Data-Driven Prediction Method for Truck Fuel Consumption Based on Car Networking

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Keke Long ◽  
Guanqun Wang ◽  
Zhigang Xu ◽  
Xiaoguang Yang
Keyword(s):  
Fuel Consumption ◽  
Prediction Method ◽  
Data Driven

Data-driven prediction method for characteristics of voltage sag based on fuzzy time series

2022 ◽  
Vol 134 ◽  
pp. 107394
Author(s):  
Ying Wang ◽  
Min-hui Yang ◽  
Hua-ying Zhang ◽  
Xian Wu ◽  
Wen-xi Hu
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Fuzzy Time Series ◽  
Data Driven ◽  
Voltage Sag

scholarly journals An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Emir Žunić ◽  
Dženana Đonko ◽  
Emir Buza
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Prediction Method ◽  
Transportation Systems ◽  
Data Driven ◽  
Support Vector ◽  
Control Parameters ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Benchmark Instances

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world vehicle routing problems (VRPs) in the field of logistics. The work consists of two basic units: (i) an innovative multistep algorithm for successful and entirely feasible solving of the VRPs in logistics and (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics, and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the decision support system with predictive models: generalized linear models (GLMs) and support vector machine (SVM). The algorithm, along with the control parameters, which uses the prediction method, was acquired and was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

A Stochastic Asset Life Prediction Method for Large Fleet Datasets in Big Data Environment

2015 ◽  
Author(s):  
Behrad Bagheri ◽  
David Siegel ◽  
Wenyu Zhao ◽  
Jay Lee
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Evaluation Method ◽  
Health Management ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Engineering System ◽  
Useful Life ◽  
Data Environment

Preventing catastrophic failures is the most important task of prognostics and health management approaches in industry where Remaining Useful Life (RUL) prediction plays a significant role to schedule required preventive actions. Regarding recent advances and trends in data analysis and in Big Data environment, industries with such foreseeing approach are able to maintain their fleet of assets more efficiently with higher assurance. To address this requirement, several physics-based and data-driven methods have been developed to predict the remaining useful life of various engineering systems. In current paper, we present a simple, yet accurate stochastic method for data-driven RUL prediction of complex engineering system. The approach is constructed based on selecting the most significant parameters from raw data by using the improved distance evaluation method as feature selection algorithms. Subsequently, the health value of units is assessed by logistic regression and the assessment output is used in a Monte Carlo simulation to estimate the remaining useful life of the desired system. During Monte Carlo iterations, several features are extracted to help filtering less accurate estimations and improve the overall prediction accuracy. The proposed algorithm is validated in two ways. First of all, the accuracy of RUL prediction is measured by applying the method to 2008 PHM data challenge gas-turbine dataset. Subsequently, gradual changes in RUL prediction of a particular test unit are measured to verify the behavior of the algorithm upon availability of additional historical data.

scholarly journals A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Anna L Buczak ◽  
Phillip T Koshute ◽  
Steven M Babin ◽  
Brian H Feighner ◽  
Sheryl H Lewis
Keyword(s):  
Remote Sensing ◽  
Prediction Method ◽  
Remote Sensing Data ◽  
Data Driven ◽  
Sensing Data ◽  
Dengue Outbreaks

scholarly journals Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 187
Author(s):  
Balázs Németh ◽  
Dániel Fényes ◽  
Zsuzsanna Bede ◽  
Péter Gáspár
Keyword(s):  
Traffic Flow ◽  
Optimization Problem ◽  
Control Design ◽  
Prediction Method ◽  
Data Driven ◽  
Traffic Network ◽  
Control Methods ◽  
Prediction And Control ◽  
Data Driven Modeling ◽  
And Control

This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values.

scholarly journals Cocrystal Prediction by Artificial Neural Networks

2020 ◽  
Author(s):  
Jan-Joris Devogelaer ◽  
Hugo Meekes ◽  
Paul Tinnemans ◽  
Elias Vlieg ◽  
Rene de Gelder
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Method ◽  
Network Models ◽  
Data Driven ◽  
Neural Network Models ◽  
Design And Synthesis ◽  
Structural Database ◽  
Artificial Neural ◽  
Artificial Neural Network Models

<div>A significant amount of attention has been given to the design and synthesis of cocrystals by both industry and academia because of its potential to change a molecule’s physicochemical properties. This paper reports on the application of a data-driven cocrystal prediction method, based on two types of artificial neural network models and cocrystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a cocrystal is likely to form.</div>

Sign in / Sign up

Export Citation Format

Share Document