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1
Liu, Jing Xian, De Qiang Chang, Yue Xie, Ning Mao, and Xi Sun. "Research on Fine Particles Capture of Baghouse Filter Media." Applied Mechanics and Materials 300-301 (February 2013): 1293–97. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.1293.
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Industrial dust emission is one of the main sources of fine particle especially for PM2.5 in atmosphere. Filter media is the key component for baghouse. In the paper, several normal needle felt filters and membrane filter are studied through experiments. The performances of filter such as resistance, capture efficiency for total particle and number counting efficiency for fine particle are tested.The experimental results show that the resistance of filter increase after aging, the resisitance increasing rate of membrane filter is the smallest; The efficiencies for total particles of normal filter are above 99.9% at clean state, and 99.99% at stable state after aging; Although the filters have high efficiency for total particle, but the number counting efficiency for 10μm particle is 94%-99%, and 52%-92% for 2μm particles. New filters for fine particles are needed.
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Kelly, Wayne, Donald Grant, and Joseph Zahka. "Optimization of Filter Properties for Recirculating Etch Baths." Journal of the IEST 43, no. 3 (June 14, 2000): 30–40. http://dx.doi.org/10.17764/jiet.43.3.72574h1883p177u4.
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Membrane filters are often used to remove small particles from liquids in recirculating etch baths (REBs). The ideal filter for this application would have both high particle capture efficiency and high flow permeability. Unfortunately, filters with high particle capture efficiency often have low permeability because the dominant particle capture mechanism is mechanical sieving. This paper describes more efficient capture mechanisms, interception and diffusion, and a filter with a surface modified to capture particles by these mechanisms. The filter has the same permeability as a conventional 0.45-μm filter and retains particles more efficiently than a conventional 0.05-μm filter. The filter is capable of removing multiple types of small (0.065-μm) particles (polystyrene latex, silicon nitride, alumina, etc.) with high efficiency (> 99.9 percent).
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Örenbaş, Halit, and Muharrem Mercimek. "Clustered Exact Daum-Huang Particle Flow Filter." Mathematical Problems in Engineering 2019 (May 13, 2019): 1–8. http://dx.doi.org/10.1155/2019/8369565.
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Unlike the conventional particle filters, particle flow filters do not rely on proposal density and importance sampling; they employ flow of the particles through a methodology derived from the log-homotopy scheme and ensure successful migration of the particles. Amongst the efficient implementations of particle filters, Exact Daum-Huang (EDH) filter pursues the calculation of migration parameters all together. An improved version of it, Localized Exact Daum-Huang (LEDH) filter, calculates the migration parameters separately. In this study, the main objective is to reduce the cost of calculation in LEDH filters which is due to exhaustive calculation of each migration parameter. We proposed the Clustered Exact Daum-Huang (CEDH) filter. The main impact of CEDH is the clustering of the particles considering the ones producing similar errors and then calculating the same migration parameters for the particles within each cluster. Through clustering and handling the particles with high errors, their engagement and influence can be balanced, and the system can greatly reduce the negative effects of such particles on the overall system. We implement the filter successfully for the scenario of high dimensional target tracking. The results are compared to those obtained with EDH and LEDH filters to validate its efficiency.
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Pei, Fujun, Mei Wu, and Simin Zhang. "Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/239531.
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The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness.
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Papavasiliou, Anastasia. "A uniformly convergent adaptive particle filter." Journal of Applied Probability 42, no. 4 (December 2005): 1053–68. http://dx.doi.org/10.1239/jap/1134587816.
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Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.
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Papavasiliou, Anastasia. "A uniformly convergent adaptive particle filter." Journal of Applied Probability 42, no. 04 (December 2005): 1053–68. http://dx.doi.org/10.1017/s0021900200001108.
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Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.
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Zhang, Yinhang, Xiuhua Men, Shuai Wang, Xiuli Fu, and Liwen Chen. "CFD-DEM Study of Pleated Filter Plugging Process Based on Porous Media Model." Machines 10, no. 10 (September 26, 2022): 862. http://dx.doi.org/10.3390/machines10100862.
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The pneumatic conveying process of fine particles through filters was studied by CFD-DEM simulation method. The porous media model and porous structure were used to simulate the airflow state and the blocking effect of fine particles when they flowed through the filter. Under different particle feed rates and initial particle velocities, the effects of the plugging rate and settling velocity in pleated filters were analyzed, and the effect of particle deposition height on fluid zone was studied. The results showed that particles should avoid the feed rate of 250–750 g/s and choose the initial particle velocity of 3–6 m/s to achieve lower plugging rate and faster settling velocity. The position of the filter should avoid the particle inlet to avoid the increase of non-uniformity. Timely cleaning of particles in the filter box can improve the filtering performance.
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Kim, Inkyu, Nosan Kwak, Heoncheol Lee, and Beomhee Lee. "Improved particle fusing geometric relation between particles in FastSLAM." Robotica 27, no. 6 (January 6, 2009): 853–59. http://dx.doi.org/10.1017/s0263574708005250.
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SUMMARYFastSLAM is a framework for simultaneous localization and mapping using a Rao-Blackwellized particle filter (RBPF). But, FastSLAM is known to degenerate over time due to the loss of particle diversity, mainly caused by the particle depletion problem in resampling phase. In this work, improved particle filter using geometric relation between particles is proposed to restrain particle depletion and to reduce estimation errors and error variances. It uses a KD tree (k-dimensional tree) to derive geometric relation among particles and filters particles with importance weight conditions for resampling. Compared to the original particle filter used in FastSLAM, this technique showed less estimation error with lower error standard deviation in computer simulations.
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Lee, Yoonsang, and Andrew J. Majda. "State estimation and prediction using clustered particle filters." Proceedings of the National Academy of Sciences 113, no. 51 (December 5, 2016): 14609–14. http://dx.doi.org/10.1073/pnas.1617398113.
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Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors.
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Michalski, Jacek, Piotr Kozierski, Wojciech Giernacki, Joanna Zietkiewicz, and Marek Retinger. "MultiPDF particle filtering in state estimation of nonlinear objects." Nonlinear Dynamics 106, no. 3 (October 25, 2021): 2165–82. http://dx.doi.org/10.1007/s11071-021-06913-2.
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AbstractThis paper presents a new particle filter algorithm (MultiPDF) for state estimation of nonlinear systems. The proposed method is a modification of the standard particle filter approach. Due to the strong need for the acceleration of calculations and an improvement in the estimation quality of state estimation, the authors propose a method which enables one to divide the main particle filter into smaller sub-filters with an accordingly smaller number of particles for each one of them. The algorithm has been implemented for various numbers of particles and subordinate parallel filters. Estimation quality has been checked for nine nonlinear objects (both one- and multidimensional) and evaluated through the quality index, average root-mean-squared error. The computation time of the particle filter algorithm for several hardware configurations has been compared. Based on the obtained results, it can be concluded that, besides the computation acceleration, the parallelization of the particle filter’s operation also improves the estimation quality.
