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Dissertations / Theses on the topic 'Predicting filter'

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Published: 30 May 2022
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1

Murthy, Sudhir N. "Predicting dewatering equipment performance from laboratory tests." Thesis, Virginia Tech, 1992. http://hdl.handle.net/10919/43976.

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2

Mathema, Najma. "Predicting Plans and Actions in Two-Player Repeated Games." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8683.

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Artificial intelligence (AI) agents will need to interact with both other AI agents and humans. One way to enable effective interaction is to create models of associates to help to predict the modeled agents' actions, plans, and intentions. If AI agents are able to predict what other agents in their environment will be doing in the future and can understand the intentions of these other agents, the AI agents can use these predictions in their planning, decision-making and assessing their own potential. Prior work [13, 14] introduced the S# algorithm, which is designed as a robust algorithm for many two-player repeated games (RGs) to enable cooperation among players. Because S# generates actions, has (internal) experts that seek to accomplish an internal intent, and associates plans with each expert, it is a useful algorithm for exploring intent, plan, and action in RGs. This thesis presents a graphical Bayesian model for predicting actions, plans, and intents of an S# agent. The same model is also used to predict human action. The actions, plans and intentions associated with each S# expert are (a) identified from the literature and (b) grouped by expert type. The Bayesian model then uses its transition probabilities to predict the action and expert type from observing human or S# play. Two techniques were explored for translating probability distributions into specific predictions: Maximum A Posteriori (MAP) and Aggregation approach. The Bayesian model was evaluated for three RGs (Prisoners Dilemma, Chicken and Alternator) as follows. Prediction accuracy of the model was compared to predictions from machine learning models (J48, Multi layer perceptron and Random Forest) as well as from the fixed strategies presented in [20]. Prediction accuracy was obtained by comparing the model's predictions against the actual player's actions. Accuracy for plan and intent prediction was measured by comparing predictions to the actual plans and intents followed by the S# agent. Since the plans and the intents of human players were not recorded in the dataset, this thesis does not measure the accuracy of the Bayesian model against actual human plans and intents. Results show that the Bayesian model effectively models the actions, plans, and intents of the S# algorithm across the various games. Additionally, the Bayesian model outperforms other methods for predicting human actions. When the games do not allow players to communicate using so-called “cheap talk”, the MAP-based predictions are significantly better than Aggregation-based predictions. There is no significant difference in the performance of MAP-based and Aggregation-based predictions for modeling human behavior when cheaptalk is allowed, except in the game of Chicken.
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3

Mild, Andreas, and Thomas Reutterer. "An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/414/1/document.pdf.

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Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This paper investigates the suitability of such methods for situations when only binary pick-any customer information (i.e., choice/nonchoice of items, such as shopping basket data) is available. We present an extension of collaborative filtering algorithms for such data situations and apply it to a real-world retail transaction dataset. The new method is benchmarked against more conventional algorithms and can be shown to deliver superior results in terms of predictive accuracy. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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4

Wells, James Z. "Application of Path Prediction Techniques for Unmanned Aerial System Operations in the National Airspace." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161710909594714.

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5

Kalapati, Raga S. "Analysis of Ozone Data Trends as an Effect of Meteorology and Development of Forecasting Models for Predicting Hourly Ozone Concentrations and Exceedances for Dayton, OH, Using MM5 Real-Time Forecasts." University of Toledo / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1091216133.

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6

Евсеенко, Олег Николаевич, and Сергей Михайлович Савицкий. "Описание метода управления тепловым объектом с распределёнными параметрами с помощью широтно-импульсной модуляции и предсказывающего фильтра." Thesis, Національний технічний університет "Харківський політехнічний інститут", 2013. http://repository.kpi.kharkov.ua/handle/KhPI-Press/48405.

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Предложен метод управления для тепловых объектов, получены разгонные кривые теплового объекта, рассчитан максимально допустимый период дискретизации переходного процесса по теореме Котельникова, выбрана частота дискретизации, проведён эксперимент по управлению инерционным тепловым объектом.
Proposed a method of control thermal objects, obtained the acceleration curves of an object, calculated the maximum allowable sampling period of transition with the theorem of Kotelnikov, chosen the sampling frequency, conducted experiments for control the temperature of inertial object.
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7

Wang, Zijian. "Conducted EMI Noise Prediction and Filter Design Optimization." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73166.

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Power factor correction (PFC) converter is a species of switching mode power supply (SMPS) which is widely used in offline frond-end converter for the distributed power systems to reduce the grid harmonic distortion. With the fast development of information technology and multi-media systems, high frequency PFC power supplies for servers, desktops, laptops and flat-panel TVs, etc. are required for more efficient power delivery within limited spaces. Therefore the critical conduction mode (CRM) PFC converter has been becoming more and more popular for these information technology applications due to its advantages in inherent zero-voltage soft switching (ZVS) and negligible diode reverse recovery. With the emerging of the high voltage GaN devices, the goal of achieving soft switching for high frequency PFC converters is the top priority and the trend of adopting the CRM PFC converter is becoming clearer. However, there is the stringent electromagnetic interference (EMI) regulation worldwide. For the CRM PFC converter, there are several challenges on meeting the EMI standards. First, for the CRM PFC converter, the switching frequency is variable during the half line cycle and has very wide range dependent on the AC line RMS voltage and the load, which makes it unlike the traditional constant-frequency PFC converter and therefore the knowledge and experience of the EMI characteristics for the traditional constant-frequency PFC converter cannot be directly applied to the CRM PFC converter. Second, for the CRM PFC converter, the switching frequency is also dependent on the inductance of the boost inductor. It means the EMI spectrum of the CRM PFC converter is tightly related the boost inductor selection during the design of the PFC power stage. Therefore, unlike the traditional constant-frequency PFC converter, the selection of the boost inductor is also part of the EMI filter design process and EMI filter optimization should begin at the same time when the power stage design starts. Third, since the EMI filter optimization needs to begin before the proto-type of the CRM PFC converter is completed, the traditional EMI-measurement based EMI filter design will become much more complex and time-consuming if it is applied to the CRM PFC converter. Therefore, a new methodology must be developed to evaluate the EMI performance of the CRM PFC converter, help to simplify the process of the EMI filter design and achieve the EMI filter optimization. To overcome these challenges, a novel mathematical analysis method for variable frequency PFC converter is thus proposed in this dissertation. Based on the mathematical analysis, the quasi-peak EMI noise, which is specifically required in most EMI regulation standards, is investigated and accurately predicted for the first time. A complete approximate model is derived to predict the quasi-peak DM EMI noise for the CRM PFC converter. Experiments are carried out to verify the validity of the prediction. Based on the DM EMI noise prediction, worst case analysis is carried out and the worst DM EMI noise case for all the input line and load conditions can be found to avoid the overdesign of the EMI filter. Based on the discovered worst case, criteria to ease the DM EMI filter design procedure of the CRM boost PFC are given for different boost inductor selection. Optimized design procedure of the EMI filter for the front-end converter is then discussed. Experiments are carried out to verify the validity of the whole methodology.
Ph. D.
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8

Park, Keun Joo. "GPS receiver self survey and attitude determination using pseudolite signals." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/1250.

