scholarly journals Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8422
Author(s):  
Radel Sultanbekov ◽  
Ilia Beloglazov ◽  
Shamil Islamov ◽  
Muk Chen Ong
Keyword(s):  
Machine Learning ◽  
Sulfur Content ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Machine Learning Algorithms ◽  
New Approach ◽  
Priority Task ◽  
Sediment Formation ◽  
Solution Studies

Providing quality fuel to ships with reduced SOx content is a priority task. Marine residual fuels are one of the main sources of atmospheric pollution during the operation of ships and sea tankers. Hence, the International Maritime Organization (IMO) has established strict regulations for the sulfur content of marine fuels. One of the possible technological solutions allowing for adherence to the sulfur content limits is use of mixed fuels. However, it carries with it risks of ingredient incompatibilities. This article explores a new approach to the study of active sedimentation of residual and mixed fuels. An assessment of the sedimentation process during mixing, storage, and transportation of marine fuels is made based on estimation three-dimensional diagrams developed by the authors. In an effort to find the optimal solution, studies have been carried out to determine the influence of marine residual fuel compositions on sediment formation via machine learning algorithms. Thus, a model which can be used to predict incompatibilities in fuel compositions as well as sedimentation processes is proposed. The model can be used to determine the sediment content of mixed marine residual fuels with the desired sulfur concentration.

scholarly journals Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110135
Author(s):  
Florian Jaton
Keyword(s):  
Machine Learning ◽  
William James ◽  
A Priori ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ground Truthing ◽  
Set Up ◽  
The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms

2022 ◽  
Vol 301 ◽  
pp. 113868
Author(s):  
Xuan Cuong Nguyen ◽  
Thi Thanh Huyen Nguyen ◽  
Quyet V. Le ◽  
Phuoc Cuong Le ◽  
Arun Lal Srivastav ◽  
...  
Keyword(s):  
Machine Learning ◽  
Constructed Wetland ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Design Support ◽  
New Approach

Geometric Accuracy Prediction for Additive Manufacturing Through Machine Learning of Triangular Mesh Data

2019 ◽  
Author(s):  
Nathan Decker ◽  
Qiang Huang
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Three Dimensional ◽  
Triangular Mesh ◽  
Geometric Accuracy ◽  
New Approach ◽  
Machine Learning Model ◽  
3D Printed ◽  
3D Shapes ◽  
Complex Parts

Abstract While additive manufacturing has seen tremendous growth in recent years, a number of challenges remain, including the presence of substantial geometric differences between a three dimensional (3D) printed part, and the shape that was intended. There are a number of approaches for addressing this issue, including statistical models that seek to account for errors caused by the geometry of the object being printed. Currently, these models are largely unable to account for errors generated in freeform 3D shapes. This paper proposes a new approach using machine learning with a set of predictors based on the geometric properties of the triangular mesh file used for printing. A direct advantage of this method is the simplicity with which it can describe important properties of a 3D shape and allow for predictive modeling of dimensional inaccuracies for complex parts. To evaluate the efficacy of this approach, a sample dataset of 3D printed objects and their corresponding deviations was generated. This dataset was used to train a random forest machine learning model and generate predictions of deviation for a new object. These predicted deviations were found to compare favorably to the actual deviations, demonstrating the potential of this approach for applications in error prediction and compensation.

scholarly journals Multiple similarly effective solutions exist for biomedical feature selection and classification problems

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jiamei Liu ◽  
Cheng Xu ◽  
Weifeng Yang ◽  
Yayun Shu ◽  
Weiwei Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Association Studies ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Optimal Solution ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Genome Wide Association Studies ◽  
Classification Problems

Abstract Binary classification is a widely employed problem to facilitate the decisions on various biomedical big data questions, such as clinical drug trials between treated participants and controls, and genome-wide association studies (GWASs) between participants with or without a phenotype. A machine learning model is trained for this purpose by optimizing the power of discriminating samples from two groups. However, most of the classification algorithms tend to generate one locally optimal solution according to the input dataset and the mathematical presumptions of the dataset. Here we demonstrated from the aspects of both disease classification and feature selection that multiple different solutions may have similar classification performances. So the existing machine learning algorithms may have ignored a horde of fishes by catching only a good one. Since most of the existing machine learning algorithms generate a solution by optimizing a mathematical goal, it may be essential for understanding the biological mechanisms for the investigated classification question, by considering both the generated solution and the ignored ones.

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