Relevant bibliographies by topics / Interactive Multiple Model algorithm

Contents

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

Academic literature on the topic 'Interactive Multiple Model algorithm'

Author: Grafiati
Published: 28 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Interactive Multiple Model algorithm"

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Liu, Qiaoran, and Xun Yang. "Improved Interacting Multiple Model Particle Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (February 2018): 169–75. http://dx.doi.org/10.1051/jnwpu/20183610169.

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For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.
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Lim, Jaechan, Hun-Seok Kim, and Hyung-Min Park. "Interactive-Multiple-Model Algorithm Based on Minimax Particle Filtering." IEEE Signal Processing Letters 27 (2020): 36–40. http://dx.doi.org/10.1109/lsp.2019.2954000.

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Zhu, Meiyu, Guanyu Chen, and Weicun Zhang. "Maneuvering target tracking with improved interactive multiple model algorithm." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 373–76. http://dx.doi.org/10.5954/icarob.2021.os6-5.

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Li, Jihan, Xiaoli Li, and Kang Wang. "Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach." Advances in Meteorology 2019 (October 15, 2019): 1–11. http://dx.doi.org/10.1155/2019/1279565.

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Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5is the main particulate matter in air pollution. Therefore, how to predict PM2.5accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5prediction, and it is effective.
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Funkhouser, Thomas, Seth Teller, Carlo Séquin, and Delnaz Khorramabadi. "The UC Berkeley System for Interactive Visualization of Large Architectural Models." Presence: Teleoperators and Virtual Environments 5, no. 1 (January 1996): 13–44. http://dx.doi.org/10.1162/pres.1996.5.1.13.

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Realistic-looking architectural models with furniture may consist of millions of polygons and require gigabytes of data—far than today's workstations can render at interactive frame rates or store in physical memory. We have developed data structures and algorithms for identifying a small portion of a large model to load into memory and render during each frame of an interactive walkthrough. Our algorithms rely upon an efficient display database that represents a building model as a set of objects, each of which can be described at multiple levels of detail, and contains an index of spatial cells with precomputed cell-to-cell and cell-to-object visibility information. As the observer moves through the model interactively, a real-time visibility algorithm traces sightline beams through transparent cell boundaries to determine a small set of objects potentially visible to the observer. An optimization algorithm dynamically selects a level of detail and rendering algorithm with which to display each potentially visible object to meet a userspecified target frame time. Throughout, memory management algorithms predict observer motion and prefetch objects from disk that may become visible during imminent frames. This paper describes an interactive building walkthrough system that uses these data structures and algorithms to maintain interactive frame rates during visualization of very large models. So far, the implementation supports models whose major occluding surfaces are axis-aligned rectangles (e.g., typical buildings). This system is able to maintain over twenty frames per second with little noticeable detail elision during interactive walkthroughs of a building model containing over one million polygons.
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Wan, Jian, Peiwen Ren, and Qiang Guo. "Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking." Symmetry 11, no. 6 (June 5, 2019): 767. http://dx.doi.org/10.3390/sym11060767.

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Aiming at the shortcomings of low precision, hysteresis, and poor robustness of the general interactive multimodel algorithm in the “snake-like” maneuver tracking of anti-ship missiles, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic (FLIMM5ACKF) is proposed. The algorithm mainly includes adaptive five-degree cubature Kalman algorithm (A5CKF) and fuzzy logic algorithm (FL). A5CKF uses the Sage–Husa noise estimation principle to propose a state error covariance adaptive five-degree cubature Kalman algorithm to improve the performance of state estimation. Then, the fuzzy logic algorithm (FL) is added to the model probability update module to control the model probability update module. Finally, by setting the same tracking model simulation analysis, the algorithm has better convergence speed, tracking effect and robustness than the interactive multimodel cubature Kalman algorithm (IMMCKF), the interactive multimodel five-degree cubature Kalman algorithm (IMM5CKF) and the interactive multimodel adaptive five-degree cubature Kalman (IMMA5CKF).
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Liu, J., and R. Li. "Hierarchical adaptive interacting multiple model algorithm." IET Control Theory & Applications 2, no. 6 (June 1, 2008): 479–87. http://dx.doi.org/10.1049/iet-cta:20070340.