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Kobler, Daniel, and Markus Boller. "Particle removal in different filtration systems for tertiary wastewater treatment - a comparison." Water Science and Technology 36, no. 4 (August 1, 1997): 259–67. http://dx.doi.org/10.2166/wst.1997.0133.
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In seven wastewater installations in the area of Zürich, Switzerland, four different filtration systems for tertiary wastewater treatment (deep bed, cloth drum, continuous up-flow and cell filters) were investigated with special attention to particle size distributions in the in- and outflow of the flocculation and the filtration step. Flocculation and filter performance was characterised by the shift of particle number curves. For each filter, particle number, surface and volume removal as a function of particle size was determined and revealed to be an excellent tool to evaluate the filter plants under practical conditions. The number removal efficiency for small particles in the colloidal size range proved to be the most sensitive parameter and could be related to operating parameters such as flocculant dose and filtration rate. The results of the particle analysis were compared to quality parameters related to small particles such as micro-organism and heavy metal concentrations. A good agreement between the removal of small size particles (< 8 μm) and bacteria counts as well as copper was found in the different filter systems. The filters were finally classified according to their particle removal performance.
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Fan, Ying, Yonggang Zhang, Guoqing Wang, Xiaoyu Wang, and Ning Li. "Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises." Sensors 18, no. 10 (September 20, 2018): 3183. http://dx.doi.org/10.3390/s18103183.
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In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.
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Chow, Judith C., John G. Watson, Xiaoliang Wang, Behrooz Abbasi, Wm Randolph Reed, and David Parks. "Review of Filters for Air Sampling and Chemical Analysis in Mining Workplaces." Minerals 12, no. 10 (October 18, 2022): 1314. http://dx.doi.org/10.3390/min12101314.
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This review considers the use of filters to sample air in mining workplace environments for dust concentration measurement and subsequent analysis of hazardous contaminants, especially respirable crystalline silica (RCS) on filters compatible with wearable personal dust monitors (PDM). The review summarizes filter vendors, sizes, costs, chemical and physical properties, and information available on filter modeling, laboratory testing, and field performance. Filter media testing and selection should consider the characteristics required for mass by gravimetry in addition to RCS quantification by Fourier-transform infrared (FTIR) or Raman spectroscopic analysis. For mass determination, the filters need to have high filtration efficiency (≥99% for the most penetrable particle sizes) and a reasonable pressure drop (up to 16.7 kPa) to accommodate high dust loading. Additional requirements include: negligible uptake of water vapor and gaseous volatile compounds; adequate particle adhesion as a function of particle loading; sufficient particle loading capacity to form a stable particle deposit layer during sampling in wet and dusty environments; mechanical strength to withstand vibrations and pressure drops across the filter; and appropriate filter mass compatible with the tapered element oscillating microbalance. FTIR and Raman measurements require filters to be free of spectral interference. Furthermore, because the irradiated area does not completely cover the sample deposit, particles should be uniformly deposited on the filter.
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Lee, Jae-Keun, Benjamin Liu, Kenneth Rubow, and Seong-Ho Yoo. "Filtration of Real-World Particles in Liquids by Microporous Membrane Filters." Journal of the IEST 38, no. 3 (May 31, 1995): 19–25. http://dx.doi.org/10.17764/jiet.2.38.3.3187876094028675.
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The filtration effect of irregular-shaped, real-world particles, such as silicon, alumina, and silicon nitride particles, has been experimentally investigated using 0.1, 0.22, and 0.45-μm-rated polyvinylidence difluoride (PVDF) and 0.2-μm polytetrafluoroethylene (PTFE) membrane filters. Particle retention tests were performed by measuring particle concentrations upstream and downstream of the test filter using an automatic liquid-borne laser particle counter (LPC). The test apparatus for the particle retention studies used a pump-driven water recirculating system. Zeta potential values play an important role in liquid filtration. The higher-charged particles, such as PSL and silicon, degraded filter efficiency during filter loading, while the lower-charged alumina and silicon nitride particles achieved complete retention over the entire range of test particle size, i.e., from 0.1 μm to 0.4 μm. Experimental results showed that if the filter and contaminant particles had a high charge of similar polarity, the electric double layers would interact in a repulsive manner, making the adsorptive deposition of the particle unfavorable and leaving sieving as the active filtration mechanism. If the contaminants had a low charge, they coagulated due to colloidal instability and were easily retained on the pore surfaces.
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Suoth, Verna Albert, and Maria D. Bobanto. "PENGUKURAN KONSENTRASI PARTIKEL ASAP ROKOK DENGAN MENGGUNAKAN P-TRAK." JURNAL ILMIAH SAINS 16, no. 2 (November 9, 2016): 80. http://dx.doi.org/10.35799/jis.16.2.2016.14109.
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ABSTRAK Penelitian ini bertujuan untuk menentukan konsentrasi partikel pada asap rokok pada beberapa sampel rokok yang menggunakan filter dan sampel rokok tersebut merupakan rokok yang beredar di Indonesia. Ada 4 sampel rokok berfilter yang digunakan yakni filter x, filter B, filter DE, dan filter DF. Jumlah konsentrasi partikel asap rokok diukur dengan menggunakan P-Track. Hasil penelitian yang di dapat bervariasi bergantung dari karakteristik filter yang ada pada masing-masing rokok tersebut. Konsentrasi partikel asap rokok yang terukur dengan menggunakan beberapa filter didapatkan bahwa dengan menggunakan Filter B akan mendapatkan konsentrasi partikel paling tinggi yaitu 384.100 pt/cc sedangkan Filter DE konsentrasi partikel 234.000 pt/cc untuk Filter DF konsentrasi partikel 204.400 pt/cc dan Filter X konsentrasi partikel 43.320 pt/cc. Faktor emisi partikel asap rokok yang dikeluarkan oleh batang rokok filter B lebih besar dibanding dengan filter lain oleh karena pada masing-masing filter memiliki karakteristik yang berbeda sehingga asap yang dikeluarkan akan sangat berbeda. Dari data tersebut dapat diketahui tingkat bahaya partikel asap rokok sebanding dengan banyaknya asap rokok yang masuk ke detector. Kata kunci: P-Trak, konsentrasi Partikel, asap rokok SMOKE PARTICLE CONCENTRATION MEASURING USING P-TRAK ABSTRACT This research aims to determine the concentration of particles in cigarette smoke in some samples of cigarettes and cigarette samples using the filter is a cigarette that circulated in Indonesia. There are 4 samples of filtered cigarettes used the filter x, filter B, filter DE, and DF filter. The amount of smoke particle concentrations were measured by using the P-Track. Results of research on may vary depending on the characteristics of the existing filter on each of these cigarettes. The concentration of particles of cigarette smoke measured by using multiple filters found that by using Filter B will get a concentration of particles highest of 384.100 pt/cc while Filter DE particle concentration of 234.000 pt/cc to filter DF particle concentration 204.400 pt/cc and Filter X concentration of particles 43.320 pt/cc. Cigarette smoke particles emission factors released by cigarette filter rod B is greater than the other filters because at each filter has different characteristics so that the smoke released will be very different. From these data it can be seen the danger level of cigarette smoke particles is proportional to the number of cigarette smoke coming into the detector. Keywords: P-Trak, the concentration of particles, cigarette smoke
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Jiao, Ziquan, Zhiqiang Feng, Na Lv, Wenjing Liu, and Haijian Qin. "Improved Particle Filter Using Clustering Similarity of the State Trajectory with Application to Nonlinear Estimation: Theory, Modeling, and Applications." Journal of Sensors 2021 (May 26, 2021): 1–19. http://dx.doi.org/10.1155/2021/9916339.
