Particle Filter Research Articles

Effects of Low Emission Zones on Air Quality, New Vehicle Registrations, and Birthweights: Evidence from Japan

In October 2003 four contiguous prefectures in Greater Tokyo introduced Low Emission Zones (LEZs) from which diesel trucks and buses without particulate filters have been banned from entering. This paper analyzes the effects of this large-scale intervention on air quality, new vehicle registrations, and birthweights. We use a matching approach to construct a control group comparable to the designated areas in terms of propensity scores based on municipality characteristics during the pre-intervention period and apply a difference-in-differences design. We find evidence that the intervention led to reductions in hourly particulate matter concentrations and the incidence of low birthweights in the Greater Tokyo LEZ relative to the control group. We also find that the LEZs led to increases in registrations of new trucks and buses. This is not the case for passenger cars, which were exempt from the regulations. Our paper provides the first evidence of a significant link between LEZs and reduced incidence of low birthweights.

An investigation of the filtration performance and mechanism of oil-solid mixed particles on filter materials with different wettability

In the process of air filtration, filter materials may encounter oil-solid mixed particles from various sources such as industry and household, and the influence of filter material with different wettability on the filtration performance of oil-solid mixed particles has not been fully reported. The objective of this study was to prepare filter materials with varying wettability properties and investigate their filtration performance for oil-solid mixed particles with diverse oil contents. In this study, four filter materials were prepared using the impregnation method, exhibiting 0° (superoleophilic), 46° (oleophilic), 90° and 122° (oleophobic) oil contact angles. The oil-solid mixed particles with varying oil contents were loaded on the four different filter materials. The loading behavior of the filter materials was characterized from two aspects: macroscopic loading behavior (changes in pressure drop and DHC) and microscopic particle deposition behavior. The results demonstrated that, under the conditions of 25% and 50% oil content, the enhanced liquid bridge force between the oil-solid mixed particles on the oleophobic filter material facilitated the formation of a loose and porous cake layer structure compared with other filter materials, resulting in a lower growth rate of pressure drop and a higher dust holding capacity (DHC). However, in 75% and 100% oil content environments, the superoleophilic filter material owned a remarkably high affinity for oil particles compared with other filter materials, leading to the gradual formation of an oil film structure on the filter material, yielding lower the growth rate of pressure drop and higher DHC. These findings shed light on the mechanism of particle deposition under diverse filtration conditions and are applicable to the filtration of various oil-solid mixed particles over a wide range of oil contents.

Effect analysis on passive and active regeneration performance of catalytic diesel particulate filters at the equilibrium point in the presence of ash layer

AbstractCatalytic diesel particulate filters (CDPF) form the main post‐processing devices for the diesel engines. Particulates trapped by CDPF can be oxidized by nitrogen dioxide (NO2) and oxygen (O2). The CDPF passive and active regeneration model on platinum‐based catalyst in the presence of ash layer, including the mass, momentum, and enthalpy balances of the gas and the solid phase, the gas diffusion, and the kinetic of the soot oxidation reaction is performed and validated by the bench test. The effects of different temperatures on CDPF passive and active regeneration at the equilibrium point are analyzed. Further, the effective analysis of ash loading and ash layer distribution on the equilibrium state is conducted. The research on CDPF passive and active regeneration at the equilibrium point in the presence of ash layer provides a theoretical basis for CDPF application.

Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking

Welding seam tracking based on online programming is the future trend of intelligent production. However, most of the existing image processing methods have certain limitations in the adaptability, accuracy, and robustness of weld feature point detection. The online welding method of gas metal arc welding (GMAW) based on active vision sensing is studied in this paper. The Steger sub-pixel detection method is used to guarantee the accuracy of feature point extraction, and a self-adaptive search window and self-adaptive slope extraction are proposed on this basis. The self-adaptive window is generated according to the linear information of the weld area, and the scale factor and range threshold constraint are added to realize the real-time detection of the weld feature information. Screening the center pixel of the laser stripe in the self-adaptive window of the current frame by the initial slope or the self-adaptive slope of the previous frame, the linear information of the weld area is obtained. The self-adaptive slope of the current frame is fitted by the random sampling consistency method, and the pixel margin is retained to adapt to the linear detection of different continuous welds. When arc light and other serious interference make it difficult to obtain weld information, a particle filter is used to make the best prediction of the weld position. Finally, the welding robot platform based on laser vision sensing was built to test various continuous welds of the butt weld, fillet weld, and lap weld. Experimental results show that the detection speed is 27 ms, and the accuracy of detection and tracking can respectively reach 0.03 mm and 0.78 mm, which meets the requirements of weld detection and tracking.

Cellulose-Based Triboelectric Nanogenerator Prepared by Multi-Fluid Electrospinning for Respiratory Protection and Self-Powered Sensing

A cellulose-based triboelectric nanogenerator (TENG) with fiber–wave–arch structure was prepared through a multi-fluid electrospinning process for air filtration and wind sensing. The TENG is composed of a cellulose nanocrystals (CNC)/zein membrane and a cyanoethyl cellulose (CEC)/polyvinylidene fluoride (PVDF) membrane. The results show that the addition of CEC improves the output performance and filterability of TENG. At the same time, the reduced diameter and high roughness of CEC/PVDF nanofibers improve the output performance of the TENG. The TENG with a 6 wt% CEC/PVDF solution concentration has the highest output performance with a short-circuit current of 3.30 μA and an open-circuit voltage of 10.01 V. The particle filtration of 12 wt% CEC/PVDF TENG is the best, showing an efficiency of 98.84% and a pressure drop of 50 Pa. The TENG also has a good formaldehyde filtration capability with an efficiency of 92% at 0.25 mg/m3. The TENG shows great potential in self-powered sensor applications.

Experimental study on enhancing electrostatic charging of nonwoven filter media through novel electrode configurations

The COVID-19 pandemic has underscored the urgent need for efficient particle filtration to tackle public health challenges. This study compares the electrostatic charging capabilities of two types of electrodes: conventional Metal Lamellar Electrodes (MLE) and Modified Lamellar Plate Electrodes (MLPE) with PMMA insulation, in their role of charging nonwoven polypropylene filter media. Experimental analysis unequivocally demonstrates the superiority of MLPE in promoting effective electrostatic charge, as influenced by applied high voltage and inter-electrode distance, resulting in a significant enhancement in fine particle filtration. Numerical simulations of the electric field conducted using the COMSOL software and employing the Finite Element Method (FEM) validate these findings. FEM discretizes the domain into small finite elements and applies Laplace's equation in charge-free regions, providing a comprehensive understanding of the behavior of dual-electrode corona discharge systems. These simulations indicate higher field intensities for MLPE compared to MLE.By integrating experimental and numerical analyses, this study sheds light on the potential benefits of MLPE for air filtration systems, offering a noteworthy advancement in the control of airborne infections and reduction of environmental health risks. This research lays the groundwork for the development of more efficient and durable filtration systems, meeting the increasing demands for public health protection.The findings of this study suggest that the adoption of MLPE could significantly contribute to improving indoor air quality and mitigating the transmission of respiratory diseases. MLPE may also find utility in industrial filtration and air purification in sensitive work environments. Ultimately, this research sets the stage for the development of more efficient and sustainable filtration systems, addressing the growing needs for public health protection.

