Relevant bibliographies by topics / PM sensor - Particle sensor

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'PM sensor - Particle sensor'

Author: Grafiati
Published: 1 March 2025

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Journal articles on the topic "PM sensor - Particle sensor"

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Huang, Ching-Hsuan, Jiayang He, Elena Austin, Edmund Seto, and Igor Novosselov. "Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements." PLOS ONE 16, no. 11 (November 11, 2021): e0259745. http://dx.doi.org/10.1371/journal.pone.0259745.

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Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles’ properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors’ performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM’s mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.
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Hagan, David H., and Jesse H. Kroll. "Assessing the accuracy of low-cost optical particle sensors using a physics-based approach." Atmospheric Measurement Techniques 13, no. 11 (November 26, 2020): 6343–55. http://dx.doi.org/10.5194/amt-13-6343-2020.

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Abstract. Low-cost sensors for measuring particulate matter (PM) offer the ability to understand human exposure to air pollution at spatiotemporal scales that have previously been impractical. However, such low-cost PM sensors tend to be poorly characterized, and their measurements of mass concentration can be subject to considerable error. Recent studies have investigated how individual factors can contribute to this error, but these studies are largely based on empirical comparisons and generally do not examine the role of multiple factors simultaneously. Here, we present a new physics-based framework and open-source software package (opcsim) for evaluating the ability of low-cost optical particle sensors (optical particle counters and nephelometers) to accurately characterize the size distribution and/or mass loading of aerosol particles. This framework, which uses Mie theory to calculate the response of a given sensor to a given particle population, is used to estimate the fractional error in mass loading for different sensor types given variations in relative humidity, aerosol optical properties, and the underlying particle size distribution. Results indicate that such error, which can be substantial, is dependent on the sensor technology (nephelometer vs. optical particle counter), the specific parameters of the individual sensor, and differences between the aerosol used to calibrate the sensor and the aerosol being measured. We conclude with a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer general recommendations for the choice of calibrant under different measurement scenarios.
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Kuo, Yu-Mei, Shin-Yu Weng, Sheng-Hsiu Huang, Chih-Wei Lin, and Chih-Chieh Chen. "2 Low-Cost Pm Sensor Performance Testing." Annals of Work Exposures and Health 67, Supplement_1 (May 1, 2023): i3. http://dx.doi.org/10.1093/annweh/wxac087.008.

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Abstract Low-cost particulate matters sensors (LCPMS) are widely used in indoor and outdoor air quality monitoring. Previous studies have explored the accuracy and precision of sensors, showing that LCPMS were most accurate for PM with diameters below 1 μm, and they poorly measured PM in the 2.5–5 μm range. The study aimed to investigate the aspiration and transmission efficiency of LCPMS, to set up a standard aerosol mass concentration generation system, to further comprehend the performance characteristics of LCPMS, and to examine the effect of aerosol loading. Three models of Plantower sensors were tested in this work. An ultrasonic atomizing nozzle was used to generate micrometer-sized NaCl aerosol particles. A TSI aerodynamic particle sizer was used to measure the aerosol concentrations and size distributions upstream and downstream of the LCPMS, to determine the aspiration and transmission efficiency. The mass concentration, from 20 µg/m3 to 200 mg/m3, could be varied by controlling the solution feeding rate and the solution concentration. The high mass concentration was mainly designed for aerosol loading study. The results showed that the aspiration efficiencies of the tested LCPMS were all almost 100% for particle smaller than 5 µm. The transmission efficiency of these sensors was function of particle size and strongly dependent on sampling flow. All sensors tested showed significant performance degrading when challenged with high mass concentration. The orientation of the sensor also played a role affecting the aerosol loading. These sensors performed better when mass concentration is below 100 µg/m3.
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Bulot, Florentin Michel Jacques, Hugo Savill Russell, Mohsen Rezaei, Matthew Stanley Johnson, Steven James Ossont, Andrew Kevin Richard Morris, Philip James Basford, et al. "Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution—Part B—Particle Number Concentrations." Sensors 23, no. 17 (September 4, 2023): 7657. http://dx.doi.org/10.3390/s23177657.

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Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors’ to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity.
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Reynaud, Adrien, Mickael Leblanc, Stéphane Zinola, Philippe Breuil, and Jean-Paul Viricelle. "Soot Particle Classifications in the Context of a Resistive Sensor Study." Proceedings 2, no. 13 (December 7, 2018): 987. http://dx.doi.org/10.3390/proceedings2130987.

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Since 2011, Euro 5b European standard limits the particle number (PN) emissions in addition to the particulate matter (PM) emissions. New thermal engines equipped vehicles have to auto-diagnose their own Diesel particulate filter (DPF) using on-board diagnostic (OBD) sensors. Accumulative resistive soot sensors seem to be good candidates for PM measurements. The aim of this study is to bring more comprehension about soot micro-structures construction in order to link the response of such a sensor to particle size and PN concentration. The sensor sensitivity to the particle size has been studied using successively an electrostatic and an aerodynamic classification, showing the same trend.
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Reynaud, Adrien, Mickaël Leblanc, Stéphane Zinola, Philippe Breuil, and Jean-Paul Viricelle. "Responses of a Resistive Soot Sensor to Different Mono-Disperse Soot Aerosols." Sensors 19, no. 3 (February 9, 2019): 705. http://dx.doi.org/10.3390/s19030705.