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A clustering similarity particle filter based on state trajectory consistency is presented for the mathematical modeling, performance estimation, and smart sensing of nonlinear systems. Starting from an information fusion model based on the consistency principle of the spatial state trajectory, the predicted observation information of the current particle filter (original trajectory) and future multistage Gaussian particle filter (modified trajectory) are selected as the state trajectories of the sampling particles. Clustering similarity methods are used to measure the state trajectories of the sampling particles and the actual system (reference trajectory). The importance weight of a first-order Markov model is updated with the measurement results. By integrating the targeted compensation scheme of the latest measurement information into the sequential importance sampling process, the adverse effects of the particle degradation phenomenon are effectively reduced. The convergence theorems of the improved particle filter are proposed and proved. The improved filter is applied to practical cases of nonlinear process estimation, economic statistical prediction, and battery health assessment, and the simulation results show that the improved particle filter is superior to traditional filters in estimation accuracy, efficiency, and robustness.
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Hou, Yu Bao, and Shu Yan Tang. "Breeding Estimated Particle Filter." Advanced Materials Research 740 (August 2013): 332–37. http://dx.doi.org/10.4028/www.scientific.net/amr.740.332.
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As the normal particle filter has an expensive computation and degeneracy problem, a propagation-prediction particle filter is proposed. In this scheme, particles after transfer are propagated under the distribution of state noise, and then the produced filial particles are used to predict the corresponding parent particle referring to measurement, in which step the newest measure information is added into estimation. Therefore predicted particle would be closer to the true state, which improves the precision of particle filter. Experiment results have proved the efficiency of the algorithm and the great predominance in little particles case.
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Liu, Fanming, Fangming Li, and Xin Jing. "INS/gravity gradient aided navigation based on gravitation field particle filter." Open Physics 17, no. 1 (December 30, 2019): 709–18. http://dx.doi.org/10.1515/phys-2019-0073.
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Abstract Swarm intelligence method is an effective way to improve the particle degradation and sample depletion of the traditional particle filter. This paper proposes a particle filer based on the gravitation field algorithm (GF-PF), and the gravitation field algorithm is introduced into the resampling process to improve particle degradation and sample depletion. The gravitation field algorithm simulates the solar nebular disk model, and introduces the virtual central attractive force and virtual rotation repulsion force between particles. The particles are moves rapidly to the high-likelihood region under action of the virtual central attractive force. The virtual rotation repulsion force makes the particles keep a certain distance from each other. These operations improve estimation performance, avoid overlapping of particles and maintain the diversity of particles. The proposed method is applied into INS/gravity gradient aided navigation, by combining the sea experimental data of an inertial navigation system. Compared with the particle swarm optimization particle filter(PSO-PF) and artificial physics optimized particle filter (APO-PF), the GF-PF has higher position estimate accuracy and faster convergence speed with the same experimental conditions.
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Sweeney, Daniel, Cigdem Keles, and Emily Sarver. "Exploring the Effect of Particle Loading Density on Respirable Dust Classification by SEM-EDX." Minerals 14, no. 7 (July 20, 2024): 728. http://dx.doi.org/10.3390/min14070728.
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Exposure to respirable coal mine dust (RCMD) still poses health risks to miners. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) is a powerful tool for RCMD characterization because it provides particle-level data, including elemental ratios (via the EDX signals) that can enable classification by inferred mineralogy. However, if the particle loading density (PLD) is high on the analyzed substrate (filter sample), interference between neighboring particles could cause misclassification. To investigate this possibility, a two-part study was conducted. First, the effect of PLD on RCMD classification was isolated by comparing dust particles recovered from the same parent filters under both low- and high-PLD conditions, and a set of modified classification criteria were established to correct for high PLD. Second, the modified criteria were applied to RCMD particles on pairs of filters, with each pair having one filter that was analyzed directly (frequently high PLD) and another filter from which particles were recovered and redeposited prior to analysis (frequently lower PLD). It was expected that application of the modified criteria would improve the agreement between mineralogy distributions for paired filters; however, relatively little change was observed for most pairs. These results suggest that factors other than PLD, including particle agglomeration, can have a substantial effect on the particle EDX data collected during direct-on-filter analysis.
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KIRSH, V. A., and A. A. KIRSH. "COLLECTION OF SUBMICRON AEROSOL PARTICLES BY FILTERS COMPOSED OF NANOFIBERS." Коллоидный журнал 85, no. 1 (January 1, 2023): 38–46. http://dx.doi.org/10.31857/s0023291222600316.
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The deposition of aerosol particles from a Stokes flow in filters composed of nanofibers has been considered at Knudsen numbers Kn ∼ 1. The efficiency of particle collection by model filters with 2D and 3D structures has been determined by numerical simulation as depending on particle radius rp , filter parameters (nanofiber radius a, packing density α and filter thickness), and filtration conditions taking into account the gas slip at the fibers.It has been shown that the efficiencies of particle collection by nanofibers in model 2D and 3D filters are almost equal at the same low packing density 0.02. It has been found that the dependence of the penetration of particles on their radius at a constant velocity on the order of several centimeters per second and at Kn ∼ 1 passes through a maximum, which corresponds to particle radius rp ∼ a. The calculated sizes of the most penetrating particles agree with experimental data. The results obtained will be used when selecting aerosols for testing nanofibrous filters.
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Li, Hai Xia, Zhi Jun Sun, Li Jun Zhao, and Zhan Xu Tie. "Filtration and Pulse Cleaning System for Particle Removal from Gas." Applied Mechanics and Materials 42 (November 2010): 188–91. http://dx.doi.org/10.4028/www.scientific.net/amm.42.188.
