Academic literature on the topic 'Filtre intervalle'
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Journal articles on the topic "Filtre intervalle"
Scherman, Mathieu. "Travail et conscience : la présentation de soi dans les estimi de Trévise du XVe siècle." Mélanges de l École française de Rome Moyen Âge 118, no. 1 (2006): 127–48. http://dx.doi.org/10.3406/mefr.2006.9408.
Full textXin Lingyi, 忻伶怡, 周雪芳 Zhou Xuefang, 毕美华 Bi Meihua, 杨国伟 Yang Guowei, 胡淼 Hu Miao, 李浩珍 Li Haozhen, and 王天枢 Wang Tianshu. "通道间隔可切换的双Lyot滤波器的结构设计与性能分析." Chinese Journal of Lasers 49, no. 21 (2022): 2106002. http://dx.doi.org/10.3788/cjl202249.2106002.
Full textHicks, Hilary, Alex Laffer, Genna Losinski, and Amber Watts. "Low Frequency Extension Filter and ActiGraph-Calculated Sleep Intervals in Older Adults." Innovation in Aging 4, Supplement_1 (December 1, 2020): 428. http://dx.doi.org/10.1093/geroni/igaa057.1381.
Full textChen, Feng-Wu, Ning-Yuan Lue, Mei-Yin Chou, and Yu-Shu G. Wu. "All-electrical valley filtering in graphene systems. I. A path to integrated electro-valleytronics." Journal of Applied Physics 132, no. 16 (October 28, 2022): 164303. http://dx.doi.org/10.1063/5.0114386.
Full textOliveira, Danian Steinkirch de, Paulo Eduardo Miranda Cunha, Luiz Gallisa Guimaraes, and Andre Fabiano Steklain. "High-Resolution Ray Tracing Migration." Brazilian Journal of Geophysics 39, no. 4 (December 6, 2021): 521. http://dx.doi.org/10.22564/rbgf.v39i4.2112.
Full textKlinke, Rainer, Marcus Müller, Claus-Peter Richter, and Jean Smolders. "Preferred intervals in birds and mammals: A filter response to noise?" Hearing Research 74, no. 1-2 (April 1994): 238–46. http://dx.doi.org/10.1016/0378-5955(94)90192-9.
Full textCOLAK, OMER H. "AN EFFICIENT METHOD FOR CORRECTION OF ECTOPIC BEATS IN R-R INTERVALS." Fluctuation and Noise Letters 08, no. 03n04 (December 2008): L359—L368. http://dx.doi.org/10.1142/s0219477508005112.
Full textYost, William A., Dan Mapes-Riordan, Raymond Dye, Stanley Sheft, and William Shofner. "Discrimination of first- and second-order regular intervals from random intervals as a function of high-pass filter cutoff frequency." Journal of the Acoustical Society of America 117, no. 1 (January 2005): 59–62. http://dx.doi.org/10.1121/1.1830671.
Full textAshkenazy, Y., M. Lewkowicz, J. Levitan, H. Moelgaard, P. E. Bloch Thomsen, and K. Saermark. "Discrimination of the Healthy and Sick Cardiac Autonomic Nervous System by a New Wavelet Analysis of Heartbeat Intervals." Fractals 06, no. 03 (September 1998): 197–203. http://dx.doi.org/10.1142/s0218348x98000249.
Full textXu, XueQing, and YongHong Zhou. "EOP prediction using least square fitting and autoregressive filter over optimized data intervals." Advances in Space Research 56, no. 10 (November 2015): 2248–53. http://dx.doi.org/10.1016/j.asr.2015.08.007.
Full textDissertations / Theses on the topic "Filtre intervalle"
Mohammedi, Irryhl. "Contribution à l’estimation robuste par intervalle des systèmes multivariables LTI et LPV : Application aux systèmes aérospatiaux." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0142.
Full textThe work of this thesis aims at developing new approaches based on a new particular class of state estimators, the so-called interval or ensemble filters.Like the class of interval observers, the objective is to estimate, in a guaranteed way, the upper and lower bounds of the states of a system, at each time instant.The proposed approach is based on the theory of monotonic systems and on the knowledge of the domain of membership, supposedly bounded, of the uncertainties of the system, such as disturbances, noise and bias of sensors, etc.The key element of the proposed approach is to use a filter structure advantage, rather than an observer-based structure (relying only on a dynamic structure of the studied system).The synthesis of the filter parameters is based on the resolution of a constrained optimization problem of linear and bilinear matrix inequalities (LMI and BMI) allowing to guarantee simultaneously the existence conditions of the filter as well as a performance level, either in an energy context for LTI systems, or in an amplitude context or in a mixed energy/amplitude context for LPV systemsThe proposed synthesis methodology is illustrated on an academic example and is compared with other existing methods in the literature. Finally, the methodology is applied to the case of attitude and acceleration control of a satellite, under realistic simulation conditions
Tran, Tuan Anh. "Cadre unifié pour la modélisation des incertitudes statistiques et bornées : application à la détection et isolation de défauts dans les systèmes dynamiques incertains par estimation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30292/document.
