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1
Zhang, Siqi, Yucai Xie, Lianfeng Zhang, Yuwei Zhang, Shuyao Zhang, Chenzhao Bai, and Wei Li. "Investigation of the Effect of Debris Position on the Detection Stability of a Magnetic Plug Sensor Based on Alternating Current Bridge." Sensors 24, no. 1 (December 21, 2023): 55. http://dx.doi.org/10.3390/s24010055.
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Magnetic plug-type abrasive particle sensors have a wide range of applications in oil detection, but there is little literature on the effect of abrasive particle position on detection accuracy. In this paper, an alternating current (AC) bridge-type abrasive particle detection sensor is designed, in which the sensing module utilizes permanent magnets to attract iron particles, and the induction coil is specially designed to detect the magnetic field fluctuation caused by iron particles. A corresponding model was also designed to evaluate the sensor’s sensitivity at different locations. In this paper, the magnetic field distribution of the sensor was first analyzed using finite element analysis software to obtain the magnetic field strength at different positions. Then, the response sensitivity of the sensor to particles and the effect of different positions on the detection results are explored through experiments. The simulation and the experimental results show substantial signal difference signal at different sensor positions. The method outlined in this article can determine the optimal sensing range for subsequent magnetic plug-type abrasive particle detection sensors and subsequently improve their reliability.
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Fardi, B., B. MacGibbon, S. Tripathi, and F. Moghadam. "Feasibility of an In-Situ Particle Monitor on a Tungsten LPCVD Reactor." Journal of the IEST 39, no. 3 (May 31, 1996): 25–30. http://dx.doi.org/10.17764/jiet.2.39.3.f109749056q17677.
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Reduction or elimination of particles generated within the semiconductor process equipment is vital to achieving high yields in the semiconductor industry. In-situ laser particle sensors that detect contaminants within the process equipment are gaining increasing acceptance because of their ability to detect problems in real-time. The prompt detection of particle problems followed by corrective actions will minimize impact to parts (wafers) sensitive to particle contamination. It is hypothesized that a relationship exists between the quantities of particles depositing on a wafer and particles detected by an in-situ particle sensor in the exhaust line. An extensive study was conducted to evaluate this hypothesis. An in-situ laser particle sensor was installed on the exhaust line of a Tungsten Low Pressure Chemical Vapor Deposition (W-LPCVD) chamber. The exhaust line particle counts were collected during the processing of device wafers and during particle tests using monitor wafers. Correlations between the ISPM counts, particles deposited on the wafer, and yields are examined. The response of the particle sensor to the periodic chamber cleans is also presented and discussed.
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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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Hong, Sung-Ho. "Numerical Approach and Verification Method for Improving the Sensitivity of Ferrous Particle Sensors with a Permanent Magnet." Sensors 23, no. 12 (June 6, 2023): 5381. http://dx.doi.org/10.3390/s23125381.
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This study aimed to improve the sensitivity of ferrous particle sensors used in various mechanical systems such as engines to detect abnormalities by measuring the number of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, their ability to detect abnormalities is limited because they only measure the number of ferrous particles collected on the top of the sensor. This study provides a design strategy to boost the sensitivity of an existing sensor using a multi-physics analysis method, and a practical numerical method was recommended to assess the sensitivity of the enhanced sensor. The sensor’s maximum magnetic flux density was increased by around 210% compared to the original sensor by changing the core’s form. In addition, in the numerical evaluation of the sensitivity of the sensor, the suggested sensor model has improved sensitivity. This study is important because it offers a numerical model and verification technique that may be used to enhance the functionality of a ferrous particle sensor that uses a permanent magnet.
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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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Hong, Sung-Ho. "Numerical Analysis for Appropriate Positioning of Ferrous Wear Debris Sensors with Permanent Magnet in Gearbox Systems." Sensors 24, no. 3 (January 26, 2024): 810. http://dx.doi.org/10.3390/s24030810.
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In order to improve the measurement sensitivity of ferrous wear debris sensors with a permanent magnet, a new numerical approach to the appropriate position of the sensor is presented. Moreover, a flow guide wall is proposed as a way to concentrate flow around the ferrous particle sensors. The flow guide wall is intended to further improve measurement sensitivity by allowing the flow containing ferrous particles to flow around the sensor. Numerical analysis was performed using the multi-physics analysis method for the most representative gearbox of the sump-tank type. In condition diagnosis using ferrous wear debris sensors, the position of the sensor has a great influence. In other words, there are cases where no measurements occur, despite the presence of abnormal wear and damage due to the wrong sensor position. To determine the optimal sensor position, this study used flow analysis for the flow caused by the movement of the gear, electric and magnetic field analysis to implement the sensor, and a particle tracing technique to track particle trajectory. The new analysis method and results of this study will provide important information for selecting the optimal sensor location and for the effective application of ferrous wear debris sensors, and will contribute to the oil sensor-based condition diagnosis technology.
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Kittimanapun, Kritsada, Natthawut Laojamnongwong, Jetnipit Kaewjai, Chinorat Kobdaj, and Wanchaloem Poonsawat. "Commissioning of Pixel Sensor Telescope for Monolithic Active Pixel Sensor Characterization." Journal of Physics: Conference Series 2653, no. 1 (December 1, 2023): 012029. http://dx.doi.org/10.1088/1742-6596/2653/1/012029.
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Abstract The Monolithic Active Pixel Sensors (MAPS) have been promisingly demonstrated as a core part of tracking detectors used in many experiments. In order to obtain decent particle tracks generated after high-energy particle collision, it is especially important that the detector must consist of high-performance MAPS. A pixel sensor telescope is a tool employed to investigate the properties of MAPS using high-energy particle test beams. At the Synchrotron Light Research Institute (SLRI), the pixel sensor telescope assembled with five layers of 50-µm thick pixel sensors has been commissioned and tested both in the laboratory with a radioactive source and by a high-energy electron test beam produced at the SLRI Beam Test Facility. With sensor signal threshold ranging from 9.73 to 10.15 DAC and noises between 0.62 and 0.87 DAC, each plane of the pixel sensor telescope can detect particles decaying from the radioactive source. Moreover, the test beam profiles have been measured to be 4.00 - 4.56 mm and 1.15 - 2.29 mm in horizontal and vertical axes, respectively. Correlations between each plane have been observed and confirm the proper operation of the pixel sensor telescope. Consequently, the pixel sensor telescope can be used to characterize the pixel sensor prototype.
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Yuan, Changrong, Zhongsheng Sun, and Xiaoning Li. "Mechanism and Modeling of Contaminant Accumulation on Hot-Film Air Flow Sensor." Mathematical Problems in Engineering 2019 (February 19, 2019): 1–15. http://dx.doi.org/10.1155/2019/6246259.
