scholarly journals Machine Learning MOSA Monitoring System

2021 ◽  
Vol 20 (4) ◽  
pp. 203-208
Author(s):  
Aditya G. Manjunath ◽  
Sabahudin Vrtagić ◽  
Fatih Dogan ◽  
Milan Dordevic ◽  
Mileta Zarkovic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Operating Voltage ◽  
Machine Learning Algorithm ◽  
Applied Learning ◽  
Proposed Model ◽  
Simulated System ◽  
Monitoring And Diagnostics

This research paper deals with the problem of Metal-Oxide Surge Arrester (MOSA) condition monitoring and a new methodology in surge arrester monitoring and diagnostics is presented. A machine learning algorithm (back propagation regression) is used to estimate the non-linearity coefficient of the surge arrester, based on operating voltage and leakage current of the arrester. Using a simulated system, this research investigates the possibility of application and efficiency of machine learning. It is shown that the applied learning algorithm results are competitive with the model results parameters calculated as R2 = 0.999 and mean absolute real error computed as 0.005 which has shown that the proposed model can be used for MOSA monitoring and diagnostic purposes.

scholarly journals Inversion of chlorophyll-a concentration in Donghu Lake based on machine learning algorithm

2021 ◽  
Author(s):  
Xiaodong Tang ◽  
Mutao Huang
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Chlorophyll A Concentration ◽  
Inversion Model ◽  
Donghu Lake ◽  
Better Than

Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. This work aims to build an effective inversion model of chlorophyll-a concentration in Lake Donghu based on machine learning algorithm. Toward this aim, a variety of models were built by applying five kinds of dataset and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The model accuracy analysis results revealed that multi-factor dataset for modeling has the possibility to improve the accuracy of the single-factor model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. SVM3 is the best inversion one among all multi-factor models that the MRE, MAE, RMSE of SF-SVM are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs a better inversion effect than SF-BPNN and SF-ELM, the MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69μg/L and 16.49μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

scholarly journals Research on target feature extraction and location positioning with machine learning algorithm

2020 ◽  
Vol 30 (1) ◽  
pp. 429-437
Author(s):  
Licheng Li
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Experimental Results ◽  
Machine Learning Algorithm ◽  
Machine Learning Method ◽  
Target Feature ◽  
Speeded Up Robust Features ◽  
Theoretical Support

Abstract The accurate positioning of target is an important link in robot technology. Based on machine learning algorithm, this study firstly analyzed the location positioning principle of binocular vision of robot, then extracted features of the target using speeded-up robust features (SURF) method, positioned the location using Back Propagation Neural Networks (BPNN) method, and tested the method through experiments. The experimental results showed that the feature extraction of SURF method was fast, about 0.2 s, and was less affected by noise. It was found from the positioning results that the output position of the BPNN method was basically consistent with the actual position, and errors in X, Y and Z directions were very small, which could meet the positioning needs of the robot. The experimental results verify the effectiveness of machine learning method and provide some theoretical support for its further promotion and application in practice.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

Sign in / Sign up

Export Citation Format

Share Document