scholarly journals A Model Trend and Application of Niger Delta Ultrasonic Field Data to Manage Erosion Corrosion in Pipelines and Flowlines

2019 ◽  
Vol 4 (11) ◽  
pp. 46-51
Author(s):  
Azubuike Hope Amadi ◽  
Boniface A. Oriji
Keyword(s):  
Petroleum Industry ◽  
Ultrasonic Field ◽  
Machine Learning Algorithms ◽  
Industrial Facilities ◽  
Software Application ◽  
Management Procedure ◽  
Erosion Corrosion ◽  
Inspection Data ◽  
Python Programming ◽  
Major Decision

Corrosion of pipelines and other industrial facilities has led to several hazards and catastrophic events in the petroleum industry. The corrosion of pipelines can occur in different ways, as materials tend to go back to original state. This work focused on the erosion corrosion of pipelines due to sand production. Several analysis following best practices introduced in this work where carried out on field inspection data on pipelines and flowlines in which a trend was identified. This trend generated models such as erosion trend angle (ETA) and erosion trend factor (ETF) which will be used for reviews, judgment and sensitivity analysis during corrosion management. The criterion for criticality of ETA was 30 degrees for the purpose of this work and recommended that users could vary to fit work objectives. Five test points where used to establish ETA and ETF values for major decision of criticality for erosion corrosion as shown in Table 2. To enhance the use of these models and known corrosion tools, a software application known as HAZUTREND was developed using python programming and machine learning algorithms for linear regression. An erosion corrosion management procedure was also introduced as a result of analysis made, to optimize decision making.

scholarly journals APPLICATION OF NEURAL NETWORK TECHNOLOGIES IN THE DENTAL CARIES FORECAST

2020 ◽  
Vol 73 (7) ◽  
pp. 1499-1504
Author(s):  
Oleksandr A. Udod ◽  
Hanna S. Voronina ◽  
Olena Yu. Ivchenkova
Keyword(s):  
Neural Network ◽  
Dental Caries ◽  
Old Patients ◽  
Software Application ◽  
The Neural Network ◽  
Software Product ◽  
High Level ◽  
Network Technologies ◽  
Python Programming ◽  
And Training

The aim: of the work was to develop and apply in the clinical trial a software product for the dental caries prediction based on neural network programming. Materials and methods: Dental examination of 73 persons aged 6-7, 12-15 and 35-44 years was carried out. The data obtained during the survey were used as input for the construction and training of the neural network. The output index was determined by the increase in the intensity of caries, taking into account the number of cavities. To build a neural network, a high-level Python programming language with the NumPay extension was used. Results: The intensity of carious dental lesions was the highest in 35-44 years old patients – 6.69 ± 0.38, in 6-7 years old children and 12-15 years old children it was 3.85 ± 0.27 and 2.15 ± 0.24, respectively (p <0.05). After constructing and training the neural network, 61 true and 12 false predictions were obtained based on these indices, the accuracy of predicting the occurrence of caries was 83.56%. Based on these results, a graphical user interface for the “CariesPro” software application was created. Conclusions: The resulting neural network and the software product based on it permit to predict the development of dental caries in persons of all ages with a probability of 83.56%.

scholarly journals Prediction Model for Occupational Incidents in Chemical and Gas Industries

2019 ◽  
Vol 8 (4) ◽  
pp. 3836-3840
Keyword(s):  
Prediction Model ◽  
Technical Information ◽  
Workplace Safety ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Industrial Safety ◽  
Prediction Algorithm ◽  
Scoring Algorithm ◽  
Inspection Data ◽  
The Right

