitsreichweite dieser Methode, indem ich sie auf bekannte Rutschungsereignisse weltweit beziehe.
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Raykhel, Ilya. "Real-time automatic price prediction for eBay online trading /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2697.pdf.

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Cosma, Andrei Claudiu. "Real-Time Individual Thermal Preferences Prediction Using Visual Sensors." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=13422566.

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The thermal comfort of a building’s occupants is an important aspect of building design. Providing an increased level of thermal comfort is critical given that humans spend the majority of the day indoors, and that their well-being, productivity, and comfort depend on the quality of these environments. In today’s world, Heating, Ventilation, and Air Conditioning (HVAC) systems deliver heated or cooled air based on a fixed operating point or target temperature; individuals or building managers are able to adjust this operating point through human communication of dissatisfaction. Currently, there is a lack in automatic detection of an individual’s thermal preferences in real-time, and the integration of these measurements in an HVAC system controller.

To achieve this, a non-invasive approach to automatically predict personal thermal comfort and the mean time to discomfort in real-time is proposed and studied in this thesis. The goal of this research is to explore the consequences of human body thermoregulation on skin temperature and tone as a means to predict thermal comfort. For this reason, the temperature information extracted from multiple local body parts, and the skin tone information extracted from the face will be investigated as a means to model individual thermal preferences.

In a first study, we proposed a real-time system for individual thermal preferences prediction in transient conditions using temperature values from multiple local body parts. The proposed solution consists of a novel visual sensing platform, which we called RGB-DT, that fused information from three sensors: a color camera, a depth sensor, and a thermographic camera. This platform was used to extract skin and clothing temperature from multiple local body parts in real-time. Using this method, personal thermal comfort was predicted with more than 80% accuracy, while mean time to warm discomfort was predicted with more than 85% accuracy.

In a second study, we introduced a new visual sensing platform and method that uses a single thermal image of the occupant to predict personal thermal comfort. We focused on close-up images of the occupant’s face to extract fine-grained details of the skin temperature. We extracted manually selected features, as well as a set of automated features. Results showed that the automated features outperformed the manual features in all the tests that were run, and that these features predicted personal thermal comfort with more than 76% accuracy.

The last proposed study analyzed the thermoregulation activity at the face level to predict skin temperature in the context of thermal comfort assessment. This solution uses a single color camera to model thermoregulation based on the side effects of the vasodilatation and vasoconstriction. To achieve this, new methods to isolate skin tone response to an individual’s thermal regulation were explored. The relation between the extracted skin tone measurement and the skin temperature was analyzed using a regression model.

Our experiments showed that a thermal model generated using noninvasive and contactless visual sensors could be used to accurately predict individual thermal preferences in real-time. Therefore, instantaneous feedback with respect to the occupants' thermal comfort can be provided to the HVAC system controller to adjust the room temperature.

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Raykhel, Ilya Igorevitch. "Real-Time Automatic Price Prediction for eBay Online Trading." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1631.

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While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model.
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Su, Yibing. "Real-time prediction of stream water temperature for Iowa." Thesis, University of Iowa, 2017. https://ir.uiowa.edu/etd/5653.

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In the agricultural state of Iowa, water quality research is of great importance for monitoring and managing the health of aquatic systems. Among many water quality parameters, water temperature is a critical variable that governs the rates of chemical and biological processes which affect river health. The main objective of this thesis is to develop a real-time high resolution predictive stream temperature model for the entire state of Iowa. A statistical model based solely on the water-air temperature relationship was developed using logistic regression approach. With hourly High Resolution Rapid Refresh (HRRR) air temperature estimations, the implemented stream temperature model produces current state-wide estimations. The results are updated hourly in real-time and presented on a web-based visualization platform: the Iowa Water Quality Information System, Beta version (IWQIS Beta). Streams of 4th order and up are color-coded according to the estimated temperatures. Hourly forecasts for lead time of up to 18 hours are also available. A model was developed separately for spring (March to May), summer (June to August), and autumn (September to November) seasons. 2016 model estimation results generate less than 3 °C average RMSE for the three seasons, with a summer season RMSE of below 2 °C. The model is transferrable to basins of different catchment sizes within the state of Iowa and requires hourly air temperature as the only input variable. The product will assist Iowa water quality research and provide information to support public management decisions.
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Naye, Edouard. "Real-time arrival prediction models for light rail train systems." Thesis, KTH, Systemanalys och ekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170645.

