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Relevant bibliographies by topics / GENETIC PARTICLE FILTER

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Academic literature on the topic 'GENETIC PARTICLE FILTER'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "GENETIC PARTICLE FILTER"

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Wang, Er Shen, Tao Pang, and Zhi Xian Zhang. "Accuracy Improvement of GPS Positioning Based on GA-Aided Particle Filter." Applied Mechanics and Materials 719-720 (January 2015): 737–43. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.737.

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Aiming at the weight degeneracy phenomena in particle filter algorithm, a resampling method improving the diversity based on GA-aided particle filter was presented. Taking the advantage of genetic algorithm ( GA ) in selection ,crossover and inheritance to make up for the shortcoming of resampling. Genetic operation on particles in real number domain is adapted to reduce the complex of the genetic algorithm. And the evolutionary idea of genetic algorithm was combined with particle filter, by using selection, and mutation to improve the weight degeneracy and diversity of particle filter. This G

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Liu, Zhigang, Jin Shang, and Xufen Hua. "Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter." Wireless Communications and Mobile Computing 2020 (June 20, 2020): 1–9. http://dx.doi.org/10.1155/2020/8865298.

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In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method o

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You, Yuanhong, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu. "A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates." Hydrology and Earth System Sciences 27, no. 15 (2023): 2919–33. http://dx.doi.org/10.5194/hess-27-2919-2023.

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Abstract. Accurate snowpack simulations are critical for regional hydrological predictions, snow avalanche prevention, water resource management, and agricultural production, particularly during the snow ablation period. Data assimilation methodologies are increasingly being applied for operational purposes to reduce the uncertainty in snowpack simulations and to enhance their predictive capabilities. This study aims to investigate the feasibility of using a genetic particle filter (GPF) as a snow data assimilation scheme designed to assimilate ground-based snow depth (SD) measurements across

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Li, Tao, and Qi Yuan Sun. "A Visual Tracking Based on Particle Filter of Multi-Algorithm Fusion." Applied Mechanics and Materials 513-517 (February 2014): 2893–96. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2893.

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A novel visual tracking algorithm based on particle filter with multi-algorithm fusion is proposed. Mean shift is employed to make particles distribute more reasonably in order to maintain tracking accuracy by using fewer particles, and the genetic evolution ideas is introduced to increase the diversity of samples by applying selection, crossover and mutation operator to achieve particles resampling. The experiments show that the tracking performance of the proposed method, compared with Mean Shift Embedded Particle Filter (MSEPF), is significantly improved.

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Huo, Lina. "Intelligent Recognition Method of Vehicle Path with Time Window Based on Genetic Algorithm." Security and Communication Networks 2021 (August 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/3614291.

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Based on particle filter and improved cuckoo genetic algorithm, an algorithm for intelligent vehicle path recognition with a time window is designed. Particle filter (PF) is an influential visual tracking tool; it relies on the Monte Carlo Chain framework and Bayesian probability, which are essential for intelligent monitoring systems. The algorithm first uses particle filters for visual tracking and then obtains the current operating environment of the vehicle, then performs cluster analysis on customer locations, and finally performs path recognition in each area. The algorithm not only intr

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Yang, Jin, Xuerong Cui, Juan Li, Shibao Li, Jianhang Liu, and Haihua Chen. "Particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization." Procedia Computer Science 187 (2021): 206–11. http://dx.doi.org/10.1016/j.procs.2021.04.052.

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Mechri, Rihab, Catherine Ottlé, Olivier Pannekoucke, and Abdelaziz Kallel. "Genetic particle filter application to land surface temperature downscaling." Journal of Geophysical Research: Atmospheres 119, no. 5 (2014): 2131–46. http://dx.doi.org/10.1002/2013jd020354.

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Chen, Xiyuan, Chong Shen, and Yuefang Zhao. "Study on GPS/INS System Using Novel Filtering Methods for Vessel Attitude Determination." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/678943.

