scholarly journals Machine Learning for Light Sensor Calibration

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6259
Author(s):  
Yichao Zhang ◽  
Lakitha O. H. Wijeratne ◽  
Shawhin Talebi ◽  
David J. Lary
Keyword(s):  
Machine Learning ◽  
Driving Force ◽  
Low Cost ◽  
Radiative Heating ◽  
Sensor Calibration ◽  
Total Cost ◽  
Environmental Processes ◽  
Neighborhood Scale ◽  
The Cost ◽  
Reference Sensor

Sunlight incident on the Earth’s atmosphere is essential for life, and it is the driving force of a host of photo-chemical and environmental processes, such as the radiative heating of the atmosphere. We report the description and application of a physical methodology relative to how an ensemble of very low-cost sensors (with a total cost of <$20, less than 0.5% of the cost of the reference sensor) can be used to provide wavelength resolved irradiance spectra with a resolution of 1 nm between 360–780 nm by calibrating against a reference sensor using machine learning. These low-cost sensor ensembles are calibrated using machine learning and can effectively reproduce the observations made by an NIST calibrated reference instrument (Konica Minolta CL-500A with a cost of around USD 6000). The correlation coefficient between the reference sensor and the calibrated low-cost sensor ensemble has been optimized to have R2> 0.99. Both the circuits used and the code have been made publicly available. By accurately calibrating the low-cost sensors, we are able to distribute a large number of low-cost sensors in a neighborhood scale area. It provides unprecedented spatial and temporal insights into the micro-scale variability of the wavelength resolved irradiance, which is relevant for air quality, environmental and agronomy applications.

scholarly journals Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

2020 ◽  
Author(s):  
Chenru Duan ◽  
Fang Liu ◽  
Aditya Nandy ◽  
Heather Kulik
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Density Functional ◽  
Low Cost ◽  
Supervised Machine Learning ◽  
Strongly Correlated ◽  
Robust Detection ◽  
Distinct Property ◽  
The Cost ◽  
Decision Engine

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wavefunction theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in a MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.

MODELING AND SIMULATION OF THE PRODUCTION OF HYDROGEN USING HYDROELECTRICITY IN VENEZUELA

2018 ◽  
pp. 88-95 ◽  
Author(s):  
A. Contrerasa ◽  
F. Possob ◽  
Т. N. Veziroglu
Keyword(s):  
Mathematical Model ◽  
Hydrogen Production ◽  
Modeling And Simulation ◽  
Regression Models ◽  
Low Cost ◽  
Total Cost ◽  
Time Horizons ◽  
Proposed Model ◽  
Production Of Hydrogen ◽  
The Cost

The purpose of this work is to develop and evaluate a mathematical model for the process of hydrogen production in Venezuela, via electrolysis and using hydroelectricity, with a view to using it as an energy vector in rural sectors of the country. Regression models were prepared to estimate the fluctuation of the main variables involved in the process: the production of hydrogen, the efficiency of energy conversion, the cost of hydroelectricity and the cost of the electrolyser. Finally, the proposed model was applied to various different time-horizons and populations, obtaining the cost of hydrogen production in each case. The results obtained are well below those mentioned in the references, owing largely to the low cost of the electricity used, which accounts for around 45% of the total cost of the system.

scholarly journals Using Clustering Techniques  to Guide Refactoring of  Object-Oriented Classes

2021 ◽  
Author(s):  
◽  
Keith Cassell
Keyword(s):  
Software Development ◽  
Open Source ◽  
Low Cost ◽  
Research Question ◽  
Object Oriented ◽  
Fundamental Unit ◽  
Total Cost ◽  
Clustering Techniques ◽  
The Cost ◽  
Refactoring Tools

<p>Much of the cost of software development is maintenance. Well structured software tends to be cheaper to maintain than poorly structured software, because it is easier to analyze and modify. The research described in this thesis concentrates on determining how to improve the structure of object-oriented classes, the fundamental unit of organization for object-oriented programs. Some refactoring tools can mechanically restructure object-oriented classes, given the appropriate inputs regarding what attributes and methods belong in the revised classes. We address the research question of determining what belongs in those classes, i.e., determining which methods and attributes most belong together and how those methods and attributes can be organized into classes. Clustering techniques can be useful for grouping entities that belong together; however, doing so requires matching an appropriate algorithm to the domain task and choosing appropriate inputs. This thesis identifies clustering techniques suitable for determining the redistribution of existing attributes and methods among object-oriented classes, and discusses the strengths and weaknesses of these techniques. It then describes experiments using these techniques as the basis for refactoring open source Java classes and the changes in the class quality metrics that resulted. Based on these results and on others reported in the literature, it recommends particular clustering techniques for particular refactoring problems. These clustering techniques have been incorporated into an open source refactoring tool that provides low-cost assistance to programmers maintaining object-oriented classes. Such maintenance can reduce the total cost of software development.</p>

