Classification Of Particles Research Articles

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model.

Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.

Approaches to Detect Microplastics in Water Using Electrical Impedance Measurements and Support Vector Machines

We propose electrical impedance spectroscopy (EIS) enhanced by machine learning (ML), in particular support vector machines (SVMs), as a noninvasive, in situ method for detecting microplastics in water and as an alternative to slow, expensive laboratory measurements. The primary measurand is the complex-valued electrical impedance of a water-filled measuring cell. We carried out stationary measurements on numerous water samples contaminated with different plastic concentrations in a cylindrical measuring cell and in the frequency range from 20 Hz to 2 MHz. The effects of various influence quantities, such as the concentration of organic material (1.0%, 3.0%, 5.0%) or the salinity of water (0.5%, 1.0%, 3.5%), were also investigated. Measurements at 2 MHz with water flows carrying microplastic particles of different materials and sizes served to investigate the dynamic capabilities of the measurement method. The impedance spectra (stationary measurements) or the measured impedances (dynamic measurements) were then evaluated by specific SVMs. The classification task consisted of distinguishing different plastic materials and particle sizes. In the stationary case, the application of the SVM resulted in assignment accuracies of over 98%. In the dynamic case, the classification accuracies exceeded 91% for the mere classification of particle sizes and 85% for the classification of plastic particles by size and material.

Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction

Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction

Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction

Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction

Study on the Application of a Reflux Classifier in the Classification of Ultrafine Ilmenite

Mineral classification is an important preparation operation in the process of beneficiation. The classification effect directly affects the production capacity of grinding machines, product quality, subsequent separation of the concentrate grade, and recovery. To improve mineral classification accuracy and provide technical ideas for enriching the separation of fine materials, this paper proposes the use of a trapezoidal inclined channel agitated reflux classifier (TARC) to classify ultra-fine ilmenite. The principle of this separating system is based on a hindered settling effect and fluidization theory. This study focuses on the factors influencing the particle separation through the optimization of different experimental conditions. A satisfactory 10 µm classification efficiency of 67.89% could be obtained when the ore pulp concentration was 15%, the agitation speed was 350 r/min, and the underflow flux was 16 cm3/min. The TARC realized a continuous feeding classification of fine particles within a narrow particle size range, with 10 µm as the limit size, and an effective reduction in the entrainment of fine particles in the underflow and coarse particles in the overflow phenomena, thus achieving improved classification efficiency.

In situ biological particle analyzer based on digital inline holography.

Obtaining in situ measurements of biological microparticles is crucial for both scientific research and numerous industrial applications (e.g., early detection of harmful algal blooms, monitoring yeast during fermentation). However, existing methods are limited to offer timely diagnostics of these particles with sufficient accuracy and information. Here, we introduce a novel method for real-time, in situ analysis using machine learning (ML)-assisted digital inline holography (DIH). Our ML model uses a customized YOLOv5 architecture specialized for the detection and classification of small biological particles. We demonstrate the effectiveness of our method in the analysis of 10 plankton species with equivalent high accuracy and significantly reduced processing time compared to previous methods. We also applied our method to differentiate yeast cells under four metabolic states and from two strains. Our results show that the proposed method can accurately detect and differentiate cellular and subcellular features related to metabolic states and strains. This study demonstrates the potential of ML-driven DIH approach as a sensitive and versatile diagnostic tool for real-time, in situ analysis of both biotic and abiotic particles. This method can be readily deployed in a distributive manner for scientific research and manufacturing on an industrial scale.

A New Method for Continuous Classification of Fine Catalyst Particles from a Slurry Bed Fischer–Tropsch Reactor

During the operation of an industrial Fischer–Tropsch plant, fine catalyst particles are continuously generated in the slurry bed, which results in a series of problems, such as reduced flowability of liquid from the filter, reduction of unit processing capacity, and increased catalyst consumption. To solve these issues, a new classification method based on hydrocyclone was proposed to classify fine particles continuously. Cold model experiments using a mimic liquid–solid medium were conducted to verify the feasibility of this new method. The optimized structure and operating conditions were determined by studying the influences of structural parameters, operating conditions, and material physical properties systematically. Side-stream tests were then conducted to evaluate the actual classification performance. The results showed that the performance of the new classification method is primarily influenced by the medium temperature, i.e., the liquid viscosity. When slurry temperature is 200 °C, the coarse loss ratio can be reduced to less than 5% in a single-turn classification test and the fine removal ratio can be maintained at around 30%. With further increases in the temperature (i.e., at lower liquid viscosities), better classification performance can be expected. Finally, a prediction model was established, which can predict the whole classification performance of the new classification method. The model can be used to guide the process design and operation of this method.