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This dissertation explores both the estimation of various parameters from a multiple antenna GPS receiver, which is used as an attitude sensor, and attitude determination using GPS-like Pseudolite signals. To use a multiple antenna GPS receiver as an attitude sensor, parameters such as baselines, integer ambiguities, line biases, and attitude, should be resolved beforehand. Also, due to a cycle slip problem a subsystem to correct this problem should be implemented. All of these tasks are called a self survey. A new algorithm to estimate these parameters from a GPS receiver is developed usingnonlinear batch filteringmethods.For convergence issues, both the nolinear least squares (NLS) and Levenberg-Marquardt (LM) methods are applied in the estimation.Acomparison ofthe NLSand LMmethods shows that the convergence of the LM method for the large initial errors is more robust than that of the NLS. In the proximity of the International Space Station (ISS), Pseudolite signals replace the GPSsignals since almostallsignals are blocked.Since the Pseudolite signals have spherical wavefronts, a new observation model should be applied. A nonlinear predictive filter, an extended Kalman filter (EKF), and an unscented filter (UF) are developed and compared using Pseudolite signals. A nonlinear predictive filter can provide a deterministic solution; however, it cannot be used for the moving case. Instead, the EKF or the UF can be used with the angular rate measurements. A comparison of EKF and UF shows that the convergence of the UF for the large initial errors is more robust than that of the EKF. Also, an alternative global navigation constellation is presented by using the Flower Constellation (FC) scheme. A comparison of FC global navigation constellation and other GPS constellations, U.S. GPS, Galileo, and GLONASS, shows that position and attitude errors of the FC constellation are smaller that those of the others.
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9

Vatis, Yuri. "Non-symmetric adaptive interpolation filter for motion compensated prediction /." Düsseldorf : VDI-Verl, 2009. http://d-nb.info/998470724/04.

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10

Aldokhayel, Abdulaziz. "A Kalman Filter-based Dynamic Model for Bus Travel Time Prediction." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38060.

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Urban areas are currently facing challenges in terms of traffic congestion due to city expansion and population increase. In some cases, physical solutions are limited. For example, in certain areas it is not possible to expand roads or build a new bridge. Therefore, making public transpiration (PT) affordable, more attractive and intelligent could be a potential solution for these challenges. Accuracy in bus running time and bus arrival time is a key component of making PT attractive to ridership. In this thesis, a dynamic model based on Kalman filter (KF) has been developed to predict bus running time and dwell time while taking into account real-time road incidents. The model uses historical data collected by Automatic Vehicle Location system (AVL) and Automatic Passenger Counters (APC) system. To predict the bus travel time, the model has two components of running time prediction (long and short distance prediction) and dwell time prediction. When the bus closes its doors before leaving a bus stop, the model predicts the travel time to all downstream bus stops. This is long distance prediction. The model will then update the prediction between the bus’s current position and the upcoming bus stop based on real-time data from AVL. This is short distance prediction. Also, the model predicts the dwell time at each coming bus stop. As a result, the model reduces the difference between the predicted arrival time and the actual arrival time and provides a better understanding for the transit network which allows lead to have a good traffic management.
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11

Odavic, Milijana. "Predictive control for multilevel active power filters." Thesis, University of Nottingham, 2008. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765261.

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12

Mutaf, Asim. "A Kalman filter with smoothing for hurricane tracking and prediction." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/26034.

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The performance of a Kalman filter used to track a hurricane was substantially improved by implementing a fixed interval smoothing algorithm. This tracking routine was designed and implemented in a microcomputer program. Several tracking scenarios were simulated and analyzed. Actual storm tracks obtained from the Joint Typhoon Warning Center in Guam, Mariana Islands, were used for this research. The application of the Kalman tracker to a tropical storm's wind speed tracking was also investigated by using the best track data and observed data
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13

Oleg, Komogortsev Vladimirovich. "EYE MOVEMENT PREDICTION BY OCULOMOTOR PLANT MODELING WITH KALMAN FILTER." Kent State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=kent1190386786.

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14

Taskin, Dogan. "The path prediction of cyclones with Kalman filters." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/34944.

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Approved for public release; distribution unlimited.
The Kalman filter is used to provide estimates of the position and velocity of a storm based upon observation of the storm's longitude and latitude. Nonstationary noise is shown to degrade the performance of the filter and cause tracking divergence. Time varying values for the noise covariance matricies R and Q, and the addition of an external forcing function to the filter, effectively compensated for this tracking error. Results for the simulations show significant performance advantages of using an external forcing function in the system.
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15

Khatib, Firas. "Topological filters for use with protein structure prediction /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.

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16

Curry, William. "Interpolation with prediction-error filters and training data /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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17

Li, Bin. "Forecasting financial time series using linear predictive filters." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11176.

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Forecasting financial time series is regarded as one of the most challenging applications of time series prediction due to their dynamic nature. However, it is the fundamental element of most investment activities thus attracting the attention of practitioners and researchers for many decades. The purpose of this research is to investigate and develop novel methods for the prediction of financial time series considering their dynamic nature. The predictive performance of asset prices time series themselves is exploited by applying digital signal processing methods to their historical observations. The novelty of the research lies in the design of predictive filters by maximising their spectrum flatness of forecast errors. The filters are then applied to forecast linear combinations of daily open, high, low and close prices of financial time series. Given the assumption that there are no structural breaks or switching regimes in a time series, the sufficient and necessary conditions that a time series can be predicted with zero errors by linear filters are examined. It is concluded that a band-limited time series can be predicted with zero errors by a predictive filter that has a constant magnitude response and constant group delay over the bandwidth of the time series. Because real world time series are not band-limited thus cannot be forecasted without errors, statistical tests of spectrum flatness which evaluate the departure of the spectral density from a constant value are introduced as measures of the predictability of time series. Properties of a time series are then investigated in the frequency domain using its spectrum flatness. A predictive filter is designed by maximising the error spectrum flatness that is equivalent to maximise the “whiteness” of forecast errors in the frequency domain. The focus is then placed on forecasting real world financial time series. By applying spectrum flatness tests, it is found that the property of the spectrum of a linear combination of daily open, high, low and close prices, which is called target prices, is different from that of a random walk process as there are much more low frequency components than high frequency ones in its spectrum. Therefore, an objective function is proposed to derive the target price time series from the historical observations of daily open, high, low and close prices. A predictive filter is then applied to obtain the one-step ahead forecast of the target prices, while profitable trading strategies are designed based on the forecast of target prices series. As a result, more than 70% success ratio could be achieved in terms of one-step ahead out-of-sample forecast of direction changes of the target price time series by taking the S&P500 index for example.
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18

Smith, Amie Michelle. "Prediction and Measurement of Thermal Exchanges within Pyranometers." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/35636.

