scholarly journals Chan-Vese segmentation of SEM ferritepearlite microstructure and prediction of grain boundary

2019 ◽  
Vol 8 (10) ◽  
pp. 1495-1498
Keyword(s):  
Image Processing ◽  
Grain Boundary ◽  
Phase Region ◽  
Set Covering ◽  
Phase Identification ◽  
Data Set ◽  
Steel Microstructure ◽  
Sem Image ◽  
Wide Range ◽  
Segmentation Task

The image processing of microstructure for design, measure and control of metal processing has been emerging as a new area of research for advancement towards the development of Industry 4.0 framework. However, exact steel phase segmentation is the key challenge for phase identification and quantification in microstructure employing proper image processing tool. In this article, we report effectiveness of a region based segmentation tool, Chan-Vese in phase segmentation task from a ferrite- pearlite steel microstructure captured in scanning electron microscopy image (SEM) image. The algorithm has been applied on microstructure images and the results are discussed in light of the effectiveness of Chan-Vese algorithms on microstructure image processing and phase segmentation application. Experiments on the ferrite perlite microstructure data set covering a wide range of resolution revealed that the Chan-Vese algorithm is efficient in segmentation of phase region and predicting the grain boundary.

scholarly journals Chan-Vese Segmentation of SEM FerritePearlite Microstructure and Prediction of Grain Boundary

2019 ◽  
Vol 8 (11) ◽  
pp. 3434-3437
Keyword(s):  
Image Processing ◽  
Grain Boundary ◽  
Phase Region ◽  
Set Covering ◽  
Phase Identification ◽  
Data Set ◽  
Steel Microstructure ◽  
Sem Image ◽  
Wide Range ◽  
Segmentation Task

The image processing of microstructure for design, measure and control of metal processing has been emerging as a new area of research for advancement towards the development of Industry 4.0 framework. However, exact steel phase segmentation is the key challenge for phase identification and quantification in microstructure employing proper image processing tool. In this article, we report effectiveness of a region based segmentation tool, Chan-Vese in phase segmentation task from a ferrite- pearlite steel microstructure captured in scanning electron microscopy image (SEM) image. The algorithm has been applied on microstructure images and the results are discussed in light of the effectiveness of Chan-Vese algorithms on microstructure image processing and phase segmentation application. Experiments on the ferrite perlite microstructure data set covering a wide range of resolution revealed that the Chan-Vese algorithm is efficient in segmentation of phase region and predicting the grain boundary.

A Color Coded STEM/SEM Image Storage System

1981 ◽  
Vol 39 ◽  
pp. 272-273
Author(s):  
Y. Kokubo ◽  
W. H. Hardy ◽  
J. Dance ◽  
K. Jones
Keyword(s):  
Image Processing ◽  
Electron Beam ◽  
Storage System ◽  
Particle Analysis ◽  
Specific Information ◽  
X Rays ◽  
Sem Image ◽  
Backscattered Electrons ◽  
Wide Range ◽  
Scanning Electron

A color coded digital image processing is accomplished by using JEM100CX TEM SCAN and ORTEC’s LSI-11 computer based multi-channel analyzer (EEDS-II-System III) for image analysis and display. Color coding of the recorded image enables enhanced visualization of the image using mathematical techniques such as compression, gray scale expansion, gamma-processing, filtering, etc., without subjecting the sample to further electron beam irradiation once images have been stored in the memory.The powerful combination between a scanning electron microscope and computer is starting to be widely used 1) - 4) for the purpose of image processing and particle analysis. Especially, in scanning electron microscopy it is possible to get all information resulting from the interactions between the electron beam and specimen materials, by using different detectors for signals such as secondary electron, backscattered electrons, elastic scattered electrons, inelastic scattered electrons, un-scattered electrons, X-rays, etc., each of which contains specific information arising from their physical origin, study of a wide range of effects becomes possible.

scholarly journals Use of routinely collected data on psychiatric in-patient care

2003 ◽  
Vol 9 (4) ◽  
pp. 300-307 ◽  
Author(s):  
Gyles Glover
Keyword(s):  
Mental Health ◽  
Set Covering ◽  
Services Research ◽  
Data Set ◽  
Research Issues ◽  
Wide Range ◽  
Policy Work ◽  
Data Source ◽  
Set Up ◽  
Service Data