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Hot-film air flow sensors are now widely applied in many industrial fields. Their accuracy can be seriously affected when there are contaminants accumulated on the sensor chip. To predict the decrease of accuracy caused by contamination, the accumulation mechanism of contaminant particles on the surface of the sensor chip is studied in this paper. The adsorption process of particle is analyzed and a theoretical model of the cumulative thickness of the particles over time is established. A test platform for the particle accumulation is built and a long-term cumulative test is conducted. According to the tests, cumulative thickness of the particles increases while the growth rate slows over time. The results of the test fit those of the theoretical model. Various factors affecting the accumulation of contaminants are analyzed. The results indicate that the cumulative thickness of the particles increases along with the increase of the particle concentration, the particle charge, and the electric field strength on the chip surface but decreases along with the increase of the particle radius. The test results also show that the electrical force is the dominant reason of particle adsorption accumulation. By decreasing the electric field strength on the sensor surface, the cumulative thickness of the particles can be effectively reduced.
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Santos da Silva, Safire Torres, Nikola Jerance, and Harijaona Lalao Rakotoarison. "Simulating metallic contamination in permanent magnets used in magnetic sensors." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 5 (September 2, 2019): 1683–95. http://dx.doi.org/10.1108/compel-12-2018-0515.
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Purpose The purpose of this paper is to provide a model for simulating contamination by ferromagnetic particles in sensors that use permanent magnets. This topic is especially important for automotive applications, where magnetic sensors are extensively used and where metallic particles are present, particularly because of friction between mechanical parts. The aim of the model is to predict the particle accumulation and its effect on the sensor performance. Design/methodology/approach Magnetostatic moment method is used to calculate particles' magnetization and magnetic field. Magnetic saturation is included and Newton–Raphson method is used to solve the non-linear system. Magnetic force on particles is calculated as a gradient of energy. Dynamic simulation provides the positions of agglomerated particles. Findings A simulation of magnetic park lock sensor shows a significant impact of ferromagnetic particles on sensor's accuracy. Moreover, gains on computational time because of model optimizations are reported. Research limitations/implications Only magnetic force and gravity are taken into account for particle dynamics. Mechanical forces such as friction and particle interactions might be considered in future works. Practical implications This paper provides the possibility to evaluate and improve magnetic sensor design with respect to particles contamination. Originality/value The paper presents a novel simulation tool developed to answer the growing need for reliable and fast prediction of magnetic position sensors’ degradation in the presence of metallic particles.
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Fan, Bin, Lianfu Wang, Yong Liu, Peng Zhang, and Song Feng. "Simulation and Optimization Design of Inductive Wear Particle Sensor." Sensors 23, no. 10 (May 19, 2023): 4890. http://dx.doi.org/10.3390/s23104890.
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In order to monitor the diagnosis of mechanical equipment by monitoring the metal wear particles carried in large aperture lubricating oil tubes, the simulation optimization structure design was carried out based on the traditional three-coil inductance wear particle sensor. The numerical model of electromotive force induced by the wear particle sensor was established, and the coil distance and coil turns were simulated by finite element analysis software. When permalloy is covered on the surface of the excitation coil and induction coil, the background magnetic field at the air gap increases, and the induced electromotive force amplitude generated by wear particles is increased. The effect of alloy thickness on the induced voltage and magnetic field was analyzed to determine the optimum thickness, and increase the induction voltage of the alloy chamfer detection at the air gap. The optimal parameter structure was determined to improve the detection ability of the sensor. Ultimately, by comparing the extreme values of the induced voltage of various types of sensors, the simulation determined that the minimum allowable detection of the optimal sensor was 27.5 µm ferromagnetic particles.
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Kumar, Paras, Harish Hirani, and Atul Kumar Agrawal. "Online condition monitoring of misaligned meshing gears using wear debris and oil quality sensors." Industrial Lubrication and Tribology 70, no. 4 (May 8, 2018): 645–55. http://dx.doi.org/10.1108/ilt-05-2016-0106.
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Purpose This paper aims to investigate the effect of misalignment on wear of spur gears and on oil degradation using online sensors. Design/methodology/approach The misalignment effect on gears is created through a self-alignment bearing, and is measured using laser alignment system. Several online sensors such as Fe-concentration sensor, moisture sensor, oil condition sensor, oil temperature sensor and metallic particle sensor are installed in the gear test rig to monitor lubricant quality and wear debris in real time to assess gearbox failure. Findings Offset and angular misalignments are detected in both vertical and horizontal planes. The failure of misaligned gear is observed at both the ends and on both the surfaces of the gear teeth. Larger-size ferrous and non-ferrous particles are traced by metallic particle sensor due to gear and seal wear caused by misalignment. Scanning electron microscope (SEM) images examine chuck, spherical and flat platelet particles, and confirm the presence of fatigue (pitting) and adhesion (scuffing) wear mechanism. Energy-dispersive X-ray spectroscopy analysis of SEM particles traces carbon (C) and iron (Fe) elements due to gear failure. Originality/value Gear misalignment is one of the major causes of gearbox failure and the lubricant analysis is as important as wear debris analysis. A reliable online gearbox condition monitoring system is developed by integrating wear and oil analyses for misaligned spur gear pair in contact.
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Cheng, Gong, and Huangfu Wei. "Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks." Sensors 21, no. 8 (April 19, 2021): 2868. http://dx.doi.org/10.3390/s21082868.
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With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.
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Nyang’au, Wilson Ombati, Andi Setiono, Maik Bertke, Harald Bosse, and Erwin Peiner. "Cantilever-Droplet-Based Sensing of Magnetic Particle Concentrations in Liquids." Sensors 19, no. 21 (November 1, 2019): 4758. http://dx.doi.org/10.3390/s19214758.
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Cantilever-based sensors have attracted considerable attention in the recent past due to their enormous and endless potential and possibilities coupled with their dynamic and unprecedented sensitivity in sensing applications. In this paper, we present a technique that involves depositing and vaporizing (at ambient conditions) a particle-laden water droplet onto a defined sensing area on in-house fabricated and commercial-based silicon microcantilever sensors. This process entailed the optimization of dispensing pressure and time to generate and realize a small water droplet volume (Vd = 49.7 ± 1.9 pL). Moreover, we monitored the water evaporation trends on the sensing surface and observed total evaporation time per droplet of 39.0 ± 1.8 s against a theoretically determined value of about 37.14 s. By using monodispersed particles in water, i.e., magnetic polystyrene particles (MPS) and polymethyl methacrylate (PMMA), and adsorbing them on a dynamic cantilever sensor, the mass and number of these particles were measured and determined comparatively using resonant frequency response measurements and SEM particle count analysis, respectively. As a result, we observed and reported monolayer particles assembled on the sensor with the lowest MPS particles count of about 19 ± 2.