A Study on the Thickness of Heat Insulation Materials Based on Total Cost of the Optimal Principle

2014 ◽  
Vol 1079-1080 ◽  
pp. 579-583
Author(s):  
Hui Han
Keyword(s):  
Heat Insulation ◽  
Low Cost ◽  
Simulation Software ◽  
Shopping Mall ◽  
Insulation Material ◽  
Original Design ◽  
Total Cost ◽  
Insulation Materials ◽  
The Cost ◽  
Whole Life Cycle

The cost of building insulation materials in construction phase has a significant impact on its energy costs in operational phase, so that it plays a meaningful role to make the total cost of insulation material low in both phases. From the point of the selection to thickness of outer heat insulation materials, this paper, making full use of energy simulation software, is keen to study on the thickness of heat insulation materials based on optimal cost principle in perspective of whole life cycle, in case of an artitecture in a some shopping mall. With the aid of eQUEST energy smulation software, this paper tries to simulate the cost of cooling and heating technology, besides it simulates different thick insultion materials, which takes advantage of mathematical models to acquire the relationship between energy consumption and thermal insulation. At last, it can be summarised the best thickness of heat insulation materials with low cost, and it can also provide some proposed improvements for the original design through comparision.

scholarly journals Using Clustering Techniques  to Guide Refactoring of  Object-Oriented Classes

2021 ◽  
Author(s):  
◽  
Keith Cassell
Keyword(s):  
Software Development ◽  
Open Source ◽  
Low Cost ◽  
Research Question ◽  
Object Oriented ◽  
Fundamental Unit ◽  
Total Cost ◽  
Clustering Techniques ◽  
The Cost ◽  
Refactoring Tools

<p>Much of the cost of software development is maintenance. Well structured software tends to be cheaper to maintain than poorly structured software, because it is easier to analyze and modify. The research described in this thesis concentrates on determining how to improve the structure of object-oriented classes, the fundamental unit of organization for object-oriented programs. Some refactoring tools can mechanically restructure object-oriented classes, given the appropriate inputs regarding what attributes and methods belong in the revised classes. We address the research question of determining what belongs in those classes, i.e., determining which methods and attributes most belong together and how those methods and attributes can be organized into classes. Clustering techniques can be useful for grouping entities that belong together; however, doing so requires matching an appropriate algorithm to the domain task and choosing appropriate inputs. This thesis identifies clustering techniques suitable for determining the redistribution of existing attributes and methods among object-oriented classes, and discusses the strengths and weaknesses of these techniques. It then describes experiments using these techniques as the basis for refactoring open source Java classes and the changes in the class quality metrics that resulted. Based on these results and on others reported in the literature, it recommends particular clustering techniques for particular refactoring problems. These clustering techniques have been incorporated into an open source refactoring tool that provides low-cost assistance to programmers maintaining object-oriented classes. Such maintenance can reduce the total cost of software development.</p>

An aviation approach to space transportation

2009 ◽  
Vol 113 (1146) ◽  
pp. 499-515 ◽  
Author(s):  
D. Ashford
Keyword(s):  
Low Cost ◽  
Launch Vehicles ◽  
Cost Estimates ◽  
Total Cost ◽  
Potential Benefits ◽  
Orbital Space ◽  
The Cost ◽  
Space Transportation

Abstract This paper presents a strategy for developing the first orbital spaceplane soon and at low cost and risk. The paper then shows how this vehicle will introduce an aviation approach to orbital space transportation to replace the present missile paradigm, leading to far lower costs and improved safety. To illustrate the potential benefits, the paper presents preliminary sizing and cost estimates of a simple lunar base. Even including the cost of developing the spaceplanes and other vehicles required, the total cost is about ten times less than that of present plans that use large new expendable launch vehicles. Timescales need not be greatly affected.

scholarly journals Analysis of the optimization of the construction process of ecological brick compared to ceramic brick