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The Eppley Precision Spectral Pyranometer (PSP) is a shortwave radiometer that is widely used in global networks to monitor solar irradiances at the earth's surface. Within the instrument, a blackened surface is in intimate thermal contact with the hot junction of a thermopile. The cold junction of the thermopile communicates thermally with the large thermal capacitance of the instrument body, which acts as a heat sink. Radiation arrives at the blackened surface through one or two hemispherical dome-shaped filters that limit the instrument response to the solar spectrum. The voltage developed by the thermopile is then interpreted in terms of the incident irradiance. Measurements taken with the pyranometer are compared with results from theoretical models. Discrepancies between model results and measurements are used to isolate inaccuracies in the optical properties of the atmosphere used in the models. As the accuracy of the models increases, the reliability of the measurements must be examined in order to assure that the models keep up with reality. The sources of error in the pyranometer are examined in order to determine the accuracy of the instrument. Measurements obtained using the pyranometer are known to be influenced by environmental conditions such as ambient temperature, wind, and cloud cover [Bush, et al., 1998]. It is surmised that at least some of the observed environmental variability in these data is due to parasitic thermal exchanges within the instrument [Haeffelin et al., 1999]. Thermal radiation absorbed and emitted by the filters, as well as that reflected and re-reflected among the internal surfaces, influences the net radiation at the detector surface and produces an offset from the signal that would result from the incident shortwave radiation alone. Described is an ongoing effort to model these exchanges and to use experimental results to verify the model. The ultimate goal of the work described is to provide reliable protocols, based on an appropriate instrument model, for correcting measured shortwave irradiance for a variable thermal radiation environment.
Master of Science
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19

Simmons, Anthony L. "A discrete, digital filter for forward prediction of seaway elevation response." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA331882.

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Thesis (M.S. in Mechanical Engineering) Naval Postgraduate School, March 1997.
Thesis advisor(s): Anthony J. Healey. "March 1997." Includes bibliographical references (p. 69-70). Also Available online.
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20

Schuler, Paul Joseph. "Polymer dose prediction for sludge dewatering with a belt filter press." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/42227.

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This study was undertaken to examine the polymer mixing requirements for sludge dewatering with a belt filter press. This involved correlating full-scale field studies to small scale laboratory testing. Bench testing involved the use of a high-speed mixer and two sludge dewatering response tests: the capillary suction time test and the time-to filter test. Full-scale testing measured the belt press response to belt speed, sludge throughput, and polymer dose. Data indicated that the conditioning and dewatering scheme of the three belt filter presses was a low shear, low total mixing energy operation. The Gt, or total mixing energy, of these operations was in the range of 8,000-12,000. Optimal dose predicted by the bench-scale testing correlated well to the optimal dose for maximum cake solids coming off the belt filter press. Also, the amount of water removed from the sludge with the belt press was largely a function of the type of solids present in the sludge and less of a function of the number of rollers or residence time in the press.
Master of Science
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21

Barsk, Karl-Johan. "Model Predictive Control of a Tricopter." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79066.

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In this master thesis, a real-time control system that stabilizes the rotational rates of a tri-copter, has been studied. The tricopter is a rotorcraft with three rotors. The tricopter has been modelled and identified, using system identification algorithms. The model has been used in a Kalman filter to estimate the state of the system and for design ofa model based controller. The control approach used in this thesis is a model predictive controller, which is a multi-variable controller that uses a quadratic optimization problem to compute the optimal con-trol signal. The problem is solved subject to a linear model of the system and the physicallimitations of the system. Two different types of algorithms that solves the MPC problem have been studied. These are explicit MPC and the fast gradient method. Explicit MPC is a pre-computed solution to the problem, while the fast gradient method is an online solution. The algorithms have been simulated with the Kalman filter and were implemented on themicrocontroller of the tricopter.
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Pitre, Kevin M. "Predicting Wind Noise Inside Porous Dome Filters for Infrasound Sensing on Mars." Thesis, University of Louisiana at Lafayette, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10244134.

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The study described in this thesis aims to assess the effects of wind-generated noise on potential infrasound measurements on future Mars missions. Infrasonic sensing on Mars is being considered as a means to probe the long-scale atmospheric dynamics, thermal balance, and also to infer bolide impact statistics. In this study, a preliminary framework for predicting the principal wind noise mechanisms to the signal detected by a sensor placed inside a hemispherical porous dome on the Martian surface is developed. The method involves calculating the pressure power density spectra in the infrasonic range generated by turbulent interactions and filtered by dome shaped filters of varying porosities. Knowing the overall noise power spectrum will allow it to be subtracted from raw signals of interest and aid in the development of infrasound sensors for the Martian environment. In order to make these power spectral predictions, the study utilizes the Martian Climate Database (MCD) global circulation model, developed by Laboratoire de Meteorologie Dynamique in Paris, France. Velocity profiles are generated and used in semi empirical functions generated by von Kármán along with equations for describing the physical turbulent interactions. With these, turbulent interactions in the free atmosphere above the Martian surface are described. For interactions of turbulence with the porous filter, semi-empirical formulations are adapted to the Martian parameters generated by the MCD and plotted alongside contributions in the free atmosphere outside and inside the dome to obtain the total wind noise contribution from turbulence. In conclusion, the plots of power spectral densities versus frequency are analyzed to determine what porosity filter would provide the best wind-noise suppression when measured at the center the dome. The study shows that 55% (0.02 to 5 Hz) and 80% (6 to 20 Hz) porosities prove to be the better of the five porosities tested.

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Marks, John Hywel. "Predictive control of active power filters using neural networks." Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396419.

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24

Li, Qian. "Correlation between Simulation and Measurement of Microwave Resonator Power Handling." Thesis, Linköpings universitet, Fysik och elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-101981.

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In modern mobile wireless communication, Base Stations (BS) are the most important equipment to build up the mobile network. One of the key elements in BS is the RF filter, which plays a key role to secure the coverage and reliability of the BS. Especially, at Transmitter (Tx) side, the filter must have a high capability to handle the power sent from Power Amplifier (PA) to antenna in any circumstances to ensure the coverage demand. Otherwise, the breakdown will be encountered, setting the power flow in the BS system in an abnormal manner that, finally can lead to the shut down of BS or destroy the system permanently. In this project, three methods using two simulation tools to predict the power handling capability of the RF/microwave resonator which is the elementary component in the BS’s filter are proposed. Power handling tests of selected configurations corresponding to the simulations are implemented as well. In the next stage, the results from the prediction and measurement are compared. Finally, the conclusions of correlation between the prediction and measurement of microwave resonator power handling will be derived.
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Rufianto, Muhammad Haky. "State Prediction for Haptic Remote Teleoperation - A Kalman Filter ApproachState Prognos för haptisk Remote teleoperation – en metod baserad på Kalman-filter." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189155.