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The application of rigid ceramic filter for gas filtration on an industrial scale has shown unstable operation over periods of days. The behavior of the ceramic filtration in further application can be predict by measuring properties of a variety of dust and filtration operation condition. The aim of the experimental investigation is to analysis the pressure distribution inside and outside the ceramic filter along the filter length, and to analyze the pressure drop across the filter along the filter length. Ceramic filter filtration system composes multi-pipe filters for gas clean up is introduced. The operation parameters can be measured by this test system, such as the pressure in the filter cavity and outside the filter surface, velocity outside the filter, gas temperature and humidity, flow rate, dust particle size concentration before gad entering the filter tank and after the gas leaving the filer tank, the pulse cleaning pressure.
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Lee, Jae-Keun, Benjamin Liu, and Kenneth Rubow. "Latex Sphere Retention by Microporous Membranes in Liquid Filtration." Journal of the IEST 36, no. 1 (January 1, 1993): 26–36. http://dx.doi.org/10.17764/jiet.2.36.1.3013036291k8n475.
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An experimental study of particulate matter retention by microporous membranes during liquid filtration has been conducted using 0.1, 0.22, 0.45, and 0.65-μm-rated hydrophilic and hydrophobic membrane filters. Retention measurements have been made with polystyrene latex spheres using an automated filter test system and a laser particle counter to measure the upstream and downstream particle concentration. Particle filtration during loading tests was found to begin with a sieving dominant regime followed by a transition regime and a cake filtration regime as particles accumulate inside the filter pores and on the filter surface. For latex sphere sizes equal to the nominal pore size of the filter, the initial filter efficiencies ranged from 97 to 99.9 percent. Complete retention (>99.9999999 percent) was achieved for a range of particle sizes two to three times the rated pore sizes of the filter. With the addition of a surfactant to the liquid, the retention was found to be lowered as a result of enhanced particle passage through the filter due to modified surface adsorption and steric stabilization. It was found that particle retention by sieving with the addition of surfactant provided the "worst-case" test for filter performance.
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Li, Xin, Yan Wang, and Dawei Liu. "Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning." Mobile Information Systems 2018 (July 3, 2018): 1–17. http://dx.doi.org/10.1155/2018/1587253.
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As UWB high-precision positioning in NLOS environment has become one of the hot topics in the research of indoor positioning, this paper firstly presents a method for the smoothing of original range data based on the Kalman filter by the analysis of the range error caused by UWB signals in LOS and NLOS environment. Then, it studies a UWB and foot-mounted IMU fusion positioning method with the integration of particle filter with extended Kalman filter. This method adopts EKF algorithm in the kinematic equation of particle filters algorithm to calculate the position of each particle, which is like the way of running N (number of particles) extended Kalman filters, and overcomes the disadvantages of the inconformity between kinematic equation and observation equation as well as the problem of sample degeneration under the nonlinear condition of the standard particle filters algorithm. The comparison with the foot-mounted IMU positioning algorithm, the optimization-based UWB positioning algorithm, the particle filter-based UWB positioning algorithm, and the particle filter-based IMU/UWB fusion positioning algorithm shows that our algorithm works very well in LOS and NLOS environment. Especially in an NLOS environment, our algorithm can better use the foot-mounted IMU positioning trajectory maintained by every particle to weaken the influence of range error caused by signal blockage. It outperforms the other four algorithms described as above in terms of the average and maximum positioning error.
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Long, Yangjuan, Zhenji Liu, Quanli Zong, Heyi Jing, and Chen Lu. "Study on the Structural Characteristics of Mesh Filter Cake in Drip Irrigation: Based on the Growth Stage of Filter Cake." Agriculture 14, no. 8 (August 6, 2024): 1296. http://dx.doi.org/10.3390/agriculture14081296.
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Mesh filters are frequently employed in water-saving irrigation fields. Studies addressing the method of cake formation and the characteristics of the cake during the mesh filter’s growing phase are still missing. One-way and orthogonal experiments were carried out using mesh filters with 220 μm and 320 μm aperture sizes as the research objects, taking particle concentrations, inlet flow, and growth phases as experimental factors. According to the variation rule of seed pressure drop in the formation process of filter cake, the growth process of filter cake is divided into four stages, which are as follows: slow blockage first and second stages (M1, M2), fast blockage stage (M3), and filter cake filtration stage (M4). Moreover, the size distribution, porosity (ε), pore-to-particle ratio (KP), and median size (d50) of the filter cake were used to represent the structural characteristics. The results show that the growth of filter cake was a process that started with the filling of mesh pores by intercepted particles and progressed to the filling of large-particle skeleton pores by subsequently filtered particles. During this process, the proportion of intercepted particles gradually decreased, while the proportion of filtered particles increased incrementally, and the median size (d50) and porosity (ε) decreased. Meanwhile, the smaller the aperture size of the screen, the smaller the filter cake’s median size (d50) was, but the larger the pore-to-particle ratio (KP) was. As the flow rate increased, the porosity (ε) was augmented in the M1 and M2 stages; however, it decreased in the M3 and M4 stages. The concentration had a minor influence on the filter cake’s porosity. Lastly, the regression model for filter cake porosity under two aperture size conditions was established, based on factors such as flow rate, concentration, and growth stage. The coefficients of determination, R2, for the model were 90.33% and 80.73%, indicating a good fit.
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Gorczyca, Beata, and David London. "Characterization of Particles in Slow Sand Filtration at North Caribou Water Treatment Plant." Water Quality Research Journal 38, no. 1 (February 1, 2003): 153–68. http://dx.doi.org/10.2166/wqrj.2003.010.
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Abstract Microscopic analysis of particles in water can indicate the size of filter media required, and can be used to monitor filter performance. This study investigated a malfunctioning slow sand filter in a water treatment facility on a First Nations community in Northern Ontario. There has been a boil-water advisory in the community due to high turbidity in the drinking water since the plant was put into operation. Also, the slow sand filters in the plant clog frequently resulting in outflow volumes significantly below the plant design capacity. On-line particle counts and microscopic analysis of water were conducted in the plant at various points before and after the slow sand filters. Microscopic analysis of the slow sand filter effluent showed that the high water turbidity was due to an increased concentration of particles smaller than 2 µm in size. This observation could not have been made with the particle counter data alone, as it is not capable of measuring particles of that size. Visual inspection of microscopic images of these small particles indicated that they were being washed out from the filter media, and did not originate from the raw water. Significant numbers of large filter-clogging diatoms (up to 50 µm in size) were identified under the microscope; yet, the particle counter did not report particles larger than 15 µm in raw water supply. Turbidimeters and particle counters were found to be unable to identify these diatoms, due to the transparent nature of the cell walls of these microorganisms. It is likely that most of these diatoms originated from sediment accumulated in the raw water intake pipe.