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Qu, HongQuan, LiPing Pang, and ShaoHong Li. "A novel interacting multiple model algorithm." Signal Processing 89, no. 11 (November 2009): 2171–77. http://dx.doi.org/10.1016/j.sigpro.2009.04.033.

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Shen, Nan, Liang Chen, Xiangchen Lu, Hao Hu, Yuanjin Pan, Zhouzheng Gao, Xiaoyan Liu, Zhaoliang Liu, and Ruizhi Chen. "Online displacement extraction and vibration detection based on interactive multiple model algorithm." Mechanical Systems and Signal Processing 155 (June 2021): 107581. http://dx.doi.org/10.1016/j.ymssp.2020.107581.

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Pan, Quan, Yan Liang, Gang Liu, Hongcai Zhang, and Guanzhong Dai. "Performance analysis of interacting multiple model algorithm." IFAC Proceedings Volumes 32, no. 2 (July 1999): 3932–37. http://dx.doi.org/10.1016/s1474-6670(17)56671-9.

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Dissertations / Theses on the topic "Interactive Multiple Model algorithm"

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Munir, Arshed. "Manoeuvring target tracking using different forms of the interacting multiple model algorithm." Thesis, University of Sussex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240430.

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Alat, Gokcen. "A Variable Structure - Autonomous - Interacting Multiple Model Ground Target Tracking Algorithm In Dense Clutter." Phd thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615512/index.pdf.

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Tracking of a single ground target using GMTI radar detections is considered. A Variable Structure- Autonomous- Interactive Multiple Model (VS-A-IMM) structure is developed to address challenges of ground target tracking, while maintaining an acceptable level computational complexity at the same time. The following approach is used in this thesis: Use simple tracker structures
incorporate a priori information such as topographic constraints, road maps as much as possible
use enhanced gating techniques to minimize the eect of clutter
develop methods against stop-move motion and hide motion of the target
tackle on-road/o-road transitions and junction crossings
establish measures against non-detections caused by environment. The tracker structure is derived using a composite state estimation set-up that incorporate multi models and MAP and MMSE estimations. The root mean square position and velocity error performances of the VS-A-IMM algorithm are compared with respect to the baseline IMM and the VS-IMM methods found in the literature. It is observed that the newly developed VS-A-IMM algorithm performs better than the baseline methods in realistic conditions such as on-road/o-road transitions, tunnels, stops, junction crossings, non-detections.
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Benko, Matej. "Hledaní modelů pohybu a jejich parametrů pro identifikaci trajektorie cílů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445467.

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Táto práca sa zaoberá odstraňovaním šumu, ktorý vzniká z tzv. multilateračných meraní leteckých cieľov. Na tento účel bude využitá najmä teória Bayesovských odhadov. Odvodí sa aposteriórna hustota skutočnej (presnej) polohy lietadla. Spolu s polohou (alebo aj rýchlosťou) lietadla bude odhadovaná tiež geometria trajektórie lietadla, ktorú lietadlo v aktuálnom čase sleduje a tzv. procesný šum, ktorý charakterizuje ako moc sa skutočná trajektória môže od tejto líšiť. Odhad spomínaného procesného šumu je najdôležitejšou časťou tejto práce. Je odvodený prístup maximálnej vierohodnosti a Bayesovský prístup a ďalšie rôzne vylepšenia a úpravy týchto prístupov. Tie zlepšujú odhad pri napr. zmene manévru cieľa alebo riešia problém počiatočnej nepresnosti odhadu maximálnej vierohodnosti. Na záver je ukázaná možnosť kombinácie prístupov, t.j. odhad spolu aj geometrie aj procesného šumu.
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Ege, Emre. "A Comparative Study Of Tracking Algorithms In Underwater Environment Using Sonar Simulation." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608866/index.pdf.

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Target tracking is one the most fundamental elements of a radar system. The aim of target tracking is the reliable estimation of a target'
s true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, because of the significant differences in the underwater propagation environment and the associated differences in the corresponding sensors, the successful use of similar principles and techniques in an underwater scenario is still an active topic of research. A comparative study of the effects of the underwater environment on a number of tracking algorithms is the focus of the present thesis. The tracking algorithms inspected are: the Kalman Filter, the Extended Kalman Filter and the Particle Filter. We also investigate in particular the IMM extension to KF and EKF filters. These algorithms are tested under several underwater environment scenarios.
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Rastgoufard, Rastin. "The Interacting Multiple Models Algorithm with State-Dependent Value Assignment." ScholarWorks@UNO, 2012. http://scholarworks.uno.edu/td/1477.