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One of the main objectives of public transport operators is to adhere to the planned timetable and to provide accurate information to passengers in order to improve actual and perceived service reliability. The aim of this thesis is to address the flowing question: how can the accuracy of a prediction system for light rail systems be measured and improved? The real-time prediction is an output of a telecommunication system, named Automatic Vehicle Location System, which computerizes the predictions. In order to improve a system, it is first important to understand how it works. The mechanism of the prediction computation will be analyzed and each part of the process will be studied in order to seek potential improvements. The first part of the prediction scheme development consists in a statistical analysis of historical data to provide the reference travel times and dwell times and their variations along a day or along a week. Then, two models (the designed-speed model and the speed/position model) will be studied to estimate the remaining time to reach the downstream stop. This estimation is mainly based on the current data (vehicle position and speed). The proposed prediction schemes were implemented and applied for a case study light rail line. Bybanen, a light rail train in Bergen was selected as case study. Real-time information displays are available at all platforms and refer to the waiting to the next two light rail trains. This study focuses on improving the accuracy of these waiting times predictions. In order to establish and analyze the performance of the current prediction scheme, a model for reproducing these computations was developed. Then, the possible improvements have been implemented in the model and the accuracy of the new predictions has been compared to the base case. The assessment and the comparison of prediction systems are not trivial tasks. Which predictions should be taken into account? How does the model identify inconsistency in the data? How could the perception of passengers be taken into account? A set of measures has been used in order to evaluate alternative prediction schemes. The comparison of the different models shows that it is possible to improve the accuracy of the short-term predictions, but it is more difficult to improve the accuracy of long-term predictions because the incertitude of small changes has more impact in long-term predictions. This thesis shows that the reference travel times and dwell times should be assimilated to the most common value instead of the average which is too dependent on high values. Moreover, the dwell time variations are related to the flow passengers. Finally, the most accurate and efficient model is the designed-speed model. The speed/position model is a bit less accurate except in the case of disturbances along the line but its modularity made easier possible improvements. Finally, this paper highlights the time-depending variations of the dwell time in the case of a light rail train system. It could be interesting to analyze the behavior of variations of two consequent dwell times and to implement a forgetting factor. Moreover, the speed-position model shows really good results and a better understanding of the drivers’ behaviors is a key to improve the model. Finally, the differences between the different models will be probably larger for a middle-distance train system, which could be an interesting application of this thesis.
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Bataineh, Mohammad Hindi. "New neural network for real-time human dynamic motion prediction." Thesis, The University of Iowa, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3711174.

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Artificial neural networks (ANNs) have been used successfully in various practical problems. Though extensive improvements on different types of ANNs have been made to improve their performance, each ANN design still experiences its own limitations. The existing digital human models are mature enough to provide accurate and useful results for different tasks and scenarios under various conditions. There is, however, a critical need for these models to run in real time, especially those with large-scale problems like motion prediction which can be computationally demanding. For even small changes to the task conditions, the motion simulation needs to run for a relatively long time (minutes to tens of minutes). Thus, there can be a limited number of training cases due to the computational time and cost associated with collecting training data. In addition, the motion problem is relatively large with respect to the number of outputs, where there are hundreds of outputs (between 500-700 outputs) to predict for a single problem. Therefore, the aforementioned necessities in motion problems lead to the use of tools like the ANN in this work.

This work introduces new algorithms for the design of the radial-basis network (RBN) for problems with minimal available training data. The new RBN design incorporates new training stages with approaches to facilitate proper setting of necessary network parameters. The use of training algorithms with minimal heuristics allows the new RBN design to produce results with quality that none of the competing methods have achieved. The new RBN design, called Opt_RBN, is tested on experimental and practical problems, and the results outperform those produced from standard regression and ANN models. In general, the Opt_RBN shows stable and robust performance for a given set of training cases.

When the Opt_RBN is applied on the large-scale motion prediction application, the network experiences a CPU memory issue when performing the optimization step in the training process. Therefore, new algorithms are introduced to modify some steps of the new Opt_RBN training process to address the memory issue. The modified steps should only be used for large-scale applications similar to the motion problem. The new RBN design proposes an ANN that is capable of improved learning without needing more training data. Although the new design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems in various engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory.

The results of evaluating the modified Opt_RBN design on two motion problems are promising, with relatively small errors obtained when predicting approximately 500-700 outputs. In addition, new methods for constraint implementation within the new RBN design are introduced. Moreover, the new RBN design and its associated parameters are used as a tool for simulated task analysis. This work initiates the idea that output weights (W) can be used to determine the most critical basis functions that cause the greatest reduction in the network test error. Then, the critical basis functions can specify the most significant training cases that are responsible for the proper performance achieved by the network. The inputs with the most change in value can be extracted from the basis function centers (U) in order to determine the dominant inputs. The outputs with the most change in value and their corresponding key body degrees-of-freedom for a motion task can also be specified using the training cases that are used to create the network's basis functions.