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KAWASAKI, Yousuke, Haruzou MIYASITA, and Tosio KIKUCHI. "Particles. Particle Measurements in Vacuum by In Situ Particle Monitor Sensor." SHINKU 41, no. 9 (1998): 771–75. http://dx.doi.org/10.3131/jvsj.41.771.
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Novak, A., C. Granja, A. Sagatova, V. Zach, J. Stursa, and C. Oancea. "Spectral tracking of proton beams by the Timepix3 detector with GaAs, CdTe and Si sensors." Journal of Instrumentation 18, no. 01 (January 1, 2023): C01022. http://dx.doi.org/10.1088/1748-0221/18/01/c01022.
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Abstract Position and directional-sensitive spectrometry of energetic charged particles can be performed with high resolution and wide dynamic range (energy, direction) with the hybrid semiconductor pixel detectors Timepix/Timepix3. The choice of semiconductor sensor material, thickness, and properties such as the reverse bias voltage, greatly determine detector sensitivity and resolving power for spectrometry and particle tracking. We investigated and evaluated the spectral tracking resolving power such as deposited energy and linear-energy-transfer (LET) spectra with the Timepix3 detector with different semiconductor sensors, based on GaAs:Cr, CdTe, and Si, using well-defined radiation sources in terms of radiation type (protons), energy, and incident direction to the detector sensor. Measurements of particle incident direction in a wide range were performed with collimated monoenergetic proton beams of various energies in the range 8–31 MeV at the U120-M cyclotron at the NPI CAS Rez near Prague. All detectors were per-pixel calibrated. This work enables to examine and perform a detailed study of charge sharing and charge collection efficiency in semiconductor sensors. The results serve to optimise the detector chip-sensor assembly configuration for measurements especially with high-LET particles in ion radiotherapy and outer space. The work underway includes evaluation of newly refined semi-insulating GaAs sensors and improved radiation hard semiconductor sensors SiC.
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Kunić, Zdravko, Leo Mršić, Goran Đambić, and Tomislav Ražov. "Innovative Air-Preconditioning Method for Accurate Particulate Matter Sensing in Humid Environments." Sensors 24, no. 17 (August 23, 2024): 5477. http://dx.doi.org/10.3390/s24175477.
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Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.
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Raciti, B., Y. Gao, R. Schimassek, A. Andreazza, Z. Feng, H. Fox, Y. Han, et al. "Characterisation of HV-MAPS ATLASPix3 and its applications for future lepton colliders." Journal of Instrumentation 17, no. 09 (September 1, 2022): C09031. http://dx.doi.org/10.1088/1748-0221/17/09/c09031.
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Abstract HV-MAPS are a novel type of CMOS depleted active pixel sensors for ionizing particles, implemented in standard CMOS processes, that have been proposed in several future particle physics experiments for particle tracking. In depleted monolithic sensors, the sensor element is the n-well/p-substrate diode. The sensor matrix and the readout are integrated in one single piece of silicon and the electronics is embedded in shallow wells inside deep n-wells, isolated from the substrate. High voltage biasing increases the depth of the depletion region, improving sensor properties as signal amplitude, charge collection speed and radiation tolerance. ATLASPix3 is the first full reticle size high voltage Monolithic Active Pixel CMOS sensor, designed to meet the specifications of the outer layers of the ATLAS inner tracker (ITk). Its thin design, the excellent position resolution, high readout rate and high radiation tolerance make ATLASPix3 an ideal candidate for large-area tracking detector R&D of future collider experiments such as the Circular Electron Positron Collider (CEPC) silicon tracker.
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Ikegami, Akihiko, Jun Kumoi, Yutaka Matsumi, Yuji Fujitani, Gaku Ichihara, Takeo Yano, and Sahoko Ichihara. "P-193 FINER PARTICLES GENERATED FROM CARBON FIBER REINFORCED PLASTICS IN AN OCCUPATIONAL SETTING." Occupational Medicine 74, Supplement_1 (July 1, 2024): 0. http://dx.doi.org/10.1093/occmed/kqae023.0735.
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Abstract Introduction Carbon fiber reinforced plastics (CFRP) is a leading functional material with superior strength and low mass density compared to metal. Our previous factory site analyses have confirmed that the processing of CFRP generates fibrous debris and finer particles, from nano to micro sized particles, of various shapes. However, methods for real-time monitoring of worker exposure to finer particles with simple devices have limited. Methods The present interventional study was conducted at a factory located in Japan and evaluated debris of various particles from nano to micro sizes (nano-micro particles) generated during the industrial processing of CFRP using several real-time particle monitoring devices. In addition, personal exposure of workers was evaluated using a novel PM2.5-compatible device (P-sensor). Results It was confirmed that nano-micro particles were generated during grinding, cutting, and turning processing of CFRP by real-time particle monitoring devices; PM4 dust monitor, Optical Particle Counter (OPC), Condensation Particle Counter (CPC), NanoScan SMPS Nanoparticle Sizer (SMPS), and a novel P-sensor. Under conditions where many finer particles were generated, the P-sensor detected the generation of finer particles although PM4 dust monitor and OPC failed to detect them. Discussion Nano-micro particles generated from CFRP were detected with the nanoparticle-compatible devices; CPC and SMPS. Particle count recorded by the P-sensor showed the same change over time as recorded by the nanoparticle-compatible devices. Conclusion The novel P-sensor is a simple device to estimate personal exposure of workers and can be used to measure finer particles generated by CFRP processing in an occupational setting.
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Budde, Matthias, Simon Leiner, Marcel Köpke, Johannes Riesterer, Till Riedel, and Michael Beigl. "FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor." Sensors 19, no. 3 (February 12, 2019): 749. http://dx.doi.org/10.3390/s19030749.
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Precise, location-specific fine dust measurement is central for the assessment of urban air quality. Classic measurement approaches require dedicated hardware, of which professional equipment is still prohibitively expensive (>10k$) for dense measurements, and inexpensive sensors do not meet accuracy demands. As a step towards filling this gap, we propose FeinPhone, a phone-based fine dust measurement system that uses camera and flashlight functions that are readily available on today’s off-the-shelf smart phones. We introduce a cost-effective passive hardware add-on together with a novel counting approach based on light-scattering particle sensors. Since our approach features a 2D sensor (the camera) instead of a single photodiode, we can employ it to capture the scatter traces from individual particles rather than just retaining a light intensity sum signal as in simple photometers. This is a more direct way of assessing the particle count, it is robust against side effects, e.g., from camera image compression, and enables gaining information on the size spectrum of the particles. Our proof-of-concept evaluation comparing several FeinPhone sensors with data from a high-quality APS/SMPS (Aerodynamic Particle Sizer/Scanning Mobility Particle Sizer) reference device at the World Calibration Center for Aerosol Physics shows that the collected data shows excellent correlation with the inhalable coarse fraction of fine dust particles (r > 0.9) and can successfully capture its levels under realistic conditions.