Full textThis thesis deals with state estimation in discrete-time dynamic systems in the context of the integration of statistical and bounded error uncertainties. Motivated by the drawbacks of the interval Kalman filter (IKF) and its improvement (iIKF), we propose a filtering algorithm for linear systems subject to uncertain Gaussian noises, i.e. with the mean and covariance matrix defined by their membership to intervals. This new interval Kalman filter (UBIKF) relies on finding a punctual gain matrix minimizing an upper bound of the set of estimation error covariance matrices by respecting the bounds of the parametric uncertainties. An envelope containing all possible estimates is then determined using interval analysis. The UBIKF reduces not only the computational complexity of the set inversion of the matrices intervals appearing in the iIKF, but also the conservatism of the estimates. We then discuss different frameworks for representing incomplete or imprecise knowledge, including the cumulative distribution functions, the possibility theory and the theory of belief functions. Thanks to the last, a model in the form of a mass function for an uncertain multivariate Gaussian distribution is proposed. A box particle filter based on this theory is developed for non-linear dynamic systems in which the process noises are bounded and the measurement errors are represented by an uncertain Gaussian mass function. Finally, the UBIKF is applied to fault detection and isolation by implementing the generalized observer scheme and structural analysis. Through various examples, the capacity for detecting and isolating sensor/actuator faults of this tool is illustrated and compared to other approaches
Pepy, Romain. "Vers une planification robuste et sûre pour les systèmes autonomes." Phd thesis, Université Paris Sud - Paris XI, 2009. http://tel.archives-ouvertes.fr/tel-00845477.
Full textNicola, Jérémy. "Robust, precise and reliable simultaneous localization and mapping for and underwater robot. Comparison and combination of probabilistic and set-membership methods for the SLAM problem." Thesis, Brest, 2017. http://www.theses.fr/2017BRES0066/document.
Full textIn this thesis, we work on the problem of simultaneously localizing an underwater robot while mapping a set of acoustic beacons lying on the seafloor, using an acoustic range-meter and an inertial navigation system. We focus on the two main approaches classically used to solve this type of problem: Kalman filtering and set-membership filtering using interval analysis. The Kalman filter is optimal when the state equations of the robot are linear, and the noises are additive, white and Gaussian. The interval-based filter do not model uncertainties in a probabilistic framework, and makes only one assumption about their nature: they are bounded. Moreover, the interval-based approach allows to rigorously propagate the uncertainties, even when the equations are non-linear. This results in a high reliability in the set estimate, at the cost of a reduced precision.We show that in a subsea context, when the robot is equipped with a high precision inertial navigation system, a part of the SLAM equations can reasonably be seen as linear with additive Gaussian noise, making it the ideal playground of a Kalman filter. On the other hand, the equations related to the acoustic range-meter are much more problematic: the system is not observable, the equations are non-linear, and the outliers are frequent. These conditions are ideal for a set-based approach using interval analysis.By taking advantage of the properties of Gaussian noises, this thesis reconciles the probabilistic and set-membership processing of uncertainties for both linear and non-linear systems with additive Gaussian noises. By reasoning geometrically, we are able to express the part of the Kalman filter equations linked to the dynamics of the vehicle in a set-membership context. In the same way, a more rigorous and precise treatment of uncertainties is described for the part of the Kalman filter linked to the range-measurements. These two tools can then be combined to obtain a SLAM algorithm that is reliable, precise and robust. Some of the methods developed during this thesis are demonstrated on real data
Akhbari, Mahsa. "Analyse des intervalles ECG inter- et intra-battement sur des modèles d'espace d'état et de Markov cachés." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT026.