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Teleoperation system is an important tool to control a device or model in an isolated area remotely where the operator cannot perform the task locally. The vast majority of teleoperation systems provides the operator with visual and haptic control to accomplish the assignment as naturally as possible. However, on a teleoperation system with considerable distance, the time delay could cause a drop in performance. This thesis aims to minimize delay problem by implementing a prediction approach using Kalman Filter. Kalman Filter algorithm has been widely used to estimate user movement for tracking systems. Kalman filter provides an efficient mechanism to predict future state based on Bayesian estimation to sequentially predict future states and measure an actual system to update system parameters. The primary objective of this work is to extract information generated by our prototyping model and visualizing the data to reflect the performance of the system. We use Phantom Omni devices and 3D arm as a model. Different type of Kalman filter algorithms is used to test the accuracy and performance of predicted state generated by the filter. The result shows that the implementation of Extended Kalman Filter (EKF) and smoothing function could overcome the networking delay on certain degrees. The comparison shows that the EKF has better accuracy and performance compared to Unscented Kalman Filter (UKF) when estimating the future state. Additionally, the implementation of smoothing function could improve the stability of teleoperation system.
Teleoperation systemet är ett viktigt verktyg för att styra en enhet eller modell i ett isolerat område på distans där operatören inte kan utföra uppgiften lokalt. De allra flesta av teleoperation system ger föraren visuell och haptisk kontroll för att utföra uppdraget så naturligt som möjligt. Men på en teleoperation system med stort avstånd, kan tidsfördröjningen medföra en nedgång i prestanda. Denna avhandling syftar till att minimera förseningar problem genom att implementera en förutsägelse tillvägagångssätt med Kalman Filter. Kalman filteralgoritm har i stor utsträckning används för att uppskatta användarens rörlighet för spårning. Kalman filter ger en effektiv mekanism för att förutsäga framtida stat grundad på Bayesian uppskattningen att sekventiellt förutsäga framtida tillstånd och mäta ett verkligt system för att uppdatera systemparametrar. Det primära syftet med detta arbete är att extrahera information som genereras av vår prototypmodell och visualisera data för att återspegla systemets prestanda. Vi använder Phantom Omni enheter och 3D-arm som en modell. Olika typer av Kalman filter algoritmer används för att testa riktigheten och prestandan hos förutsagda tillståndet genereras av filtret. Resultatet visar att genomförandet av Extended Kalman filter (EKF) och utjämningsfunktionen kan övervinna nätverk dröjsmålsvissa grader. Jämförelsen visar att EKF har bättre noggrannhet och prestanda jämfört med Unscented Kalman Filter (UKF) vid bedömningen av framtida tillstånd. Dessutom, genomförandet av utjämningsfunktionen skulle kunna förbättra stabiliteten hos teleoperation systemet.
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Rosdal, David. "Missilstyrning med Model Predictive Control." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2748.

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This thesis has been conducted at Saab Bofors Dynamics AB. The purpose was to investigate if a non-linear missile model could be stabilized when the optimal control signal is computed considering constraints on the control input. This is particularly interesting because the missile is controlled with rudders that have physical bounds. This strategy is called Model Predictive Control. Simulations are conducted to compare this strategy with others; firstly simulations with step responses and secondly simulations when the missile is supposed to hit a moving target. The latter is performed to show that the missile can be stabilized in its whole area of operation. The simulations show that the controller indeed can stabilize the missile for the given scenarios. However, this control strategy does not show any obvious improvements in comparison with alternative ones.

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27

Wiklander, Jonas. "Performance comparison of the Extended Kalman Filter and the Recursive Prediction Error Method." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1832.

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In several projects within ABB there is a need of state and parameter estimation for nonlinear dynamic systems. One example is a project investigating optimisation of gas turbine operation. In a gas turbine there are several parameters and states which are not measured, but are crucial for the performance. Such parameters are polytropic efficiencies in compressor and turbine stages, cooling mass flows, friction coefficients and temperatures. Different methods are being tested to solve this problem of system identification or parameter estimation. This thesis describes the implementation of such a method and compares it with previously implemented identification methods. The comparison is carried out in the context of parameter estimation in gas turbine models, a dynamic load model used in power systems as well as models of other dynamic systems. Both simulated and real plant measurements are used in the study.

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Tom, Adam Sean. "Prediction of FIR pre- and post-filter performance based upon a visual model." Thesis, Massachusetts Institute of Technology, 1986. http://hdl.handle.net/1721.1/15084.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1986.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING
Bibliography: leaves 143-145.
by Adam Sean Tom.
M.S.
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29

Hwang, Mitchell D. "Temperature prediction using thermal fluctuations from wireless sensor networks in adaptive filter model." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129903.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 97-98).
In many scientific experiments, it is imperative to minimize the unintended effects of variables other than the independent variables. Temperature, pressure, and gas levels are factors controlled to a certain extent using expensive climate-controlling technology, yet the resolution for monitoring their levels is generally low. The downward scaling of communication-enabled electronics in size, cost, and energy provides a potential toolset for tracking such data with high spatial and temporal resolutions. We establish a data collection methodology through a low-cost, small footprint distributed network system of modules that records data in a remote server. The system architecture allows for increased spatial resolutions, demonstrates high precision of measurements, and investigates room dynamics. Modules are fabricated using commercial sensors such as the ESP8266, BME680, and TCS34725. In this paper, we propose a temperature prediction model using adaptive filter methodologies to learn the relationship between thermal fluctuations at distinct locations within a lab environment.
by .Mitchell D. Hwang
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Fan, Zheyu Jerry. "Kalman Filter Based Approach : Real-time Control-based Human Motion Prediction in Teleoperation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189210.