Since the start of the National Health Service, data have been collected on admissions to psychiatric in-patient units, first as the Mental Health Enquiry, then as part of Hospital Episode Statistics. Some details have changed but many have stayed remarkably consistent. Published literature on the wide range of research and policy work undertaken using this data source is reviewed. Early work was central to the government's deinstitutionalisation policy in the early 1960s. Subsequent studies cover a wide range of epidemiological and health services research issues. A new statistical base, the Mental Health Minimum Data Set, covering individuals receiving all types of health care is currently being set up. This will supplement (but not replace) admission statistics.

scholarly journals Image Processing of SEM Image Nano Silver Using K-means MATLAB Technique

2019 ◽  
Vol 29 (3) ◽  
pp. 150 ◽  
Author(s):  
Elham Jasim Mohammad
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Sem Image ◽  
Huge Data ◽  
And Performance ◽  
Biometric Information

Nanotechnology is one of the non-exhaustive applications in which image processing is used. For optimal nanoparticle visualization and characterization, the high resolution Scanning Electron Microscope (SEM) and the Atomic Force Microscope (AFM) are used. Image segmentation is one of the critical steps in nanoscale processing. There are also different ways to reach retail, including statistical approximations.In this study; we used the K-means method to determine the optimal threshold using statistical approximation. This technique is thoroughly studied for the SEM nanostructure Silver image. Note that, the image obtained by SEM is good enough to analyze more recently images. The analysis is being used in the field of nanotechnology. The K-means algorithm classifies the data set given to k groups based on certain measurements of certain distances. K-means technology is the most widely used among all clustering algorithms. It is one of the common techniques used in statistical data analysis, image analysis, neural networks, classification analysis and biometric information. K-means is one of the fastest collection algorithms and can be easily used in image segmentation. The results showed that K-means is highly sensitive to small data sets and performance can degrade at any time. When exposed to a huge data set such as 100.000, the performance increases significantly. The algorithm also works well when the number of clusters is small. This technology has helped to provide a good performance algorithm for the state of the image being tested.

Early Prediction of University Dropouts – A Random Forest Approach

2020 ◽  
Vol 240 (6) ◽  
pp. 743-789 ◽  
Author(s):  
Andreas Behr ◽  
Marco Giese ◽  
Herve D. Teguim K ◽  
Katja Theune
Keyword(s):  
Student Satisfaction ◽  
Early Stage ◽  
Binary Classification ◽  
Early Warning Systems ◽  
Conditional Inference ◽  
Set Covering ◽  
Study Phase ◽  
Early Prediction ◽  
Data Set ◽  
Wide Range

AbstractWe predict university dropout using random forests based on conditional inference trees and on a broad German data set covering a wide range of aspects of student life and study courses. We model the dropout decision as a binary classification (graduate or dropout) and focus on very early prediction of student dropout by stepwise modeling students’ transition from school (pre-study) over the study-decision phase (decision phase) to the first semesters at university (early study phase). We evaluate how predictive performance changes over the three models, and observe a substantially increased performance when including variables from the first study experiences, resulting in an AUC (area under the curve) of 0.86. Important predictors are the final grade at secondary school, and also determinants associated with student satisfaction and their subjective academic self-concept and self-assessment. A direct outcome of this research is the provision of information to universities wishing to implement early warning systems and more personalized counseling services to support students at risk of dropping out during an early stage of study.

Computer vision approach for phase identification from steel microstructure

2019 ◽  
Vol 36 (6) ◽  
pp. 1913-1933
Author(s):  
Amitava Choudhury ◽  
Snehanshu Pal ◽  
Ruchira Naskar ◽  
Amitava Basumallick
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
High Carbon Steel ◽  
Phase Identification ◽  
Content Type ◽  
Steel Microstructure ◽  
Segmentation Boundary ◽  
Transformation Algorithms ◽  
Processing Techniques

PurposeThe purpose of this paper is to develop an automated phase segmentation model from complex microstructure. The mechanical and physical properties of metals and alloys are influenced by their microstructure, and therefore the investigation of microstructure is essential. Coexistence of random or sometimes patterned distribution of different microstructural features such as phase, grains and defects makes microstructure highly complex, and accordingly identification or recognition of individual phase, grains and defects within a microstructure is difficult.Design/methodology/approachIn this perspective, computer vision and image processing techniques are effective to help in understanding and proper interpretation of microscopic image. Microstructure-based image processing mainly focuses on image segmentation, boundary detection and grain size approximation. In this paper, a new approach is presented for automated phase segmentation from 2D microstructure images. The benefit of the proposed work is to identify dominated phase from complex microstructure images. The proposed model is trained and tested with 373 different ultra-high carbon steel (UHCS) microscopic images.FindingsIn this paper, Sobel and Watershed transformation algorithms are used for identification of dominating phases, and deep learning model has been used for identification of phase class from microstructural images.Originality/valueFor the first time, the authors have implemented edge detection followed by watershed segmentation and deep learning (convolutional neural network) to identify phases of UHCS microstructure.