Full textCardiovascular diseases are one of the major causes of mortality in humans. One way to diagnose heart diseases and abnormalities is processing of cardiac signals such as ECG. In many of these processes, inter-beat and intra-beat features of ECG signal must be extracted. These features include peak, onset and offset of ECG waves, meaningful intervals and segments that can be defined for ECG signal. ECG fiducial point (FP) extraction refers to identifying the location of the peak as well as the onset and offset of the P-wave, QRS complex and T-wave which convey clinically useful information. However, the precise segmentation of each ECG beat is a difficult task, even for experienced cardiologists.In this thesis, we use a Bayesian framework based on the McSharry ECG dynamical model for ECG FP extraction. Since this framework is based on the morphology of ECG waves, it can be useful for ECG segmentation and interval analysis. In order to consider the time sequential property of ECG signal, we also use the Markovian approach and hidden Markov models (HMM). In brief in this thesis, we use dynamic model (Kalman filter), sequential model (HMM) and their combination (switching Kalman filter (SKF)). We propose three Kalman-based methods, an HMM-based method and a SKF-based method. We use the proposed methods for ECG FP extraction and ECG interval analysis. Kalman-based methods are also used for ECG denoising, T-wave alternans (TWA) detection and fetal ECG R-peak detection.To evaluate the performance of proposed methods for ECG FP extraction, we use the "Physionet QT database", and a "Swine ECG database" that include ECG signal annotations by physicians. For ECG denoising, we use the "MIT-BIH Normal Sinus Rhythm", "MIT-BIH Arrhythmia" and "MIT-BIH noise stress test" databases. "TWA Challenge 2008 database" is used for TWA detection and finally, "Physionet Computing in Cardiology Challenge 2013 database" is used for R-peak detection of fetal ECG. In ECG FP extraction, the performance of the proposed methods are evaluated in terms of mean, standard deviation and root mean square of error. We also calculate the Sensitivity for methods. For ECG denoising, we compare methods in their obtained SNR improvement
Dandach, Hoda. "Prédiction de l'espace navigable par l'approche ensembliste pour un véhicule routier." Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP1892/document.
Full textIn this thesis, we aim to characterize a vehicle stable state domain, as well as vehicle state estimation, using interval methods.In the first part of this thesis, we are interested in the intelligent vehicle state estimation.The Bayesian approach is one of the most popular and used approaches of estimation. It is based on the calculated probability of the density function which is neither evident nor simple all the time, conditioned on the available measurements.Among the Bayesian approaches, we know the Kalman filter (KF) in its three forms(linear, non linear and unscented). All the Kalman filters assume unimodal Gaussian state and measurement distributions. As an alternative, the Particle Filter(PF) is a sequential Monte Carlo Bayesian estimator. Contrary to Kalman filter,PF is supposed to give more information about the posterior even when it has a multimodal shape or when the noise follows non-Gaussian distribution. However,the PF is very sensitive to the imprecision due by bias or noise, and its efficiency and accuracy depend mainly on the number of propagated particles which can easily and significantly increase as a result of this imprecision. In this part, we introduce the interval framework to deal with the problems of the non-white biased measurements and bounded errors. We use the Box Particle Filter (BPF), an estimator based simultaneously on the interval analysis and on the particle approach. We aim to estimate some immeasurable state from the vehicle dynamics using the bounded error Box Particle algorithm, like the roll angle and the lateral load transfer, which are two dynamic states of the vehicle. BPF gives a guaranteed estimation of the state vector. The box encountering the estimation is guaranteed to encounter thereal value of the estimated variable as well.In the second part of this thesis, we aim to compute a vehicle stable state domain.An algorithm, based on the set inversion principle and the constraints satisfaction,is used. Considering the longitudinal velocity and the side slip angle at the vehicle centre of gravity, we characterize the set of these two state variables that corresponds to a stable behaviour : neither roll-over nor sliding. Concerning the roll-over risk,we use the lateral transfer ratio LTR as a risk indicator. Concerning the sliding risk, we use the wheels side slip angles. All these variables are related geometrically to the longitudinal velocity and the side slip angle at the centre of gravity. Using these constraints, the set inversion principle is applied in order to define the set ofthe state variables where the two mentioned risks are avoided. The algorithm of Sivia is implemented. Knowing the vehicle trajectory, a maximal allowed velocityon every part of this trajectory is deduced
Avcu, Soner. "Radar Pulse Repetition Interval Tracking With Kalman Filter." Thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607691/index.pdf.