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Books on the topic "Real time prediction"

1

Bell, M. G. H. Journey time prediction for real time bus monitoring and passenger information systems. Newcastle upon Tyne: University of Newcastle upon Tyne, Transport Operations Research Group, 1988.

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Amato, Jeffery D. The real-time predictive content of money for output. Basel, Switzerland: Bank for International Settlements, Monetary and Economic Dept., 2000.

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Rendine, John J. Real-time airborne ocean sampling and applications to naval operations. Monterey, Calif: Naval Postgraduate School, 1986.

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Marsteller, Gary E. Comparison of the Naval Operational Global Atmospheric Prediction System cloud analyses and forecasts with the Air Force Real Time nephanalyses cloud model. Monterey, Calif: Naval Postgraduate School, 1998.

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Clark, Todd E. Tests of equal predictive ability with real-time data. Kansas City [Mo.]: Research Division, Federal Reserve Bank of Kansas City, 2007.

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Harrington, Edward J. A real time sharpening of NOGAPS predictions of mid-latitude Central Pacific cyclones. Monterey, Calif: Naval Postgraduate School, 1992.

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Bauer, Jeffrey E. An impact-location estimation algorithm for subsonic uninhabited aircraft. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 1997.

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Nihan, N. L. Predictive algorithm improvements for a real-time ramp control system: Final report, Research Project GC 8286, Task 16, Ramp Control Volume Forecast. [Olympia, Wash.]: Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1991.

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Moshgbar, Mojgan. Prediction and real-time compensation of line wear in cone crushers. 1996.

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I, Cleveland Jeff, and Langley Research Center, eds. A study of workstation computational performance for real-time flight simulation. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1995.

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Book chapters on the topic "Real time prediction"

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Georgakakos, Konstantine P. "Real-Time Flash Flood Prediction." In Collected Reprint Series, 9615–29. Washington, DC: American Geophysical Union, 2013. http://dx.doi.org/10.1002/9781118782071.ch8.

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Bee Dagum, Estela, and Silvia Bianconcini. "Real Time Trend-Cycle Prediction." In Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation, 243–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31822-6_10.

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Abdelrahman, S. A., Omar Khaled, Amr Alaa, Mohamed Ali, Injy Mohy, and Ahmed H. ElDieb. "Real-Time Spectrum Occupancy Prediction." In Lecture Notes on Data Engineering and Communications Technologies, 219–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11437-4_17.

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Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In 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.

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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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Li, Haiguang, Zhao Li, Robert T. White, and Xindong Wu. "A Real-Time Transportation Prediction System." In Advanced Research in Applied Artificial Intelligence, 68–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31087-4_8.

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Chu, Deming, Zhitao Shen, Yu Zhang, Shiyu Yang, and Xuemin Lin. "Real-Time Popularity Prediction on Instagram." In Lecture Notes in Computer Science, 275–79. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68155-9_21.

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Robinson, Robert, Ozan Oktay, Wenjia Bai, Vanya V. Valindria, Mihir M. Sanghvi, Nay Aung, José M. Paiva, et al. "Real-Time Prediction of Segmentation Quality." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 578–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00937-3_66.

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Colnarič, Matjaž. "Run-Time Prediction for Hard Real-Time Programs." In PEARL 90 — Workshop über Realzeitsysteme, 59–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-46725-7_6.

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Boytsov, Andrey, and Arkady Zaslavsky. "Context Prediction in Pervasive Computing Systems: Achievements and Challenges." In Supporting Real Time Decision-Making, 35–63. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7406-8_3.

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Onishi, Ryo, Joe Hirai, Dmitry Kolomenskiy, and Yuki Yasuda. "Real-Time High-Resolution Prediction of Orographic Rainfall for Early Warning of Landslides." In Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022, 237–48. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16898-7_17.

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AbstractHeavy rainfall often causes devastating landslides. Early warning based on reliable rainfall prediction can help reduce human and economic damages. This paper describes a recent development of reliable high-resolution prediction of orographic (topographic) rainfall using our next-generation numerical weather prediction model, the Multi-Scale Simulator for the Geoenvironment (MSSG). High-resolution computing is required for reliable rainfall prediction, and the MSSG can run with very high resolutions. Robust cloud microphysics is another key to realizing reliable predictions of orographic clouds, where the atmospheric boundary turbulence can affect. This paper clarifies that in-cloud turbulence can enhance cloud development. The recent cloud microphysics model that can consider turbulence enhancement is newly implemented in the MSSG. The emerging machine-learning technology is also coupled with the MSSG for reliable operational predictions. We show the recent development towards reliable predictions of orographic rainfall for realizing early warning of landslides.
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Conference papers on the topic "Real time prediction"

1

Bartetzko, A., J. H. Figenschou, S. Schimschal, S. Wessling, and T. Dahl. "Real-time Pore Pressure Modelling Workflow Automation." In First EAGE Workshop on Pore Pressure Prediction. Netherlands: EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201700058.