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Since 2011, the Euro 5b European standard limits the particle number (PN) emissions in addition to the particulate mass (PM) emissions. New thermal engine equipped vehicles also have to auto-diagnose their own particulate filter (Diesel particulate filter or gasoil particulate filter) using on-board diagnostic (OBD) sensors. Accumulative resistive soot sensors seem to be good candidates for PM measurements. The aim of this study is to bring more comprehension about soot microstructures construction in order to link the response of such a sensor to particle size and concentration. The sensor sensitivity to the particle size has been studied using successively an electrostatic and an aerodynamic classification, showing the same trend.
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Bächler, P., J. Meyer, and A. Dittler. "Characterization of the emission behavior of pulse-jet cleaned filters using a low-cost particulate matter sensor/Charakterisierung der Emission von druckstoßgereinigten Oberflächenfiltern mit einem Low-Cost-Feinstaubsensor." Gefahrstoffe 79, no. 11-12 (2019): 443–50. http://dx.doi.org/10.37544/0949-8036-2019-11-12-49.

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The reduction of fine dust emissions with pulse-jet cleaned filters plays an important role in industrial gas cleaning to meet emission standards and protect the environment. The dust emission of technical facilities is typically measured “end of pipe”, so that no information about the local emission contribution of individual filter elements exists. Cheap and compact low-cost sensors for the detection of particulate matter (PM) concentrations, which have been prominently applied for immission monitoring in recent years have the potential for emission measurement of filters to improve process monitoring. This publication discusses the suitability of a low-cost PM-sensor, the model SPS30 from the manufacturer Sensirion, in terms of the potential for particle emission measurement of surface filters in a filter test rig based on DIN ISO 11057. A Promo® 2000 in combination with a Welas® 2100 sensor serves as the optical reference device for the evaluation of the detected PM2.5 concentration and particle size distribution of the emission measured by the low-cost sensor. The Sensirion sensor shows qualitatively similar results of the detected PM2.5 emission as the low-cost sensor SDS011 from the manufacturer Nova Fitness, which was investigated by Schwarz et al. in a former study. The typical emission peak after jet-pulse cleaning of the filter, due to the penetration of particles through the filter medium, is detected during Δp-controlled operation. The particle size distribution calculated from the size resolved number concentrations of the low-cost sensor yields a distinct distribution for three different employed filter media and qualitatively fits the size distribution detected by the Palas® reference. The emission of these three different types of filter media can be distinguished clearly by the measured PM2.5 concentration and the emitted mass per cycle and filter area, demonstrating the potential for PM emission monitoring by the low-cost PM-sensor. During the period of Δt-controlled filter aging, a decreasing emission, caused by an increasing amount of stored particles in the filter medium, is detected. Due to the reduced particle emission after filter aging, the specified maximum concentration of the low-cost sensor is not exceeded so that coincidence is unlikely to affect the measurement results of the sensor for all but the very first stage of filter life.
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Li, Liangbo, Ang Chen, Tian Deng, Jin Zeng, Feifan Xu, Shu Yan, Shu Wang, Wenqing Cheng, Ming Zhu, and Wenbo Xu. "A Simple Optical Aerosol Sensing Method of Sauter Mean Diameter for Particulate Matter Monitoring." Biosensors 12, no. 7 (June 21, 2022): 436. http://dx.doi.org/10.3390/bios12070436.

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Mass concentration is a commonly used but insufficient metric to evaluate the particulate matter (PM) exposure hazard. Recent studies have declared that small particles have more serious impacts on human health than big particles given the same mass concentration. However, state-of-the-art PM sensors cannot provide explicit information of the particle size for further analysis. In this work, we adopt Sauter mean diameter (SMD) as a key metric to reflect the particle size besides the mass concentration. To measure SMD, an effective optical sensing method and a proof-of-concept prototype sensor are proposed by using dual wavelengths technology. In the proposed method, a non-linear conversion model is developed to improve the SMD measurement accuracy for aerosol samples of different particle size distributions and reflective indices based on multiple scattering channels. In the experiment of Di-Ethyl-Hexyl-Sebacate (DEHS) aerosols, the outputs of our prototype sensor demonstrated a good agreement with existing laboratory reference instruments with maximum SMD measurement error down to 7.04%. Furthermore, the simplicity, feasibility and low-cost features of this new method present great potential for distributed PM monitoring, to support sophisticated human exposure hazard assessment.
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Di Antonio, Andrea, Olalekan Popoola, Bin Ouyang, John Saffell, and Roderic Jones. "Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter." Sensors 18, no. 9 (August 24, 2018): 2790. http://dx.doi.org/10.3390/s18092790.

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There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on κ -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.
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Oh, Kwang Chul, Kyoung Bok Lee, and Byeong Gyu Jeong. "Characteristics of Resistive PM Sensors for Onboard Diagnostics of Diesel Particulate Filter Failure." Sensors 22, no. 10 (May 16, 2022): 3767. http://dx.doi.org/10.3390/s22103767.

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In accordance with the recently reinforced exhaust regulations and onboard diagnostics regulations, it is essential to adopt diesel particulate filter systems in diesel vehicles; a sensor that directly measures particulate matter (PM) in exhaust gas is installed to precisely monitor diesel particulate filter (DPF) failure. Because the reduction of particulate matter in the diesel particulate filter system is greatly influenced by the physical wall structure of the substrate, the presence or absence of damage to the substrate wall (cracks or local melting, etc.) determines the reliability of normal DPF operation. Therefore, an onboard diagnostics sensor for particle matter is being developed with a focus on monitoring damage to the DPF wall. In this study, as a sensor for determining damage to the substrate wall, an accumulation-type sensor whose resistance changes as soot particles are deposited between two electrodes was fabricated. The sensor characteristics were investigated by changing the gap between the sensor electrodes, sensor cap shape, and electrode bias voltage to improve resistive soot sensor sensitivity and response. From the signal characteristics of various sensor configurations, a combination sensor with improved signal stability and response time is manufactured, and they were compared with the characteristics of commercially available sensors in the engine-simulated NEDC mode in terms of the degree of DPF crack. As a result of transient mode, PM monitoring cycle was improved by 1.2~1.5 times during the same vehicle driving time compared to the existing commercial sensor.
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Dissertations / Theses on the topic "PM sensor - Particle sensor"

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Chander, Bhan Chander Bhan. "Photonics-based environmental sensors for automotive air quality monitoring." Electronic Thesis or Diss., Lyon, INSA, 2024. http://www.theses.fr/2024ISAL0120.