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Any vehicle such as vessel has three attitude parameters, which are mostly defined as pitch, roll, and heading from true north. In hydrographic surveying, determination of these parameters by using GPS or INS technologies is essential for the requirements of vehicle measurements. Recently, integration of GPS/INS by using data fusion algorithm became more and more popular. Therefore, the data fusion algorithm plays an important role in vehicle attitude determination. To improve attitude determination accuracy and efficiency, two improved data fusion algorithms are presented, which are extended

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Sun, Meng, Yunjia Wang, Shenglei Xu, Hongji Cao, and Minghao Si. "Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter." Applied Sciences 10, no. 2 (2020): 668. http://dx.doi.org/10.3390/app10020668.

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This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate syste

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Bi, Jun, Wei Guan, and Long-Tao Qi. "A genetic resampling particle filter for freeway traffic-state estimation." Chinese Physics B 21, no. 6 (2012): 068901. http://dx.doi.org/10.1088/1674-1056/21/6/068901.

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Dissertations / Theses on the topic "GENETIC PARTICLE FILTER"

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Mullen, Patrick Bowen. "Learning in Short-Time Horizons with Measurable Costs." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/808.

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Dynamic pricing is a difficult problem for machine learning. The environment is noisy, dynamic and has a measurable cost associated with exploration that necessitates that learning be done in short-time horizons. These short-time horizons force the learning algorithms to make pricing decisions based on scarce data. In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle

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Hussain, M. S. "Real-coded genetic algorithm particle filters for high-dimensional state spaces." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1426733/.

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This thesis successfully addresses the issues faced by particle filters in high-dimensional state-spaces by comparing them with genetic algorithms and then using genetic algorithm theory to address these issues. Sequential Monte Carlo methods are a class of online posterior density estimation algorithms that are suitable for non-Gaussian and nonlinear environments, however they are known to suffer from particle degeneracy; where the sample of particles becomes too sparse to approximate the posterior accurately. Various techniques have been proposed to address this issue but these techniques fa

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Boudjelaba, Kamal. "Contribution à la conception des filtres bidimensionnels non récursifs en utilisant les techniques de l’intelligence artificielle : application au traitement d’images." Thesis, Orléans, 2014. http://www.theses.fr/2014ORLE2015/document.

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La conception des filtres a réponse impulsionnelle finie (RIF) peut être formulée comme un problème d'optimisation non linéaire réputé pour être difficile sa résolution par les approches conventionnelles. Afin d'optimiser la conception des filtres RIF, nous explorons plusieurs méthodes stochastiques capables de traiter de grands espaces. Nous proposons un nouvel algorithme génétique dans lequel certains concepts innovants sont introduits pour améliorer la convergence et rendre son utilisation plus facile pour les praticiens. Le point clé de notre approche découle de la capacité de l'algorithme

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SNEKHA. "GENETIC ALGORITHM BASED ECG SIGNAL DE-NOISING USING EEMD AND FUZZY THRESHOLDING." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15346.

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ElectroCardioGram (ECG) signal records electrical conduction activity of heart. These are very small signals in strength with narrow bandwidth of 0.05-120 Hz. Physicians especially cardiologist use these signals for diagnosis of the heart’s condition or heart diseases. ECG signal is contaminated with various artifacts such as Power Line Interference (PLI), Patient–electrode motion artifacts, Electrode-pop or contact noise, and Baseline Wandering and ElectroMyoGraphic (EMG) noise during acquisition. Analysis of ECG signals becomes difficult to inspect the cardiac activity in the presence

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Book chapters on the topic "GENETIC PARTICLE FILTER"

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Wang, Qicong, Jilin Liu, and Zhigang Wu. "Object Tracking Using Genetic Evolution Based Kernel Particle Filter." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11774938_37.

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Liang, Yue, Zhong Liu, and Guodong Zhang. "Passive Target Tracking Using an Improved Particle Filter Algorithm Based on Genetic Algorithm." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12990-2_65.