Understanding occupational incidents is one of the important measures in workplace safety strategy. Analyzing the trends of the occupational incident data helps to identify the potential pain points and helps to reduce the loss. Optimizing the Machine Learning algorithms is a relatively new trend to fit the prediction model and algorithms in the right place to support human beneficial factors. The aim of this research is to build a prediction model to identify the occupational incidents in chemical and gas industries. This paper describes the architecture and approach of building and implementing the prediction model to predict the cause of the incident which can be used as a key index for achieving industrial safety in specific to chemical and gas industries. The implementation of the scoring algorithm coupled with prediction model should bring unbiased data to obtain logical conclusion. The prediction model has been trained against FACTS (Failure and Accidents Technical information system) is an incidents database which have 25,700 chemical industrial incidents with accident descriptions for the years span from 2004 to 2014. Inspection data and sensor logs should be fed on top of the trained dataset to verify and validate the implementation. The outcome of the implementation provides insight towards the understanding of the patterns, classifications, and also contributes to an enhanced understanding of quantitative and qualitative analytics. Cutting edge cloud-based technology opens up the gate to process the continuous in-streaming data, process it and output the desired result in real-time. The primary technology stack used in this architecture is Apache Kafka, Apache Spark Streaming, KSQL, Data frames, and AWS Lambda functions. Lambda functions are used to implement the scoring algorithm and prediction algorithm to write out the results back to AWS S3 buckets. Proof of concept implementation of the prediction model helps the industries to see through the incidents and will layout the base platform for the various safety-related implementations which always benefits the workplace's reputation, growth, and have less attrition in human resources.

scholarly journals PROTOTYPE DESIGN OF SPEED DETECTION MOBILE APPLICATION FOR GOLFER’S SWING MOVEMENT USING COMPUTER VISION COMPARED TO PORTABLE RADAR AND ACCELEROMETER SYSTEMS

2019 ◽  
Vol 1 (1) ◽  
pp. 49-52
Author(s):  
Bagus Wibowo ◽  
Edi Sofyan ◽  
Gembong Baskoro
Keyword(s):  
Computer Vision ◽  
Mobile Application ◽  
Golf Club ◽  
Accelerometer Data ◽  
Application Development ◽  
Software Application ◽  
The Moment ◽  
Swing Speed ◽  
Python Programming ◽  
Prototype Design

Prototype Design of Golf Swing Speed Detection Mobile Application (GSSDMA)/Swing Vision (SV) has been researched and developed on this thesis with computer vision technique. Frames detection method has been implemented to performed calculation of the swing speed by manually identification of the frames from start of down swing to impact of the ball with Matlab Video Viewer as initial reference calculation, when head of golf club start to move to downswing as frame-zero/fr0 and frame-n/frn as end of the frame after the head of golf club impact to the ball and then the total frames can be determined by frn minus fr0 (frn - fr0) which will be used for speed calculation reference formula using Python programming.Both measurements have been recorded using RADAR and accelerometer systems to get references of swing speed data measurement from some golfers in golf driving ranges. Accelerometer data measurements have been selected to use as reference of speed calculation with Python programming for software application development since deviation standard is lower than the RADAR systems.There is a limitation on the hand phone camera speed which only have thirty frames per second (30 fps) and the maximum swing speed can be tested with this camera is 101,2 mph at the moment which has three frames (frn-fr0). Found a swing speed formula y = - 0,53x3 + 9,53x2 – 61,27x + 213,35 from experimental data’s of Driver, 6 Iron, 8 Iron and Pitching and maximum swing speed can be predicted is 124,69 mph which has two frames.

scholarly journals Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1108
Author(s):  
Oliver Mey ◽  
André Schneider ◽  
Olaf Enge-Rosenblatt ◽  
Dirk Mayer ◽  
Christian Schmidt ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Manufacturing Systems ◽  
Acoustic Emissions ◽  
Machine Learning Algorithms ◽  
Damage Classification ◽  
Industrial Facilities ◽  
Vibration Sensors ◽  
Drive Trains ◽  
Condition Monitoring Systems

Early damage detection and classification by condition monitoring systems is crucial to enable predictive maintenance of manufacturing systems and industrial facilities. Data analysis can be improved by applying machine learning algorithms and fusion of data from heterogenous sensors. This paper presents an approach for a step-wise integration of classifications gained from vibration and acoustic emission sensors in order to combine the information from signals acquired in the low and high frequency ranges. A test rig comprising a drive train and bearings with small artificial damages is used for acquisition of experimental data. The results indicate that an improvement of damage classification can be obtained using the proposed algorithm of combining classifiers for vibrations and acoustic emissions.

Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language

2019 ◽  
Vol 44 (3) ◽  
pp. 348-361 ◽  
Author(s):  
Jiangang Hao ◽  
Tin Kam Ho
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Machine Learning Methods ◽  
Python Programming Language ◽  
And Behavior ◽  
Python Programming

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

scholarly journals Hybrid Heart Disease Prediction Supervised, Unsupervised Opinion Mining Algorithms

2021 ◽  
pp. 36-41
Author(s):  
Miss. Samiksha Arvind Kale ◽  
Prof. Dr A .B . Gadicha
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Living Organisms ◽  
Python Programming ◽  
Mining Algorithms ◽  
Death Cases

Heart plays significant role in living organisms. Diagnosis and prediction of heart related diseases requires more precision, perfection and correctness because slightly mistake can cause fatigue problem or death of the person, there are numerous death cases related to heart and their counting is increasing exponentially day by day. To affect the matter there's essential need of prediction system for awareness about diseases Machine learning is that the branch of AI (AI), it provides prestigious support in predicting any quite event which take training from natural events. During this paper, we calculate accuracy of machine learning algorithms for predicting heart condition, for this algorithms are k-nearest neighbor, decision tree, linear regression and support vector machine (SVM) by using UCI repository dataset for training and testing. For implementation of Python programming Anaconda (jupytor) notebook is best tool, which have many kind of library, header file, that make the work more accurate and precise.

scholarly journals R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

2020 ◽  
Vol 13 (3) ◽  
pp. 5-12
Author(s):  
Polina Lemenkova
Keyword(s):  
Cluster Analysis ◽  
Programming Languages ◽  
Spatial Data ◽  
Hierarchical Cluster ◽  
Machine Learning Algorithms ◽  
Spatial Data Analysis ◽  
Group Data ◽  
Data Output ◽  
Effective Visualization ◽  
Python Programming

AbstractThe paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis.

scholarly journals FEM modeling of spatial structural systems in evaluation of the real structural performances

2011 ◽  
Vol 9 (3) ◽  
pp. 347-356
Author(s):  
Dusan Kovacevic ◽  
Slobodan Rankovic
Keyword(s):  
Structural Systems ◽  
Analysis Software ◽  
Fem Modeling ◽  
Industrial Facilities ◽  
Software Application ◽  
Evaluation Procedures ◽  
Structural Evaluation ◽  
Computer Aided ◽  
Alternative Type ◽  
Technical Regulations

The paper is a review of enhanced concept of CASA (Computer Aided Structural Analysis) software application in FEM modeling of spatial structural systems of bridges, buildings, industrial facilities, machine devices, etc. in evaluation procedures of real structural performances. Contrary to everyday engineering design circumstances, which comprehend, primarily, respect of technical regulations, FEM modeling in case of structural evaluation, needs, in some sense, alternative type of creativity. Chosen examples could be illustrative for this attitude.

scholarly journals Heart Disease Detection Using Machine Learning

2020 ◽  
Author(s):  
Chithambaram T ◽  
Logesh Kannan N ◽  
Gowsalya M
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Human Body ◽  
Cardiac Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Individual Data ◽  
Different Types ◽  
Python Programming ◽  
Target Output

Abstract This paper analyzes the detection of heart disease using machine learning algorithms and python programming. Over the post decades, heart disease is common and dangerous disease caused by fat containment. This disease occurs due to over pressure in the human body. Using different types of parameters in the dataset we can predict the cardiac-disease. We have observed a dataset consists of 12 parameters and 70000 individual data values[5] to analyze the performance of patients. The main objective of the paper is to get a better accuracy to detect the heart-disease using algorithms in which the target output counts that a person having heart disease or not.

scholarly journals Applying Ensemble Learning Algorithm in Early Prognosis of Heart Illness

2021 ◽  
Vol 12 (2) ◽  
pp. 1769-1774
Author(s):  
Lijetha.C. Jaffrin, Et. al.
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Heart Attacks ◽  
Ensemble Machine Learning ◽  
Ensemble Learning Algorithm ◽  
Cardiac Illness ◽  
Python Programming

Medical diagnosis and treatment of diseases are the key elements of machine learning algorithms nowadays. To find similarities between various diseases, machine learning algorithms are used. Many people are now dying due to sudden heart attacks. Predicting and diagnosing heart disease is a daunting aspect faced by physicians and hospitals around the world. There is a need to foreknow whether or not a person is at risk of heart syndrome in advance, in order to minimize the number of deaths due to heart disease. In this field, machine learning algorithms play a very significant role. Many researchers are carrying out their research in this field to create software that can assist doctors to make decisions about cardiac illness prognosis. In this paper, Random Forest and AdaBoost ensemble Machine Learning Procedures are used in advance to predict heart disease. The datasets are handled in python programming by means of Anaconda Spyder IDE to validate the machine learning algorithm.

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