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The value of a state is a measure of its worth, so that, for example, waypoints have high value and regions inside of obstacles have very small value. We propose two methods of incorporating world information as state-dependent modifications to the interacting multiple models (IMM) algorithm, and then we use a game's player-controlled trajectories as ground truths to compare the normal IMM algorithm to versions with our proposed modifications. The two methods involve modifying the model probabilities in the update step and modifying the transition probability matrix in the mixing step based on the assigned values of different target states. The state-dependent value assignment modifications are shown experimentally to perform better than the normal IMM algorithm in both estimating the target's current state and predicting the target's next state.
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Sahin, Mehmet Alper. "Performance Optimization Of Monopulse Tracking Radar." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605364/index.pdf.

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An analysis and simulation tool is developed for optimizing system parameters of the monopulse target tracking radar and observing effects of the system parameters on the performance of the system over different scenarios. A monopulse tracking radar is modeled for measuring the performance of the radar with given parameters, during the thesis studies. The radar model simulates the operation of a Class IA type monopulse automatic tracking radar, which uses a planar phased array. The interacting multiple model (IMM) estimator with the Probabilistic Data Association (PDA) technique is used as the tracking filter. In addition to modeling of the tracking radar model, an optimization tool is developed to optimize system parameters of this tracking radar model. The optimization tool implements a Genetic Algorithm (GA) belonging to a GA Toolbox distributed by Department of Automatic Control and System Engineering at University of Sheffield. The thesis presents optimization results over some given optimization scenarios and concludes on effect of tracking filter parameters, beamwidth and dwell interval for the confirmed track.
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Aslan, Murat Samil. "Tracker-aware Detection: A Theoretical And An Experimental Study." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610474/index.pdf.

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A promising line of research attempts to bridge the gap between detector and tracker by means of considering jointly optimal parameter settings for both of these subsystems. Along this fruitful path, this thesis study focuses on the problem of detection threshold optimization in a tracker-aware manner so that a feedback from the tracker to the detector is established to maximize the overall system performance. Special emphasis is given to the optimization schemes based on two non-simulation performance prediction (NSPP) methodologies for the probabilistic data association filter (PDAF), namely, the modified Riccati equation (MRE) and the hybrid conditional averaging (HYCA) algorithm. The possible improvements are presented in two domains: Non-maneuvering and maneuvering target tracking. In the first domain, a number of algorithmic and experimental evaluation gaps are identified and newly proposed methods are compared with the existing ones in a unified theoretical and experimental framework. Furthermore, for the MRE based dynamic threshold optimization problem, a closed-form solution is proposed. This solution brings a theoretical lower bound on the operating signal-to-noise ratio (SNR) concerning when the tracking system should be switched to the track before detect (TBD) mode. As the improvements of the second domain, some of the ideas used in the first domain are extended to the maneuvering target tracking case. The primary contribution is made by extending the dynamic optimization schemes applicable to the PDAF to the interacting multiple model probabilistic data association filter (IMM-PDAF). Resulting in an online feedback from the filter to the detector, this extension makes the tracking system robust against track losses under low SNR values.
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Canolla, Adriano. "Interactive Multiple Model Estimation for Unmanned Aircraft Systems Detect and Avoid." Thesis, Illinois Institute of Technology, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13419136.

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This research presents new methods to apply safety standards to Detect and Avoid (DAA) functions for Unmanned Aircraft Systems (UAS), using maneuvering target tracking and encounter models.

Previous DAA research methods focused on predefined, linear encounter generation. The new estimation and prediction methods in this research are based on the target tracking of maneuvering intruders using Multiple Model Adaptive Estimation and a realistic random encounter generation based on an established encounter model.

When tracking maneuvering intruders there is limited knowledge of changes in intruder behavior beyond the current measurement. The standard Kalman filter (KF) with a single motion model is limited in performance for such problems due to ineffective responses as the target maneuvers. In these cases, state estimation can be improved using MMAE. It is assumed that the current active dynamic model is one of a discrete set of models, each of which is the basis for a separate filter. These filters run in parallel to estimate the states of targets with changing dynamics.

