Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Detection of size of manufactured sand particles based on digital image processing.

ABSTRACT: The size distribution of manufactured sand particles has a significant influence on the quality of concrete. To overcome the shortcomings of the traditional vibration-sieving method, a manufactured sand casting/dispersing system was developed, based on the characteristics of the sand particle contours (as determined by backlit image acquisition) and an extraction mechanism. Algorithms for eliminating particles from the image that had be repeatedly captured, as well as for identifying incomplete particles at the boundaries of the image, granular contour segmentation, and the determination of an equivalent particle size, are studied. The hardware and software for the image-based detection device were developed. A particle size repeatability experiment was carried out on the single-grade sands, grading the size fractions of the manufactured sand over a range of 0.6-4.75 mm. A method of particle-size correction is proposed to compensate for the difference in the results obtained by the image-based method and those obtained by the sieving method. The experimental results show that the maximum repeatability error of single-grade fractions is 3.46% and the grading size fraction is 0.51%. After the correction of the image method, the error between the grading size fractions obtained by the two methods was reduced from 7.22%, 6.10% and 5% to 1.47%, 1.65%, and 3.23%, respectively. The accuracy of the particle-size detection can thus satisfy real-world measuring requirements.

SUBMITTER: Yang J

PROVIDER: S-EPMC6294553 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Service reliability evaluation of highway tunnel based on digital image processing.

Project description:China is gradually transitioning from the "tunnel construction exploration era" to the "tunnel high-quality construction and operation era", and the maintenance demand of highway tunnels has increased sharply. Therefore, there is an urgent need for an evaluation method to evaluate the service reliability of highway tunnels, so as to provide reference for tunnel maintenance personnel to carry out maintenance work. Taking highway tunnels as the research object, this paper extracts three parameters, including length, maximum width and fractal dimension, from the binary image of highway tunnel lining cracks. The standard for dividing the length of the highway tunnel section is 500m as the tunnel section, and a section disease sample space including multiple highway tunnels is constructed. The EM clustering algorithm was used to determine the number of graded grades of disease, and the relative Euclidean distance was used as the evaluation index to divide the safety grade of the tunnel into five grades: normal, degraded, inferior, deteriorated and hazardous. The partial least squares method is used to establish the lining service reliability evaluation formula and verify the residual of each sample point in the sample space. The smaller the average value of the residual, the better fitting effect of the established evaluation formula. The service reliability evaluation method proposed in this paper is applied to engineering practice and compared with the expert scoring method and the national standard method, which proves that the evaluation method in this paper has the advantages of strong visibility, simple evaluation method, and is conducive to engineering practice.

| S-EPMC10420349 | biostudies-literature

Size measurement and filled/unfilled detection of rice grains using backlight image processing.

Project description:Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.

| S-EPMC10613065 | biostudies-literature

QTL Analysis for Bread Wheat Seed Size, Shape and Color Characteristics Estimated by Digital Image Processing.

Project description:The size, shape, and color of wheat seeds are important traits that are associated with yield and flour quality (size, shape), nutritional value, and pre-harvest sprouting (coat color). These traits are under multigenic control, and to dissect their molecular and genetic basis, quantitative trait loci (QTL) analysis is used. We evaluated 114 recombinant inbred lines (RILs) in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative, ITMI/MP) grown in 2014 season. We used digital image analysis for seed phenotyping and obtained data for seven traits describing seed size and shape and 48 traits of seed coat color. We identified 212 additive and 34 pairs of epistatic QTLs on all the chromosomes of wheat genome except chromosomes 1A and 5D. Many QTLs were overlapping. We demonstrated that the overlap between QTL regions was low for seed size/shape traits and high for coat color traits. Using the literature and KEGG data, we identified sets of genes in Arabidopsis and rice from the networks controlling seed size and color. Further, we identified 29 and 14 candidate genes for seed size-related loci and for loci associated with seed coat color, respectively.

| S-EPMC9414870 | biostudies-literature

An automatic screening method for strabismus detection based on image processing.

