scholarly journals Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning

2021 ◽  
Vol 6 (11) ◽  
pp. 157
Author(s):  
Gonçalo Pereira ◽  
Manuel Parente ◽  
João Moutinho ◽  
Manuel Sampaio
Keyword(s):  
Machine Learning ◽  
Fuel Consumption ◽  
Great Variability ◽  
Accurate Estimation ◽  
Estimation Model ◽  
Digital Twins ◽  
Significant Step ◽  
The Cost ◽  
Consumption Prediction ◽  
Insight Into

Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed datalogger with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.

scholarly journals Forecasting the Fuel Consumption of Passenger Ships with a Combination of Shallow and Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 776 ◽  
Author(s):  
Ioannis Panapakidis ◽  
Vasiliki-Marianna Sourtzi ◽  
Athanasios Dagoumas
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fuel Consumption ◽  
Initial Study ◽  
Forecasting Model ◽  
Time Series Models ◽  
Prediction System ◽  
Fuel Management ◽  
Passenger Ships ◽  
Consumption Prediction

An accurate fuel consumption prediction system for transportation units is the pillar that a more efficient fuel management can rely on. This in turn may eventually lead to cost and emission savings for the unit’s owner. Numerous studies have been conducted for predicting the fuel usage in various means of transportation (i.e., airplanes, trucks, and vehicles). However, there is a limited number of researches that focus on passenger ships. These researches involve traditional machine learning models. There is a lack of literature on deep-learning-based forecasting models. The present paper serves as an initial study for exploring the potential of deep learning in day-ahead fuel consumption on a passenger ship. Firstly, a discussion is provided for the parameters that influence the fuel consumption. Secondly, the day-ahead fuel forecasting problem is formulated. To fully examine the influence of exogenous parameters on the consumption, various scenarios are formulated that differ in the types and number of inputs. The proposed forecasting model combines shallow and deep learning. Several machine learning and time series models were compared, and the results indicate the robustness of the proposed approach.

scholarly journals The Model of Vehicle and Route Selection for Energy Saving

2021 ◽  
Vol 13 (8) ◽  
pp. 4528
Author(s):  
Olga Lingaitienė ◽  
Juozas Merkevičius ◽  
Vida Davidavičienė
Keyword(s):  
Sustainable Development ◽  
Fuel Consumption ◽  
Motor Vehicles ◽  
The Sustainable Development ◽  
Supply Chain Efficiency ◽  
The World Bank ◽  
Cargo Delivery ◽  
Efficiency And Effectiveness ◽  
The Cost ◽  
Source Use

The World Bank, United Nations, the Organization for Economic Cooperation and Development, and others are in line with the governments of countries that are strongly interested in the sustainable development of countries, regions, and enterprises. One of the aspects that affects the indicators and prospects of sustainable development is the efficiency of energy source use. Nationwide reductions in the greenhouse gas emissions of motor vehicles could have a direct effect on ambient temperature and reducing the effects of global warming, which can affect future environmental, societal, and economic development. Significant reductions in fuel consumption can be achieved by increasing the efficiency of use, and the performance, of current cargo vehicles. This aspect is directly related to cargo delivery systems and supply chain efficiency and effectiveness. The article solves the problem of increasing the effectiveness of cargo delivery and proposes a model that would minimize transportation costs that are directly related to fuel consumption, shortening transportation time. The model addresses the problem of a lack of models evaluating the efficiency of cargo to Lithuania that is using several different modes of transportation. For the solution to this problem, the article examines the complexity of the rational use of land and water vehicles depending on the type of cargo transported, the technical capabilities of the vehicles (loading, speed, environmental pollution, fuel consumption, etc.), and the type (cars, railways, ships). The novelty of the findings is based on the availability to select the most appropriate vehicles, on a case-by-case basis, from the available options, depending on their environmental performance and energy efficiency. This model, later in this article, is used for calculations of Lithuanian companies for selecting the most rational vehicle by identifying the most appropriate route, as well as assessing the dynamics of the economic and physical indicators. The model allows for creating dependencies between the main indicators characterizing the transport process—the cost, the time of transport, and the safety, taking into account the dynamics of economic and physical indicators, that lead to a very important issue—reducing the amount of energy required to provide products and services.