In practical applications of multiple model systems, one of the most popular algorithms for the MMAE is the Interacting Multiple Model (IMM) estimator. In the IMM, the regime switching is modeled by a finite state homogeneous Markov Chain. This is represented by a transition probability matrix characterizing the mode transitions. A Markov Chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the previous event.

This research uses the hazard states estimates (which are derived from DAA standards) to analyze the IMM performance, and then presents a new method to predict the hazard states. To reduce the prediction error, this new method accounts for maneuvering intruders. The new prediction method uses the prediction phase in the IMM algorithm to predict the future intruder aircraft states based on the current and past sensor measurements.

The estimation and prediction methods described in this thesis can help ensure safe encounters between UAS and manned aircraft in the National Airspace System through improvement of the trajectory estimation used to inform the DAA sensor certification process.

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Vince, Robert Johnston. "An electromagnetic radome model using an interactive micro-computer finite element algorithm." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25893.

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Caglar, Musa. "Multiple Criteria Project Selection Problems." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610945/index.pdf.

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In this study, we propose two biobjective mathematical models based on PROMETHEE V method for project selection problems. We develop an interactive approach (ib-PROMETHEE V) including data mining techniques to solve the first proposed mathematical model. For the second model, we propose NSGA-II with constraint handling method. We also develop a Preference Based Interactive Multiobjective Genetic Algorithm (IMGA) to solve the second proposed mathematical model. We test the performance of NSGA-II with constraint handling method and IMGA on randomly generated test problems.
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Books on the topic "Interactive Multiple Model algorithm"

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Julien, Benoît. A fuzzy interactive screening model for multiple attribute decision-making. Ottawa: National Library of Canada, 1990.

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Vince, Robert Johnston. An electromagnetic radome model using an interactive micro-computer finite element algorithm. Monterey, Calif: Naval Postgraduate School, 1989.

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Lincoln, Patrick. A formally verified algorithm for interactive consistency under a hybrid fault model. Hampton, Va: Langley Research Center, 1993.

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A Novel Electrocardiogram Segmentation Algorithm Using a Multiple Model Adaptive Estimator. Storming Media, 2002.

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Arnold, Robert M., Anthony L. Back, Walter F. Baile, Kelly A. Edwards, and James A. Tulsky. The Oncotalk/Vitaltalk model. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198736134.003.0056.

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Clinicians can, with training, improve their communication skills. In this chapter, we describe an interactive, evidence-based method for teaching clinicians to communicate with seriously ill patients. The programme, Vitaltalk, emphasizes small-group teaching with simulated patients and immediate feedback to allow learners to practice how to give serious news, talk about goals of care, and about what is most important to dying patients. This chapter describes common evidence-based principles used in developing an advanced communication skills programme based on Oncotalk experiences, identifies unique aspects of the learning context within an intensive retreat structure, and illustrates the lessons learned that can be tested in other settings. The programme is effective in improving learners’ communication skills in clinical studies. The growth of this programme in multiple specialties is discussed, as are our plans for disseminating the programme in the future.
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Book chapters on the topic "Interactive Multiple Model algorithm"

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Zhang, Yang, and Yuncai Liu. "Traffic Forecasts Using Interacting Multiple Model Algorithm." In Next-Generation Applied Intelligence, 360–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02568-6_37.

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Wei, Wenqiang, Jidong Suo, and Xiaoming Liu. "An Improved Interacting Multiple Model Algorithm Based on Switching of AR Model Set." In Lecture Notes in Electrical Engineering, 459–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6504-1_56.

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Hussain, D. M. Akbar, Shaiq A. Haq, M. Zafar Ullah Khan, and Zaki Ahmed. "Case Study: Investigating the Performance of Interactive Multiple Motion Model Algorithm for a Crossing Target Scenario." In Wireless Networks, Information Processing and Systems, 332–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89853-5_36.

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Song, Liping, and Hongbing Ji. "Least Squares Interacting Multiple Model Algorithm for Passive Multi-sensor Maneuvering Target Tracking." In Lecture Notes in Computer Science, 479–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_66.