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This work is to investigate the performance of two Kalman Filter Algorithms, namely Linear Kalman Filter and Extended Kalman Filter on control-based human motion prediction in a real-time teleoperation. The Kalman Filter Algorithm has been widely used in research areas of motion tracking and GPS-navigation. However, the potential of human motion prediction by utilizing this algorithm is rarely being mentioned. Combine with the known issue - the delay issue in today’s teleoperation services, the author decided to build a prototype of simple teleoperation model based on the Kalman Filter Algorithm with the aim of eliminated the unsynchronization between the user’s inputs and the visual frames, where all the data were transferred over the network. In the first part of the thesis, two types of Kalman Filter Algorithm are applied on the prototype to predict the movement of the robotic arm based on the user’s motion applied on a Haptic Device. The comparisons in performance among the Kalman Filters have also been focused. In the second part, the thesis focuses on optimizing the motion prediction which based on the results of Kalman filtering by using the smoothing algorithm. The last part of the thesis examines the limitation of the prototype, such as how much the delays are accepted and how fast the movement speed of the Phantom Haptic can be, to still be able to obtain reasonable predations with acceptable error rate.   The results show that the Extended Kalman Filter has achieved more advantages in motion prediction than the Linear Kalman Filter during the experiments. The unsynchronization issue has been effectively improved by applying the Kalman Filter Algorithm on both state and measurement models when the latency is set to below 200 milliseconds. The additional smoothing algorithm further increases the accuracy. More important, it also solves shaking issue on the visual frames on robotic arm which is caused by the wavy property of the Kalman Filter Algorithm. Furthermore, the optimization method effectively synchronizes the timing when robotic arm touches the interactable object in the prediction.   The method which is utilized in this research can be a good reference for the future researches in control-based human motion tracking and prediction.
Detta arbete fokuserar på att undersöka prestandan hos två Kalman Filter Algoritmer, nämligen Linear Kalman Filter och Extended Kalman Filter som används i realtids uppskattningar av kontrollbaserad mänsklig rörelse i teleoperationen. Dessa Kalman Filter Algoritmer har används i stor utsträckning forskningsområden i rörelsespårning och GPS-navigering. Emellertid är potentialen i uppskattning av mänsklig rörelse genom att utnyttja denna algoritm sällan nämnas. Genom att kombinera med det kända problemet – fördröjningsproblem i dagens teleoperation tjänster beslutar författaren att bygga en prototyp av en enkel teleoperation modell vilket är baserad på Kalman Filter algoritmen i syftet att eliminera icke-synkronisering mellan användarens inmatningssignaler och visuella information, där alla data överfördes via nätverket. I den första delen av avhandlingen appliceras både Kalman Filter Algoritmer på prototypen för att uppskatta rörelsen av robotarmen baserat på användarens rörelse som anbringas på en haptik enhet. Jämförelserna i prestandan bland de Kalman Filter Algoritmerna har också fokuserats. I den andra delen fokuserar avhandlingen på att optimera uppskattningar av rörelsen som baserat på resultaten av Kalman-filtrering med hjälp av en utjämningsalgoritm. Den sista delen av avhandlingen undersökes begräsning av prototypen, som till exempel hur mycket fördröjningar accepteras och hur snabbt den haptik enheten kan vara, för att kunna erhålla skäliga uppskattningar med acceptabel felfrekvens.   Resultaten visar att den Extended Kalman Filter har bättre prestandan i rörelse uppskattningarna än den Linear Kalman Filter under experimenten. Det icke-synkroniseringsproblemet har förbättrats genom att tillämpa de Kalman Filter Algoritmerna på både statliga och värderingsmodeller när latensen är inställd på under 200 millisekunder. Den extra utjämningsalgoritmen ökar ytterligare noggrannheten. Denna algoritm löser också det skakande problem hos de visuella bilder på robotarmen som orsakas av den vågiga egenskapen hos Kalman Filter Algoritmen. Dessutom effektivt synkroniserar den optimeringsmetoden tidpunkten när robotarmen berör objekten i uppskattningarna.   Den metod som används i denna forskning kan vara en god referens för framtida undersökningar i kontrollbaserad rörelse- spåning och uppskattning.
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31

SLAMANI, YOUCEF. "Etude comparative de differents modeles mathematiques pour la prediction des niveaux de pollution atmospherique." Caen, 1988. http://www.theses.fr/1988CAEN2017.

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Prediction a court terme des concentrations de quelques polluants atmospheriques en utilisant un ensemble de donnees experimentales. Deux types d'analyses sont envisagees : l'analyse univariable et l'analyse multivariable. Evaluation de la qualite des differents modeles. Les meilleurs resultats sont obtenus avec le modele arma
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32

Conte, Dean Edward. "Autonomous Robotic Escort Incorporating Motion Prediction with Human Intention." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102581.

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This thesis presents a framework for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses accurate path prediction incorporating human intention to locate the robot in front of the human while walking. Human intention is inferred by the head pose, an effective past-proven implicit indicator of intention, and fused with conventional physics-based motion prediction. The human trajectory is estimated and predicted using a particle filter because of the human's nonlinear and non-Gaussian behavior, and the robot control action is determined from the predicted human pose allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an omnidirectional mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate.
Master of Science
This thesis presents a method for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses human intention to predict the walk path allowing the robot to be in front of the human while walking. Human intention is inferred by the head direction, an effective past-proven indicator of intention, and is combined with conventional motion prediction. The robot motion is then determined from the predicted human position allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate. The unique escorting interaction method proposed has applications such as touch-less shopping cart robots, exercise companions, collaborative rescue robots, and sanitary transportation for hospitals.
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Ramachandran, Ravi P. "Pitch filtering in adaptive predictive coding of speech." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=65345.

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34

Castillo, Carlos L. "Fault-tolerant adaptive model predictive control using joint kalman filter for small-scale helicopter." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002711.

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35

Miller, Alan C. "The prediction of adhesion in filled polymeric composites /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/9885.

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36

Premanode, Bhusana. "Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23954.

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Volatility is a key parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving- Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression.
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Mannix, Michael G. "The prediction of edge raggedness via a single-channel filter model of the visual system /." Online version of thesis, 1987. http://hdl.handle.net/1850/9675.

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38

McGonigal, Denis. "A study on a Kalman filter and recursive parameter estimation approach applied to stock prediction." Thesis, University of Ottawa (Canada), 1996. http://hdl.handle.net/10393/10127.

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This thesis describes a first experimental project using a recursive parameter estimation and Kalman filter approach to on-line modelling and prediction of stock market time-series. On-line (real-time) and daily closing price stock data are identified as Box-Jenkins ARIMA models. Differencing is performed to obtain a locally wide sense stationary process which is identified through spectral estimation methods. The initial model parameters are updated on-line via the Recursive Prediction Error algorithm and predictions are performed using the Kalman filter. This approach is studied and compared to the traditional Box-Jenkins SISO approach. The daily stock processes are also modeled as autoregressive processes embedded in white noise, which make an ideal investigation for the Kalman filter.
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39

Fri, Johannes. "Path Prediction for a Night Vision System." Thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68895.

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In modern cars, advanced driver assistance systems are used to aid the driver and increase the automobile safety. An example of such a system is the night vision system designed to detect and warn for pedestrians in danger of being hit by the car. To determine if a warning should be given when a pedestrian is detected, the system requires a prediction of the future path of the car for up to four seconds ahead in time. In this master's thesis, a new path prediction algorithm based on satellite positioning and a digital map database has been developed. The algorithm uses an extended Kalman filter to get an accurate estimate of the current position and heading direction of the car. The estimate is then matched to a position in the map database and the possible future paths of the vehicle are predicted using the road network. The performance of the path prediction algorithm has been evaluated on recorded night vision sequences corresponding to 15 hours of driving. The results show that map-based path prediction algorithms are superior to dead-reckoning methods for longer time horizons. It has also been investigated whether vision-based lane detection and tracking can be used to improve the path prediction. A prediction method using lane markings has been implemented and evaluated on recorded sequences. Based on the results, the conclusion is that lane detection can be used to support a path prediction system when lane markings are clearly visible.
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40

Rönnqvist, Hans. "Predicting surfacing internal erosion in moraine core dams." Licentiate thesis, KTH, Hydraulic Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-14084.

