scholarly journals Evolutionary Design of a System for Online Surface Roughness Measurements

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1904
Author(s):  
Valentin Koblar ◽  
Bogdan Filipič
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Machine Vision ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Paper Machine ◽  
Machine Learning Algorithm ◽  
Key Characteristics ◽  
Evolutionary Optimization Algorithms ◽  
Vision Algorithm

Surface roughness is one of the key characteristics of machined components as it affects the surface quality and, consequently, the lifetime of the components themselves. The most common method of measuring the surface roughness is contact profilometry. Although this method is still widely applied, it has several drawbacks, such as limited measurement speed, sensitivity to vibrations, and requirement for precise positioning of the measured samples. In this paper, machine vision, machine learning and evolutionary optimization algorithms are used to induce a model for predicting the surface roughness of automotive components. Based on the attributes extracted by a machine vision algorithm, a machine learning algorithm generates the roughness predictive model. In addition, an evolutionary algorithm is used to tune the machine vision and machine learning algorithm parameters in order to find the most accurate predictive model. The developed methodology is comparable to the existing contact measurement method with respect to accuracy, but advantageous in that it is capable of predicting the surface roughness online and in real time.

Comparing a Machine Learning Predictive Model with Federal Transit Administration (FTA)’s Default Useful Life Benchmark to Predict Replacement Costs for Revenue Vehicles

2020 ◽  
Vol 2674 (2) ◽  
pp. 181-190
Author(s):  
Dilip Mistry ◽  
Jill Hough
Keyword(s):  
Machine Learning ◽  
Service Life ◽  
Predictive Model ◽  
Learning Algorithm ◽  
The State ◽  
Decision Makers ◽  
Machine Learning Algorithm ◽  
Useful Life ◽  
Transit Agencies ◽  
The U.S

A predictive model is developed that uses a machine learning algorithm to predict the service life of transit vehicles and calculates backlog and yearly replacement costs to achieve and maintain transit vehicles in a state of good repair. The model is applied to data from the State of Oklahoma. The vehicle service lives predicted by the machine learning predictive model (MLPM) are compared with the default useful life benchmark (ULB) of the U.S. Federal Transit Administration (FTA). The model shows that the service life predicted by the MLPM provides relatively more realistic predictions of replacement costs of revenue vehicles than the predictions generated using FTA’s default ULB. The MLPM will help Oklahoma’s transit agencies facilitate the state of good repair analysis of their transit vehicles and guide decision makers when investing in rehabilitation and replacement needs. The paper demonstrates that it is advantageous to use a MLPM to predict the service life of revenue vehicles in place of the FTA’s default ULB.

scholarly journals Flood and Drought Prediction Using the Machine Learning Algorithm Support Vector Regression

2019 ◽  
Vol 9 (1) ◽  
pp. 5194-5199
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Predictive Skill ◽  
Drought Prediction ◽  
Rbf Kernel ◽  
Search Approach

Flood and drought are frequently happening natural disasters in most of the countries. These disasters can cause considerable damage to agriculture, ecology and economy of the country. Mitigating the impacts of flood and drought is a valuable help to the human being. The main cause of these disasters is precipitation. If the past precipitation data are analyzed properly, the future flood and drought events can be easily found. Prediction using the Standard Precipitation Index (SPI) is a way to find the wet or dry condition of a region or country. In this paper the SPI values with different lead times are calculated for a long period of time. These SPI indices are analysed by a predictive model using the machine learning algorithm called Support Vector Regression (SVR) with RBF (Radial Basis Function) kernel. In this model the Grid Search approach is used for optimization. The forecast result of this predictive model shows the predictive skill of the SVR-RBF kernel.

Finding the Real Differences Between Learning Algorithms

2015 ◽  
Vol 24 (03) ◽  
pp. 1550001 ◽  
Author(s):  
George Rudolph ◽  
Tony Martinez
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Hybrid Systems ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithm ◽  
The Real ◽  
Reasonable Alternative ◽  
Output Behavior ◽  
Diversity Metrics

In the process of selecting a machine learning algorithm to solve a problem, questions like the following commonly arise: (1) Are some algorithms basically the same, or are they fundamentally different? (2) How different? (3) How do we measure that difference? (4) If we want to combine algorithms, what algorithms and combinators should be tried? This research proposes COD (Classifier Output Difference) distance as a diversity metric. COD separates difference from accuracy, COD goes beyond accuracy to consider differences in output behavior as the basis for comparison. The paper extends earlier on COD by giving a basic comparison to other diversity metrics, and by giving an example of using COD data as a predictive model from which to select algorithms to include in an ensemble. COD may fill a niche in metalearning as a predictive aid to selecting algorithms for ensembles and hybrid systems by providing a simple, straightforward, computationally reasonable alternative to other approaches.

Testicular salvage: Using machine learning algorithm to develop a more predictive model in testicular torsion

2021 ◽  
Vol 79 ◽  
pp. S1612-S1613
Author(s):  
M. Ekşi ◽  
A.H. Yavuzsan ◽  
İ. Evren ◽  
A. Ayten ◽  
A.E. Fakir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Testicular Torsion ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

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
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