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Cette thèse explore les dispositifs de circuits photoniques intégrés (PIC) basés sur une plateforme en nitrure de silicium (SiN), en mettant l'accent sur leur potentiel pour la détection de particules dans l'air, un élément clé des capteurs de l'indice de qualité de l'air (AQI). La haute sensibilité volumique des résonateurs à anneaux (MRR) dans ces dispositifs permet la détection de faibles concentrations de particules, tandis que les forces optiques permettent le piégeage spécifique à la taille des particules. Pour relever les défis liés au piégeage de particules diélectriques de moins de 100 nm, cette recherche explore diverses structures photoniques, y compris des guides d'onde diélectriques, des résonateurs en mode d'ordre supérieur (HOM) et des guides d'onde plasmoniques hybrides. cette étude comprend leur conception, leur fabrication et leur compatibilité avec des plateformes industrielles telles que DAPHNE de STMicroelectronics. L'analyse des forces optiques, réalisée à l'aide de méthodes telles que le tenseur de contrainte de Maxwell (MST) et l'approximation de dipôle discret (DDA), fournit un cadre rigoureux pour optimiser la conception et évaluer différentes structures. Les résultats soulignent le potentiel des guides d'ondes HOM et des guides d'ondes plasmoniques hybrides pour le piégeage optique avancé et la détection de l'IQA, ouvrant ainsi la voie à des approches innovantes pour les applications de surveillance de l'environnement
This thesis explores photonic integrated circuit (PIC) devices based on a silicon nitride (SiN) platform, focusing on their potential for airborne particle detection, a key component of air quality index (AQI) sensors. The bulk sensitivity of the ring resonators (MRR) in these devices enables detection of low particle concentrations, while the optical forces enable size-specific trapping of particles. To address the challenges of trapping dielectric particles smaller than 100 nm, this research explores various photonic structures, including dielectric waveguides, higher-order mode (HOM) resonators and hybrid plasmonic waveguides. The study includes their design, fabrication and compatibility with industrial platforms such as STMicroelectronics' DAPHNE. Optical force analysis, using methods such as Maxwell's stress tensor (MST) and discrete dipole approximation (DDA), provides a rigorous framework for optimizing the design and evaluating different structures.The findings underscore the potential of HOM waveguides and hybrid plasmonic waveguides for advanced optical trapping and AQI sensing, paving the way for innovative approaches to environmental monitoring applications
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Grondin, Didier. "Développement d'un capteur de suies pour application automobile - Etude des paramètres clés affectant sa réponse." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEM012/document.

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Le transport routier est responsable d’une part des émissions de particules fines, notamment dans les grandes agglomérations. Celles-ci ayant des répercussions graves sur la santé humaine et l’environnement, des normes d’émissions des véhicules de plus en plus strictes sont mises en place. Des limites en nombre de particules sont imposées et une obligation de connaitre l’état de santé des organes de post-traitement des gaz d’échappement est également entrée en vigueur (On-Board Diagnostic : OBD).Les capteurs résistifs ont montré de bons résultats pour la mesure de la concentration massique des particules et ont l’avantage d’être simples à mettre en oeuvre, robustes et peu onéreux. Le principe de fonctionnement du capteur est basé sur la mesure de la conductance entre des électrodes de platine. Celle-ci augmente avec le dépôt de suies.L’objectif de ce travail de thèse est de parvenir à déterminer les paramètres clés qui affectent la réponse du capteur. Trois flux de suies présentant des distributions en taille différentes ont été sélectionnés et caractérisés. Un quatrième flux de suies a permis d’appréhender l’impact d’une concentration massique inférieure. La réponse du capteur à ces différents flux de suies a été étudiée. Nous avons montré que la sensibilité et le temps de réponse du capteur sont optimaux pour une tension de polarisation donnée entre les deux électrodes dont la valeur dépend de la distribution en taille des suies. Ce phénomène a été expliqué par les propriétés électriques différentes des suies et modélisé par un équilibre entre accumulation et combustion par effet Joule des suies déposées permettant de simuler la réponse temporelle du capteur
Road transport contributes to a part of the particulate matter emissions, especially in big cities. Due to the negative effect of these pollutants on the human health and environment, more and more stringent emission standards for automotive are applied. These emissions are now limited in number of particles per kilometer and the vehicle need to indicate when there is some failure of the systems of depollution (OBD: On-Board diagnostic).Resistive sensors have shown good results to measure soot particles mass concentration. They have advantage of being a simple and robust technology that can be easily manufactured at a cheap price. The sensor principle consists of conductance measurement between two platinum electrodes. Conductance increases with soot deposition. This work aims to define the key parameters that affect the sensors response. Three particles flow with different particles size distributions (centered at 90, 70 and 50 nm) were used and characterized. A fourth flow was used to see the impact of a lower mass concentration. The sensor response exposed to these different flows was studied. It was shown that the sensor sensibility and response times are optimal for a given polarization voltage between the electrodes whose value depends on the size distribution. This phenomenon was explained by the different electrical properties of the soot particles and modeled by equilibrium of soot accumulation and their combustion by Joule heating that permitting to simulate the sensor temporal response
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Ing, Garrick. "Distributed particle filters for object tracking in sensor networks." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98971.