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Dams that comprise broadly and widely graded glacial materials, such as moraines, have been found to be susceptible to internal erosion, perhaps more than dams of other soil types. Internal erosion washes out fine-grained particles from the filling material; the erosion occurs within the material itself or at an interface to another dam zone, depending on the mode of initiation. Whether or not internal erosion proceeds depend on the adequacy of the filter material. If internal erosion is allowed, it may manifest itself as sinkholes on the crest, increased leakage and muddy seepage once it surfaces, which here is called surfacing internal erosion (i.e. internal erosion in the excessive erosion or continuation phase). In spite of significant developments since the 1980s in the field of internal erosion assessment, the validity of methods developed by others on broadly graded materials are still less clear because most available criteria are based on tests of narrowly graded granular soils. This thesis specifically addresses dams that are composed of broadly graded glacial soils and investigates typical indicators, signs and behaviors of internal erosion prone dams. Based on a review of 90+ existing moraine core dams, which are located mainly in Scandinavia as well as North America and Australia/New Zealand, this thesis will show that not only the filter’s coarseness needs to be reviewed when assessing the potential for internal erosion to surface (i.e., erosion in the excessive or continuing phase); in addition, the grading stability of the filter and the core material as well as non-homogeneities that are caused by filter segregation need to be studied. Cross-referencing between these aspects improves the assessment of potential for internal erosion in dams of broadly graded soils and furthermore it provides aid-to-judgment.


QC 20100715
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41

Xia, Jingxin. "DYNAMIC FREEWAY TRAVEL TIME PREDICTION USING SINGLE LOOP DETECTOR AND INCIDENT DATA." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_diss/315.

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The accurate estimation of travel time is valuable for a variety of transportation applications such as freeway performance evaluation and real-time traveler information. Given the extensive availability of traffic data collected by intelligent transportation systems, a variety of travel time estimation methods have been developed. Despite limited success under light traffic conditions, traditional corridor travel time prediction methods have suffered various drawbacks. First, most of these methods are developed based on data generated by dual-loop detectors that contain average spot speeds. However, single-loop detectors (and other devices that emulate its operation) are the most commonly used devices in traffic monitoring systems. There has not been a reliable methodology for travel time prediction based on data generated by such devices due to the lack of speed measurements. Moreover, the majority of existing studies focus on travel time estimation. Secondly, the effect of traffic progression along the freeway has not been considered in the travel time prediction process. Moreover, the impact of incidents on travel time estimates has not been effectively accounted for in existing studies.The objective of this dissertation is to develop a methodology for dynamic travel time prediction based on continuous data generated by single-loop detectors (and similar devices) and incident reports generated by the traffic monitoring system. This method involves multiple-step-ahead prediction for flow rate and occupancy in real time. A seasonal autoregressive integrated moving average (SARIMA) model is developed with an embedded adaptive predictor. This predictor adjusts the prediction error based on traffic data that becomes available every five minutes at each station. The impact of incidents is evaluated based on estimates of incident duration and the queue incurred.Tests and comparative analyses show that this method is able to capture the real-time characteristics of the traffic and provide more accurate travel time estimates particularly when incidents occur. The sensitivities of the models to the variations of the flow and occupancy data are analyzed and future research has been identified.The potential of this methodology in dealing with less than perfect data sources has been demonstrated. This provides good opportunity for the wide application of the proposed method since single-loop type detectors are most extensively installed in various intelligent transportation system deployments.
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42

Das, Subhro. "Distributed Linear Filtering and Prediction of Time-varying Random Fields." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/765.

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We study distributed estimation of dynamic random fields observed by a sparsely connected network of agents/sensors. The sensors are inexpensive, low power, and they communicate locally and perform computation tasks. In the era of large-scale systems and big data, distributed estimators, yielding robust and reliable field estimates, are capable of significantly reducing the large computation and communication load required by centralized estimators, by running local parallel inference algorithms. The distributed estimators have applications in estimation, for example, of temperature, rainfall or wind-speed over a large geographical area; dynamic states of a power grid; location of a group of cooperating vehicles; or beliefs in social networks. The thesis develops distributed estimators where each sensor reconstructs the estimate of the entire field. Since the local estimators have direct access to only local innovations, local observations or a local state, the agents need a consensus-type step to construct locally an estimate of their global versions. This is akin to what we refer to as distributed dynamic averaging. Dynamic averaged quantities, which we call pseudo-quantities, are then used by the distributed local estimators to yield at each sensor an estimate of the whole field. Using terminology from the literature, we refer to the distributed estimators presented in this thesis as Consensus+Innovations-type Kalman filters. We propose three distinct types of distributed estimators according to the quantity that is dynamically averaged: (1) Pseudo-Innovations Kalman Filter (PIKF), (2) Distributed Information Kalman Filter (DIKF), and (3) Consensus+Innovations Kalman Filter (CIKF). The thesis proves that under minimal assumptions the distributed estimators, PIKF, DIKF and CIKF converge to unbiased and bounded mean-squared error (MSE) distributed estimates of the field. These distributed algorithms exhibit a Network Tracking Capacity (NTC) behavior – the MSE is bounded if the degree of instability of the field dynamics is below a threshold. We derive the threshold for each of the filters. The thesis establishes trade-offs between these three distributed estimators. The NTC of the PIKF depends on the network connectivity only, while the NTC of the DIKF and of the CIKF depend also on the observation models. On the other hand, when all the three estimators converge, numerical simulations show that the DIKF improves 2dB over the PIKF. Since the DIKF uses scalar gains, it is simpler to implement than the CIKF. Of the three estimators, the CIKF provides the best MSE performance using optimized gain matrices, yielding an improvement of 3dB over the DIKF. Keywords: Kalman filter, distributed state estimation, multi-agent networks, sensor networks, distributed algorithms, consensus, innovation, asymptotic convergence, mean-squared error, dynamic averaging, Riccati equation, Lyapunov iterations, distributed signal processing, random dynamical systems.
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43

Gill, M. E. "Surge prediction in multistage axial and centrifugal compressors." Thesis, Cranfield University, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370630.

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44

Pozo, Palma Paúl Marcelo [Verfasser]. "Finite set model predictive control of the PMSM with sine-wave filter / Paúl Marcelo Pozo Palma." Siegen : Universitätsbibliothek der Universität Siegen, 2016. http://d-nb.info/108124741X/34.