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Li, Yeming, Yidan Xia, Dailiang Xie, Ya Xu, Zhipeng Xu, and Yuebing Wang. "Application of artificial bee colony algorithm for particle size distribution measurement of suspended sediment based on focused ultrasonic sensor." Transactions of the Institute of Measurement and Control 43, no. 7 (February 11, 2021): 1680–90. http://dx.doi.org/10.1177/0142331221989115.
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A new focused ultrasonic sensor is proposed, based on which the measurement system for particle size distribution measurement of suspended sediment is established. Compared with the traditional ultrasonic sensors, the one used in this paper is equipped with piezoelectric transducer (PZT) on an arc-shaped shell, to concentrate ultrasonic beams on one measurement point. The sensor is used to measure the particle size distribution of suspended sediment. The experiments were carried out on water-sediment mixtures with different particle size distribution. Due to multiple parameters and non-linearity of the ultrasonic attenuation model, the artificial bee colony (ABC) inversion algorithm is used to estimate particle size distribution, thus improving measurement accuracy. The particle sizes obtained by sieving method are seen as reference values. The results indicate that whether the suspended particles are subject to a unimodal distribution, uniform distribution or random distribution, the particle size distribution obtained by ABC inversion algorithm is consistent with the result obtained by the sieve method. The results demonstrate that the method has good utility and accuracy within the low concentration range.
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Martínez-Barberá, Humberto, Pablo Bernal-Polo, and David Herrero-Pérez. "Sensor Modeling for Underwater Localization Using a Particle Filter." Sensors 21, no. 4 (February 23, 2021): 1549. http://dx.doi.org/10.3390/s21041549.
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This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.
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Gu, C., C. Bai, Y. Cheng, H. Shi, I. J. Lebile, H. Zhang, and Y. Sun. "Design and experimental research of abrasive particle detection sensor based on coil magnetic field." Journal of Instrumentation 17, no. 06 (June 1, 2022): P06017. http://dx.doi.org/10.1088/1748-0221/17/06/p06017.
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Abstract This paper develops a dual-coil sensor integrated with high-permeability materials based on inductive sensor technology to detect wear particles in oil. This method is mainly used for online detection and fault analysis of pollutants in hydraulic and lubricating oil systems. The sensor innovatively embeds permalloy into the sensing unit of the sensor to generate high gradient magnetic field in the sensing area, consequently improving the detection sensitivity of the sensor. The detection unit consists of two pieces of permalloy and two plane coils. The permalloys have a rectangular groove at the center, which is placed in line with the inner hole of the coil, thereby forming the detection area. Particles in the microchannel can be detected as they flow through the detection area. The article theoretically analyzes the working principle of the sensor and establishes a verification experiment system. The experimental results show that after adding permalloy to the sensing unit, the signal-to-noise ratio (SNR) of iron particles is increased by more than 40%, and the SNR of copper particles is increased by more than 30%. As the particle size increases, the SNR decreases. Using this design, the range of the lower limit detection for ferromagnetic metal particles increased to 10–15 μm, while that of non-ferromagnetic metal particles increased to 60 μm. Compared with traditional inductive sensors, the addition of permalloy greatly improves the sensor's performance, which significantly boosts the sensitivity of the dual-coil type sensor.
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Schriefl, Mario Anton, Matthias Longin, and Alexander Bergmann. "Charging-Based PN Sensing of Automotive Exhaust Particles." Proceedings 2, no. 13 (January 3, 2019): 805. http://dx.doi.org/10.3390/proceedings2130805.
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Mobile measurement of particle number concentration (PN) in the exhaust of motor vehicles has recently become an integral part of emission legislation. Charge-based sensing techniques for the examination of PN, like Diffusion Charging (DC), represent a promising alternative to condensational particle counters (CPCs) as established PN sensors, because they enable to build robust, compact and energy efficient systems. However, due to the charging process, particle properties like size and morphology have a big impact on the sensor’s PN response. For particles of different size and shape we experimentally investigated those impacts using own-built charging-based sensors. The PN response of the DC sensor showed desired behavior for compact NaCl particles, but less satisfying behavior for combustion aerosol standard (CAST) particles, which is a widely used test aerosol for automotive applications. With a photoelectric charger, the PN response of CAST particles was significantly better.
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Jayaratne, Rohan, Xiaoting Liu, Phong Thai, Matthew Dunbabin, and Lidia Morawska. "The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog." Atmospheric Measurement Techniques 11, no. 8 (August 27, 2018): 4883–90. http://dx.doi.org/10.5194/amt-11-4883-2018.
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Abstract. While low-cost particle sensors are increasingly being used in numerous applications, most of them have no heater or dryer at the inlet to remove water from the sample before measurement. Deliquescent growth of particles and the formation of fog droplets in the atmosphere can lead to significant increases in particle number concentration (PNC) and mass concentrations reported by such sensors. We carried out a detailed study using a Plantower PMS1003 low-cost particle sensor, both in the laboratory and under actual ambient field conditions, to investigate its response to increasing humidity and the presence of fog in the air. We found significant increases in particle number and mass concentrations at relative humidity above about 75 %. During a period of fog, the total PNC increased by 28 %, while the PNC larger than 2.5 µm increased by over 50 %. The PM10 concentration reported by the PMS1003 was 46 % greater than that on the standard monitor with a charcoal dryer at the inlet. While there is a causal link between particle pollution and adverse health effects, the presence of water on the particles is not harmful to humans. Therefore, air quality standards for particles are specifically limited to solid particles and standard particle monitoring instruments are fitted with a heater or dryer at the inlet to remove all liquid material from the sample before the concentrations are measured. This study shows that it is important to understand that the results provided by low-cost particle sensors, such as the PMS1003, cannot be used to ascertain if air quality standards are being met.
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Duan, Shu Min, and Jian Jun Wu. "Using Particle Swarm Optimization Algorithm to Construction Detection and Node Management System in Wireless Sensor Network." Advanced Materials Research 1078 (December 2014): 426–29. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.426.
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Particle swarm optimization objective is spatial neighborhood directly in the search space according to the distance between particles. This paper analyses method of detection and node management in wireless sensor networks. Sensor nodes are randomly placed in the particle swarm optimization method, data acquisition, processing and transmission of the work. The paper presents using particle swarm optimization algorithm to construction detection and node management system in wireless sensor network. Simulation experiments show the validity of this method.
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Jia, Ran, Biao Ma, Changsong Zheng, Xin Ba, Liyong Wang, Qiu Du, and Kai Wang. "Comprehensive Improvement of the Sensitivity and Detectability of a Large-Aperture Electromagnetic Wear Particle Detector." Sensors 19, no. 14 (July 18, 2019): 3162. http://dx.doi.org/10.3390/s19143162.