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A particle filter (PF) is a simulation-based algorithm used to solve estimation problems, such as object tracking. The PF works by maintaining a set of "particles" as candidate state descriptions of an object's position. The filter determines how well the set of particles describe the observations and fit the dynamic model, in order to form an object state estimate. The drawback of the basic PF is that the algorithm functions by collecting all data at a fusion centre. This leads to high communication and energy costs in a resource-limited network such as the sensor network. In this thesis, we analyze the PF to determine how it can be modified for efficient use in a sensor network. Our main priority is to keep communication and energy costs low since this increases the network lifetime. We propose two innovative particle filtering algorithms which minimizes the associated costs.
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Latiff, Nurul Mu'azzah Abdul. "Particle swarm optimisation for clustering in wireless sensor networks." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489298.

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One of the mam characteristics which set wireless sensor networks apart from traditional networks is the inclusion of energy consumption as the highest priority optimisation goal. This is because these types of networks work under the general concept that the system lifetime needs to be extended as much as possible, whilst at the same time achieving efficient data forwarding and preventing route disconnections due to sensor node failure. Hence, the use of energy efficient infrastructure such as clustering n:ay lengthen the lifetime of the network and prevent network connectivity degradation through the utilisation of cluster heads. Since the optimal selection of cluster heads in a network belongs to nondeterministic polynomial (NP) hard problem, the use of approximation algorithms such as Particle Swarm Optimisation (PSO) are generally more suitable due to its simplicity and outstanding search strength. This PhD thesis investigates the application of the PSO algorithm in clustering ofwireless sensor networks. In view of the need to prolong sensor network lifetime, a centralised, energy efficient, cluster-based protocol is developed using the PSO algorithm. A new cost function has been defined, which takes into consideration three important factors, namely the expected network energy consumption, the intracluster distance and the remaining energy of the cluster heads. The clustering problem is then transformed into an optimisation problem, and the PSO algorithm is employed to search for the optimal set of cluster heads. Simulation results demonstrate that the proposed protocol using PSO obtains better data delivery and network lifetime, as well as improves network connectivity over its comparatives. In addition, the results confirm the efficiency of PSG in clustering problems, compared to other evolutionary algorithms. This thesis also considers the use of the PSO algorithm in clustering the wireless sensor networks with mobile nodes. For this purpose, the 'mobility factor is taken into account when defining the cluster membership and selecting the cluster heads in order to maintain network connectivity. Simulation results prove that this approach outperforms other well known protocols in terms of data delivery and network lifetime. Finally, a dynamic multi-objective clustering algorithm which automatically determines the optimum number of clusters is introduced. This algorithm, based on binary PSG, eliminates the need to set the number of clusters a priori. Furthermore, the use of multi-objective PSG can tackle the difficulty of tuning the cost function weights that properly scales the sub-objectives. Performance evaluation through simulation exhibits the superior strength of this algorithm in enhancing the network survivability.
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Ihler, Alexander T. (Alexander Thomas) 1976. "Inference in sensor networks : graphical models and particle methods." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33206.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 175-183).
Sensor networks have quickly risen in importance over the last several years to become an active field of research, full of difficult problems and applications. At the same time, graphical models have shown themselves to be an extremely useful formalism for describing the underlying statistical structure of problems for sensor networks. In part, this is due to a number of efficient methods for solving inference problems defined on graphical models, but even more important is the fact that many of these methods (such as belief propagation) can be interpreted as a set of message passing operations, for which it is not difficult to describe a simple, distributed architecture in which each sensor performs local processing and fusion of information, and passes messages locally among neighboring sensors. At the same time, many of the tasks which are most important in sensor networks are characterized by such features as complex uncertainty and nonlinear observation processes. Particle filtering is one common technique for dealing with inference under these conditions in certain types of sequential problems, such as tracking of mobile objects.
(cont.) However, many sensor network applications do not have the necessary structure to apply particle filtering, and even when they do there are subtleties which arise due to the nature of a distributed inference process performed on a system with limited resources (such as power, bandwidth, and so forth). This thesis explores how the ideas of graphical models and sample-based representations of uncertainty such as are used in particle filtering can be applied to problems defined for sensor networks, in which we must consider the impact of resource limitations on our algorithms. In particular, we explore three related themes. We begin by describing how sample-based representations can be applied to solve inference problems defined on general graphical models. Limited communications, the primary restriction in most practical sensor networks, means that the messages which are passed in the inference process must be approximated in some way. Our second theme explores the consequences of such message approximations, and leads to results with implications both for distributed systems and the use of belief propagation more generally.
(cont.) This naturally raises a third theme, investigating the optimal cost of representing sample-based estimates of uncertainty so as to minimize the communications required. Our analysis shows several interesting differences between this problem and traditional source coding methods. We also use the metrics for message errors to define lossy or approximate4 encoders, and provide an example encoder capable of balancing communication costs with a measure on inferential error. Finally, we put all of these three themes to work to solve a difficult and important task in sensor networks. The self-localization problem for sensors networks involves the estimation of all sensor positions given a set of relative inter-sensor measurements in the network. We describe this problem as a graphical model, illustrate the complex uncertainties involved in the estimation process, and present a method of finding for both estimates of the sensor positions and their remaining uncertainty using a sample-based message passing algorithm. This method is capable of incorporating arbitrary noise distributions, including outlier processes, and by applying our lossy encoding algorithm can be used even when communications is relatively limited.
(cont.) We conclude the thesis with a summary of the work and its contributions, and a description of some of the many problems which remain open within the field.
y Alexander T. Ihler.
Ph.D.
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Campbell, Steven Conner. "DETERMINATION OF ACOUSTIC RADIATION EFFICIENCY VIA PARTICLE VELOCITY SENSOR WITH APPLICATIONS." UKnowledge, 2019. https://uknowledge.uky.edu/me_etds/133.