A SC Financial Credit Risk Assessment Model Based on Particle Filter and SVM with Gain Information

The accuracy of credit risk prediction in SC financing is critical for many enterprises, based on machine learning algorithms can be good for SME credit risk assessment research, for this reason, this paper establishes a combinatorial model that can improve credit risk prediction, using support vector machine (SVM) and particle filtering to achieve credit risk classification and prediction, we and introduce information gain (IG) to extract the prediction of The model uses SVM and particle filtering to classify and predict credit risk, and we introduce information gain (IG) to extract feature variables that contribute significantly to the prediction results and optimize model feature inputs. Compared with the benchmark model, the prediction accuracy of the model in this paper is 97.62%, which is 8.97% higher than that of SVM, and the performance of IG with feature optimization improves the prediction accuracy by another 3%.

Electrostatic vehicle exhaust particle sensor for the evaluation of the diesel particulate filter (DPF)

A real-time electrostatic particle sensor has been developed to measure the concentration of soot in diesel exhaust. Based on the Venturi effect, the self-priming sampling probe of the sensor was designed to generate a pressure difference to drive particles into the detection region of the electrostatic trap. A detection module was developed for real-time measurement of the nanoamp current generated by charged soot within the probe. The microcurrent was amplified by the signal processing system of the sensor. To guarantee the precision of particles, it was essential to test the performance. The sensor was able to measure currents from −100 nA to −30 nA with a relative error of −0.01%. In addition, experiments comparing the sensor with a commercial instrument have shown that they correlate well. Furthermore, the standard deviation of the sensor was 0.001049 during a 36 h long-term test, which is lower than a commercial instrument with better stability. The sensor was investigated to demonstrate the detection of diesel particulate filter (DPF) failure. The results showed that the consistency between the sensor and the regulatory measurement equipment was good. The data ratios of the sensors installed at the front and rear of the DPF differed by many fold, which indicated that the sensor was able to determine the status of the exhaust system.

A simulation-driven prediction model for state of charge estimation of electric vehicle lithium battery

Accurately predicting the state of charge (SOC) of lithium-ion batteries in electric vehicles is crucial for ensuring their stable operation. However, the component values related to SOC in the circuit typically require estimation through parameter identification. This paper proposes a three-stage method for estimating the SOC of lithium batteries in electric vehicles. Firstly, the parameters of the constructed second-order RC circuit are identified using the Forgetting Factor Recursive Least Squares (FFRLS) method. Secondly, an innovative approach is employed to construct a battery simulation model using modal-data fusion method. Finally, the predicted values of the simulation model are corrected using the unscented Kalman filter (UKF). Validation through datasets demonstrates the high precision of this method in parameter identification. Moreover, in the comparison of SOC prediction corrections with Particle Filter (PF), Extended Kalman Filter (EKF), and the proposed UKF on simulated prediction data and experimental test data. The proposed method achieves the lowest root mean square error (RMSE) of 0.0025 for simulation prediction data and 0.0186 for experimental test data. It also maintained its error within 5 % on actual data.

High throughput clogging free microfluidic particle filter by femtosecond laser micromachining.

In recent decades, driven by the needs of industry and medicine, researchers have been investigating how to remove carefully from the main flow microscopic particles or clusters of them. Among all the approaches proposed, crossflow filtration is one of the most attractive as it provides a non-destructive, label-free and in-flow sorting method. In general, the separation performance shows capture and separation efficiencies ranging from 70% up to 100%. However, the maximum flow rate achievable (µL/min) is still orders of magnitude away from those suitable for clinical or industrial applications mainly due to the low stiffness of the materials typically used. In this work, we propose an innovative hydrodynamic-crossflow hybrid filter geometry, buried in a fused silica substrate by means of the femtosecond laser irradiation followed by chemical etching technique. The material high stiffness combined with the accuracy of our manufacturing technique allows the 3D fabrication of non-deformable channels with higher aspect ratio posts, while keeping the overall device dimensions compact. The filter performance has been validated through experiments with both Newtonian (water-based solution of microbeads) and non-Newtonian fluids (blood), achieving separation efficiencies of up to 94% and large particles recovery rates of 100%, even at very high flow rates (mL/h).

Exploration-Based Planning for Multiple-Target Search with Real-Drone Results.