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Gui, Yi, and Nong Cheng. "An Improved Approach of Terrain-Aided Navigation Based on RBPF." Applied Mechanics and Materials 336-338 (July 2013): 336–42. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.336.
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Rao-Blackwellized Particle Filter (RBPF) is suitable for solving the linear/nonlinear mixed Terrain-Aided Navigation (TAN) problem. But the Particle Filter (PF) part of RBPF is Standard Particle Filter (SPF), causing particle diversity reduction and even filters divergence under extreme conditions. To get a better estimation of the errors of INS, this paper proposes an improved approach called Regularized Rao-Blackwellized Particle Filter (RRBPF). After updating the nonlinear state and corresponding importance weights, RRBPF resamples from the Epanechnikov kernel and then get the resampled particles through a linear transition process. Theoretically, the resampling part of RRBPF is equivalent to resampling from the approximated continuous posterior probability density function. Shuttle Radar Topography Mission (SRTM) terrain data is used in simulations to investigate the performance of RRBPF. Results show that RRBPF can provide more accurate estimation of TAN and bear larger initial position error than Sandia Inertial Terrain Aided Navigation (SITAN).
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Xue, Changhu, Guigen Nie, Haiyang Li, and Jing Wang. "Data assimilation with an improved particle filter and its application in the TRIGRS landslide model." Natural Hazards and Earth System Sciences 18, no. 10 (October 26, 2018): 2801–7. http://dx.doi.org/10.5194/nhess-18-2801-2018.
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Abstract. Particle filters have become a popular algorithm in data assimilation for their ability to handle nonlinear or non-Gaussian state-space models, but they have significant disadvantages. In this work, an improved particle filter algorithm is proposed. To overcome the particle degeneration and improve particles' efficiency, the processes of particle resampling and particle transfer are updated. In this improved algorithm, particle propagation and the resampling method are ameliorated. The new particle filter is applied to the Lorenz-63 model, and its feasibility and effectiveness are verified using only 20 particles. The root-mean-square difference (RMSD) of estimations converges to stable when there are more than 20 particles. Finally, we choose a peristaltic landslide model and carry out an assimilation experiment of 20 days. Results show that the estimations of states can effectively correct the running offset of the model and the RMSD is convergent after 3 days of assimilation.
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Hoteit, I., D.-T. Pham, G. Triantafyllou, and G. Korres. "A New Approximate Solution of the Optimal Nonlinear Filter for Data Assimilation in Meteorology and Oceanography." Monthly Weather Review 136, no. 1 (January 1, 2008): 317–34. http://dx.doi.org/10.1175/2007mwr1927.1.
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Abstract This paper introduces a new approximate solution of the optimal nonlinear filter suitable for nonlinear oceanic and atmospheric data assimilation problems. The method is based on a local linearization in a low-rank kernel representation of the state’s probability density function. In the resulting low-rank kernel particle Kalman (LRKPK) filter, the standard (weight type) particle filter correction is complemented by a Kalman-type correction for each particle using the covariance matrix of the kernel mixture. The LRKPK filter’s solution is then obtained as the weighted average of several low-rank square root Kalman filters operating in parallel. The Kalman-type correction reduces the risk of ensemble degeneracy, which enables the filter to efficiently operate with fewer particles than the particle filter. Combined with the low-rank approximation, it allows the implementation of the LRKPK filter with high-dimensional oceanic and atmospheric systems. The new filter is described and its relevance demonstrated through applications with the simple Lorenz model and a realistic configuration of the Princeton Ocean Model (POM) in the Mediterranean Sea.
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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 GA-aided particle filter was applied in the established GPS system nonlinear dynamic state space model. The experimental results based on the collected real GPS data is compared with the tradition particle filter, and compared with the effective number of particles and particle distribution. The experimental results indicated that the GA-aided particle filter can increase the number of particle, and effectively solve the particle degradation phenomena, the estimation accuracy of GA-aided particle filter is better than that of particle filter (PF).
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Yao, Hai Tao, Hai Qiang Chen, and Tuan Fa Qin. "Niche PSO Particle Filter with Particles Fusion for Target Tracking." Applied Mechanics and Materials 239-240 (December 2012): 1368–72. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1368.
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An improved particle filter algorithm is proposed to track a randomly moving target in video. In particle filter framework, a particle swarm optimization improved by niche technique which implemented by restricted competition selection is integrated. It can move particles into high likelihood area of target and form multi-population distribution, so that the searching capability of particles is enhanced and then the adaptation to the change of dynamic target state is improved. The particles of niching particle swarm optimization and the particles of particle filter are integrated for new particle weight calculation and finally realize a new particle filter for target tracking in video sequence.
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Zhang, Wenzheng, Jiumao Cai, Guoliang Zhai, Lei Song, and Mouchao Lv. "Experimental Study on the Filtration Characteristics and Sediment Distribution Influencing Factors of Sand Media Filters." Water 15, no. 24 (December 18, 2023): 4303. http://dx.doi.org/10.3390/w15244303.
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The sand media filter is a crucial component of micro-irrigation filtration systems. Investigating the effects of various factors on filtration performance and the migration patterns of sediment particles within the filter can enhance the healthy operation of sand media filters. This study, based on a sand media filter model, conducts indoor hydraulic experiments, selecting different experimental factors and observation indices for research under varying conditions. Results indicate that filter thickness and raw water concentration are positively correlated with the turbidity of filtered water samples, while changes in filtration rate have no obvious impact on turbidity. When filter thickness is large, particle content differs obviously from other filter thicknesses, and the effect of raw water concentration changes on particle content is similar to that of filter thickness changes. Sediment particle size distribution within the filter layer is primarily concentrated in the upper region. By focusing on the area situated 20 cm below the filter layer surface, the sediment retention rate reached 80% or higher at a depth of 20 cm below the filter layer surface. As the depth of the filter layer increases, the sediment retention characteristics show a decreasing trend for larger particle sizes and an increasing trend for smaller particle sizes. Head loss is positively correlated with filtration rate, raw water concentrations, and filter thickness changes. It is suggested that, when meeting micro-irrigation water quality requirements, the filter material particle size can be appropriately increased to improve filtration efficiency and reduce energy consumption. These research findings are highly significant for sand media filter material selection and working condition design.
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Kim, J., and J. E. Tobiason. "Origins of filter effluent particles: experimental study of particle deposition and detachment." Water Science and Technology 50, no. 12 (December 1, 2004): 215–22. http://dx.doi.org/10.2166/wst.2004.0716.