scholarly journals Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning

Polymers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 353
Author(s):  
Kun-Cheng Ke ◽  
Ming-Shyan Huang
Keyword(s):  
Machine Learning ◽  
Integrated Circuit ◽  
Quality Indices ◽  
Statistical Process ◽  
Suitable Alternative ◽  
Quality Grading ◽  
Quality Of Products ◽  
The Cost ◽  
Mlp Model

Conventional methods for assessing the quality of components mass produced using injection molding are expensive and time-consuming or involve imprecise statistical process control parameters. A suitable alternative would be to employ machine learning to classify the quality of parts by using quality indices and quality grading. In this study, we used a multilayer perceptron (MLP) neural network along with a few quality indices to accurately predict the quality of “qualified” and “unqualified” geometric shapes of a finished product. These quality indices, which exhibited a strong correlation with part quality, were extracted from pressure curves and input into the MLP model for learning and prediction. By filtering outliers from the input data and converting the measured quality into quality grades used as output data, we increased the prediction accuracy of the MLP model and classified the quality of finished parts into various quality levels. The MLP model may misjudge datapoints in the “to-be-confirmed” area, which is located between the “qualified” and “unqualified” areas. We classified the “to-be-confirmed” area, and only the quality of products in this area were evaluated further, which reduced the cost of quality control considerably. An integrated circuit tray was manufactured to experimentally demonstrate the feasibility of the proposed method.

scholarly journals Smooth input preparation for quantum and quantum-inspired machine learning

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhikuan Zhao ◽  
Jack K. Fitzsimons ◽  
Patrick Rebentrost ◽  
Vedran Dunjko ◽  
Joseph F. Fitzsimons
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Machine Learning Algorithms ◽  
Low Rank ◽  
Smoothed Analysis ◽  
Machine Learning Applications ◽  
Data Points ◽  
Input Model ◽  
The Cost ◽  
Input Perturbation

AbstractMachine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

scholarly journals Factors Affecting the Rate of Fuel Consumption in Aircrafts

2021 ◽  
Vol 13 (14) ◽  
pp. 8066
Author(s):  
Thowayeb H. Hassan ◽  
Abu Elnasr E. Sobaih ◽  
Amany E. Salem
Keyword(s):  
Fuel Consumption ◽  
Positive Association ◽  
Pollutant Emissions ◽  
Aviation Industry ◽  
Factors Affecting ◽  
Fuel Flow ◽  
Homogeneity Of Variance ◽  
Strong Positive Association ◽  
Ground Distance ◽  
The Cost

The cost of fuel and its availability are among the most major concerns for aircrafts and the aviation industry overall. Environmental difficulties with chemical pollutant emissions emitted by aviation machines are also connected to fuel consumption. As a result, it is crucial to examine factors that affect the overall fuel usage and consumption in the airport-based aviation industry. Several variables were investigated related to the total fuel consumed, such as dry operating weight (DOW) (KG), zero-fuel weight (ZFW), take-off weight (TOW), air distance (AIR DIST) (KM), and ground distance (GDN DIST). Analysis of the correlation between total fuel consumed as well as the extra fuel and selected variables was conducted. The results showed that the most positively associated factors with the total used fuel were the air distance (r2 = 0.86, p < 0.01), ground distance (r2 = 0.78, p < 0.01), TOW (r2 = 0.68, p < 0.01), and flight time (r2 = 0.68, p < 0.01). There was also a strong positive association between the average fuel flow (FF) and actual TOW (r2 = 0.74, p < 0.01) as well as ZFW (r2 = 0.61, p < 0.01). The generalized linear model (GLM) was utilized to assess the predictions of total energy usage after evaluating important outliers, stability of the homogeneity of variance, and the normalization of the parameter estimation. The results of multiple linear regression revealed that the most significant predictors of the total consumed fuel were the actual ZFW (p < 0.01), actual TOW (p < 0.01), and actual average FF (p < 0.05). The results interestingly confirmed that wind speed has some consequences and effects on arrival fuel usage. The result reflects that thermal and hydrodynamic economies impact on the flying fuel economy. The research has various implications for both scholars and practitioners of aviation industry.

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