scholarly journals Experimental duration and predator satiation levels systematically affect functional response parameters

2017 ◽  
Author(s):  
Yuanheng Li ◽  
Björn C. Rall ◽  
Gregor Kalinkat
Keyword(s):  
Food Web ◽  
Functional Response ◽  
Large Data ◽  
Handling Time ◽  
Set Covering ◽  
Parameter Estimates ◽  
Functional Responses ◽  
Predator Satiation ◽  
Data Set ◽  
Wide Range

AbstractEmpirical feeding studies where density-dependent consumption rates are fitted to functional response models are often used to parametrize the interaction strengths in models of population or food-web dynamics. However, the relationship between functional response parameter estimates from short-term feeding studies and real-world, long-term, trophic interaction strengths remains largely untested. In a critical first step to address this void, we tested for systematic effects of experimental duration and predator satiation on the estimation of functional response parameters, namely attack rate and handling time. Analyzing a large data set covering a wide range of predator taxonomies and body sizes we show that attack rates decrease with increasing experimental duration, and that handling times of starved predators are consistently shorter than those of satiated predators. Therefore, both the experimental duration and the predator satiation level have a strong and systematic impact on the predictions of population dynamics and food-web stability. Our study highlights potential pitfalls at the intersection of empirical and theoretical applications of functional responses. We conclude our study with some practical suggestions how these implications should be addressed in the future to improve predictive abilities and realism in models of predator-prey interactions.

Measuring the use of human resources practices and employee attitudes

2016 ◽  
Vol 4 (2) ◽  
pp. 94-115 ◽  
Author(s):  
Patrick Kampkötter ◽  
Jens Mohrenweiser ◽  
Dirk Sliwka ◽  
Susanne Steffes ◽  
Stefanie Wolter
Keyword(s):  
Human Resources ◽  
Human Resources Management ◽  
Personal Characteristics ◽  
Employee Attitudes ◽  
Set Covering ◽  
Human Resource Research ◽  
Data Set ◽  
Employee Survey ◽  
Content Type ◽  
Wide Range

Purpose – The purpose of this paper is to introduce a new data source available for researchers with interest in human resources management (HRM) and personnel economics, the Linked Personnel Panel (LPP). Design/methodology/approach – The LPP is a longitudinal and representative employer-employee data set covering establishments in Germany and a subset of their workforce and is designed for quantitative empirical human resource research. Findings – The LPP employee survey applies a number of established scales to measure job characteristics and job perceptions, personal characteristics, employee attitudes towards the organization and employee behaviour. This paper gives an overview of both the employer and employee survey and outlines the definitions, origins, and statistical properties of the scales used in the individual questionnaire. Practical implications – The paper describes how researchers can access the data. Originality/value – First, the data set combines employer and employee surveys that can be matched to each other. Second, it can also be linked to a number of additional administrative data sets. Third, the LPP covers a wide range of firms and workers from different backgrounds. Finally, because of its longitudinal dimension, the LPP should facilitate the study of causal effects of HRM practices.

Recent Applications of High Resolution Electron Microscopy to Crystalline Arrays of Virus Particles

1978 ◽  
Vol 36 (3) ◽  
pp. 470-482 ◽  
Author(s):  
R.W. Horne
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Analytical Techniques ◽  
Staining Technique ◽  
High Resolution Electron Microscopy ◽  
Optical Diffraction ◽  
Full Potential ◽  
Virus Particles ◽  
Resolution Electron ◽  
Wide Range

The technique of surrounding virus particles with a neutralised electron dense stain was described at the Fourth International Congress on Electron Microscopy, Berlin 1958 (see Home & Brenner, 1960, p. 625). For many years the negative staining technique in one form or another, has been applied to a wide range of biological materials. However, the full potential of the method has only recently been explored following the development and applications of optical diffraction and computer image analytical techniques to electron micrographs (cf. De Hosier & Klug, 1968; Markham 1968; Crowther et al., 1970; Home & Markham, 1973; Klug & Berger, 1974; Crowther & Klug, 1975). These image processing procedures have allowed a more precise and quantitative approach to be made concerning the interpretation, measurement and reconstruction of repeating features in certain biological systems.

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

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