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Volpe, Kyle Clarke. "Application of the backward-smoothing extended Kalman filter to attitude estimation and prediction using radar observations." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/51646.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.
Includes bibliographical references (p. 317-322).
The Lincoln Attitude Estimation System (LAES), a new tool being developed for the Space Situational Awareness Group (SSAG) at MIT Lincoln Laboratory, integrates several existing hardware and software systems, with a backward-smoothing extended Kalman filter (BSEKF). LAES is intended to determine the rotational motion of a freely tumbling spacecraft from a sequence of discrete-time radar images. The raw range-Doppler returns are collected using a ground-based sensor, which is owned and operated by the SSAG, and processed into a set of range/cross-range images. A three-dimensional geometric model is, through computer graphics procedures, displayed on top of the two-dimensional radar images, enabling an analyst to rotate (and scale in cross-range) the model in order to align it to the object's image. Therefore, the orthographic projection matrix that the computer graphics procedures computed to display the computer model, simultaneously describes the projection of the object onto the radar image plane. These measurements are essentially corrections to a nominal or baseline motion which had to be assumed in order to generate the images in the first place. Combining the reference motion, which describes the orientation of the image plane in inertial space, with the sequence of rotations describing the attitude of the spacecraft within the image plane, yields the final set of attitude measurements which are then passed to the BSEKF for processing.
(cont.) The existing free motion software currently in use within the Space Situational Awareness Group makes two critical assumptions: 1) that that the spacecraft is a symmetric rigid body and 2) that there are no disturbance torques acting on the spacecraft during the imaging period. The Lincoln Attitude Estimation System removes these simplifying assumptions in favor of a more flexible approach which is better suited for long-term studies of rigid body motion. Accordingly, several additions have been made to the backward-smoothing extended Kalman filter, including the addition of environmental torque models and an algorithm which generates an initial estimate for the inertia tensor using the same geometric model used in the image-model matching process. The BSEKF solves a nonlinear smoothing problem for the current and past sample intervals using iterative numerical techniques. This approach retains the nonlinearities of a fixed number of stages that precede the time of interest, and processes information from earlier stages in an approximate manner. The algorithm has been tested using simulated and actual data from a challenging spacecraft attitude estimation problem in which there is significant measurement noise, poor initial state estimates, and highly nonlinear system dynamics. The filter compensates for this uncertainty through concurrent estimation of the attitude and moment of inertia parameters. The filter has been demonstrated to accurately and reliably converge on a motion solution in both types of test cases.
by Kyle Clarke Volpe.
S.M.
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46

Magnano, Alexander. "Predictive Mobile IP Handover for Vehicular Networks." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34350.

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Vehicular networks are an emerging technology that offer potential for providing a variety of new services. However, extending vehicular networks to include IP connections is still problematic, due in part to the incompatibility of mobile IP handovers with the increased mobility of vehicles. The handover process, consisting of discovery, registration, and packet forwarding, has a large overhead and disrupts connectivity. With increased handover frequency and smaller access point dwell times in vehicular networks, the handover causes a large degradation in performance. This thesis proposes a predictive handover solution, using a combination of a Kalman filter and an online hidden Markov model, to minimize the effects of prediction errors and to capitalize on advanced handover registration. Extensive simulated experiments were carried out in NS-2 to study the performance of the proposed solution within a variety of traffic and network topology scenarios. Results show a significant improvement to both prediction accuracy and network performance when compared to recent proposed approaches.
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Paim, Anderson de Campos. "Controle preditivo retroalimentado por estados estimados, aplicado a uma planta laboratorial." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/21258.

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A retroalimentação de controladores preditivos que utilizam modelos em espaço de estado pode ser realizada de duas formas: (a) correção por bias, em que as saídas preditas são corrigidas adicionando-se um valor proporcional a discrepância encontrada entre o valor medido atual e sua respectiva predição e por (b) retroalimentação dos estados, onde se determinam as condições iniciais através da estimação dos estados, e a partir de uma melhor condição inicial se realizam as predições futuras usadas no cálculo das ações de controle. Nesta dissertação estas duas abordagens são comparadas utilizando a Planta Laboratorial de Seis Tanques Esféricos. As técnicas de Filtro de Kalman Estendido (EKF) e Filtro de Kalman Estendido com Restrições (CEKF) foram empregadas para estimar os estados não medidos. Inicialmente foram feitos testes off-line destes algoritmos de estimação. Para estes testes são utilizados uma série de dados da planta laboratorial do estudo de caso, na qual são estudadas as influências de diversos fatores de ajuste que determinam a qualidade final de estimação. Estes ajustes serviram de base para a aplicação destes algoritmos em tempo real, quando então, estimadores de estados estão associados ao sistema de controle do processo baseado em um algoritmo de controle preditivo. Após se ter certificado a qualidade das estimações de estado, partiu-se para sua utilização como uma alternativa de retroalimentação de controladores preditivos. Estes resultados foram comparados com os obtidos através da correção simples por bias. Os resultados experimentais apontam para uma marginal piora devido à retroalimentação por estimadores de estados frente à correção por bias, pelo menos para o caso do controlador preditivo linear utilizado na comparação. Entretanto, espera-se que resultados melhores sejam obtidos no caso de modelos preditivos não-lineares, uma vez que nestes casos o modelo é bem mais sensível à qualidade da condição inicial.
The feedback of controllers that use predictive models in state space can be accomplished in two ways: (a) bias correction, where the predicted outputs are corrected by adding a value proportional to the discrepancy found between the current measurement and its respective prediction; and by (b) state feedback, which establishes the initial conditions through the states estimation, and from a better initial condition are carried out the future predictions used in the calculation of control. In this thesis these two approaches are compared using a Laboratorial Plant of Six Spherical Tanks. The techniques of Extended Kalman Filter (EKF) and Constraint Extended Kalman Filter (CEKF) were used to estimate the unmeasured states. Initially, tests were carried out off-line for theses estimation algorithms. For such testing are used a dataset of the plant in case study, in which are studied the influences of several adjustment factors that they determine the final quality of estimation. These adjustments were used of base for the application of these algorithms in real time, when then state estimators are associated with the system of process control based on a predictive control algorithm. After having ascertained the quality of the state estimates, begins its use as an alternative for feedback of predictive controllers. These results were compared with those obtained by the simple correction of bias. The experimental results show a marginal worsening due to feedback from state estimated compared with bias correction, at least for the case of linear predictive controller used in the comparison. However, one expects that better results will be obtained in the case of non-linear predictive models, since in these cases the model is much more sensitive to the quality of the initial condition.
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48

Nilson, Jonas. "Inter-Picture Prediction for Video Compression using Low Pass and High Pass Filters." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-331202.