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This paper investigates the relative roles of particle deposition and detachment in controlling the origin of filter effluent particles. A conceptual mathematical model was developed and laboratory-scale experiments were conducted. Laboratory experiments were performed using three sizes of fluorescent microspheres (FMs), to determine the fraction of filter effluent particles that are filter influent particles that were never removed, as well as the fraction of filter effluent particles that were detached after deposition. Experimental results indicated that particle detachment is significant beginning from the early phase of filtration. FM removal increased with filter run time, depth and particle size. For each size FM at one filter depth, FM removal increased with filter runtime to a maximum due to ripening and then decreased with filter runtime after ripening due to limited pore space remaining in the filter. The fraction of effluent particles that were detached particles increased with particle size and filter bed depth.
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Christensen, Morten Lykkegaard, Morten Møller Klausen, and Peter Vittrup Christensen. "Test of precoat filtration technology for treatment of swimming pool water." Water Science and Technology 77, no. 3 (November 21, 2017): 748–58. http://dx.doi.org/10.2166/wst.2017.593.
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Abstract The technical performance of a precoat filter was compared with that of a traditional sand filter. Particle concentration and size distribution were measured before and after the filtration of swimming pool water. Both the sand and precoat filters could reduce the particle concentration in the effluent. However, higher particle removal efficiency was generally observed for the precoat filter, especially for particles smaller than 10 μm in diameter. Adding flocculant improved the removal efficiency of the sand filter, resulting in removal efficiencies comparable to those of the precoat filter. Three powders, i.e., two types of perlite (Harbolite® and Aquatec perlite) and cellulose fibers (Arbocel®), were tested for the precoat filter, but no significant difference in particle removal efficiency was observed among them. The maximum efficiency was reached within 30–40 min of filtration. The energy required for the pumps increased by approximately 35% over a period of 14 days. The energy consumption could be reduced by replacing the powder on the filter cloth. The sand filter was backwashed once a week, while the powder on the precoat filter was replaced every two weeks. Under these conditions, it was possible to reduce the water used for cleaning by 88% if the precoat filter was used instead of the sand filter.
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Naddeo, V., and V. Belgiorno. "Tertiary filtration in small wastewater treatment plants." Water Science and Technology 55, no. 7 (April 1, 2007): 219–25. http://dx.doi.org/10.2166/wst.2008.148.
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Tertiary filtration can be proposed in small wastewater treatment plants with impact on protected water bodies. Rotating disk filters may be adopted, in respect to conventional sand filters, when low availability of space and low investment costs are the prevailing conditions. The overall objective of this research was to evaluate the filtration efficiency of rotating disk filters; to compare effectiveness with traditional sand filters; to analyse thoroughly the importance of particle size distribution in wastewater tertiary filtration. In the experimental activity, conventional wastewater quality parameters were investigated and particle size distribution (PSD) was characterized to discuss the filter effectiveness. The effect of design and operation parameters of tertiary filters were discussed related to particle removal curves derived from particles counts. Analysis of particle size distribution can be very useful to help comprehension of filtration processes, design of filtration treatments and to decide the best measures to improve filter performance.
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Murata, Masaya, and Kaoru Hiramatsu. "On Ensemble Kalman Filter, Particle Filter, and Gaussian Particle Filter." Transactions of the Institute of Systems, Control and Information Engineers 29, no. 10 (2016): 448–62. http://dx.doi.org/10.5687/iscie.29.448.
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Feng, Jie, Xuguang Wang, and Jonathan Poterjoy. "A Comparison of Two Local Moment-Matching Nonlinear Filters: Local Particle Filter (LPF) and Local Nonlinear Ensemble Transform Filter (LNETF)." Monthly Weather Review 148, no. 11 (November 2020): 4377–95. http://dx.doi.org/10.1175/mwr-d-19-0368.1.
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AbstractThe local particle filter (LPF) and the local nonlinear ensemble transform filter (LNETF) are two moment-matching nonlinear filters to approximate the classical particle filter (PF). They adopt different strategies to alleviate filter degeneracy. LPF and LNETF localize observational impact but use different localization functions. They assimilate observations in a partially sequential and a simultaneous manner, respectively. In addition, LPF applies the resampling step, whereas LNETF applies the deterministic square root transformation to update particles. Both methods preserve the posterior mean and variance of the PF. LNETF additionally preserves the posterior correlation of the PF for state variables within a local volume. These differences lead to their differing performance in filter stability and posterior moment estimation. LPF and LNETF are systematically compared and analyzed here through a set of experiments with a Lorenz model. Strategies to improve the LNETF are proposed. The original LNETF is inferior to the original LPF in filter stability and analysis accuracy, particularly for small particle numbers. This is attributed to both the localization function and particle update differences. The LNETF localization function imposes a stronger observation impact than the LPF for remote grids and thus is more susceptible to filter degeneracy. The LNETF update causes an overall narrower range of posteriors that excludes true states more frequently. After applying the same localization function as the LPF and additional posterior inflation to the LNETF, the two filters reach similar filter stability and analysis accuracy for all particle numbers. The improved LNETF shows more accurate posterior probability distribution but slightly worse spatial correlation of posteriors than the LPF.
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Xue, Li, Chunning Na, and Yulan Han. "Improved Auxiliary Particle Filter for SINS/SAR Navigation." Mathematical Problems in Engineering 2021 (January 31, 2021): 1–9. http://dx.doi.org/10.1155/2021/6635390.
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In order to obtain the relatively appropriate importance density function and alleviate the problem of particle degradation, a new improved auxiliary particle filter algorithm is proposed. After calculating the auxiliary variable, the adaptive regulator is employed to obtain the state estimation. So, the latest measurement information is efficiently utilized to establish a better importance density function in the importance sampling process. Then, the process of particle weights’ adaptive adjustment and random-weighted calculation can keep the diversity of particles and improve the filter precision; thus, it can better solve the filter problem of nonlinear system model error and noise interference. The simulation and analysis result show that the proposed algorithm can optimize the filter performance and improve the calculation precision in the positioning of the SINS/SAR integrated navigation system, compared with the other two existing filters.
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Poterjoy, Jonathan. "A Localized Particle Filter for High-Dimensional Nonlinear Systems." Monthly Weather Review 144, no. 1 (December 22, 2015): 59–76. http://dx.doi.org/10.1175/mwr-d-15-0163.1.