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Acoustic radiation efficiency is defined as the ratio of sound power radiated to the surface vibration power of a piston with equivalent surface area. It has been shown that the radiation efficiency is maximized and may exceed unity when the structural and acoustic wavelengths are approximately equal. The frequency at which this occurs is called the critical frequency and can be shifted with structural modifications. This has proven to be an effective way to reduce noise. The standard radiation efficiency measurement is comprised of an intensity scan for sound power measurement and accelerometer array for spatially averaged vibration determination. This method is difficult to apply to lightweight structures, complicated geometries, and when acoustic sources are in close proximity to one another. Recently, robust particle velocity sensors have been developed. Combined with a small microphone in the same instrument, particle velocity and sound pressure can be measured simultaneously and at the same location. This permits radiation efficiency to be measured using a non-contact approach with a single sensor. A suggested practice for measuring radiation efficiency has been developed and validated with several examples including two flat plates of different thickness, an oil pan, and components on a running small engine.
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Liu, Xiaoting. "Developing a scientific basis for utilisation of low-cost sensing technologies towards quantitative assessments of air pollution and its sources." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212115/1/Xiaoting_Liu_Thesis.pdf.

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This thesis develops a scientific basis for the use of low-cost sensing technologies for quantitative assessment of air pollution and its sources. It has extensively identified the capabilities and limitations of low-cost sensors in laboratory and field environments. Essentially, it has demonstrated that low-cost sensors are capable of monitoring air quality with a high degree of accuracy in different locations (e.g., highly polluted areas) and for different purposes (e.g., citizen science projects for raising environmental awareness).
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Jagtiani, Ashish V. "DEVELOPMENT OF NOVEL MULTICHANNEL RESISTIVE PULSE SENSORS FOR MICRO-PARTICLE DETECTION AND DIFFERENTIATION." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1196284929.

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Kohlbacher, Anton. "Development of a Novel Relative Localization Sensor." Thesis, Luleå tekniska universitet, Rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65515.

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By enabling coordinated task execution and movement, robotic swarms can achieve efficient exploration or disaster site management. When utilizing Ultra-wideband (UWB) radio technology for ranging, the proposed relative localization sensor can be made lightweight and relatively indifferent to the ambient environment. Infrastructure dependency is eliminated by making the whole sensor fit on a swarm agent, while allowing for a certain amount of positional error. In this thesis, a novel algorithm is implemented in to constrained hardware and compared to a more traditional trilateration approach. Both algorithms utilize Particle Swarm Optimization (PSO) to be more robust towards noise and achieves similar accuracy, but the proposed algorithm can run up to ten times faster. The antenna array which forms the localization sensor weighs only 56g, and achieves around 0.5m RMSE with a 10Hz update rate. Experiments show that the accuracy can be further improved if the rotational bias observed in the UWB devices are compensated for.
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Zhang, Zheng. "RESISTIVE PULSE SENSORS FOR POLLEN PARTICLE MEASUREMENTS." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1145070142.

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Books on the topic "PM sensor - Particle sensor"

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Hartmann, Frank. Evolution of Silicon Sensor Technology in Particle Physics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64436-3.

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Hartmann, Frank. Evolution of Silicon Sensor Technology in Particle Physics. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-59720-6.

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Goddard Space Flight Center. Engineering Procurement Office., ed. [Measuring electrically charged particle fluxes in space using a fiber optic loop sensor]: Final report. Greenbelt, MD: NASA Goddard Space Flight Center, Engineering Procurement Office, 1993.

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A, Lindemulder Elizabeth, Jovaag Kari, and United States. National Aeronautics and Space Administration., eds. Temperature-dependent daily variability of precipitable water in special sensor microwave/imager observations. [Washington, DC: National Aeronautics and Space Administration, 1995.

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A, Lindemulder Elizabeth, Jovaag Kari, and United States. National Aeronautics and Space Administration., eds. Temperature-dependent daily variability of precipitable water in special sensor microwave/imager observations. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Evolution of Silicon Sensor Technology in Particle Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/b106762.

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Hartmann, Frank. Evolution of Silicon Sensor Technology in Particle Physics. Springer, 2010.

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Evolution of Silicon Sensor Technology in Particle Physics. Springer Nature, 2017.

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Hartmann, Frank. Evolution of Silicon Sensor Technology in Particle Physics. Springer, 2008.

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Hartmann, Frank. Evolution of Silicon Sensor Technology in Particle Physics. Springer, 2018.

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Book chapters on the topic "PM sensor - Particle sensor"

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Gitahi, Joseph, and Michael Hahn. "Evaluation of Crowd-Sourced PM2.5 Measurements from Low-Cost Sensors for Air Quality Mapping in Stuttgart City." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 225–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_14.

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AbstractExposure to particulate matter (PM) pollution poses a major risk to the environment and human health. Monitoring PM pollution is thus crucial to understand particle distribution and mitigation. There has been rapid development of low-cost PM sensors and advancement in the field of Internet of Things (IoT) that has led to the deployment of the sensors by technology-aware people in cities. In this study, we evaluate the stability and accuracy of PM measurements from low-cost sensors crowd-sourced from a citizen science project in Stuttgart. Long-term measurements from the sensors show a strong correlation with measurements from reference stations with most of the selected sensors achieving Pearson correlation coefficients of r > 0.7. We investigate the stability of the sensors for reproducibility of measurements using five sensors installed at different height levels and horizontal distances. They exhibit minor variations with low correlation of variation (CV) values of between 10 and 14%. A CV of ≤10% is recommended for low-cost sensors. In a dense network, the sensors enable extraction pollution patterns and trends. We analyse PM measurements from 2 years using space-time pattern analysis and generate two clusters of sensors that have similar trends. The clustering shows the relationship between traffic and pollution with most sensors near major roads being in the same cluster.
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Eveland, Christopher K. "Particle Filtering with Evidential Reasoning." In Sensor Based Intelligent Robots, 305–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45993-6_17.