Detection of ice core particles via deep neural networks

Abstract. Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented.

Encyclopedia of emergent particles in 528 magnetic layer groups and 394 magnetic rod groups

We present a systematic classification of emergent particles in all 528 magnetic layer groups and 394 magnetic rod groups, which describe two-dimensional and one-dimensional crystals, respectively. Our approach is via constructing a correspondence between a given magnetic layer/rod group and one of the magnetic space groups, such that all irreducible representations of the layer/rod group can be derived from those of the corresponding space group. Based on these group representations, we explicitly construct the effective models for possible band degeneracies and identify all emergent particles, including both spinless and spinful cases. We find that there are six kinds of particles protected by magnetic layer groups and three kinds by magnetic rod groups. Our work provides a useful reference for the search and design of emergent particles in lower dimensional crystals.

Lipid translocation in the omega-3 fatty-acid transporter MFSD2A.

The omega-3 fatty-acid docosahexaenoic acid (DHA) is a key component of cellular membranes in the central nervous system (CNS) and essential for neurological functions. In human cerebral cortex, DHA contributes to around 25% of total fatty acids. Deficiency of DHA results in several CNS-related disorders, such as memory deficits, dyspraxia, and dyslexia. The major source of DHA in the brain is obtained from the diet and transported in the form of lysophosphatidylcholine (LPC) across the blood-brain-barrier (BBB) by MFSD2A in a Na+-dependent manner. The detailed transport mechanism of LPC across the lipid bilayer is still elusive. Here, by using single particle cryo-EM, we revealed the structure of Danio rerio MFSD2A (59 kDa) in a complex with FAB in the inward-open conformation with ligands bound to amphipathic pockets between N- and C-terminal domains at 2.9 Å. Multiple MFSD2A structures with individual ligands located at unique positions along the translocation pathway were further identified by extensive classification of particles. These structures depicted the DHA-LPC translocating trajectory along the cavity, where a ∼70 degree rotation of LPC headgroup followed by translation of the ligand towards the cytoplasmic exit occurred after the initial lipid flipping step. Together these results provide a detailed model for omega-3 fatty-acid transport and potential for the design of the delivery strategies for amphipathic drugs across the BBB.

Sparse Convolutional Neural Networks for particle classification in ProtoDUNE-SP events

Deep Learning (DL) methods and Computer Vision are becoming important tools for event reconstruction in particle physics detectors. In this work, we report on the use of submanifold sparse convolutional neural networks (SparseNets) for the classification of track and shower hits from a DUNE prototype liquid-argon detector at CERN (ProtoDUNE-SP). By taking advantage of the three-dimensional nature of the problem we use a set of nine input features to classify sparse and locally dense hits associated to track or shower particles. The SparseNet has been trained on a test sample and shows promising results: efficiencies and purities greater than 90%. This has also been achieved with a considerable speedup and substantially less resource utilization with respect to other DL networks such as graph neural networks. This method offers great scalability advantages for future large neutrino detectors such as the planned DUNE experiment.

Particle classification of iron ore sinter green bed mixtures by 3D X-ray microcomputed tomography and machine learning

Particle classification of iron ore sinter green bed mixtures by 3D X-ray microcomputed tomography and machine learning

A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

We improve convergence speed by two orders of magnitude and the global exploration capabilities of particle swarm optimization (PSO) through targeted position-mutated elitism (TPME). The proposed fast-converging TPME operator requires a fitness-based classification technique to categorize the particles. The introduced classification is motivated by its simplicity, low memory requirements, and automated termination criteria based on convergence. The three key innovations address particle classification, elitism, and mutation in the cognitive and social model. PSO-TPME is benchmarked against five popular PSO variants for multi-dimensional functions, which are extensively adopted in the optimization field, In particular, the convergence accuracy, convergence speed, and the capability to find global minima are investigated. The statistical error is assessed by numerous repetitions. The simulations confirmed that in ten of the thirteen investigated functions, the proposed PSO variant outperforms other variants in terms of convergence rate and accuracy by at least two orders of magnitude. On the other hand, the simulations demonstrated the early exploration capabilities of PSO-TPME in all tested functions. In the first ten iterations, PSO-TPME outperformed all the investigated PSO variants by at least two orders of magnitude.