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Most of the IP network traffic today consists of video content. In 2015 the videotraffic was estimated to be 70% of the Internet traffic and it is expected to rise past80% by 2020. The demand for high quality video, including high definition and ultrahigh definition, is steadily increasing with high resolution TV and computer monitorsbeing available to the general consumer. The video content streamed over theInternet have typically been compressed using a video encoder to reduce the bitsrequired to store and send the video. The increase of high resolution video contentalso requires updates to the video compression techniques. AVC is one of the mostcommonly used video compression standards, and its successor, HEVC, was able toachieve a 50% reduction of bitrate for the same perceived quality. Today there isongoing work to improve the compression efficiency of HEVC even further. Interpicture prediction, available in AVC and HEVC, is used to find redundant pixelsbetween adjacent frames in a video sequence and describe the movement of thepixels using motion vectors. This thesis focuses on exploring possible improvements to the inter prediction of thesuccessor to HEVC, currently under development, using low and high pass filteringwithin motion estimation and motion compensation to find potentially betterpredictions. The evaluation of the implemented filtering extension shows that themotion estimation and compensation filtering result can yield small benefits in somevideo sequences, with most of the video sequences in the test set resulting in smalllosses. There are still improvements to be made to the implementation, so there arepotentially more benefits to be gained by performing filtering within motionestimation and compensation.
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Yevseienko, Oleg, Anatoliy Gapon, and Dmytro Salnikov. "Searching for Optimal Control Parameters of Thermal Object Using Pulse-Width Modulation (PWM) Control with Predictive Filter." Thesis, Lviv Polytechnic Publishing House, 2015. http://repository.kpi.kharkov.ua/handle/KhPI-Press/41116.

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The thesis is devote to the temperature control of objects with lumped or distributed parameters. The problems of choosing the right value of regulator’s heater power and prediction period are discussed. The major attention is paid to the process of searching the minimum value of control quantities. It is shown that the approximated second-order plane has an exact accordance with the original data. It is concluded that algorithm of PWM-control with prediction filter provides good quality control.
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50

Boulanouar, Ibtissem. "Algorithmes de suivi de cible mobile pour les réseaux de capteurs sans fils." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1077/document.

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Les réseaux de capteurs sans fils se définissent comme un ensemble de petits appareils autonomes et interconnectés. Ces capteurs sont déployés dans une zone d'intérêt dans le but de collecter des informations de l'environnement comme la température ou la qualité de l'air, suivant l'application envisagée. L'évolution de ces dispositifs de capture vers le multimédia ouvre l'accès à une plus large palette d'applications et de services pour une meilleure maitrise de notre environnement. Dans cette thèse nous nous intéressons au suivi de cible mobile dans les réseaux de capteurs sans fils, certains de ces capteurs pouvant collecter des images. Le suivi de cible (Tracking) consiste à détecter et à localiser sur l'ensemble de sa trajectoire une cible traversant une zone d'intérêt. Cette application peut s'avérer très utile, par exemple, pour détecter et enregistrer les déplacements d'un intrus dans une zone sensible ou encore pour suivre les déplacements d'une personne assistée et munie d'un appareil avec interface radio. Contrairement aux systèmes de surveillance classiques qui nécessitent une infrastructure fixe, les réseaux de capteurs sans fils sont aussi faciles à installer qu'à désinstaller. De plus, grâce à leur polyvalence, ils peuvent être utilisés dans de nombreux environnements hostiles et inaccessibles pour l'être humain. Toutefois, étant restreints en énergie, ils ne peuvent rester actifs en permanence au risque de limiter considérablement leur durée de vie. Afin de résoudre ce problème, l'idée est d'activer uniquement les capteurs qui sont sur la trajectoire de la cible au moment ou cette dernière est à leur portée radio ou visuelle. La question est donc : comment et sur quels critères activer ces capteurs afin d'obtenir à tout moment le meilleur compromis entre la précision du suivi et la préservation des ressources énergétiques ? C'est à cette question que nous essayerons de répondre tout au long de cette thèse. Dans un premier temps nous nous intéressons aux cibles communicantes qui ont la faculté d'émettre des signaux et donc de faciliter grandement le processus de suivi. Le défi ici est de relayer l'information entre les différents capteurs concernés. Nous utilisons pour cela un algorithme de déploiement basé sur le concept de forces virtuelles (VFA : Virtual Forces Algorithm) associé à un algorithme de suivi collaboratif et distribué implémenté sur un réseau organisé en clusters. Ensuite, nous traitons le cas, plus complexe et plus fréquent, des cibles non communicantes. L'objectif est de détecter la présence de la cible uniquement à l'aide de capteurs de présence. Pour cela nous proposons le déploiement d'un réseau de capteurs sans fil hétérogènes composé decapteurs de mouvement en charge de la partie détection de la cible et de capteurs vidéo en charge de la partie localisation. Lorsqu'une cible est détectée par un capteur de mouvement, l'information est communiquée aux capteurs vidéo voisins qui décident d'activer ou non leurs caméras en se basant sur des critères prédéfinis tenant compte de l'angle d'orientation des caméras. Enfin, dans une dernière contribution nous nous intéressons plus spécifique mentaux modèles de mobilité de la cible. Ces modèles nous permettent d'anticiper ses déplacements et d'affiner le processus d'activation des capteurs qui sont sur sa trajectoire. Nous utilisons pour cela le filtre de Kalman étendu combiné à un mécanisme de détection de changements de direction nommé CuSum (Cumulative Summuray). Ce mécanisme nous permet de calculer efficacement les futures coordonnées de la cible et de réveiller les capteurs en conséquence
Wireless Sensor Networks (WSN) are a set of tiny autonomous and interconnected devices. These Sensors are scattered in a region of interest to collect information about the surrounding environment depending on the intended application. Nowadays, sensors allow handling more complex data such as multimedia flow. Thus, we observe the emergence of Wireless Multimedia Sensor Networks opening a wider range of applications. In this work, we focus on tracking moving target in these kinds of networks. Target tracking is defined as a two-stage application: detection and localization of the target through its evolution inside an area of interest. This application can be very useful. For example, the presence of an intruder can be detected and its position inside a sensitive area reported, elderly or sick persons carrying sensors can be tracked anytime and so on. Unlike classical monitoring systems, WSN are more flexible and more easy to set up. Moreover, due to their versatility and autonomy they can be used in hostile regions, inaccessible for human. However, these kinds of networks have some limitations: wireless links are not reliable and data processing and transmission are greedy processes in term of energy. To overcome the energy constraint, only the sensors located in target pathway should be activated. Thus, the question is : how to select these sensors to obtain the best compromise between the tracking precision and the energy consumption? This is the question we are trying to answer in this dissertation. Firstly, we focus on communicating targets which have the ability to transmit signals and greatly facilitate the tracking process. The challenge here is to relay the information between the concerned sensors. In order to deal with this challenge, we use a deployment strategy based on virtual forces (VFA: Virtual Forces Algorithm) associated to a distributed tracking algorithm implemented in a cluster-based network. Secondly, we handle a more complex and more frequent case of non-communicating targets. The objective is to detect the presence of such target using movement sensors. We propose the deployment of an heterogeneous wireless sensor networks composed of movement sensors used to detect the target and camera sensors used to locate it. When the target is detected the information is sent to the camera sensors which decide whether to activate or not their cameras based on probabilistic criteria which include the camera orientation angle. Finally, as our last contribution, we specifically focus on target mobility models. These models help us to predict target behaviour and refine the sensor activation process. We use the Extended Kalamn filter as prediction model combined with a change detection mechanism named CuSum (Cumulative Summuray). This mechanism allows to efficiently compute the future target coordinates, and to select which sensors to activate
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