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Jacobsen, Finn, and Hans-Elias de Bree. "The Microflown Particle Velocity Sensor." In Handbook of Signal Processing in Acoustics, 1283–91. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_68.

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Becker, Aaron, Erik D. Demaine, Sándor P. Fekete, Golnaz Habibi, and James McLurkin. "Reconfiguring Massive Particle Swarms with Limited, Global Control." In Algorithms for Sensor Systems, 51–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45346-5_5.

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Barretta, L., and F. Foncellino. "PM Sensor Based on Piezoelettric MEMS: Mock Up." In Lecture Notes in Electrical Engineering, 180–85. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25706-3_29.

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Kreucher, Christopher M., Mark Morelande, Keith Kastella, and Alfred O. Hero. "Joint Multi-Target Particle Filtering." In Foundations and Applications of Sensor Management, 59–93. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-49819-5_4.

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Trejo, Raúl Emiliano Gómez, Bernardo Buitrón Rossainz, Jorge Alfredo García Torres, and Antonio Hernández Zavala. "A Study on the Behavior of Different Low-Cost Particle Counter Sensors for PM-10 and PM-2.5 Suspended Air Particles." In Communications in Computer and Information Science, 33–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18082-8_3.

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Majumdar, Ivy, B. N. Chatterji, and Avijit Kar. "Particle Swarm Optimisation Method for Texture Image Retrieval." In Computational Intelligence in Sensor Networks, 405–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57277-1_17.

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Narkhede, Parag, Shripad Deshpande, and Rahee Walambe. "Sensor Data Cleaning Using Particle Swarm Optimization." In Advances in Intelligent Systems and Computing, 182–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16681-6_18.

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Ristic, Branko. "Sensor Control for Random Set BasedParticle Filters." In Particle Filters for Random Set Models, 85–119. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6316-0_5.

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Conference papers on the topic "PM sensor - Particle sensor"

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Yaqoob, Irfan, Vijay Kumar, and Shafique Ahmad Chaudhry. "Machine Learning Calibration of Low-Cost Sensor PM2.5 data." In 2024 IEEE International Symposium on Systems Engineering (ISSE), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/isse63315.2024.10741149.

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Besch, Marc C., Arvind Thiruvengadam, Hemanth K. Kappanna, Alessandro Cozzolini, Daniel K. Carder, Mridul Gautam, and Juha Tikkanen. "Assessment of Novel In-Line Particulate Matter Sensor With Respect to OBD and Emissions Control Applications." In ASME 2011 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/icef2011-60142.

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Upcoming emissions legislations and On-Board-Diagnostics (OBD) system requirements, both in the US and Europe, impose new challenges for particulate matter (PM) control strategies, especially with regard to upcoming EURO-VI regulations that are expected to limit the particulate number (PN) emissions. Indeed, the US-EPA Heavy-Duty-OBD regulations already require monitoring of diesel particulate filters (DPF) in 2010 for at least one engine series, with extension to all engine series by 2013. Due to the current absence of reliable in-line PM sensors to monitor DPF filtration efficiencies, manufacturers adopted alternative methods based on pressure drop measurements and semi-empirical models that require extensive calibration efforts; hence, driving upward the development costs. In order to meet upcoming OBD requirements and reduce unnecessary DPF regeneration frequencies, so as to minimize fuel consumption penalties, reliable sensors need to be integrated into the aftertreatment control environment. These next-generation sensors must be capable of performing actual real-time PM concentration measurements on a continuous basis within the exhaust stream. The primary objective of this study was to assess the capabilities, limitations and sensitivity of a newly developed inline PM sensor, with regard to its future application for OBD monitoring or control strategies of PM filter systems. The operation of the so called Pegasor Particulate Sensor (PPS) [1] is based on the escaping current principle. The instrument is capable of performing continuous PM measurements, directly from the exhaust stack, while providing a real-time signal with a resolution of 100 Hz. The sensor’s output signal can be calibrated to either measure the concentration of mass, surface or number of exhaust particles. Designed as a flow-through device, the PPS has a tungsten corona wire imposing an equal charge on particles that is subsequently measured from the outflowing particles via a built-in electrometer. The system does not involve collection or contact with particles in the exhaust stream, which is especially advantageous for long-term stability and operation without frequent maintenance; hence, best suited for in-use application. A comprehensive test matrix was developed to gain a more pronounced understanding of the sensor’s measurement technology by comparing it to other proven aerosol instruments, namely the Ultrafine Condensation Particle Counter (UCPC), Engine Exhaust Particle Sizer (EEPS™) spectrometer, Tapered Element Oscillating Microbalance (TEOM), and gravimetric PM. Test results demonstrated a stable and repeatable response over consecutive European Transient Cycle (ETC) and Federal Test Procedure (FTP) cycles, as well as over idle and constant load operation with coefficients of variation below 2%, which is a prerequisite for OBD algorithm implementation.
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Jagtiani, Ashish, Jiang Zhe, and Bi-min Zhang Newby. "Simultaneous Detection of Multiple Bioparticles With a High Throughput Resistive Pulse Sensor." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15565.

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We describe an all-electronic, label-free, resistive pulse sensor that utilizes multiple micropores for simultaneous detection and counting of multiple biological particles. Four particle samples were utilized for the sensor testing: 20μm and 40μm polymethacrylate (PM) particles, Juniper pollen and Eastern Cottonwood pollen. Experiments demonstrated that this sensor was able to differentiate and count multiple particle solutions simultaneously through its four micropores fabricated on polymer membranes. Thus the sensing throughput has been improved significantly. Furthermore, the experimental results also proved the feasibility of differentiating various pollens from PM microparticles with the multi-pore resistive pulse sensor, with no need for labeling of samples.
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Osara, Jude A., Timothy T. Diller, Matthew J. Hall, Ronald D. Matthews, and Jakob Heinrich. "Particulate Matter Emissions From a High-Emitting Diesel Vehicle Measured With an On-Board Electronic PM Sensor." In ASME 2010 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/icef2010-35055.