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Abstract This paper presents a new data assimilation approach based on the particle filter (PF) that has potential for nonlinear/non-Gaussian applications in geoscience. Particle filters provide a Monte Carlo approximation of a system’s probability density, while making no assumptions regarding the underlying error distribution. The proposed method is similar to the PF in that particles—also referred to as ensemble members—are weighted based on the likelihood of observations in order to approximate posterior probabilities of the system state. The new approach, denoted the local PF, extends the particle weights into vector quantities to reduce the influence of distant observations on the weight calculations via a localization function. While the number of particles required for standard PFs scales exponentially with the dimension of the system, the local PF provides accurate results using relatively few particles. In sensitivity experiments performed with a 40-variable dynamical system, the local PF requires only five particles to prevent filter divergence for both dense and sparse observation networks. Comparisons of the local PF and ensemble Kalman filters (EnKFs) reveal advantages of the new method in situations resembling geophysical data assimilation applications. In particular, the new filter demonstrates substantial benefits over EnKFs when observation networks consist of densely spaced measurements that relate nonlinearly to the model state—analogous to remotely sensed data used frequently in weather analyses.
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Qiao, Yu Kun, Qi Zhang, and Jin Sheng Zhang. "A Fault Predication Algorithm Based on Artificial Immune Particle Filter." Applied Mechanics and Materials 44-47 (December 2010): 3459–63. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3459.
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Degeneracy problem is an inevitable result of sequential importance re-sampling (SIR) particle filter, and a mass of degenerated particles will influence the tracking ability of particle filter seriously. As a result, SIR particle filter based predication algorithm can’t predict system faults accurately. Artificial immune algorithm is characterized by a global ability to search for optimum, so it is introduced into the particle filter, named artificial immune particle filter (AIPF). Particles are regarded as antibodies in AIPF and particles with large weight aberrance and are cloned, and then the better particles are selected for states evaluation. A fault predication algorithm based on AIPF is proposed to improve the predication accuracy, and simulation results have demonstrated the feasibility of the proposed algorithm.
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Thaysen, Clara, Keenan Munno, Ludovic Hermabessiere, and Chelsea M. Rochman. "Towards Raman Automation for Microplastics: Developing Strategies for Particle Adhesion and Filter Subsampling." Applied Spectroscopy 74, no. 9 (June 30, 2020): 976–88. http://dx.doi.org/10.1177/0003702820922900.
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Automation and subsampling have been proposed as solutions to reduce the time required to quantify and characterize microplastics in samples using spectroscopy. However, there are methodological dilemmas associated with automation that are preventing its widespread implementation including ensuring particles stay adhered to the filter during filter mapping and developing an appropriate subsampling strategy to reduce the time needed for analysis. We provide a solution to the particle adherence issue by applying Skin Tac, a non-polymeric permeable adhesive that allows microplastic particles to adhere to the filter without having their Raman signal masked by the adhesive. We also explore different subsampling strategies to help inform how to take a representative subsample. Based on the particle distributions observed on filters, we determined that assuming a homogenous particle distribution is inappropriate and can lead to over- and under-estimations of extrapolated particle counts. Instead, we provide recommendations for future studies that wish to subsample to increase the throughput of samples for spectroscopic analysis.
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Hoteit, Ibrahim, Xiaodong Luo, and Dinh-Tuan Pham. "Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters." Monthly Weather Review 140, no. 2 (February 2012): 528–42. http://dx.doi.org/10.1175/2011mwr3640.1.
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This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. The authors show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an “ensemble of Kalman filters” operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, the authors consider the construction of the PKF through an “ensemble” of ensemble Kalman filters (EnKFs) instead, and call the implementation the particle EnKF (PEnKF). It is shown that different types of the EnKFs can be considered as special cases of the PEnKF. Similar to the situation in the particle filter, the authors also introduce a resampling step to the PEnKF in order to reduce the risk of weights collapse and improve the performance of the filter. Numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.
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Guo, Yuyang, Xiangbo Xu, and Miaoxin Ji. "A Zero-Velocity Update Method for Adaptive Particle Filtering." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 2 (April 2020): 427–33. http://dx.doi.org/10.1051/jnwpu/20203820427.
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Aiming at the low precision of Kalman filter in dealing with non-linear and non-Gaussian models and the serious particle degradation in standard particle filter, a zero-velocity correction algorithm of adaptive particle filter is proposed in this paper. In order to improve the efficiency of resampling, the adaptive threshold is combined with particle filter. In the process of resampling, the degradation co-efficient is introduced to judge the degree of particle degradation, and the particles are re-sampled to ensure the diversity of particles. In order to verify the effectiveness and feasibility of the proposed algorithm, a hardware platform based on the inertial measurement unit (IMU) is built, and the state space model of the system is established by using the data collected by IMU, and experiments are carried out. The experimental results show that, compared with Kalman filter and classical particle filter, the positioning accuracy of adaptive particle filter in zero-velocity range is improved by 40.6% and 19.4% respectively. The adaptive particle filter (APF) can correct navigation errors better and improve pedestrian trajectory accuracy.
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Cong-An, Xu, Xu Congqi, Dong Yunlong, Xiong Wei, Chai Yong, and Li Tianmei. "A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6872–77. http://dx.doi.org/10.1166/jctn.2016.5640.
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As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.
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Chan, Tak W., Marie Lee, Gary Mallach, and David Buote. "Efficiency of the Vehicle Cabin Air Filters for Removing Black Carbon Particles and BTEX from the Air Intake." Applied Sciences 11, no. 19 (September 28, 2021): 9048. http://dx.doi.org/10.3390/app11199048.
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A laboratory study was conducted to evaluate 11 vehicular cabin filters (including electrostatic filters) in removing fine particles. Two filters with charcoal were also evaluated to understand their usefulness in removing five common volatile organic compounds, including benzene, toluene, ethylbenzene, and xylene isomers (BTEX). Filters were found to show considerably different particle filtration efficiencies (FE). Electrostatic filters were found to provide 20–60% better FE across all particle diameters (6–520 nm). For 6 nm particles, FE from 78 to 94% were observed (from the worst to the best filters), while at 520 nm, FE varied from 35 to 60%. The best group of filters provided 44–46% FE for capturing the most penetrating particles (100–300 nm), while the worst group of filters provided only 10–11% FE. The filtration behavior of nominal filters was typically stable (with respect to particle number, black carbon, and particulate matter mass) over the course of 1–2 years of usage. The benefits of the electrostatic filters were significant, but such advantages were observed to gradually dissipate over the course of about 1 year; by then, the electrostatic filter becomes no different compared to a nominal filter in terms of filtration behavior. Charcoal filters showed variabilities in removing BTEX, and removal efficiencies varied from 11 to 41%.
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Lee, J. J., J. H. Im, R. BenAim, J. R. Kim, Y. J. Kim, K. M. Poo, and C. W. Kim. "Better understanding of the filtration characteristics in the flexible fibre filter module (3FM)." Water Science and Technology 55, no. 1-2 (January 1, 2007): 77–83. http://dx.doi.org/10.2166/wst.2007.049.