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Amoozgar, Mohammad Hadi, Abbas Chamseddine, and Youmin M. Zhang. "Experimental Test of an Interacting Multiple Model Filtering Algorithm for Actuator Fault Detection and Diagnosis of an Unmanned Quadrotor Helicopter." In Intelligent Robotics and Applications, 473–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33509-9_47.

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Ghannem, Adnane, Ghizlane El Boussaidi, and Marouane Kessentini. "Model Refactoring Using Interactive Genetic Algorithm." In Search Based Software Engineering, 96–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39742-4_9.

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Narula, Subhash C., Leonid Kirilov, and Vassil Vassilev. "An Interactive Algorithm for Solving Multiple Objective Nonlinear Programming Problems." In Multiple Criteria Decision Making, 119–27. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2666-6_13.

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Kang, Hyun-Soo. "A Fast Successive Elimination Algorithm for Multiple Reference Images." In Interactive Multimedia and Next Generation Networks, 187–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30493-7_17.

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Durso, Anthony. "An Interactive Combined Branch-and-Bound/Tchebycheff Algorithm for Multiple Criteria Optimization." In Multiple Criteria Decision Making, 107–22. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2918-6_9.

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Pan, Zhaoqing, Sam Kwong, and Yun Zhang. "A Multiple Hexagon Search Algorithm for Motion and Disparity Estimation in Multiview Video Coding." In The Era of Interactive Media, 113–22. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3501-3_10.

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Conference papers on the topic "Interactive Multiple Model algorithm"

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Wang, Weijie, Shengli Wang, and Zhi Zhang. "Improved Adaptive Grid Interactive Multiple Model Algorithm based on Maneuverability Division." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9391035.

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Liu, Jianshu, and Renhou Li. "A Hierarchical Adaptive Interacting Multiple Model Algorithm." In 2007 IEEE International Symposium on Signal Processing and Information Technology. IEEE, 2007. http://dx.doi.org/10.1109/isspit.2007.4458122.

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Ji, Bing, Gan-lin Shan, and Hai Chen. "A yaw-aided interacting multiple model tracking algorithm." In 2012 5th International Congress on Image and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/cisp.2012.6469912.

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Chze Eng Seah and Inseok Hwang. "Stability analysis of the Interacting Multiple Model algorithm." In 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4586853.

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He Yan, Guo Zhi-jiang, and Jiang Jing-ping. "Design of the adaptive interacting multiple model algorithm." In Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1023240.

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Li, X. Rong, and Yaakov Bar-Shalom. "Performance Prediction of the Interacting Multiple Model Algorithm." In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792500.

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Watson, Gregory A., and W. Dale Blair. "Tracking maneuvering targets with multiple sensors using the interacting multiple model algorithm." In Optical Engineering and Photonics in Aerospace Sensing, edited by Oliver E. Drummond. SPIE, 1993. http://dx.doi.org/10.1117/12.157781.

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Wang, Shuai, Xiaocun Guan, Denghua Guo, and Shaohua Guan. "Position Detection Method of Linear Motor Based on Interactive Multiple Model Cubature Kalman Filter Algorithm." In 2019 22nd International Conference on Electrical Machines and Systems (ICEMS). IEEE, 2019. http://dx.doi.org/10.1109/icems.2019.8922386.

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Tianlai, Xu, and Cui Pingyuan. "Fuzzy Adaptive Interacting Multiple Model Algorithm for INS/GPS." In 2007 International Conference on Mechatronics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icma.2007.4304031.

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Zhang Yuan, Dong Shou-quan, Yang Xing-bao, Liu Shu-bo, and Chu Jun-bo. "Interacting multiple model algorithm based on S-amended UKF." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053351.

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Reports on the topic "Interactive Multiple Model algorithm"

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Chapman, Ray, Phu Luong, Sung-Chan Kim, and Earl Hayter. Development of three-dimensional wetting and drying algorithm for the Geophysical Scale Transport Multi-Block Hydrodynamic Sediment and Water Quality Transport Modeling System (GSMB). Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41085.