Project description:PurposeThis study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility.Materials and methodsThe proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facial landmarks to extract the eye region for a frontal facial image. Second, Otsu's binarization and the HSV color model are applied to the image to eliminate the influence of eyelashes and canthi. Then, the method samples all of the pixel points on the limbus and applies the least square method to obtain the coordinate of the pupil center. Lastly, we calculated the distances from the pupil center to the medial and lateral canthus to measure the deviation of the positional similarity of two eyes for strabismus screening.ResultWe used a total of 60 frontal facial images (30 strabismus images, 30 normal images) to validate the proposed method. The average value of the iris positional similarity of normal images was smaller than one of the strabismus images via the method (p-value<0.001). The sample mean and sample standard deviation of the positional similarity of the normal and strabismus images were 1.073 ± 0.014 and 0.039, as well as 1.924 ± 0.169 and 0.472, respectively.ConclusionThe experimental results of 60 images show that the proposed method is a promising automatic strabismus screening method for people living in remote areas with poor medical accessibility.

| S-EPMC8330949 | biostudies-literature

Spectral image contrast-based flow digital nanoplasmon-metry for ultrasensitive antibody detection.

Project description:BackgroundGold nanoparticles (AuNPs) have been widely used in local surface plasmon resonance (LSPR) immunoassays for biomolecule sensing, which is primarily based on two conventional methods: absorption spectra analysis and colorimetry. The low figure of merit (FoM) of the LSPR and high-concentration AuNP requirement restrict their limit of detection (LOD), which is approximately ng to μg mL-1 in antibody detection if there is no other signal or analyte amplification. Improvements in sensitivity have been slow in recent for a long time, and pushing the boundary of the current LOD is a great challenge of current LSPR immunoassays in biosensing.ResultsIn this work, we developed spectral image contrast-based flow digital nanoplasmon-metry (Flow DiNM) to push the LOD boundary. Comparing the scattering image brightness of AuNPs in two neighboring wavelength bands near the LSPR peak, the peak shift signal is strongly amplified and quickly detected. Introducing digital analysis, the Flow DiNM provides an ultrahigh signal-to-noise ratio and has a lower sample volume requirement. Compared to the conventional analog LSPR immunoassay, Flow DiNM for anti-BSA detection in pure samples has an LOD as low as 1 pg mL-1 within only a 15-min detection time and 500 μL sample volume. Antibody assays against spike proteins of SARS-CoV-2 in artificial saliva that contained various proteins were also conducted to validate the detection of Flow DiNM in complicated samples. Flow DiNM shows significant discrimination in detection with an LOD of 10 pg mL-1 and a broad dynamic detection range of five orders of magnitude.ConclusionTogether with the quick readout time and simple operation, this work clearly demonstrated the high sensitivity and selectivity of the developed Flow DiNM in rapid antibody detection. Spectral image contrast and digital analysis further provide a new generation of LSPR immunoassay with AuNPs.

| S-EPMC8724237 | biostudies-literature

Image-Based Geometrical Characterization of Nodes in Additively Manufactured Lattice Structures.

Project description:Additive manufacturing (AM) enables the fabrication of lattice structures with optimal mechanical, fluid, and thermal properties. However, during the AM fabrication process, defects are produced in the strut and node elements, which comprise the lattice structure. This leads to discrepancies between the AM fabricated lattice and its idealized computer-aided design (CAD) model, negatively affecting the ability to predict the mechanical behavior of the fabricated lattice via numerical models. Current research is focused on quantification of geometric uncertainties in the strut elements of the lattice; as-manufactured node geometries remain relatively unexplored on an individual scale, despite their criticality to the mechanical response of the structure. Understanding the geometrical properties of as-manufactured nodes relative to CAD idealizations can be used to improve lattice designs and numerical models. In this research, X-ray microcomputed tomography (μCT) is used to analyze and quantify the as-manufactured nodal geometry, found in face-centered cubic and face-centered cubic with axial struts lattices fabricated via selective laser melting. A custom tool is developed that enables auto-isolation and classification of nodal joints from μCT-derived cross-sectional slices. Geometrical properties are extracted from the isolated nodal cross sections and compared with their idealized CAD model counterpart. Quantification of geometrical defects provides insight into how nodes within an AM lattice structure differ from each other and their idealized design. Overall, this research is an initial step toward developing accurate and efficient numerical models, as well as better node design for AM.

| S-EPMC9828589 | biostudies-literature

Digital image processing to detect subtle motion in stony coral.