Full textGomes, Adriano de Araújo. "Algoritmo das projeções sucessivas para seleção de variáveis em calibração de segunda ordem." Universidade Federal da Paraíba, 2015. http://tede.biblioteca.ufpb.br:8080/handle/tede/8196.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
In this work it was developed a new strategy for intervals selection using the successive projections algorithm (SPA) coupled to N-PLS and U-PLS models, both with residual bilinearização (RBL) as a post-calibration step. The new algorithm coupled to N-PLS/RBL models was evaluated in two cases of studies. The first was simulated data for quantitation of two analytes (A and B) in the presence of a single interfering. On the second study was conducted a quantitation of ofloxacin in water in the presence of interferents (ciprofloxacin and danofloxacin) by means of liquid chromatography with diode array detection (LC-DAD) data modeling. The results were compared to the N-PLS/RBL model and the variables selection with the genetic algorithm (GA-N-PLS/RBL). In the first case of study (simulated data) were observed RMSEP values (x 10-3 in arbitrary units) for the analytes A and B in the order of 6.7 to 47.6; 10.6 to 11.4; and 6.0 to 14.0 for the N-PLS/RBL, Ga-N-PLS/RBL and the proposed method, respectively. On the second case of study (HPLC-DAD data) RMSEP value (mg/L) of 0.72 (N-PLS/RBL); 0.70 (GA-N-PLS/RBL) and 0.64 (iSPA N-PLS/RBL) were obtained. When combined with the U-PLS/RBL, the new algorithm was evaluated in the EEM modeling in the presence of inner filter effect. Simulated data and quantitation of phenylephrine in the presence of acetaminophen in water sample and interferences (ibuprofen and acetylsalicylic acid) were used as a case of studies. The results were compared to the U-PLS/RBL and e twell established method PARAFAC. For simulated data was observed the following RMSEP values (in arbitrary units) 1.584; 0.077 and 0.066 for PARAFAC; U-PLS/RBL and the proposed method, respectively. In the quantitation of phenylephrine the found RMSEP (in μg/L) were of 0.164 (PARAFAC); 0.089 (U-PLS/RBL) and 0.069 (ISPA-U-PLS/RBL). In all cases it was shown that variables selection is a useful tool capable of improving accuracy when compared with the respective global models (model without variables selection) leading to more parsimonious models. It was observed in all cases, that the sensitivity loss promoted by variables selection is compensated by using more selective channels, justifying the obtained RMSEP smaller values. Finally, it was also observed that the models based on variables selection such as the proposed method were free from significant bias at 95% confidence.
Neste trabalho foi desenvolvida uma nova estratégia para seleção de intervalos empregando o algoritmo das projeções sucessivas (SPA) acoplado a modelos N-PLS e U-PLS, ambos com etapa pós-calibração de bilinearização residual (RBL). O novo algoritmo acoplado a modelos N-PLS/RBL, foi avaliado em dois estudos de casos. O primeiro envolvendo dados simulados para quantificação de dois analitos (A e B) na presença de um único interferente. No segundo foi conduzida a quantificação de ofloxacina em água na presença de interferentes (ciprofloxacina e danofloxacina) por meio da modelagem de dados cromatografia liquida com detecção por arranjo de diodos (LC-DAD). Os resultados obtidos foram comparados ao modelo N-PLS/RBL e a seleção de variáveis com o algoritmo genético (GA-N-PLS/RBL). No primeiro estudo de caso (dados simulados) foram observados valores de RMSEP (x 10-3 em unidades arbitrárias) para os analitos A e B da ordem de 6,7 e 47,6; 10,6 e 11,4; 6,0 e 14,0 para o N-PLS/RBL, GA-N-PLS/RBL e o método proposto, respectivamente. No segundo estudo de caso (dados HPLC-DAD) valores de RMSEP (em mg/L) de 0,72 (N-PLS/RBL); 0,70 (GA-N-PLS/RBL) e 0,64 (iSPA-N-PLS/RBL) foram obtidos. Quando combinado com o U-PLS/RBL o novo algoritmo foi avaliado na modelagem de EEM em presença efeito de filtro interno. Dados simulados e a quantificação de fenilefrina na presença de paracetamol em amostras de água e interferentes (Ibuprofeno e ácido acetil salicílico) foram usados como estudos de caso. Os resultados obtidos foram comparados ao modelo U-PLS/RBL e ao bem estabelecido método PARAFAC. Para dados simulados foram observado os seguintes valores de RMSEP (em unidades arbitrarias) 1,584; 0,077 e 0,066 para o PARAFAC; U-PLS/RBL e método proposto, respectivamente. Na quantificação de fenilefrina os RMSEP (em μg/L) encontrados foram de 0,164 (PARAFAC); 0,089 (U-PLS/RBL) e 0,069 (iSPA-U-PLS/RBL). Em todos os casos foi demostrado que seleção de variáveis é uma ferramenta útil capaz de melhorar a acurácia quando comparados aos respectivos modelos globais (modelo sem seleção de variáveis) e tornar os modelos mais parcimoniosos. Foi observado ainda para todos os casos, que a perda de sensibilidade promovida pela seleção de variáveis é compensada pelo uso de canais mais seletivos, justificando os menores valores de RMSEP obtidos. E por fim, foi também observado que os modelos baseados em seleção de variáveis como o método proposto foram isentos de bias significativos a 95% de confiança.