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The Environmental Laboratory (EL) and the Coastal and Hydraulics Laboratory (CHL) have jointly completed a number of large-scale hydrodynamic, sediment and water quality transport studies. EL and CHL have successfully executed these studies utilizing the Geophysical Scale Transport Modeling System (GSMB). The model framework of GSMB is composed of multiple process models as shown in Figure 1. Figure 1 shows that the United States Army Corps of Engineers (USACE) accepted wave, hydrodynamic, sediment and water quality transport models are directly and indirectly linked within the GSMB framework. The components of GSMB are the two-dimensional (2D) deep-water wave action model (WAM) (Komen et al. 1994, Jensen et al. 2012), data from meteorological model (MET) (e.g., Saha et al. 2010 - http://journals.ametsoc.org/doi/pdf/10.1175/2010BAMS3001.1), shallow water wave models (STWAVE) (Smith et al. 1999), Coastal Modeling System wave (CMS-WAVE) (Lin et al. 2008), the large-scale, unstructured two-dimensional Advanced Circulation (2D ADCIRC) hydrodynamic model (http://www.adcirc.org), and the regional scale models, Curvilinear Hydrodynamics in three dimensions-Multi-Block (CH3D-MB) (Luong and Chapman 2009), which is the multi-block (MB) version of Curvilinear Hydrodynamics in three-dimensions-Waterways Experiments Station (CH3D-WES) (Chapman et al. 1996, Chapman et al. 2009), MB CH3D-SEDZLJ sediment transport model (Hayter et al. 2012), and CE-QUAL Management - ICM water quality model (Bunch et al. 2003, Cerco and Cole 1994). Task 1 of the DOER project, “Modeling Transport in Wetting/Drying and Vegetated Regions,” is to implement and test three-dimensional (3D) wetting and drying (W/D) within GSMB. This technical note describes the methods and results of Task 1. The original W/D routines were restricted to a single vertical layer or depth-averaged simulations. In order to retain the required 3D or multi-layer capability of MB-CH3D, a multi-block version with variable block layers was developed (Chapman and Luong 2009). This approach requires a combination of grid decomposition, MB, and Message Passing Interface (MPI) communication (Snir et al. 1998). The MB single layer W/D has demonstrated itself as an effective tool in hyper-tide environments, such as Cook Inlet, Alaska (Hayter et al. 2012). The code modifications, implementation, and testing of a fully 3D W/D are described in the following sections of this technical note.
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2

Amela, R., R. Badia, S. Böhm, R. Tosi, C. Soriano, and R. Rossi. D4.2 Profiling report of the partner’s tools, complete with performance suggestions. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.023.

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This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks were dened in collaboration with WP5. The rst benchmark is an embarrassingly parallel benchmark that performs a read and then multiple writes of the same object, with the objective of stressing the memory and storage systems and evaluate the overhead when these reads and writes are performed in parallel. A second benchmark is dened based on the Continuation Multi Level Monte Carlo (C-MLMC) algorithm. While this algorithm is normally executed using multiple levels, for the proling and performance analysis objectives, the execution of a single level was enough since the forthcoming levels have similar performance characteristics. Additionally, while the simulation tasks can be executed as parallel (multi-threaded tasks), in the benchmark, single threaded tasks were executed to increase the number of simulations to be scheduled and stress the scheduling engines. A set of experiments based on these two benchmarks have been executed in the MareNostrum 4 supercomputer and using PyCOMPSs as underlying programming model and dynamic scheduler of the tasks involved in the executions. While the rst benchmark was executed several times in a single iteration, the second benchmark was executed in an iterative manner, with cycles of 1) Execution and trace generation; 2) Performance analysis; 3) Improvements. This had enabled to perform several improvements in the benchmark and in the scheduler of PyCOMPSs. The initial iterations focused on the C-MLMC structure itself, performing re-factors of the code to remove ne grain and sequential tasks and merging them in larger granularity tasks. The next iterations focused on improving the PyCOMPSs scheduler, removing existent bottlenecks and increasing its performance by making the scheduler a multithreaded engine. While the results can still be improved, we are satised with the results since the granularity of the simulations run in this evaluation step are much ner than the one that will be used for the real scenarios. The deliverable nishes with some recommendations that should be followed along the project in order to obtain good performance in the execution of the project codes.
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3

Over, Thomas, Riki Saito, Andrea Veilleux, Padraic O’Shea, Jennifer Sharpe, David Soong, and Audrey Ishii. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, June 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.
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