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The electromagnetic wear particle detector has been widely studied due to its prospective applications in various fields. In order to meet the requirements of the high-precision wear particle detector, a comprehensive method of improving the sensitivity and detectability of the sensor is proposed. Based on the nature of the sensor, parallel resonant exciting coils are used to increase the impedance change of the exciting circuit caused by particles, and the serial resonant topology structure and an amorphous core are applied to the inductive coil, which improves the magnetic flux change of the inductive coil and enlarges the induced electromotive force of the sensor. Moreover, the influences of the resonance frequency on the sensitivity and effective particle detection range of the sensor are studied, which forms the basis for optimizing the frequency of the magnetic field within the sensor. For further improving the detectability of micro-particles and the real-time monitoring ability of the sensor, a simple and quick extraction method for the particle signal, based on a modified lock-in amplifier and empirical mode decomposition and reverse reconstruction (EMD-RRC), is proposed, which can effectively extract the particle signal from the raw signal with low signal-to-noise ratio (SNR). The simulation and experimental results show that the proposed methods improve the sensitivity of the sensor by more than six times.
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Krishnan and Goud. "Magnetic Particle Bioconjugates: A Versatile Sensor Approach." Magnetochemistry 5, no. 4 (November 19, 2019): 64. http://dx.doi.org/10.3390/magnetochemistry5040064.
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: Nanomaterial biosensors have revolutionized the entire scientific, technology, biomedical, materials science, and engineering fields. Among all nanomaterials, magnetic nanoparticles, microparticles, and beads are unique in offering facile conjugation of biorecognition probes for selective capturing of any desired analytes from complex real sample matrices (e.g., biofluids such as whole blood, serum, urine and saliva, tissues, food, and environmental samples). In addition, rapid separation of the particle-captured analytes by the simple use of a magnet for subsequent detection on a sensor unit makes the magnetic particle sensor approach very attractive. The easy magnetic isolation feature of target analytes is not possible with other inorganic particles, both metallic (e.g., gold) and non-metallic (e.g., silica), which require difficult centrifugation and separation steps. Magnetic particle biosensors have thus enabled ultra-low detection with ultra-high sensitivity that has traditionally been achieved only by radioactive assays and other tedious optical sources. Moreover, when traditional approaches failed to selectively detect low-concentration analytes in complex matrices (e.g., colorimetric, electrochemistry, and optical methods), magnetic particle-incorporated sensing strategies enabled sample concentration into a defined microvolume of large surface area particles for a straightforward detection. The objective of this article is to highlight the ever-growing applications of magnetic materials for the detection of analytes present in various real sample matrices. The central idea of this paper was to show the versatility and advantages of using magnetic particles for a variety of sample matrices and analyte types and the adaptability of different transducers with the magnetic particle approaches.
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Becker, Andrew. "Health indicator metrics applicable to inductive wear debris sensors." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 231, no. 5 (August 16, 2016): 583–93. http://dx.doi.org/10.1177/1350650116665047.
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The Inductive Wear Debris Sensor is a relatively new invention that is increasingly being used for the detection of incipient machinery damage or failures by sensing metallic debris in lubrication systems. This type of sensor is typically used in-line and has a superior particle size detection range compared to traditional techniques such as the ubiquitous spectrometric oil analysis. There is, however, very little in the literature regarding the application and interpretation of data arising from this type of sensor. Unlike other condition monitoring sensors, no data will be generated by an Inductive Wear Debris Sensor in an ideal system; however, in real applications it is necessary to discriminate between occasional particles unrelated to a failure and incipient failure particles. Inductive Wear Debris Sensor data could be misinterpreted if a simple cumulative count limit was applied to the data. A short-term rate of particle generation is sometimes used as an alternative; however, it too can be misleading with short succession particles producing high instantaneous rates possibly causing false alarms. The purpose of this work was to develop a robust metric (or group of metrics) that when applied to Inductive Wear Debris Sensor data would reliably identify a failure event and exclude non-failure related particles. The Health Indicator described herein consists of three subordinate Condition Indices that collectively are shown to reliably detect the onset of rolling contact fatigue. The metrics have been applied to bearing test rig data (seeded fault) and data obtained from a non-seeded fault test of a complex helicopter gearbox.
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Wang, Yan Shan, Mei Ju Zhang, and De Feng Liu. "A Compact on-Line Particle Counter Sensor for Hydraulic Oil Contamination Detection." Applied Mechanics and Materials 130-134 (October 2011): 4198–201. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.4198.
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Real-time monitoring of oil contamination in hydraulic system is one of the most effective measures of prevention and early diagnosis for system failures. A compact on-line particle counter sensor based on light-blockage principle for monitoring hydraulic oil is presented in the article. A novel method is used in this sensor, which can calculate the particle number concentration without a metering pump. The experimental results show that the particle counter sensor can measure the concentration as well as the approximate size of the hydraulic oil particles with a high precision.
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Tang, Dong, Zhixuan Ju, and Li Wang. "Simulation and Experimental Research on the Charged Characteristics of Particulate Matter in the Sensor under Different Exhaust States." Sensors 20, no. 21 (October 31, 2020): 6226. http://dx.doi.org/10.3390/s20216226.
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The particulate matter sensor needs to be used in order to detect the concentration of particulate matter in diesel engine exhaust, monitor the working condition of diesel particulate filter (DPF) in real time, and ensure the reliable operation of DPF. The flow field and electric field of the sensor are studied and their distribution in the sensor is analyzed. At the same time, the particle tracking model was used to simulate the charged characteristics of particles in the sensor under different exhaust states. It is found that the exhaust gas flow rate maintains stability after entering the outer protection zone and concentration test zone. The electric field is a non-uniform electric field and the direction of electric field intensity is from the high voltage electrode to the grounding electrode. The electric charge per particle will decrease with the increase of exhaust flow rate, but the electric charge shows a slow growth trend. The charge of particles increases with the increase of exhaust temperature, exhaust gas concentration and particle size. The study of the charged characteristics under different environmental conditions provides a theoretical basis for further improving the prototype mechanism of a leakage flow particle sensor.
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Gao, Heming, and Susu Zhong. "Research on spatial filtering velocity measurement method for an interdigital electrostatic sensor." Measurement Science and Technology 33, no. 5 (February 10, 2022): 055106. http://dx.doi.org/10.1088/1361-6501/ac40a8.
