scholarly journals Restoring and Enhancing Degraded Underwater Pipelines for Identifying and Detecting Corrosion

2021 ◽  
Author(s):  
Vaibhav A. Parjane ◽  
Mohit Gangwar
Keyword(s):  
Classification Accuracy ◽  
Oil And Gas ◽  
Corrosion Detection ◽  
Learning Techniques ◽  
Processing Techniques ◽  
Traditional Approaches ◽  
Enhancement Algorithm ◽  
Preliminary Phase ◽  
Pipeline Corrosion

Detection of corrosion from underwater images is necessary for oil and gas pipelines to eliminate the internal leakages and hazards. The tests utilized a broad range of underwater pictures of various situations. A modern technique for estimating subsea pipeline corrosion based on the colour of the corroded pipe. For corrupted underwater videos, an image reconstruction and enhancement algorithm is created as a preliminary phase. The created algorithm reduces blurring and improves picture colour and contrast. The improved colours in the imaging details aid in the method of corrosion estimation. In this work we proposed a underwater corrosion detection using image processing techniques. Some machine learning and deep learning techniques have been used for classification of corrosion. In experimental analysis various features have been evaluated for detection of corrosion and it introduces better classification accuracy than traditional approaches.

scholarly journals Deep Learning Based Classification of Radar Spectral Maps

2021 ◽  
pp. 99-104
Author(s):  
Tong Lin ◽  
◽  
Xin Chen ◽  
Xiao Tang ◽  
Ling He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Region Of Interest ◽  
Radar Target ◽  
Automatic Annotation ◽  
Deep Convolutional Neural Networks ◽  
Learning Techniques ◽  
Using Data ◽  
Map Data

This paper discusses the use of deep convolutional neural networks for radar target classification. In this paper, three parts of the work are carried out: firstly, effective data enhancement methods are used to augment the dataset and address unbalanced datasets. Second, using deep learning techniques, we explore an effective framework for classifying and identifying targets based on radar spectral map data. By using data enhancement and the framework, we achieved an overall classification accuracy of 0.946. In the end, we researched the automatic annotation of image ROI (region of interest). By adjusting the model, we obtained a 93% accuracy in automatic labeling and classification of targets for both car and cyclist categories.

scholarly journals A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1614 ◽  
Author(s):  
Jacopo Aguzzi ◽  
Jan Albiez ◽  
Sascha Flögel ◽  
Olav Rune Godø ◽  
Endre Grimsbø ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Oil And Gas ◽  
Cold Water ◽  
Three Dimensional ◽  
Ecosystem Monitoring ◽  
Habitat Features ◽  
Technological Developments ◽  
Processing Techniques ◽  
Traditional Approaches

This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining.

scholarly journals Texture based Leaf Disease Classification using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 956-961
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Textural Features ◽  
Leaf Disease ◽  
Learning Techniques ◽  
Segmented Images ◽  
Agricultural Applications

Machine learning techniques has emerged as a potential field in many of present day agricultural applications. One of these applications is the identification and classification of leaf diseases. In this paper, a triangular based and OTSU based methods are applied for segmentation, Textural features primarily based on GLCM are obtained for these segmented images using kmeans clustering technique, further classification of different leaf disease is performed using an SVM based classification. The proposed method resulted in an overall classification accuracy of 70% using the triangular based segmentation with an AUC of 0.63.

scholarly journals Detecting Keratoconus by Using SVM and Decision Tree Classifiers with the Aid of Image Processing

2019 ◽  
Vol 16 (4(Suppl.)) ◽  
pp. 1022
Author(s):  
Mosa Et al.
Keyword(s):  
Image Processing ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Methods ◽  
Health It ◽  
Image Processing Techniques ◽  
Learning Techniques ◽  
Medical Instruments ◽  
Processing Techniques

 Researchers used different methods such as image processing and machine learning techniques in addition to medical instruments such as Placido disc, Keratoscopy, Pentacam;to help diagnosing variety of diseases that affect the eye. Our paper aims to detect one of these diseases that affect the cornea, which is Keratoconus. This is done by using image processing techniques and pattern classification methods. Pentacam is the device that is used to detect the cornea’s health; it provides four maps that can distinguish the changes on the surface of the cornea which can be used for Keratoconus detection. In this study, sixteen features were extracted from the four refractive maps along with five readings from the Pentacam software. The classifiers utilized in our study are Support Vector Machine (SVM) and Decision Trees classification accuracy was achieved 90% and 87.5%, respectively of detecting Keratoconus corneas. The features were extracted by using the Matlab (R2011 and R 2017) and Orange canvas (Pythonw).       