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He, Pan, Chun Tan, and Huawen Huang. "Research on Receiver Autonomous Integrity Monitoring Algorithm Using Genetic Algorithm Resampling Particle Filter." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37404-3_20.

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Nie, Lixia, Xuguang Yang, Jinglin He, Yaya Mu, and Likang Wang. "Research on the Elite Genetic Particle Filter Algorithm and Application on High-Speed Flying Target Tracking." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_105.

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Lundén, Daniel, Johannes Borgström, and David Broman. "Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages." In Programming Languages and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_15.

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AbstractProbabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic programming languages (PPLs) employ different inference algorithms, such as sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), or variational methods. Existing research on such algorithms mainly concerns their implementation and efficiency, rather than the correctness of the algorithms themselves when applied in the context of expressive PPLs. To remedy this, we give a correctness proof for SMC methods in the context of an expressive PPL calculus, representative of popular PPLs such as WebPPL, Anglican, and Birch. Previous work have studied correctness of MCMC using an operational semantics, and correctness of SMC and MCMC in a denotational setting without term recursion. However, for SMC inference—one of the most commonly used algorithms in PPLs as of today—no formal correctness proof exists in an operational setting. In particular, an open question is if the resample locations in a probabilistic program affects the correctness of SMC. We solve this fundamental problem, and make four novel contributions: (i) we extend an untyped PPL lambda calculus and operational semantics to include explicit resample terms, expressing synchronization points in SMC inference; (ii) we prove, for the first time, that subject to mild restrictions, any placement of the explicit resample terms is valid for a generic form of SMC inference; (iii) as a result of (ii), our calculus benefits from classic results from the SMC literature: a law of large numbers and an unbiased estimate of the model evidence; and (iv) we formalize the bootstrap particle filter for the calculus and discuss how our results can be further extended to other SMC algorithms.

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Goudos, Sotirios K. "Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems." In Multidisciplinary Computational Intelligence Techniques. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch006.

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Antenna and microwave design problems are, in general, multi-objective. Multi-objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. Particle Swarm Optimization (PSO) and Differential Evolution (DE) have received increased interest from the electromagnetics community. The fact that both algorithms can efficiently handle arbitrary optimization problems has made them popular for solving antenna and microwave design problems. This chapter presents three different state-of-the-art MOEAs based on PSO and DE, namely: the Multi-objective Particle Swarm Optimization (MOPSO), the Multi-objective Particle Swarm Optimization with fitness sharing (MOPSO-fs), and the Generalized Differential Evolution (GDE3). Their applications to different design cases from antenna and microwave problems are reported. These include microwave absorber, microwave filters and Yagi-uda antenna design. The algorithms are compared and evaluated against other evolutionary multi-objective algorithms like Nondominated Sorting Genetic Algorithm-II (NSGA-II). The results show the advantages of using each algorithm.

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Gorman, Sara E., and Jack M. Gorman. "Causality and Filling the Ignorance Gap." In Denying to the Grave. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780199396603.003.0008.

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There is an old adage: “What you don’t know can’t hurt you.” In the science denial arena, however, this adage seems to have been recrafted to something like: “What you don’t know is an invitation to make up fake science.” Before it was dis¬covered that tuberculosis is caused by a rather large bacteria called Mycobacterium tuberculosis it was widely believed to be the result of poor moral character. Similarly, AIDS was attributed to “deviant” lifestyles, like being gay or using intravenous drugs. When we don’t know what causes something, we are pummeled by “experts” telling us what to believe. Vaccines cause autism. ECT causes brain damage. GMOs cause cancer. Interestingly, the leap by the public to latch onto extreme theories does not extend to all branches of science. Physicists are not certain how the force of gravity is actually conveyed between two bodies. The theoretical solutions offered to address this question involve mind-boggling mathematics and seemingly weird ideas like 12 dimensional strings buzzing around the universe. But we don’t see denialist theories about gravity all over the Internet. Maybe this is simply because the answer to the question does not seem to affect our daily lives one way or the other. But it is also the case that even though particle physics is no more or less complex than molecular genetics, we all believe the former is above our heads but the latter is within our purview. Nonphysicists rarely venture an opinion on whether or not dark matter exists, but lots of nonbiologists will tell you exactly what the immune system can and cannot tolerate. Even when scientific matters become a little more frightening, when they occur in some branches of science, they register rather mild atten¬tion. Some people decided that the supercollider in Switzerland called the Large Hadron Collider (LHC) might be capable of producing black holes that would suck in all of Earth. Right before the LHC was scheduled to be tested at full capacity, there were a few lawsuits filed around the world trying to stop it on the grounds that it might induce the end of the world.