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Abstract Particle velocity is an important parameter to describe the flow characteristics of gas–solid two-phase flows. It is also a tricky problem for parameter measurement of gas–solid flows. Aiming at the problem of the spatial filtering effect of an electrostatic sensor suffering from the effect of a spatial distribution of solid particles, a new type of interdigital electrostatic sensor is here proposed to resolve it. First the spatial filtering characteristics of the interdigital electrostatic sensor are analyzed through finite element simulation, obtaining that the spatial position and size of the solid particles have no effect on the peak frequency of the sensor output signal power spectrum, and the quantitative relationship between the velocity and peak frequency is derived. Then, experimental verification is carried out on an experimental platform of a gravity-conveying particle flow. Simulation and experimental results show that the interdigital electrostatic sensor eliminates the influence of particle spatial position and particle size on the velocity measurement results. In the velocity range of 2.97 m s−1 to 4.95 m s−1, the relative error of the measurement system is better than 5%, and the relative standard deviation of repeated measurements is within 3%.
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Bousiotis, Dimitrios, Ajit Singh, Molly Haugen, David C. S. Beddows, Sebastián Diez, Killian L. Murphy, Pete M. Edwards, Adam Boies, Roy M. Harrison, and Francis D. Pope. "Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study." Atmospheric Measurement Techniques 14, no. 6 (June 7, 2021): 4139–55. http://dx.doi.org/10.5194/amt-14-4139-2021.
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Abstract. Measurement and source apportionment of atmospheric pollutants are crucial for the assessment of air quality and the implementation of policies for their improvement. In most cases, such measurements use expensive regulatory-grade instruments, which makes it difficult to achieve wide spatial coverage. Low-cost sensors may provide a more affordable alternative, but their capability and reliability in separating distinct sources of particles have not been tested extensively yet. The present study examines the ability of a low-cost optical particle counter (OPC) to identify the sources of particles and conditions that affect particle concentrations at an urban background site in Birmingham, UK. To help evaluate the results, the same analysis is performed on data from a regulatory-grade instrument (SMPS, scanning mobility particle sizer) and compared to the outcomes from the OPC analysis. The analysis of the low-cost sensor data manages to separate periods and atmospheric conditions according to the level of pollution at the site. It also successfully identifies a number of sources for the observed particles, which were also identified using the regulatory-grade instruments. The low-cost sensor, due to the particle size range measured (0.35 to 40 µm), performed rather well in differentiating sources of particles with sizes greater than 1 µm, though its ability to distinguish their diurnal variation, as well as to separate sources of smaller particles, at the site was limited. The current level of source identification demonstrated makes the technique useful for background site studies, where larger particles with smaller temporal variations are of significant importance. This study highlights the current capability of low-cost sensors in source identification and differentiation using clustering approaches. Future directions towards particulate matter source apportionment using low-cost OPCs are highlighted.
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Lim, Jaechan, and Hyung-Min Park. "Tracking by Risky Particle Filtering over Sensor Networks." Sensors 20, no. 11 (May 31, 2020): 3109. http://dx.doi.org/10.3390/s20113109.
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The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we propose minimax particle filtering (PF) for tracking a target in wireless sensor networks where multiple-RSS-measurements of received signal strength (RSS) are available at networked-sensors. The minimax PF adopts the maximum risk when computing the weights of particles, which results in the decreased variance of the weights and the immunity against the degeneracy problem of generic PF. Via the proposed approach, we can obtain improved tracking performance beyond the asymptotic-optimal performance of PF from a probabilistic perspective. We show the validity of the employed strategy in the applications of various PF variants, such as auxiliary-PF (APF), regularized-PF (RPF), Kullback–Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF), besides the standard PF (SPF) in the problem of tracking a target in wireless sensor networks.
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Arinchev, S. V. "Newton’s Third Law is not a Dogma but a Computational Hypothesis." Proceedings of Higher Educational Institutions. Маchine Building, no. 06 (723) (June 2020): 36–50. http://dx.doi.org/10.18698/0536-1044-2020-6-36-50.
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Experimental modal analysis is an important stage in the development of a flying vehicle structure. In the experiment, the eigenfrequency of the structure is identified by a corresponding resonance peak of its amplitude-frequency response characteristic. Different sensors in the vibration machine provide different amplitude-frequency response characteristics. The resonance peaks obtained through different sensors for one and the same eigenfrequency of the structure are located with a frequency shift of approximately 1 Hz. This frequency-shift effect is an obstacle for the experimental modal analysis of structures with closely located oscillation modes. This paper explains the frequency-shift effect using the particle approach. A particle is a point mass, and a structure is a system of particles connected by springs, with each particle associated with its own structural model. Each particle has a “right” for its own resonance and “lives” in its own parallel reality. Each particle is associated with an acceleration sensor. The number of simultaneously considered models is equal to the number of sensors. The obtained modal-analysis results are related only to the corresponding particle. Newton’s third law of the particle interaction is not used in full when assessing the particles’ interaction. The action and reaction forces are still applied to different particles along the same line in the opposite directions, but these forces are different. Modal-analysis simulation is limited to the 2-DOF and the 3-DOF oscillation models.
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Qin, Bo Ying, and Xian Kun Lin. "Optimal Sensor Placement Based on Particle Swarm Optimization." Advanced Materials Research 271-273 (July 2011): 1108–13. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1108.
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In order to dispose sensors to reasonable freedom degrees, and reflect adequately the dynamic characteristics of tested structure, the sensor locations of dynamic testing must be optimized. In this paper, taking MAC matrix, Fisher information matrix (FIM), and their combinations as optimization criteria respectively, the particle swarm optimization (PSO) was applied to the optimal sensor location problem (OSLP). The effect of optimization criteria and optimal method to optimal sensor locations were discussed. According to the optimized results, we can arrived at the following conclusions: using MAC and FIM as optimal criteria, introducing the PSO into the OSLP, the optimal sensor locations can ensure the better linear independence of the mode shape vectors and the better estimation of the experimental modal parameters.
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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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Jobert, Gabriel, Pierre Barritault, Maryse Fournier, Cyrielle Monpeurt, Salim Boutami, Cécile Jamois, Pietro Bernasconi, Andrea Lovera, Daniele Braga, and Christian Seassal. "Miniature Optical Particle Counter and Analyzer Involving a Fluidic-Optronic CMOS Chip Coupled with a Millimeter-Sized Glass Optical System." Sensors 21, no. 9 (May 3, 2021): 3181. http://dx.doi.org/10.3390/s21093181.
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Our latest advances in the field of miniaturized optical PM sensors are presented. This sensor combines a hybrid fluidic-optronic CMOS (holed retina) that is able to record a specific irradiance pattern scattered by an illuminated particle (scattering signature), while enabling the circulation of particles toward the sensing area. The holed retina is optically coupled with a monolithic, millimeter-sized, refracto-reflective optical system. The latter notably performs an optical pre-processing of signatures, with a very wide field of view of scattering angles. This improves the sensitivity of the sensors, and simplifies image processing. We report the precise design methodology for such a sensor, as well as its fabrication and characterization using calibrated polystyrene beads. Finally, we discuss its ability to characterize particles and its potential for further miniaturization and integration.