Consider a drone that aims to find an unknown number of static targets at unknown positions as quickly as possible. A multi-target particle filter uses imperfect measurements of the target positions to update an intensity function that represents the expected number of targets. We propose a novel receding-horizon planner that selects the next position of the drone by maximizing an objective that combines exploration and target refinement. Confidently localized targets are saved and removed from consideration along with their future measurements. A controller with an obstacle-avoidance component is used to reach the desired waypoints. We demonstrate the performance of our approach through a series of simulations as well as via a real-robot experiment in which a Parrot Mambo drone searches from a constant altitude for targets located on the floor. Target measurements are obtained on-board the drone using segmentation in the camera image, while planning is done off-board. The sensor model is adapted to the application. Both in the simulations and in the experiments, the novel framework works better than the lawnmower and active-search baselines.

Sideslip Angle Estimation for Distributed Drive Electric Vehicles Based on Robust Unscented Particle Filter

An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly, a nonlinear dynamics model of DDEV is constructed, providing a theoretical foundation for the design of the RUPF algorithm. Then, the RUPF algorithm, which incorporates the unscented Kalman filter (UKF) to update importance density and utilizes systematic random resampling to mitigate particle degradation, is designed for estimation. Eventually, the availability of the proposed RUPF algorithm is validated on the co-simulation platform with non-Gaussian noises. Simulation results demonstrate that RUPF algorithm attains a higher precision and stronger robustness compared with the traditional PF and UKF algorithms.

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.

An Adaptive Tracking Method for Moving Target in Fluctuating Reverberation Environment

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method.

Particle-Filter-Based Fault Diagnosis for the Startup Process of an Open-Cycle Liquid-Propellant Rocket Engine.

This study introduces a fault diagnosis algorithm based on particle filtering for open-cycle liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method for the startup process, accounting for more than 30% of engine failures. Similar to the previous fault detection and diagnosis (FDD) algorithm for the startup process, the algorithm in this study is composed of a nonlinear filter to generate residuals, a residual analysis, and a multiple-model (MM) approach to detect and diagnose faults from the residuals. In contrast to the previous study, this study makes use of the modified cumulative sum (CUSUM) algorithm, widely used in change-detection monitoring, and a particle filter (PF), which is theoretically the most accurate nonlinear filter. The algorithm is confirmed numerically using the CUSUM and MM methods. Subsequently, the FDD algorithm is compared with an algorithm from a previous study using a Monte Carlo simulation. Through a comparative analysis of algorithmic performance, this study demonstrates that the current PF-based FDD algorithm outperforms the algorithm based on other nonlinear filters.

Superhydrophobic Materials for Self-cleaning Applications: A Review

Abstract: Since the discovery of the “lotus effect,” the superhydrophobic structure of the research is more and more profound. When the droplets fall on the surface, it will not occur as a wetting phenomenon. Making full use of this feature will achieve the self-cleaning effect of the object surface. In the dust filtration problem, it is possible to spray superhydrophobic materials on the surface of the equipment using superhydrophobic self-cleaning characteristics to change the filtering method and to improve the self-cleaning efficiency. This paper describes the methods of preparing superhydrophobic materials, self-cleaning methods, and the application of superhydrophobic materials in the field of self-cleaning. Based on the summary of patents and papers, this paper focuses on the filtration and self-cleaning effect of superhydrophobic materials on dust particles in humid environments. With a simple structure, high filtration efficiency, and long service life, the super sparse/hydrophilic hybrid mesh can work continuously without manual operation. It can effectively filter dust particles and improve air quality in humid environments. Coal powder condensation experiments in this paper proved that the application of superhydrophobic materials in the field of self-cleaning yielded significant results. When superhydrophobic materials are sprayed on copper-based filters, the hydrophilic line mixes with the hydrophobic region, greatly improving the self-cleaning efficiency.

