scholarly journals Effect of Double Pulse MIG Welding on Porosity Formation on Aluminium 5083 Fillet Joint

2019 ◽  
Vol 269 ◽  
pp. 01002
Author(s):  
Eakkachai Warinsiriruk ◽  
Jukkapun Greebmalai ◽  
Montri Sangsuriyun
Keyword(s):  
Duty Cycle ◽  
Plate Thickness ◽  
Travel Speed ◽  
Processing Technique ◽  
Double Pulse ◽  
Image Processing Technique ◽  
Porosity Formation ◽  
High Level ◽  
Welding Consumables ◽  
Wire Feed

In this article, parameters of Double-Pulse Metal Inert Gas Welding (DP-MIG) was used for minimising a porosity formation in a T-joint fillet weld. AA5083-H112 aluminium alloy (Non-heat treatable series) with the plate thickness of 10 millimetres is base metal for this study. Welding consumables were filler wire ER5356 with a diameter of 1.2 millimetres and shielded by industrial argon gas. Three majorities parameter of DP-MIG were Delta wire feed (m/min), Frequency (Hz) and Duty cycle (%). Measurable signal current pattern and opened porosity on the fractured surface were couple observed to study their relationships. An appropriate image processing technique was employed to quantitative measuring and calculating a size grouping area of several opened porosities overall weld length, precisely. The result found that the optimal was used a low-level of Delta wire feed of 0.8 m/min, a high-level of a frequency of 5.0 Hz, a mid-level of the duty cycle 30 % and a high travel speed 60 cm/min could minimise the porosity formation with complete penetration.

scholarly journals iRIS-RT: Eye Fatigue and Reaction Time Detector

2018 ◽  
Vol 12 (1) ◽  
pp. 42-51
Author(s):  
Kedsara Rakpongsiri
Keyword(s):  
Image Processing ◽  
Reaction Time ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Time Interval ◽  
Test Results ◽  
Test Program ◽  
Eye Fatigue ◽  
Temporal Intervals ◽  
High Level

The objective of this research is to develop an instrument to measure eye fatigue and reaction time for decision making. The data were automatically analyzed by the iRIS-RT program designed to measure the contraction or expansion of pupils by means of image processing in order to analyze changes of pupil diameters. The image processing technique is a set of images with continuous and different temporal intervals. The images are then transferred into a computer, stored and analyzed to obtain changes of pupil diameters. Pupil diameter of each image in each time interval is measured and frequency of blinking is detected and displayed on the computer screen in an attempt to analyze eye fatigue and to design the assessment of reaction time. The test program is designed with red, green and blue windows that randomly changes according to the set time in order to determine reaction to the colors and accuracy of decision to select the colors that correspond to those on the test program. The test results with the iRIS-RT program among 40 male and female volunteer participants aged between 18 and 35years reveal that the program is able to preliminarily measure and process eye fatigue. Furthermore, it is able to store and record personal results in the computer and Cloud media that are accessible via global online computer and internet systems. The results can be displayed on personal computers and other mobile devices. With regard to satisfaction of the participants with operations of the device, it is found that the satisfaction was at a high level, or 66.7%, and at the highest level, or 33.3%, on the overall efficiency of the device. From interviewing the experts after the construction of the device on its performance, attributes, size, safety, installation and result display, their satisfaction was at a high level.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62-68
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

scholarly journals Developments in Segmentation of Abdominal Liver CT Images for Cancer and Other Disease Lesion Detection

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Hariharan S
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Medical Image Analysis ◽  
Clinical Information ◽  
Disease Diagnosis ◽  
Processing Technique ◽  
Ct Images ◽  
Lesion Detection ◽  
Image Processing Technique ◽  
High Level

Segmentation is one of the most important and widely used methods in medical image analysis. It is considered to be a high level image processing technique and can be used for many applications in medical imaging. CT images are commonly used in medical field and it provides clear picture of the internal organs. However in some places further processing of CT images are required for disease diagnosis and lesion detection. This work is an effort for bringing out clinical information from liver images of computed tomography based on image processing. Finally liver tumor classifications have been performed using texture based image analysis.

scholarly journals Cervical Cancer Cell Identification & Detection Using Fuzzy C Mean and K nearest Neighbor Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 1080-1084
Keyword(s):  
Cervical Cancer ◽  
Nearest Neighbor ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Shape Features ◽  
K Nearest Neighbor ◽  
Learning Skills ◽  
Color Features ◽  
High Level ◽  
Morphology Changes

Across the globe, woman has been diagnosed two major forms of cancer, in which one is identified as cervical cancer and its micro classification. Morphology changes in cells or dead nucleus in the cervix causes cervical cancer. These cells are characterized with multiple nucleuses, faulty & lack of cytoplasm and so on. Detection of cervical cancer using smear test is extremely challenging because such cells does not offer texture variations or any significant color from the normal cells. Therefore for identification in abnormality of cells we required high level Digital image processing technique which compromises an automated, comprehensive machine learning skills. An advanced Fuzzy based technique has been implied to separate nucleus and cytoplasm from the cell. KNN is instructed with the color features and shape features of the segmented units of the cell and then an unknown cervix cell samples are classified by this technique. The proposed technique gives shape and color features of nucleus and cytoplasm of the cervix cell.

scholarly journals Butterfly Family Detection and Identification using Convolutional Neural Network for Lepidopterology

2019 ◽  
Vol 8 (2S11) ◽  
pp. 635-640
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Search ◽  
Biology Students ◽  
Detection And Identification ◽  
Family Types ◽  
High Level ◽  
Image Format

Lepidopterology is a branch of entomology concerning the scientific study of moths and the three superfamilies of butterflies. The project aims to help biology students in identifying butterfly without harming the insect. In the studies of lepidopterology, the students normally need to capture the butterflies with nets and dissect the insect to identify its family types. Computer vision is a study on how computers can be used to make high-level comprehension from the input of digital image and videos. By utilizing the latest Image Processing technique, it can identify the correct species of butterfly with high accuracy by using layers of node in a Convolutional Neural Network (CNN). The work process starts with data acquisition (mining the butterfly image automatically from google image search), pre-processing (converting image format and rotation), analyzing and understanding digital images (group images into folders), and to make assumptions of the high complication data from the real world in the process of producing numerical information that can be comprehend by machines in order to form conclusions. Benefits of using CNN is to reduce the need for human and physical intervention in identifying each of the butterfly characters. This makes it easier to expand the database in the future. The image is acquired using Fatkun Batch Downloader to download large number of images. The project is develop using Tensorflow in Ubuntu operating system and interface is in HTML connected to the Python script via Flask. The results of the experiment show that CNN can identify with 92.7 percent of final accuracy with learning saturation (overfitting) of 500 cycle. While testing results shows 62.5 percent of accuracy in predicting new datasets.

A New Image Processing Technique for STEM

1974 ◽  
Vol 32 ◽  
pp. 432-433
Author(s):  
Yasushi Kokubo ◽  
Hirotami Koike ◽  
Teruo Someya
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Elastic Scattering ◽  
Field Emission ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Energy Analyzer ◽  
Scanning Microscope ◽  
Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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