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Liu, Zhenzhen, Yan Liu, Hongfu Zuo, Han Wang, and Zhixiong Chen. "An Oil Wear Particles Inline Optical Sensor Based on Motion Characteristics for Rotating Machines Condition Monitoring." Machines 10, no. 9 (August 25, 2022): 727. http://dx.doi.org/10.3390/machines10090727.
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Since inline monitoring method has the advantages of no sampling, being real-time, no human intervention, and low error, this paper innovatively proposes to study the inline monitoring of wear particles in an oil pipeline, from the perspective of the different motion characteristics of the particles. In this paper, an inline optical sensor was designed and developed by studying the velocity characteristics of different particles through theoretical calculations, numerical simulations, and experimental analysis. First, an equation for particle motion was statistically established, based on the forces acting on wear particles in an oil-filled vertical tube. Then a finite element model of particle motion in a full-flow oil pipeline was created, to simulate particle motion with various diameters, densities, locations, and shapes. Finally, the results of the theoretical study were effectively applied to design an inline optical monitoring sensor, and the experimental validation results demonstrated that the inline sensor has excellent suitability for monitoring wear particles. This study has significance for the safe operation of large rotating machinery.
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Han, Wenbo, Xiaotong Mu, Yu Liu, Xin Wang, Wei Li, Chenzhao Bai, and Hongpeng Zhang. "A Critical Review of On-Line Oil Wear Debris Particle Detection Sensors." Journal of Marine Science and Engineering 11, no. 12 (December 14, 2023): 2363. http://dx.doi.org/10.3390/jmse11122363.
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In the field of marine engineering, the friction and wear experienced by rotating mechanisms are recognized as significant contributors to the failure of marine machinery. In order to enhance the safety and dependability of marine ship operations, the implementation of on-line oil wear debris particle detection sensors enables the on-line monitoring of oil and facilitates the rapid identification of abnormal wear locations. This paper provides a critical review of the recent research progress and development trends in the field of sensors for on-line detection of oil wear debris particles. According to the method of sensor detection, wear debris particle detection sensors can be classified into two distinct categories: electrical and non-electrical sensors. Electrical sensors encompass a range of types, including inductive, capacitive, and resistive sensors. Non-electrical sensors encompass a range of technologies, such as image processing sensors, optical sensors, and ultrasonic sensors. Finally, this review addresses the future research directions for wear debris particle detection sensors in light of the challenging problems currently faced by these sensors.
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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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POLATOĞLU, Ahmet, and Cahit YEŞİLYAPRAK. "Using and Testing Camera Sensors with Different Devices at Cosmic Ray Detection." Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi 16, no. 2 (August 24, 2023): 590–97. http://dx.doi.org/10.18185/erzifbed.1167041.
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Cosmic Ray (CR) is high-energy charged particles that reach the earth from space. CR detection methods and studies have been progressing rapidly since the beginning of the 20th century. One of these methods is the use of digital cameras with Charge Coupled Device (CCD) and Complementary Metal Oxide Semiconductor (CMOS) sensors. Mobile phone cameras or webcams offer an easily using and economical measurement system for CR measurement. The sensors are exposed to CR during a long exposure. CRs leave traces in the background. Cosmic particle tracks are then separated from the background noise and can be classified. Making the traces of the particles visible is important for understanding the subject. In this context, traces of particles such as electrons, muons, and alphas can be seen with the cloud chamber experiments. Help of sensor technology and cameras have developed in recent years, CR traces can be easily detected so that it can be seen. There are many software and international projects that detect CR using CMOS sensors in cell phone cameras. In this study, related projects, programs and studies were researched; CR traces that we captured with the help of Cosmic-Ray Extremely Distributed Observatory (CREDO) and Cosmic Ray Finder (CRF) software with web cam and a mobile phone cam CMOS sensor are presented. Links have been made about astrophysical events coinciding with previously detected particle images.
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Bai, Chenzhao, Hongpeng Zhang, Chengjie Wang, Lebile Ilerioluwa Joseph, Qiang Wang, Yucai Xie, and Guobin Li. "Design and Parameter Research of Time-Harmonic Magnetic Field Sensor Based on PDMS in Microfluidic Technology." Polymers 12, no. 9 (September 4, 2020): 2022. http://dx.doi.org/10.3390/polym12092022.
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In order to improve the throughput and sensitivity of the inductive metal micro-abrasive particle detection sensor, this paper uses microfluidic detection technology to design a high-throughput abrasive particle detection sensor based on PDMS (Polydimethylsiloxane). Theoretical modeling analyzes the magnetization of metal abrasive particles in the coil’s time-harmonic magnetic field, and uses COMSOL simulation to calculate the best performance parameters of the sensor. Through the experiment of the control variable method, the corresponding signal value is obtained and the signal-to-noise ratio (SNR) is calculated. The SNR value and error value are calculated, and the SNR is corrected. The detection limit of the sensor is determined to be 10 μm iron particles and 60 μm copper particles. The optimal design parameters of the 3-D solenoid coil and the frequency characteristics of the sensor are obtained. Finally, through high-throughput experiments and analysis, it was found that there was a reasonable error between the actual throughput and the theoretical throughput. The design ideas suggested in this article can not only improve the sample throughput, but also ensure the detection accuracy. This provides a new idea for the development of an inductive on-line detection method of abrasive particle technology.
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Sharar, Nour, Konstantin Wüstefeld, Rahat Morad Talukder, Julija Skolnik, Katharina Kaufmann, Bernd Giebel, Verena Börger, et al. "The Employment of the Surface Plasmon Resonance (SPR) Microscopy Sensor for the Detection of Individual Extracellular Vesicles and Non-Biological Nanoparticles." Biosensors 13, no. 4 (April 12, 2023): 472. http://dx.doi.org/10.3390/bios13040472.
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A wide-field surface plasmon resonance (SPR) microscopy sensor employs the surface plasmon resonance phenomenon to detect individual biological and non-biological nanoparticles. This sensor enables the detection, sizing, and quantification of biological nanoparticles (bioNPs), such as extracellular vesicles (EVs), viruses, and virus-like particles. The selectivity of bioNP detection does not require biological particle labeling, and it is achieved via the functionalization of the gold sensor surface by target-bioNP-specific antibodies. In the current work, we demonstrate the ability of SPR microscopy sensors to detect, simultaneously, silica NPs that differ by four times in size. Employed silica particles are close in their refractive index to bioNPs. The literature reports the ability of SPR microscopy sensors to detect the binding of lymphocytes (around 10 μm objects) to the sensor surface. Taken together, our findings and the results reported in the literature indicate the power of SPR microscopy sensors to detect bioNPs that differ by at least two orders in size. Modifications of the optical sensor scheme, such as mounting a concave lens, help to achieve homogeneous illumination of a gold sensor chip surface. In the current work, we also characterize the improved magnification factor of the modified SPR instrument. We evaluate the effectiveness of the modified and the primary version of the SPR microscopy sensors in detecting EVs isolated via different approaches. In addition, we demonstrate the possibility of employing translation and rotation stepper motors for precise adjustments of the positions of sensor optical elements—prism and objective—in the primary version of the SPR microscopy sensor instrument, and we present an algorithm to establish effective sensor–actuator coupling.