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Measurements of time-resolved particulate matter emissions from a high-emitting light-duty diesel vehicle were made using an electronic particulate matter sensor developed at the University of Texas. The sensor, which is threaded directly through the exhaust pipe wall, detects the time-resolved mass concentration of carbonaceous PM in undiluted vehicle exhaust. The sensor works by detecting an electrical current that is created between two electrodes that have a large potential difference across them; a current is created when particles are present. The sensor was used to characterize the PM emissions from a Chevrolet Equinox SUV which had its original gasoline engine replaced with a 1.9 liter Fiat/Opel turbo-diesel. The vehicle was without a diesel particulate filter (DPF) and had transient PM emission concentrations during accelerations as high as 1000 g/m3. The sensor’s output closely followed exhaust opacity. PM emissions were found to be highest for rapid accelerations and were strongly correlated with pedal position, which can be taken as a surrogate for the fuel delivery per cycle. The sensor was calibrated against gravimetric filter measurements of dry PM mass captured from the vehicle’s exhaust in sample bags.
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Amanatidis, Stavros, Leonidas Ntziachristos, Zissis Samaras, Kauko Janka, and Juha Tikkanen. "Applicability of the Pegasor Particle Sensor to Measure Particle Number, Mass and PM Emissions." In 11th International Conference on Engines & Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2013. http://dx.doi.org/10.4271/2013-24-0167.

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Adamović, Savka, Rastko Milošević, Bojan Banjanin, Miroslav Dramićanin, Milana Ilić Mićunović, and Aleksandra Mihailović. "THE PARTICULATE MATTER IN THE WORKING ENVIRONMENT OF THE DIGITAL PRINTING MACHINE DETECTED BY STATIONARY AND PERSONAL METHODS." In INTERNATIONAL SYMPOSIUM ON GRAPHIC ENGINEERING AND DESIGN. UNIVERSITY OF NOVI SAD FACULTY OF TECHNICAL SCIENCES DEPARTMENT OF GRAPHIC ENGINEERING AND DESIGN 21000 Novi Sad, Trg Dositeja Obradovića 6, 2024. http://dx.doi.org/10.24867/grid-2024-p71.

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Particulate matter (PM) emitted during the digital printing process is potentially dangerous in the sense that it penetrates deep into the operator's lungs. The mass concentration levels of PM1, PM2.5, and PM10 particles emitted in the working environment of digital machines were measured after applying two sampling methods, spatial and individual. A sensor sampler with an optical particle counter was used for the spatial method, while a personal sampler of particulate materials was applied for the individual process. The paper's primary goal is to assess the comparability of the concentration of PM particles obtained by different sampling methods.
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Jain, Praveer Kirtimohan, Omkar Yadav, Chellapandi Chendil, P. Krishnaraj, Sivasubramamanian R, Parag Narsinha Daithankar, and Muthu Shanmugam Ramakrishnan. "Soot Sensor Elimination with DPF Substrate Failure Monitoring." In Symposium on International Automotive Technology. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-26-0153.

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<div class="section abstract"><div class="htmlview paragraph">The automobile industry is going through one of the most challenging times, with increased competition in the market which is enforcing competitive prices of the products along with meeting the stringent emission norms. One such requirement for BS6 phase 2 emission norms is monitoring for partial failure of the component if the tailpipe emissions are higher than the OBD limits. Recently PM (soot) sensor is employed for partial failure monitoring of DPF in diesel passenger cars.. PM sensor detects soot leakage in case of DPF substrate failure. There is a cost factor along with extensive calibration efforts which are needed to ensure sensor works flawlessly. This paper deals with the development of an algorithm with which robust detection of DPF substrate failure is achieved without addition of any sensor in the aftertreatment system. In order to achieve this, a thermodynamic model of DPF substate was created using empirical relations between parameters like exhaust flow rate, exhaust gas temperature and soot mass content. The modeling was done in both empty (no soot) and filled (threshold soot content) DPF substrate conditions. There were two methodologies, namely integration method and normalization method. In integration method the pressure drop across DPF substate in actual running condition is cumulatively summed up and compared with the integrated modelled value achieved from the thermodynamic model. If the ratio between modelled and actual crosses the threshold value, a DPF substrate failure flag is raised. In normalization method, actual pressure drop across DPF is corrected using empirical thermodynamic relations for exhaust temperature and soot mass content. The corrected pressure drop is normalized for the exhaust flow rate and then averaged based on release conditions. If the averaged values is lower than the threshold, a DPF substrate failure flag is raised. DPF substrate failure is intimated only when both the methodologies raise the failure flag. The algorithm was tested with actual failed DPF sample and robust detection was observed (more than 90% detection accuracy) and no misdetection.</div></div>
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Schlender, T. V., G. D. Burchanka, Y. A. Prakopchyk, and E. A. Chumakou. "CHANGE IN THE CONTENT OF PM 2.5 AND PM 10 SOLID PARTICLES IN THE ATMOSPHERIC AIR IN THE AREA OF ZAKHAROV STREET IN MINSK ACCORDING TO THE DATA OF THE AIRMQ SENSOR." In SAKHAROV READINGS 2021: ENVIRONMENTAL PROBLEMS OF THE XXI CENTURY. International Sakharov Environmental Institute of Belarusian State University, 2021. http://dx.doi.org/10.46646/sakh-2021-2-375-378.