Fabrication of porous and highly defective CeO2-based catalysts for soot combustion with the strategy of soft-template addition and matrix doping by multiple rare earth elements

In this study, aiming to optimize the reactivity of CeO2 for soot combustion, rare earth elements (La, Pr, Sm and Y) co-doped CeO2 catalysts were synthesized via polyvinylpyrrolidone (PVP) and glycine (GNC) combustion methods. The PVP-CeO2-Re catalyst maintains a high specific surface area (82.9 m2 g−1) and abundant porous structure, thus being able to enhance the soot contact efficiency. The co-doping of multiple rare earth ions improves also the redox properties of the catalyst and increases the surface defects/vacancies, which is crucial to form the active surface O2- and O22- species to participate in soot combustion. The oxygen exchange rate over the catalyst with gaseous O2 can be accelerated by raising the temperature, and the exchange mechanism changes from a mixture of the R1 and R2 mechanisms to a domination by the R2 mechanism. Notably, PVP-CeO2-Re catalyst shows not only excellent activity (T50=373 °C) but also good stability. Our work could provide some reference for the design of promising catalysts applied in diesel particulate filters.

A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range

The state of energy (SOE) is a key indicator for lithium-ion battery management systems (BMS). Based on the second-order resistance-capacitance equivalent circuit model and online parameter identification using the dynamic weights particle swarm optimization (DWPSO) method, a least-squares support vector machine-particle filter (LSSVM-PF) algorithm is proposed to construct a particle filter to estimate the SOE of a lithium-ion battery, and then transfer the resulting estimation error together with the experimentally measured voltage and current values to a trained LSSVM model, and use the LSSVM model to optimize the SOE estimates obtained by the PF algorithm twice to improve the accuracy of SOE estimation for lithium-ion batteries. The feasibility of the proposed algorithm is verified using two complex operating conditions and at three different temperatures. The validation results show that the maximum error of SOE estimation of the proposed algorithm is 0.0284 for a wide temperature range under Beijing Bus Dynamic Stress Test (BBDST) condition, and 0.0226 for a wide temperature range under Dynamic Stress Test (DST) condition. The proposed algorithm significantly improves the accuracy of SOE estimation and provides a reference for fundamental applications of lithium-ion batteries.

Jump Clustering, Information Flows, and Stock Price Efficiency

Abstract We study the clustering behavior of stock return jumps modeled by a self/cross-exciting process embedded in a stochastic volatility model. Based on the model estimates, we propose a novel measurement of stock price efficiency characterized by the extent of jump clustering that stock returns exhibit. This measurement demonstrates the capability of capturing the speed at which stock prices assimilate new information, especially at the firm-specific level. Furthermore, we assess the predictability of self-exciting (clustered) jumps in stock returns. We employ a particle filter to sample latent states in the out-of-sample period and perform one-step-ahead probabilistic forecasting on upcoming jumps. We introduce a new statistic derived from predicted probabilities of positive and negative jumps, which has been shown to be effective in return predictions.

Salience Interest Option: Temporal abstraction with salience interest functions

Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions, also known as options. The issue of discovering options has seen a considerable research effort. Most notably, the Interest Option Critic (IOC) algorithm first extends the initial set to the interest function, providing a method for learning options specialized to certain state space regions. This approach offers a specific attention mechanism for action selection. Unfortunately, this method still suffers from the classic issues of poor data efficiency and lack of flexibility in RL when learning options end-to-end through backpropagation. This paper proposes a new approach called Salience Interest Option Critic (SIOC), which chooses subsets of existing initiation sets for RL. Specifically, these subsets are not learned by backpropagation, which is slow and tends to overfit, but through particle filters. This approach enables the rapid and flexible identification of critical subsets using only reward feedback. We conducted experiments in discrete and continuous domains, and our proposed method demonstrate higher efficiency and flexibility than other methods. The generated options are more valuable within a single task and exhibited greater interpretability and reusability in multi-task learning scenarios.