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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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Nyang’au, Wilson Ombati, Andi Setiono, Angelika Schmidt, Harald Bosse, and Erwin Peiner. "Sampling and Mass Detection of a Countable Number of Microparticles Using on-Cantilever Imprinting." Sensors 20, no. 9 (April 28, 2020): 2508. http://dx.doi.org/10.3390/s20092508.
Full textAbstract:
Liquid-borne particles sampling and cantilever-based mass detection are widely applied in many industrial and scientific fields e.g., in the detection of physical, chemical, and biological particles, and disease diagnostics, etc. Microscopic analysis of particles-adsorbed cantilever-samples can provide a good basis for measurement comparison. However, when a particles-laden droplet on a solid surface is vaporized, a cluster-ring deposit is often yielded which makes particles counting difficult or impractical. Nevertheless, in this study, we present an approach, i.e., on-cantilever particles imprinting, which effectively defies such odds to sample and deposit countable single particles on a sensing surface. Initially, we designed and fabricated a triangular microcantilever sensor whose mass m0, total beam-length L, and clamped-end beam-width w are equivalent to that of a rectangular/normal cantilever but with a higher resonant frequency (271 kHz), enhanced sensitivity (0.13 Hz/pg), and quality factor (~3000). To imprint particles on these cantilever sensors, various calibrated stainless steel dispensing tips were utilized to pioneer this study by dipping and retracting each tip from a small particle-laden droplet (resting on a hydrophobic n-type silicon substrate), followed by tip-sensor-contact (at a target point on the sensing area) to detach the solution (from the tip) and adsorb the particles, and ultimately determine the particles mass concentration. Upon imprinting/adsorbing the particles on the sensor, resonant frequency response measurements were made to determine the mass (or number of particles). A minimum detectable mass of ~0.05 pg was demonstrated. To further validate and compare such results, cantilever samples (containing adsorbed particles) were imaged by scanning electron microscopy (SEM) to determine the number of particles through counting (from which, the lowest count of about 11 magnetic polystyrene particles was obtained). The practicality of particle counting was essentially due to monolayer particle arrangement on the sensing surface. Moreover, in this work, the main measurement process influences are also explicitly examined.
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Biró, Norbert, and Péter Kiss. "Euro VI-d Compliant Diesel Engine’s Sub-23 nm Particle Emission." Sensors 23, no. 2 (January 4, 2023): 590. http://dx.doi.org/10.3390/s23020590.
Full textAbstract:
Passenger and commercial transportation significantly contribute to hazardous air pollution. Exhaust gas after-treatment technology advances closely to the emission regulations throughout the world. The upcoming legislation will be EURO VII in European Union, which requirements are not set yet, but the Solid Particle Number (SPN) diameter range is expected to be more severe compared to EURO VI. This paper will revisit the measurement principle differences between over 10 nm and over 23 nm diameter particles in theory and practical engine bench measurement. Two different types of particle counters have performed the soot particle counting measurement; therefore, the applied sensors are different in terms of applied counting principles. The measurement principles of both devices will be introduced, and the experiment’s result will reflect on the sensor differences. From this, a conclusion can be derived in order to determine the severity of the upcoming EURO VII legislation in terms of SPN, and the experiment will also reflect on the measurement sensor differences. The overall results suggested that extending the lower range of the measurement increases the tailpipe particle emission by 20%, although the DPF filtration efficiency is still over 99%.
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Kuula, Joel, Timo Mäkelä, Minna Aurela, Kimmo Teinilä, Samu Varjonen, Óscar González, and Hilkka Timonen. "Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors." Atmospheric Measurement Techniques 13, no. 5 (May 15, 2020): 2413–23. http://dx.doi.org/10.5194/amt-13-2413-2020.
Full textAbstract:
Abstract. Low-cost particulate matter (PM) sensors have been under investigation as it has been hypothesized that the use of low-cost and easy-to-use sensors could allow cost-efficient extension of the currently sparse measurement coverage. While the majority of the existing literature highlights that low-cost sensors can indeed be a valuable addition to the list of commonly used measurement tools, it often reiterates that the risk of sensor misuse is still high and that the data obtained from the sensors are only representative of the specific site and its ambient conditions. This implies that there are underlying reasons for inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to investigate the particle-size selectivity of low-cost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of size selectivity was carried out in the laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes (from ∼0.55 to 8.4 µm) on-line. The results of the study show that none of the low-cost sensors adhered to the detection ranges declared by the manufacturers; moreover, cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020) indicates that the sensors can only achieve independent responses for one or two size bins, whereas the spectrometer can sufficiently characterize particles with 15 different size bins. These observations provide insight into and evidence of the notion that particle-size selectivity has an essential role in the analysis of the sources of errors in sensors.
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Jamaludin, Amirul, Norhidayah Mohamad Yatim, Zarina Mohd Noh, and Norlida Buniyamin. "Rao-Blackwellized Particle Filter Algorithm Integrated with Neural Network Sensor Model Using Laser Distance Sensor." Micromachines 14, no. 3 (February 27, 2023): 560. http://dx.doi.org/10.3390/mi14030560.
Full textAbstract:
Commonly, simultaneous localization and mapping (SLAM) algorithm is developed using high-end sensors. Alternatively, some researchers use low-end sensors due to the lower cost of the robot. However, the low-end sensor produces noisy sensor measurements that can affect the SLAM algorithm, which is prone to error. Therefore, in this paper, a SLAM algorithm, which is a Rao-Blackwellized particle filter (RBPF) integrated with artificial neural networks (ANN) sensor model, is introduced to improve the measurement accuracy of a low-end laser distance sensor (LDS) and subsequently improve the performance of SLAM. The RBPF integrated with the ANN sensor model is experimented with by using the Turtlebot3 mobile robot in simulation and real-world experiments. The experiment is validated by comparing the occupancy grid maps estimated by RBPF integrated with the ANN sensor model and RBPF without ANN. Both the results in simulation and real-world experiments show that the SLAM performance of RBPF integrated with the ANN sensor model is better than the RBPF without ANN. In the real-world experiment results, the performance of the occupied cells integrated with the ANN sensor model is increased by 107.59%. In conclusion, the SLAM algorithm integrated with the ANN sensor model is able to improve the accuracy of the map estimate for mobile robots using low-end LDS sensors.