Merlinge, Nicolas. "State estimation and trajectory planning using box particle kernels." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS425/document.
Full textState estimation and trajectory planning are two crucial functions for autonomous systems, and in particular for aerospace vehicles.Particle filters and sample-based trajectory planning have been widely considered to tackle non-linearities and non-Gaussian uncertainties.However, these approaches may produce erratic results due to the sampled approximation of the state density.In addition, they have a high computational cost which limits their practical interest.This thesis investigates the use of box kernel mixtures to describe multimodal probability density functions.A box kernel mixture is a weighted sum of basic functions (e.g., uniform kernels) that integrate to unity and whose supports are bounded by boxes, i.e., vectors of intervals.This modelling yields a more extensive description of the state density while requiring a lower computational load.New algorithms are developed, based on a derivation of the Box Particle Filter (BPF) for state estimation, and of a particle based chance constrained optimisation (Particle Control) for trajectory planning under uncertainty.In order to tackle ambiguous state estimation problems, a Box Regularised Particle Filter (BRPF) is introduced.The BRPF consists of an improved BPF with a guaranteed resampling step and a smoothing strategy based on kernel regularisation.The proposed strategy is theoretically proved to outperform the original BPF in terms of Mean Integrated Square Error (MISE), and empirically shown to reduce the Root Mean Square Error (RMSE) of estimation.BRPF reduces the computation load in a significant way and is robust to measurement ambiguity.BRPF is also integrated to federated and distributed architectures to demonstrate its efficiency in multi-sensors and multi-agents systems.In order to tackle constrained trajectory planning under non-Gaussian uncertainty, a Box Particle Control (BPC) is introduced.BPC relies on an interval bounded kernel mixture state density description, and consists of propagating the state density along a state trajectory at a given horizon.It yields a more accurate description of the state uncertainty than previous particle based algorithms.A chance constrained optimisation is performed, which consists of finding the sequence of future control inputs that minimises a cost function while ensuring that the probability of constraint violation (failure probability) remains below a given threshold.For similar performance, BPC yields a significant computation load reduction with respect to previous approaches
Janapala, Arun. "RR INTERVAL ESTIMATION FROM AN ECG USING A LINEAR DISCRETE KALMAN FILTER." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3426.
Full textM.S.E.E.
Department of Electrical and Computer Engineering
Engineering and Computer Science
Electrical Engineering
Books on the topic "Filtre intervalle"
Nicklas, Richard B. An application of a Kalman Filter Fixed Interval Smoothing Algorithm to underwater target tracking. Monterey, Calif: Naval Postgraduate School, 1989.
Find full textGalinis, William J. Fixed interval smoothing algorithm for an extended Kalman filter for over-the-horizon ship tracking. Monterey, California: Naval Postgraduate School, 1989.
Find full textWeinert, Howard L. Fixed interval smoothing for state space models. Boston: Kluwer Academic Publishers, 2001.
Find full textSoftware, Coda Music. Finale: The art of music notation. 3rd ed. Eden Prairie, MN: Coda Music Software, 1992.
Find full textAutomatic Transmission Service Guide: Fluid Capacity and Recommended Filter Change Intervals. Hearst Business Pub., 2005.
Find full textInter-Rater Reliability Using SAS: A Practical Guide for Nominal, Ordinal, and Interval Data. Advanced Analytics, LLC, 2010.
Find full textBook chapters on the topic "Filtre intervalle"
Catlin, Donald E. "Fixed Interval Smoothing." In Estimation, Control, and the Discrete Kalman Filter, 188–99. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4528-5_9.
Full textQuislant, Ricardo, Eladio Gutierrez, Oscar Plata, and Emilio L. Zapata. "Interval Filter: A Locality-Aware Alternative to Bloom Filters for Hardware Membership Queries by Interval Classification." In Intelligent Data Engineering and Automated Learning – IDEAL 2010, 162–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15381-5_20.