2021 ◽  
Vol 9 (10) ◽  
pp. 156-173
Author(s):  
Francisca das Chagas Oliveira ◽  
Paulysendra Felipe Silva ◽  
Phillype Dowglas Lopes ◽  
Rebeka Manuela Lobo Sousa ◽  
Gilvan Moreira da Paz ◽  
...  
Keyword(s):  
Literature Review ◽  
Low Cost ◽  
Hydraulic Press ◽  
Construction Process ◽  
Ceramic Brick ◽  
Total Cost ◽  
Ceramic Blocks ◽  
Block Wall ◽  
Pillar Structure ◽  
The Cost

The brick is composed of water, soil and cement, having an easy manufacturing process, with short construction and low cost is not subjected to burning is manufactured by a very different process from ceramic blocks, the same goes through a hydraulic press. This type of brick has characteristics that provide quality, sustainability, beauty, and above all savings in the total cost of the work. When used in construction are eliminated some steps and the execution time in the work. A block wall or ceramic brick will need roughcast, sketch, plaster, seamer and painting, besides considering the cost of these materials can not forget the labor that corresponds to an average 50% of the value of the work. But the great advantage over the other bricks is its construction system, once raised the wall this is ready, does not need finishing, and the pillar structure and beams are ready with it. Electrical and Hydraulic Installations are easily installed without the need for breakage and waste. These bricks do not require the use of mortars for laying, coatings such as plastering for regularization and finishing of walls, in addition to accelerating the work with their fittings that facilitate the alignment and plumb of the walls. The objective of this work is present through a literature review of optimization processes that involve the ecological brick. in addition to accelerating the work with their fittings that facilitate the alignment and plumb of the walls. The objective of this work is present through a literature re-view of optimization processes that involve the ecological brick. in addition to accelerating the work with their fittings that facilitate the alignment and plumb of the walls. The objective of this work is present through a literature review of optimization processes that involve the ecological brick.

scholarly journals Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

2020 ◽  
Author(s):  
Chenru Duan ◽  
Fang Liu ◽  
Aditya Nandy ◽  
Heather Kulik
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Density Functional ◽  
Low Cost ◽  
Supervised Machine Learning ◽  
Strongly Correlated ◽  
Robust Detection ◽  
Distinct Property ◽  
The Cost ◽  
Decision Engine

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wavefunction theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in a MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.

Advance Protection for Three Phase Induction Motor using Microcontroller Atmega32

2018 ◽  
Vol 8 (1) ◽  
pp. 172
Author(s):  
Karan S Belsare ◽  
Gajanan D Patil
Keyword(s):  
Induction Motor ◽  
Low Cost ◽  
Short Circuit ◽  
Cost Factor ◽  
Protection Scheme ◽  
Three Phase ◽  
The Cost

A low cost and reliable protection scheme has been designed for a three phase induction motor against unbalance voltages, under voltage, over voltage, short circuit and overheating protection. Taking the cost factor into consideration the design has been proposed using microcontroller Atmega32, MOSFETs, relays, small CTs and PTs. However the sensitivity of the protection scheme has been not compromised. The design has been tested online in the laboratory for small motors and the same can be implemented for larger motors by replacing the i-v converters and relays of suitable ratings.

Janus – A New Approach to Air Combat Pilot Training

2019 ◽  
Vol 2019 (4) ◽  
pp. 7-22
Author(s):  
Georges Bridel ◽  
Zdobyslaw Goraj ◽  
Lukasz Kiszkowiak ◽  
Jean-Georges Brévot ◽  
Jean-Pierre Devaux ◽  
...  
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
High Energy ◽  
Training System ◽  
Embedded Sensors ◽  
Pilot Training ◽  
New Approach ◽  
Combat Aircraft ◽  
Air Combat ◽  
The Cost

Abstract Advanced jet training still relies on old concepts and solutions that are no longer efficient when considering the current and forthcoming changes in air combat. The cost of those old solutions to develop and maintain combat pilot skills are important, adding even more constraints to the training limitations. The requirement of having a trainer aircraft able to perform also light combat aircraft operational mission is adding unnecessary complexity and cost without any real operational advantages to air combat mission training. Thanks to emerging technologies, the JANUS project will study the feasibility of a brand-new concept of agile manoeuvrable training aircraft and an integrated training system, able to provide a live, virtual and constructive environment. The JANUS concept is based on a lightweight, low-cost, high energy aircraft associated to a ground based Integrated Training System providing simulated and emulated signals, simulated and real opponents, combined with real-time feedback on pilot’s physiological characteristics: traditionally embedded sensors are replaced with emulated signals, simulated opponents are proposed to the pilot, enabling out of sight engagement. JANUS is also providing new cost effective and more realistic solutions for “Red air aircraft” missions, organised in so-called “Aggressor Squadrons”.

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