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This article presents the processed data of PM2.5 and PM10 particulate matter of the AirMQ sensor on Zakharova street, Minsk. The 15 minute readings were averaged to the daily average, monthly average and annual average values for the period August 2019 - January 2021. The analysis of data by seasons of the year, by 15-minute periods of observations and by days of the week is presented. Conclusions are made about the possible natural and anthropogenic causes of daily, weekly and seasonal changes in the content of PM2.5 and PM10 solid particles in the surface air at Zakharova Street in Minsk. An analysis is made of the relationship between solid particles and the urban heat island (surface air temperature) in the area of Victory square.
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Cung, Khanh, Gina Buffaloe, Alex Michlberger, Thomas Briggs, Chris Bitsis, Edward Smith, and Imad Khalek. "Comparison on Combustion and Emissions Performance of Biodiesel and Diesel in a Heavy-duty Diesel Engine: NO <sub>X</sub> , Particulate Matter, and Particle Size Distribution." In 2023 JSAE/SAE Powertrains, Energy and Lubricants International Meeting. 10-2 Gobancho, Chiyoda-ku, Tokyo, Japan: Society of Automotive Engineers of Japan, 2023. http://dx.doi.org/10.4271/2023-32-0100.

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<div class="section abstract"><div class="htmlview paragraph">Low carbon emissions policies for the transportation sector have recently driven more interest in using low net-carbon fuels, including biodiesel. An internal combustion engine (ICE) can operate effectively using biodiesel while achieving lower engine-out emissions, such as soot, mostly thanks to oxygenate content in biodiesel.</div><div class="htmlview paragraph">This study selected a heavy-duty (HD) single-cylinder engine (SCE) platform to test biodiesel fuel blends with 20% and 100% biodiesel content by volume, referred to as B20, and B100. Test conditions include a parametric study of exhaust gas recirculating (EGR), and the start of injection (SOI) performed at low and high engine load operating points. In-cylinder pressure and engine-out emissions (NOX and soot) measurements were collected to compare diesel and biodiesel fuels. Exhaust particulate matter (PM) emissions were collected to assess solid particle mass and particle size distribution (PSD) using a micro-soot sensor (MSS) and a Cambustion different mobility spectrometer (DMS500), respectively.</div><div class="htmlview paragraph">Fuel consumption was higher with B20 and B100 than with diesel. However, soot emissions were reduced significantly with biodiesel fuels. B20 and B100 also have lower PSD curves, which indicates a smaller total particle count at a given mobility diameter (ranging from 6 nm to 1000 nm). PSD of all fuels shows a well- known bimodal characteristic. A slight difference in nitrogen oxides (NOX) emissions was observed and could be due to the difference in the physio-chemical properties of biodiesel and diesel fuels. Engine-out emissions of carbon monoxide (CO) and unburned hydrocarbon (UHC) are lower with biodiesel fuels than diesel.</div><div class="htmlview paragraph">High-speed combustion analysis suggests that minor modification in injection strategy is potentially needed for biodiesel fuels to deliver a diesel-like combustion profile, as seen by the apparent heat release rate (AHRR) curve. Overall, findings from current study suggest the drop-in capability in modern diesel engines of biodiesel with similar diesel-like efficiency and lower PM emissions.</div></div>
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Wu, Yidong, Li Shi, Xinxin Wu, Xiaoxin Wang, and Qiankun Xiao. "Flow-Induced Vibration of Two Square Cylinders With Rounded Corners in a Tandem Arrangement." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-90995.

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Abstract Based on the design of the heat transfer tube bundles with square cross section of the steam generators in high temperature reactor-pebble bed modules (HTR-PM), a free-vibration experiment is conducted to examine flow-induced vibration (FIV) characteristic of two identical rounded square cylinders with r/D = 0.12 in tandem arrangements at spacing ratio L/D = 1.5∼5.5. One of the cylinders is two-dimensional, spring mounted, and allowed to vibrate in the cross-flow direction while the other is held stationary. Considering two cases that elastic cylinder located upstream or downstream, two mass-damping ratios m*ζ are chosen to investigate the effect of spacing between two cylinder. The vibration responses and the flow structure around two cylinders are studied, using laser displacement sensor and Particle image velocimetry. It is observed that only vortex-induced vibration (VIV) occurs when the elastic cylinder is located downstream. When elastic cylinder is located upstream, the full interaction between VIV and galloping of cylinder with lower m*ζ is invariable no matter what the spacing is. The spacing has noticeable effects on the vibration behavior of cylinder with higher m*ζ and changes the interaction between VIV and galloping. The flow structure of two cylinders indicates that the upstream shear layer reattaches on the surface of the downstream cylinder as L/D &lt; 3.5, where the St drops with increasing L/D. There is gap vortices between two cylinders at L/D &gt; 3.5 where the St rises and is close to the value of single cylinder with a larger spacing.
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Reports on the topic "PM sensor - Particle sensor"

1

SEA TECH INC CORVALLIS OR. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada251708.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada251942.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada252185.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada252186.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada255702.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, February 1994. http://dx.doi.org/10.21236/ada303901.

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Chang, Enson, and R. Patton. Moored optical particle flux sensor (MOPAR). SBIR Phase II interim report. Office of Scientific and Technical Information (OSTI), June 1993. http://dx.doi.org/10.2172/10200461.

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Orlando, Philip. Modeling Spatiotemporal Patterns of PM 2.5 at the Sub-Neighborhood Scale Using Low-Cost Sensor Networks. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7239.

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Dichter, Bronislaw K., Edward G. Mullen, and Gary E. Galica. Space Particle Modeling, Measurements, and Effects: Compact Environmental Anomaly Sensor (CEASE) Proton Calibration. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada536723.

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Bontha, Jagannadha R., Nancy G. Colton, Eric A. Daymo, T. D. Hylton, C. K. Bayne, and T. H. May. Qualification of the Lasentec M600P Particle Size Analyzer and the Red Valve Model 1151 Pressure Sensor. Office of Scientific and Technical Information (OSTI), January 2000. http://dx.doi.org/10.2172/15002697.

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