scholarly journals Through-Wall Radar Classification of Human Posture Using Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Alper Kılıç ◽  
İsmail Babaoğlu ◽  
Ahmet Babalık ◽  
Ahmet Arslan
Keyword(s):  
Deep Learning ◽  
Continuous Wave ◽  
Human Posture ◽  
Learning Techniques ◽  
Military Applications ◽  
Radar Signals ◽  
Traditional Approaches ◽  
Term Data

Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.

scholarly journals Flower Identification and Classification using Computer Vision and Machine Learning Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 277-285
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Accuracy Rate ◽  
Disk Flower ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Hybrid Methodology

Computer vision techniques plays an important role in extracting meaningful information from images. A process of extraction, analysis, and understanding of information from images may accomplished by an automated process using computer vision and machine learning techniques. The paper proposed a hybrid methodology using MKL – SVM with multi-label classification that is experimented on a dataset contained 25000 flower images of 102 different spices. Basic and morphology features including color, size, texture, petal type, petal count, disk flower, corona, aestivation of flower and flower class are extracted to increase the classification accuracy. Various classifiers are applied on extracted feature set and their performance are discussed. The result of MKL – SVM with multi-label classification is very promising with 76.92% as an accuracy rate. In brief, this paper attempts to explore a novel morphology for feature extraction and the applicability of symbolic representation schemes along with different classification strategies for effective multi-label classification of flower spices

scholarly journals Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ali Hajnayeb
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Machine Learning Techniques ◽  
Centrifugal Pumps ◽  
Test Setup ◽  
Learning Techniques ◽  
Pump Test ◽  
Processing Techniques ◽  
Cavitation Detection

Detection of cavitation in centrifugal pumps is critical in their condition monitoring. In order to detect cavitation more accurately and confidently, more advanced signal processing techniques are needed. For the classification of a pump conditions based on the outputs of these techniques, advanced machine learning techniques are needed. In this research, an automatic system for cavitation detection is proposed based on machine learning. Bispectral analysis is used for analyzing the vibration signals. The resulting bispectrum images are given to convolutional neural networks (CNNs) as inputs. The CNNs are a pretrained AlexNet and a pretrained GoogleNet, which are used in this application through transfer learning. On the contrary, a laboratory test setup is used for generating controlled cavitation in a centrifugal pump. The suggested algorithm is implemented on the vibration dataset acquired from the laboratory pump test setup. The results show that the cavitation state of the pump can be detected accurately using this system without any need to image processing or feature extraction.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Determining the discount rate when evaluating trade organizations: Some features

2020 ◽  
Vol 13 (1) ◽  
pp. 71-84
Author(s):  
E.A. Grigor'eva ◽  
A.S. Buzhikeeva
Keyword(s):  
Discount Rate ◽  
Economic Activity ◽  
Systems Approach ◽  
Market Value ◽  
Additional Risk ◽  
Evaluation Procedures ◽  
Analytical Research ◽  
Definition Of ◽  
Traditional Approaches

Subject. This article deals with the issues of determining the market value of the trading business, taking into account a number of characteristics. Objectives. The article aims to develop certain provisions of the methodology and practice of evaluating the business of trading organizations, namely, taking into account the additional risk of inventory feasibility when calculating the discount rate. Methods. For the study, we used a systems approach, and the cognition, and economic and analytical research methods. Results. The article presents a three-tiered classification of stocks and a definition of risk based on the criteria for dividing stocks by purpose, degree of implementation, and shelf life in accordance with the scale. Based on the classification, the article offers certain recommendations for determining the discount rate when evaluating trading organizations, aimed at taking into account additional risk. Conclusions. Various evaluation procedures within the framework of traditional approaches and methods in relation to trading organizations do not take into account risk specific to this type of economic activity. The proposed methodology for calculating the discount rate for trade organizations takes into account the features of their functioning.

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