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Conference papers on the topic "GENETIC PARTICLE FILTER"

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Cong Li, Qin Honglei, and Xing Juhong. "Distributed genetic resampling particle filter." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579807.

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Zhao Juan and Dong-feng Li. "The ant system-genetic algorithm particle filter." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5690150.

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Liu, Yanli, and Heng Zhang. "Real time face tracking by genetic particle filter." In 2009 Chinese Control and Decision Conference (CCDC 2009). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5192407.

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Wang, W., Q. K. Tan, J. Chen, and Z. Ren. "Particle filter based on improved genetic algorithm resampling." In 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). IEEE, 2016. http://dx.doi.org/10.1109/cgncc.2016.7828809.

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Zhao, Bin, Jian-wang Hu, and Bing Ji. "An improved particle filter based on genetic resampling." In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/amcce-15.2015.125.

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Ho, Ming-Che, Cheng-Chin Chiang, and Yu-Long Chen. "A Genetic Particle Filter for Moving Object Tracking." In Fourth International Conference on Image and Graphics (ICIG 2007). IEEE, 2007. http://dx.doi.org/10.1109/icig.2007.165.

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Ziyu, Li, Liu Yan, Song Lei, and Cheng Ying. "Particle Filter Based on Pseudo Parallel Genetic Algorithm." In 2013 Fifth International Conference on Computational and Information Sciences (ICCIS). IEEE, 2013. http://dx.doi.org/10.1109/iccis.2013.59.

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Ye, Zhu, and Zhi-Qiang Liu. "Tracking Human Hand Motion Using Genetic Particle Filter." In 2006 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icsmc.2006.385089.

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Kalami Heris, S. Mostapha, and Hamid Khaloozadeh. "Non-dominated sorting genetic filter a multi-objective evolutionary particle filter." In 2014 Iranian Conference on Intelligent Systems (ICIS). IEEE, 2014. http://dx.doi.org/10.1109/iraniancis.2014.6802580.

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Xu, Zhenyuan, Junzo Watada, and Zalili Binti Musa. "Particle Filter-Based Height Estimation in Human Tracking." In 2011 Fifth International Conference on Genetic and Evolutionary Computing (ICGEC). IEEE, 2011. http://dx.doi.org/10.1109/icgec.2011.94.

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Reports on the topic "GENETIC PARTICLE FILTER"

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are ob

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Sela, Hanan, Eduard Akhunov, and Brian J. Steffenson. Population genomics, linkage disequilibrium and association mapping of stripe rust resistance genes in wild emmer wheat, Triticum turgidum ssp. dicoccoides. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598170.bard.

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The primary goals of this project were: (1) development of a genetically characterized association panel of wild emmer for high resolution analysis of the genetic basis of complex traits; (2) characterization and mapping of genes and QTL for seedling and adult plant resistance to stripe rust in wild emmer populations; (3) characterization of LD patterns along wild emmer chromosomes; (4) elucidation of the multi-locus genetic structure of wild emmer populations and its correlation with geo-climatic variables at the collection sites. Introduction In recent years, Stripe (yellow) rust (Yr) caused

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