Particle detection and size recognition based on defocused particle images: a comparison of a deterministic algorithm and a deep neural network

The systematic manipulation of components of multimodal particle solutions is a key for the design of modern industrial products and pharmaceuticals with highly customized properties. In order to optimize innovative particle separation devices on microfluidic scales, a particle size recognition with simultaneous volumetric position determination is essential. In the present study, the astigmatism particle tracking velocimetry is extended by a deterministic algorithm and a deep neural network (DNN) to include size classification of particles of multimodal size distribution. Without any adaptation of the existing measurement setup, a reliable classification of bimodal particle solutions in the size range of 1.14~upmu hbox {m}–5.03~upmu hbox {m} is demonstrated with a precision of up to 99.9 %. Concurrently, the high detection rate of the particles, suspended in a laminar fluid flow, is quantified by a recall of 99.0 %. By extracting particle images from the experimentally acquired images and placing them on a synthetic background, semi-synthetic images with consistent ground truth are generated. These contain labeled overlapping particle images that are correctly detected and classified by the DNN. The study is complemented by employing the presented algorithms for simultaneous size recognition of up to four particle species with a particle diameter in between 1.14~upmu hbox {m} and 5.03~upmu hbox {m}. With the very high precision of up to 99.3 % at a recall of 94.8 %, the applicability to classify multimodal particle mixtures even in dense solutions is confirmed. The present contribution thus paves the way for quantitative evaluation of microfluidic separation and mixing processes.

Preliminary Study for Smoke Color Classification of Combustibles Using the Distribution of Light Scattering by Smoke Particles

Photoelectric smoke detectors are used for early detection of building fires, and sensitivity adjustment is generally performed using white smoke generated by the burning of filter paper. Therefore, when black smoke of the same concentration is introduced, the detector is often not activated. To address this problem, differences in the distribution of light scattered by smoke of various colors were analyzed. A light-scattering chamber with a light-receiving unit for various scattering angles was constructed to measure the scattered light generated inside the chamber of the smoke detector. The light scattering distribution was measured for smoke generated from three combustibles—filter paper (white smoke), kerosene (black smoke), and polyurethane (gray-black smoke)—and three analysis criteria were applied. By identifying a section where the measured values were concentrated for a specific analysis criterion and scattering angle, it was confirmed that some combustibles can be distinguished. Specifically, criterion III, a probabilistic section, was presented to determine which combustible smoke particles were close by applying the proposed section in a complex manner. A preliminary study was conducted to evaluate a methodology for the color classification of smoke particles flowing into a smoke detector chamber; this can be utilized as a foundation for determining optical properties.

The use of information technologies for the research and modernization of the separation of grain raw materials

The article presents algorithms for the operation of an information and measurement system describing the technology of grinding wheat grain. The properties of grain processing products are determined by the influence of natural and climatic factors, and in addition by the method of grinding. The spread of information technologies has made it possible to improve the existing algorithms of information and measurement systems for wheat processing, as well as to form new processing methods due to a more accurate assessment of grain properties. Various wheat varieties were studied, trial grinds of which were produced at a laboratory mill. The increase in the output of industry enterprises is ensured by heuristic selection of the most accurate operating mode of separators of various types. Empirical models serve the purpose of predicting the quality indicators of grain mass after its separation. Digitalization of the grain industry has made it possible to improve the accuracy of granulometric analysis in the field of grain particle classification by shape and size. Artificial neural networks and technical vision algorithms as part of an intelligent system were used to process the appearance of grain after grinding. Appropriate software has been developed for this purpose. To divide wheat into classes, it is proposed to use grain hardness indicators. A systematic approach was used to detect and distribute grain particles after grinding according to geometric characteristics. A brief description of the algorithm of operation of such a system consists in finding the contours of crushed grain particles and measuring their size and shape. The accuracy of using such an intelligent system when measuring grain hardness in comparison with traditional methods, as experiments have shown, is less than 3.5%. The introduction of such intelligent systems at industrial facilities of the food industry will improve the quality of the enterprise and the output of finished products.