Full textSharma, Teena, and Nishchal K. Verma. "Adaptive Interval Type-2 Fuzzy Filter." In Artificial Intelligent Algorithms for Image Dehazing and Non-Uniform Illumination Enhancement, 111–34. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2011-8_6.
Full textWang, Ziyun, Yan Wang, and Zhicheng Ji. "Fault diagnosis based on interval." In Filter Design for System Modeling, State Estimation and Fault Diagnosis, 95–148. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/b23146-5.
Full textForsythe, W., and R. M. Goodall. "Error Mechanisms, Filter Structure, and the Sampling Interval." In Digital Control, 88–111. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-21550-8_4.
Full textGuru, D. S., and N. Vinay Kumar. "Clustering of Interval Valued Data Through Interval Valued Feature Selection: Filter Based Approaches." In Mining Intelligence and Knowledge Exploration, 270–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66187-8_26.
Full textWang, Ziyun, Yan Wang, and Zhicheng Ji. "Design of Interval Set-Membership Based Fault Detection Filter." In Advances in Fault Detection and Diagnosis Using Filtering Analysis, 57–74. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5959-1_5.
Full textLi, Hongyi, Ligang Wu, Hak-Keung Lam, and Yabin Gao. "Filter Design of Interval Type-2 Fuzzy-Model-Based Systems." In Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems, 109–21. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0593-0_7.
Full textPiazena, H., W. Müller, and Peter Vaupel. "Physical and Photobiological Basics of wIRA-Hyperthermia." In Water-filtered Infrared A (wIRA) Irradiation, 35–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92880-3_3.
Full textZhao, Shen, Yunwei Zhang, XiWei Guo, and Deliang Liu. "Research on the Linear Interpolation of Equal-Interval Fractional Delay Filter." In Lecture Notes in Electrical Engineering, 512–19. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_60.
Full textConference papers on the topic "Filtre intervalle"
Liang, You, Aerambamoorthy Thavaneswaran, Juan Liyau, Areebah Muhammad, Thimani Ranathungage, and Ruppa Thulasiram. "A Cryptocurrency Multiple Trading Strategy with Kalman Filter Innovation Volatility Interval Forecasts." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), 204–9. IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00037.
Full textHu, Jie, Yan Wang, Aiguo Cheng, and Zhihua Zhong. "A Kalman Filtering Mechanism Based on Generalized Interval Probability and its Application in Process Variation Estimation." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34543.
Full textMadsen, Stian, Jørn Watvedt, and Lars E. Bakken. "Gas Turbine Fouling Offshore: Air Intake Filtration Optimization." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75613.
Full textGomes, Daiana Caroline, and Ginalber Serra. "Algoritmo de Filtro de Kalman Baseado em Modelo Fuzzy Tipo-2 Evolutivo para Rastreamento." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-010.
Full textDos Santos Gomes, Daiana Caroline, and Ginalber Luiz De Oliveira Serra. "Filtragem Computacional de Kalman Fuzzy Tipo-2 para Rastreamento e Previsão da Dinâmica de Propagação da COVID-19: Estudo de Caso Aplicado ao Brasil." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.986.
Full textMadsen, Stian, and Lars E. Bakken. "Gas Turbine Operation Offshore: Increased Operating Interval and Higher Engine Performance Through Optimized Intake Air Filter System." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56066.
Full textMarshall, Lauren, Adam Schroeder, and Brian Trease. "Comparing Fish-Inspired Ram Filters for Collection of Harmful Algae." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88797.
Full textWatson, Simon A. G., Victor W. Wong, Darrell Brownawell, and Scott P. Lockledge. "Controlling Lubricant Acidity With an Oil Conditioning Filter." In ASME 2009 Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/ices2009-76100.
Full textLyons, James W., and Alex Morrison. "Utility Perspective of Selecting Air Filter for Simple-Cycle, Heavy-Duty Combustion Turbines." In ASME 1992 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/92-gt-321.
Full textTakemura, Kentaro, Euisun Kim, and Jun Ueda. "Individualized Inter-Stimulus Interval Estimation for Neural Facilitation in Human Motor System: A Particle Filtering Approach." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9155.
Full textReports on the topic "Filtre intervalle"
Bhattarai, Rabin, Yufan Zhang, and Jacob Wood. Evaluation of Various Perimeter Barrier Products. Illinois Center for Transportation, May 2021. http://dx.doi.org/10.36501/0197-9191/21-009.
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