Project description:Coral reef ecosystems support significant biological activities and harbor huge diversity, but they are facing a severe crisis driven by anthropogenic activities and climate change. An important behavioral trait of the coral holobiont is coral motion, which may play an essential role in feeding, competition, reproduction, and thus survival and fitness. Therefore, characterizing coral behavior through motion analysis will aid our understanding of basic biological and physical coral functions. However, tissue motion in the stony scleractinian corals that contribute most to coral reef construction are subtle and may be imperceptible to both the human eye and commonly used imaging techniques. Here we propose and apply a systematic approach to quantify and visualize subtle coral motion across a series of light and dark cycles in the scleractinian coral Montipora capricornis. We use digital image correlation and optical flow techniques to quantify and characterize minute coral motions under different light conditions. In addition, as a visualization tool, motion magnification algorithm magnifies coral motions in different frequencies, which explicitly displays the distinctive dynamic modes of coral movement. Specifically, our assessment of displacement, strain, optical flow, and mode shape quantify coral motion under different light conditions, and they all show that M. capricornis exhibits more active motions at night compared to day. Our approach provides an unprecedented insight into micro-scale coral movement and behavior through macro-scale digital imaging, thus offering a useful empirical toolset for the coral research community.

| S-EPMC8032694 | biostudies-literature

A method of crack detection based on digital image correlation for simulated cracked tooth.

Project description:BackgroundEarly clinical cracked tooth can be a perplexing disorder to diagnose and manage. One of the key problems for the diagnosis of the cracked tooth is the detection of the location of the surface crack.MethodsThis paper proposes an image-based method for the detection of the micro-crack in the simulated cracked tooth. A homemade three-axis motion platform mounted with a telecentric lens was built as an image acquisition system to observe the surface of the simulated cracked tooth, which was under compression with a magnitude of the masticatory force. By using digital image correlation (DIC), the deformation map for the crown surface of the cracked tooth was calculated. Through image analysis, the micro-crack was quantitatively visualized and characterized.ResultsThe skeleton of the crack path was successfully extracted from the image of the principal strain field, which was further verified by the image from micro-CT. Based on crack kinematics, the crack opening displacement was quantitatively calculated to be 2-10 µm under the normal mastication stress, which was in good agreement with the value reported in the literature.ConclusionsThe crack on the surface of the simulated cracked tooth could be detected based on the proposed DIC-based method. The proposed method may provide a new solution for the rapid clinical diagnosis of cracked teeth and the calculated crack information would be helpful for the subsequent clinical treatment of cracked teeth.

| S-EPMC8524926 | biostudies-literature

Dataset of tensile strength development of concrete with manufactured sand.

Project description:This article presents 755 groups splitting tensile strength tests data of concrete with manufactured sand (MSC) in different curing age ranged from 1 day to 388 days related to the research article "Experimental study on tensile strength development of concrete with manufactured sand" (Zhao et al., 2017) [1]. These data were used to evaluate the precision of the prediction formulas of tensile strength of MSC, and can be applied as dataset for further studies.

| S-EPMC5338904 | biostudies-literature

Color restoration based on digital pathology image.

Project description:ObjectiveProtective color restoration of faded digital pathology images based on color transfer algorithm.MethodsTwenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irradiated with sunlight to simulate natural fading, and every 7 days was a fading cycle, and a total of 8 cycles were experienced. At the end of each cycle, the sections were digitally scanned to retain clear images, and the color changes of the sections during the fading process were recorded. The color transfer algorithm was applied to restore the color of the faded images; Adobe Lightroom Classic software presented the histogram of the image color distribution; UNet++ cell recognition segmentation model was used to identify the color restored images; Natural Image Quality Evaluator (NIQE), Information Entropy (Entropy), and Average Gradient (AG) were applied to evaluate the quality of the restored images.ResultsThe restored image color met the diagnostic needs of pathologists. Compared with the faded images, the NIQE value decreased (P<0.05), Entropy value increased (P<0.01), and AG value increased (P<0.01). The cell recognition rate of the restored image was significantly improved.ConclusionThe color transfer algorithm can effectively repair faded pathology images, restore the color contrast between nucleus and cytoplasm, improve the image quality, meet the diagnostic needs and improve the cell recognition rate of the deep learning model.

| S-EPMC10306179 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data