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Naaijen, Peter, and Rene´ Huijsmans. "Real Time Wave Forecasting for Real Time Ship Motion Predictions." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57804.

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This paper presents results of a validation study into a linear short term wave and ship motion prediction model for long crested waves. Model experiments have been carried out during which wave elevations were measured at various distances down stream of the wave maker simultaneously. Comparison between predicted and measured wave elevation are presented for 6 different wave conditions. The theoretical relation between spectral content of an irregular long crested wave system and optimal prediction distance for a desired prediction time is explained and validated. It appears that predictions can be extended further into the future than expected based on this theoretical relation.
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Meyers, Gregory, Miguel Martinez-Garcia, Yu Zhang, and Yudong Zhang. "Reliable Real-time Destination Prediction." In 2021 IEEE 19th International Conference on Industrial Informatics (INDIN). IEEE, 2021. http://dx.doi.org/10.1109/indin45523.2021.9557585.

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Suter, E., S. Alyaev, and B. Daireaux. "RT-Hub - Next Generation Real-time Data Aggregation While Drilling." In First EAGE Workshop on Pore Pressure Prediction. Netherlands: EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201700060.

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Courtaud, Cedric, Julien Sopena, Gilles Muller, and Daniel Gracia Perez. "Improving Prediction Accuracy of Memory Interferences for Multicore Platforms." In 2019 IEEE Real-Time Systems Symposium (RTSS). IEEE, 2019. http://dx.doi.org/10.1109/rtss46320.2019.00031.

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Sarukkai, Ramesh R. "Real-time user modeling and prediction." In the 22nd International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2487788.2488041.

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Bai, Jie, Linjing Li, Lan Lu, Yanwu Yang, and Daniel Zeng. "Real-time prediction of meme burst." In 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 2017. http://dx.doi.org/10.1109/isi.2017.8004900.

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Sandaruwan, Damitha, Nihal Kodikara, Rexy Rosa, and Chamath Keppitiyagama. "Real-time Ship Motion Prediction System." In Annual International Conferences on Computer Games, Multimedia and Allied Technology. Global Science & Technology Forum (GSTF), 2009. http://dx.doi.org/10.5176/978-981-08-3190-5_470.

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Van Dyke Parunak, H., Sven Brueckner, Robert Matthews, John Sauter, and Steve Brophy. "Real-time agent characterization and prediction." In the 6th international joint conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1329125.1329460.

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Reitmann, Stefan, and Michael Schultz. "Real-time Prediction of Aircraft Boarding." In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC). IEEE, 2018. http://dx.doi.org/10.1109/dasc.2018.8569370.

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Reports on the topic "Real time prediction"

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Susnjak, Teo, and Christoph Schumacher. Nowcasting: Towards Real-time GDP Prediction. Knowledge Exchange Hub, December 2018. http://dx.doi.org/10.33217/keh/gdplive/001/12.2018.

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Doyle, J. D., R. M. Hodur, S. Chen, H. Jin, Y. Jin, J. Moskaitis, A. Reinecke, P. Black, J. Cummings, and E. Hendricks. Real-Time Prediction of Tropical Cyclone Intensity Using COAMPS-TC. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada609982.

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Dinda, Peter A., Bruce Lowekamp, Loukas Kallivokas, and David R. O'Hallaron. The Case For Prediction-based Best-effort Real-time Systems. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada360937.

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Shin, Insub, and Alexander H. Levis. Performance Prediction of Real-Time Command, Control, and Communications (C3) Systems. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada388093.

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Steinmen, Jeffrey, and Craig Lammers. Real Time Estimation and Prediction using Optimistic Simulation and Control Theory Techniques. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada478368.

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Tang, Wei, and Stylianos Chatzidakis. REAL-TIME CANISTER WELDING HEALTH MONITORING AND PREDICTION SYSTEM FOR SPENT FUEL DRY STORAGE. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1649019.

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Evangelinos, Constantinos, Pierre F. Lermusiaux, Jinshan Xu, Jr Haley, Hill Patrick J., and Chris N. Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada513019.

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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.
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Martin Wilde, Principal Investigator. The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant. Office of Scientific and Technical Information (OSTI), December 2012. http://dx.doi.org/10.2172/1062998.

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