Field prototype for rapid classification of suspended particles in water based on polarized light scattering and fluorescence measurement

Field prototype for rapid classification of suspended particles in water based on polarized light scattering and fluorescence measurement

Toward a Monolithic Pixel Sensor for Heavy Ion Spectroscopy—Pixel Structure Design and Optimization

Detection and classification of cosmic particles is essential for many scientific and engineering applications. Currently available heavy ion detectors necessitate an external readout integrated circuit when conducting both measurements of particle energy deposition and particle incident position. In this paper, we propose an innovative monolithic pixel detector topology capable of distinguishing between particles in a predefined LET range. The analog and digital readout circuitry are incorporated into the pixel region while maintaining almost an 100% detection fill factor over the entire pixel array. We present comprehensive CAD 3D simulations of the device to verify the applicability of the detector for galactic cosmic rays with the specified LET levels and induced charge distribution. We evaluate the charge collection performance under heavy-ion hit conditions and provide an assessment of the impact on charge collection from the particle impinging position, particle LET, angle of penetration and circuit bias. The results indicate a linear relationship between particle LET and collected charge, regardless of the ion hit position and almost independent of the circuit voltage bias. The simulation results for various particle incident angles show correlations between charge collection and particle trajectory length. We propose an implementation which makes it possible to determine the particle angle of penetration and particle LET attributes. The radiation tolerance of the sensor was estimated. Its applicability to the non-ionizing radiation environment was shown to be adequate for low Earth orbits.

An Android Application-based Surface Oust Classifier

Surface dust can be a source carrier of viruses, bacteria and air pollution which entail common health issues such as asthma attacks, chest tightness, wheezing and difficulty in breathing. Visually perceived, cleanliness is one measure of indoor air quality and is the subjective assessment of cleaning quality. The aim of this work is to use pattern recognition mediated through a mobile application to analyse and classify dust in households, in order to obtain useful information about the dust sources for the selection of appropriate countermeasures in view to improve air quality and better manage the cleaning of indoor surfaces. The dust type categorization in this work are pollen, rock and ash. This paper also explores the concept of transfer learning techniques and adopts it for small particle classification using CNN models by developing a surface dust application for android smartphones. The behaviour of InceptionV2, InceptionV3, ResNetV2, MobileNetV2 and MobileNetV3 as dust feature extractors were analysed based on their accuracy, precision, recall and Fl-Scoreperformance metrics. Results show that MobileNetV3 model is best suited as a dust feature extractor and rapid dust prediction with an accuracy of up to 92% and low-size storage of only 30 megabytes. Additional Keywords: Android; Classification; Transfer Learning; Smartphone; Surface dust.

Phase-unwrapping algorithm combined with wavelet transform and Hilbert transform in self-mixing interference for individual microscale particle detection

The self-mixing interferometry (SMI) technique is an emerging sensing technology in microscale particle classification. However, due to the nature of the SMI effect raised by a microscattering particle, the signal analysis suffers from many problems compared with a macro target, such as lower signal-to-noise ratio (SNR), short transit time, and time-varying modulation strength. Therefore, the particle sizing measurement resolution is much lower than the one in typical displacement measurements. To solve these problems, in this paper, first, a theoretical model of the phase variation of a single-particle SMI signal burst is demonstrated in detail. The relationship between the phase variation and the particle size is investigated, which predicts that phase observation could be another alternative for particle detection. Second, combined with continuous wavelet transform and Hilbert transform, a novel phase-unwrapping algorithm is proposed. This algorithm can implement not only efficient individual burst extraction from the noisy raw signal, but also precise phase calculation for particle sizing. The measurement shows good accuracy over a range from 100 nm to 6 μm with our algorithm, proving that our algorithm enables a simple and reliable quantitative particle characteristics retrieval and analysis methodology for microscale particle detection in biomedical or laser manufacturing fields.