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This study is about the particle retention and filtration characteristics of fibre filter. Four laboratory scale fibre filters with different heights were used in parallel at various packing densities and filtration velocities. Of all of the operating parameters studied, filtration velocity had the most influence. Contrary to general theories, pressure drop increases slightly during the filtration in spite of the continuous retention of particles. This may have occurred because of large porosity of the packing (about 93%). This might be considered an advantage of the filter and something that makes it economic. The higher the filtration velocity, the larger the mass of particles retained in the filter. For filtration velocities of 20 and 40 m/h, particles smaller than 5 μm are retained as proven by the particle size distribution at the inlet and outlet.
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Dong, Rui, Zhiqing Zhang, Yanshuai Ye, Huiqiong Huang, and Chao Cao. "Review of Particle Filters for Internal Combustion Engines." Processes 10, no. 5 (May 17, 2022): 993. http://dx.doi.org/10.3390/pr10050993.
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Diesel engines have gradually become one of the main forces in the human transportation industry because of their high efficiency, good durability, and stable operation. However, compared with gasoline vehicles, the high emission of diesel vehicles forces manufacturers to introduce new pollutant control technologies. Although the particulate matter emissions of gasoline vehicles are lower than that of diesel vehicles, with the popularity of gasoline vehicles and the continuous rise of power, the impact of these particles on the environment cannot be ignored. Therefore, diesel particulate filters and gasoline particulate filters have been invented to collect the fine particles in the exhaust gas to protect the environment and meet increasingly stringent emission regulations. This paper summarizes the research progress on diesel particulate filters and gasoline particulate filters at present and comprehensively introduces the diesel particulate filter and gasoline particulate filter from the mechanism, composition, and operation processes. Additionally, the laws and regulations of various countries and the impact of gas waste particulates on the human body are described. In addition, the mechanisms of the diesel particulate filter, gasoline particulate filter, and regeneration were studied. Finally, the prospects and future directions for the development of particle filters for internal combustion engines are presented.
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Puderbach, Vanessa, Kilian Schmidt, and Sergiy Antonyuk. "A Coupled CFD-DEM Model for Resolved Simulation of Filter Cake Formation during Solid-Liquid Separation." Processes 9, no. 5 (May 9, 2021): 826. http://dx.doi.org/10.3390/pr9050826.
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In cake filtration processes, where particles in a suspension are separated by forming a filter cake on the filter medium, the resistances of filter cake and filter medium cause a specific pressure drop which consequently defines the process energy effort. The micromechanics of the filter cake formation (interactions between particles, fluid, other particles and filter medium) must be considered to describe pore clogging, filter cake growth and consolidation correctly. A precise 3D modeling approach to describe these effects is the resolved coupling of the Computational Fluid Dynamics with the Discrete Element Method (CFD-DEM). This work focuses on the development and validation of a CFD-DEM model, which is capable to predict the filter cake formation during solid-liquid separation accurately. The model uses the Lattice-Boltzmann Method (LBM) to directly solve the flow equations in the CFD part of the coupling and the DEM for the calculation of particle interactions. The developed model enables the 4-way coupling to consider particle-fluid and particle-particle interactions. The results of this work are presented in two steps. First, the developed model is validated with an empirical model of the single particle settling velocity in the transition regime of the fluid-particle flow. The model is also enhanced with additional particles to determine the particle-particle influence. Second, the separation of silica glass particles from water in a pressurized housing at constant pressure is experimentally investigated. The measured filter cake, filter medium and interference resistances are in a good agreement with the results of the 3D simulations, demonstrating the applicability of the resolved CFD-DEM coupling for analyzing and optimizing cake filtration processes.
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Kaminski, Iris, Nicolae Vescan, and Avner Adin. "Particle size distribution and wastewater filter performance." Water Science and Technology 36, no. 4 (August 1, 1997): 217–24. http://dx.doi.org/10.2166/wst.1997.0123.
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Particle size distribution (PSD) allows more accurate simulations of filtration models and better understanding of filter performance. PSD in municipal activated sludge effluent filtration is determined, varying filtration rate, grain size, flocculant type and dosage and function parameters are examined in this work. Results show, that removal efficiency varies for different size groups: small particles in the range of 5-10 μm in initialization stage, with no chemical aids, are poorly removed. Higher rate filters were more sensitive to the particle size than lower rate filters. Filtration with chemical aids is more sensitive to filtration conditions than filtration with no chemical additions. Particle size distribution in filtrate generally fits power law function behavior better than in raw effluent. The treatment smoothens the function somewhat. In a similar manner to the effect of settling in tanks or in natural lakes. Degree of correlation to power law function may indicate the mode of filter operation: high - working stage, low - breakthrough stage. β may also reflect on filters performance: high values - initial filtration stages. Decrease in β values - cycle progress towards breakthrough. Low β values, with low PSD correlation to power law function, may indicate low filtration efficiency or breakthrough.
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Akyildiz, Ömer Deniz, and Joaquín Míguez. "Nudging the particle filter." Statistics and Computing 30, no. 2 (July 13, 2019): 305–30. http://dx.doi.org/10.1007/s11222-019-09884-y.
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Abstract We investigate a new sampling scheme aimed at improving the performance of particle filters whenever (a) there is a significant mismatch between the assumed model dynamics and the actual system, or (b) the posterior probability tends to concentrate in relatively small regions of the state space. The proposed scheme pushes some particles toward specific regions where the likelihood is expected to be high, an operation known as nudging in the geophysics literature. We reinterpret nudging in a form applicable to any particle filtering scheme, as it does not involve any changes in the rest of the algorithm. Since the particles are modified, but the importance weights do not account for this modification, the use of nudging leads to additional bias in the resulting estimators. However, we prove analytically that nudged particle filters can still attain asymptotic convergence with the same error rates as conventional particle methods. Simple analysis also yields an alternative interpretation of the nudging operation that explains its robustness to model errors. Finally, we show numerical results that illustrate the improvements that can be attained using the proposed scheme. In particular, we present nonlinear tracking examples with synthetic data and a model inference example using real-world financial data.
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Siradjuddin, Indah Agustien, and Muhammad Rahmat Widyanto. "Particle Filter with Gaussian Weighting for Vehicle Tracking." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 6 (August 20, 2011): 681–86. http://dx.doi.org/10.20965/jaciii.2011.p0681.
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To track vehicle motion in data video, particle filter with Gaussian weighting is proposed. This method consists of four main stages. First, particles are generated to predict target’s location. Second, certain particles are searched and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The proposed method could reduce computational time of tracking compared to that of conventional method of particle filter, since the proposed method does not have to calculate all particles weight using likelihood function. This method has been tested on video data with car as a target object. In average, this proposed method of particle filter is 60.61% times faster than particle filter method meanwhile the accuracy of tracking with this newmethod is comparable with particle filter method, which reach up to 86.87%. Hence this method is promising for real time object tracking application.