Development of a model for assessing energy consumption of a system

Computer Science often seems distant from its natural science cousins, especially software engineering which feels closer to sociology and psychology than to physics. Physical measurements are often rare in software engineering, except in a few niches. One such important niche is that of software energy consumption, green mining, green IT, and sustainable computing, which all fall under the umbrella of green software engineering. With the physical measurement of energy consumption comes all of the limitations of measurement and experimentation that exist in the natural sciences and engineering. Issues abound, from attribution of energy use, isolation of components, to replicable experiments. These get further complicated by cloud computing whereby systems are virtualized and attribution of resource usage is a serious issue. Thus in this work we discuss the current state of software energy consumption, and where will it go.

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Green software engineering: the curse of methodology

10.7287/peerj.preprints.1470v2 ◽

2016 ◽

Cited By ~ 1

Author(s):

Abram Hindle

Keyword(s):

Energy Consumption ◽

Software Engineering ◽

Energy Use ◽

Natural Sciences ◽

Physical Measurement ◽

Green It ◽

Physical Measurements ◽

Current State ◽

Green Software ◽

Software Energy Consumption

Computer Science often seems distant from its natural science cousins, especially software engineering which feels closer to sociology and psychology than to physics. Physical measurements are often rare in software engineering, except in a few niches. One such important niche is that of software energy consumption, green mining, green IT, and sustainable computing, which all fall under the umbrella of green software engineering. With the physical measurement of energy consumption comes all of the limitations of measurement and experimentation that exist in the natural sciences and engineering. Issues abound, from attribution of energy use, isolation of components, to replicable experiments. These get further complicated by cloud computing whereby systems are virtualized and attribution of resource usage is a serious issue. Thus in this work we discuss the current state of software energy consumption, and where will it go.

Download Full-text

Green software engineering: the curse of methodology

10.7287/peerj.preprints.1470v1 ◽

2015 ◽

Author(s):

Abram Hindle

Keyword(s):

Energy Consumption ◽

Software Engineering ◽

Energy Use ◽

Natural Sciences ◽

Physical Measurement ◽

Green It ◽

Physical Measurements ◽

Current State ◽

Green Software ◽

Software Energy Consumption

Computer Science often seems distant from its natural science cousins, especially software engineering which feels closer to sociology and psychology than to physics. Physical measurements are often rare in software engineering, except in a few niches. One such important niche is that of software energy consumption, green mining, green IT, and sustainable computing, which all fall under the umbrella of green software engineering. With the physical measurement of energy consumption comes all of the limitations of measurement and experimentation that exist in the natural sciences and engineering. Issues abound, from attribution of energy use, isolation of components, to replicable experiments. These get further complicated by cloud computing whereby systems are virtualized and attribution of resource usage is a serious issue. Thus in this work we discuss the current state of software energy consumption, and where will it go.

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A Methodology for Software Cost Estimation Using Machine Learning Techniques: International conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & Applications (ICAISC-2020)

SSRN Electronic Journal ◽

10.2139/ssrn.3647639 ◽

2020 ◽

Author(s):

Md aaquib nawaz ◽

Mrityunjay kumar ◽

Md kashif zaki ◽

Sunil Kumar Gouda ◽

Prithvi Raj ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cost Estimation ◽

Smart Cities ◽

Machine Learning Techniques ◽

Software Cost Estimation ◽

Software Cost ◽

International Conference ◽

Learning Techniques ◽

Recent Trends

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A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽

10.3390/data6020012 ◽

2021 ◽

Vol 6 (2) ◽

pp. 12

Author(s):

Helder F. Castro ◽

Jaime S. Cardoso ◽

Maria T. Andrade

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Qualitative Analysis ◽

Continuous Growth ◽

Scientific Databases ◽

Extensive Survey ◽

Research Areas ◽

Current State ◽

Global View ◽

Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

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An Estimation of Ship Collision Risk Based on Relevance Vector Machine

Journal of Marine Science and Engineering ◽

10.3390/jmse9050538 ◽

2021 ◽

Vol 9 (5) ◽

pp. 538

Author(s):

Jinwan Park ◽

Jung-Sik Jeong

Keyword(s):

Machine Learning ◽

Decision Making ◽

Risk Estimation ◽

Good Method ◽

Relevance Vector Machine ◽

Machine Learning Algorithms ◽

Support Vector ◽

Collision Risk ◽

Estimation Model ◽

Ship Collision

According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

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Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

Metals ◽

10.3390/met11050833 ◽

2021 ◽

Vol 11 (5) ◽

pp. 833

Author(s):

Irene Mirandola ◽

Guido A. Berti ◽

Roberto Caracciolo ◽

Seungro Lee ◽

Naksoo Kim ◽

...

Keyword(s):

Machine Learning ◽

Finite Element ◽

Energy Consumption ◽

Metal Forming ◽

Mutual Influence ◽

Rolling Process ◽

Ring Rolling ◽

Gradient Boosting ◽

Average Error ◽

Ring Rolling Mill

This research provides an insight on the performances of machine learning (ML)-based algorithms for the estimation of the energy consumption in metal forming processes and is applied to the radial-axial ring rolling process. To define the mutual influence between ring geometry, process settings, and ring rolling mill geometries with the resulting energy consumption, measured in terms of the force integral over the processing time (FIOT), FEM simulations have been implemented in the commercial SW Simufact Forming 15. A total of 380 finite element simulations with rings ranging from 650 mm < DF < 2000 mm have been implemented and constitute the bulk of the training and validation datasets. Both finite element simulation settings (input), as well as the FI (output), have been utilized for the training of eight machine learning models, implemented with Python scripts. The results allow defining that the Gradient Boosting (GB) method is the most reliable for the FIOT prediction in forming processes, being its maximum and average errors equal to 9.03% and 3.18%, respectively. The trained ML models have been also applied to own and literature experimental cases, showing a maximum and average error equal to 8.00% and 5.70%, respectively, thus proving once again its reliability.

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Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽

10.3390/a12050099 ◽

2019 ◽

Vol 12 (5) ◽

pp. 99 ◽

Cited By ~ 2

Author(s):

Kleopatra Pirpinia ◽

Peter A. N. Bosman ◽

Jan-Jakob Sonke ◽

Marcel van Herk ◽

Tanja Alderliesten

Keyword(s):

Machine Learning ◽

Image Registration ◽

State Of The Art ◽

Deformable Image Registration ◽

Optimization Approach ◽

High Quality ◽

Trade Off ◽

Multi Objective ◽

Current State ◽

Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

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