scholarly journals Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

2017 ◽  
Vol 29 (4) ◽  
pp. 433-441 ◽  
Author(s):  
Li Linchao ◽  
Tomislav Fratrović ◽  
Zhang Jian ◽  
Ran Bin
Keyword(s):  
Symbolic Regression ◽  
Real World Data ◽  
Significant Information ◽  
Traffic State ◽  
Proposed Model ◽  
Microwave Sensor ◽  
Speed Prediction ◽  
Traffic Speed ◽  
Real Time Information ◽  
Time Information

Due to the increase of congestion on highways, providing real-time information about the traffic state has become a crucial issue. Hence, it is the aim of this research to build an accurate traffic speed prediction model using symbolic regression to generate significant information for travellers. It is built based on genetic programming using Pareto front technique. With real world data from microwave sensor, the performance of the proposed model is compared with two other widely used models. The results indicate that the symbolic regression is the most accurate among these models. Especially, after an incident occurs, the performance of the proposed model is still the best which means it is robust and suitable to predict traffic state of highway under different conditions.

An Adaptive Approach Towards Computation Offloading for Mobile Cloud Computing

2019 ◽  
Vol 14 (2) ◽  
pp. 52-73 ◽  
Author(s):  
Archana Kero ◽  
Abhirup Khanna ◽  
Devendra Kumar ◽  
Amit Agarwal
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Adaptive Computation ◽  
Proposed Model ◽  
Environment Variables ◽  
Real Time Information ◽  
Time Information

The widespread acceptability of mobile devices in present times have caused their applications to be increasingly rich in terms of the functionalities they provide to the end users. Such applications might be very prevalent among users but the execution results in dissipating many of the device end resources. Mobile cloud computing (MCC) has a solution to this problem by offloading certain parts of the application to cloud. At the first place, one might find computation offloading quite promising in terms of saving device end resources but eventually may result in being the other way around if performed in a static manner. Frequent changes in device end resources and computing environment variables may lead to a reduction in the efficiency of offloading techniques and even cause a drop in the quality of service for applications involving the use of real-time information. In order to overcome this problem, the authors propose an adaptive computation offloading framework for data stream applications wherein applications are partitioned dynamically followed by being offloaded depending upon the device end parameters, network conditions, and cloud resources. The article also talks about the proposed algorithm that depicts the workflow of the offloading model. The proposed model is simulated using the CloudSim simulator. In the end, the authors illustrate the working of the proposed system along with the simulated results.

scholarly journals One-dimensional Lumped-Circuit for Transient Thermal Study of an Induction Electric Motor

2017 ◽  
Vol 7 (4) ◽  
pp. 1714
Author(s):  
Radhia Jebahi ◽  
Helmi Aloui ◽  
Moez Ayadi
Keyword(s):  
Power Systems ◽  
Thermal State ◽  
Electrical Machines ◽  
One Dimensional ◽  
Lumped Parameter ◽  
Proposed Model ◽  
Transient Thermal ◽  
Real Time Information ◽  
Time Information ◽  
Do So

<span lang="EN-US">Electrical machines lifetime and performances could be improved when along the design process both electromagnetic and thermal behaviors are taken into account. Moreover, real time information about the device thermal state is necessary to an appropriate control with minimized losses. Models based on lumped parameter thermal circuits are: generic, rapid, accurate and qualified as a convenient solution for power systems. The purpose of the present paper is to validate a simulation platform intended for the prediction of the thermal state of an induction motor covering all operation regimes.  To do so, in steady state, the proposed model is validated using finite element calculation and experimental records. Then, in an overload situation, obtained temperatures are compared to finite element’s ones. It has been found that, in both regimes, simulation results are with closed proximity to finite element’s ones and experimental records.</span>

scholarly journals Conditional StyleGAN modelling and analysis for a machining digital twin

2021 ◽  
pp. 1-17
Author(s):  
Evgeny Zotov ◽  
Ashutosh Tiwari ◽  
Visakan Kadirkamanathan
Keyword(s):  
Research Literature ◽  
Process Conditions ◽  
Generative Adversarial Network ◽  
Digital Twin ◽  
Adversarial Network ◽  
Digital Twins ◽  
Proposed Model ◽  
Vibration Model ◽  
Real Time Information ◽  
Time Information

Manufacturing digitalisation is a critical part of the transition towards Industry 4.0. Digital twin plays a significant role as the instrument that enables digital access to precise real-time information about physical objects and supports the optimisation of the related processes through conversion of the big data associated with them into actionable information. A number of frameworks and conceptual models has been proposed in the research literature that addresses the requirements and benefits of digital twins, yet their applications are explored to a lesser extent. A time-domain machining vibration model based on a generative adversarial network (GAN) is proposed as a digital twin component in this paper. The developed conditional StyleGAN architecture enables (1) the extraction of knowledge from existing models and (2) a data-driven simulation applicable for production process optimisation. A novel solution to the challenges in GAN analysis is then developed, where the comparison of maps of generative accuracy and sensitivity reveals patterns of similarity between these metrics. The sensitivity analysis is also extended to the mid-layer network level, identifying the sources of abnormal generative behaviour. This provides a sensitivity-based simulation uncertainty estimate, which is important for validation of the optimal process conditions derived from the proposed model.

scholarly journals Traffic speed prediction using ensemble kalman filter and differential evolution

2019 ◽  
Vol 259 ◽  
pp. 02001
Author(s):  
Lukáš Rapant
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Intelligent Transportation System ◽  
Transmission Model ◽  
Traffic Data ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Machine Learning Approach ◽  
Speed Prediction ◽  
Traffic Speed

Importance of traffic state prediction steadily increases with growing volume of traffic. Ability to predict traffic speed in short to medium horizon (i.e. up to one hour) is one of the main tasks of every newly developed Intelligent Transportation System. There are two possible approaches to this prediction. The first is to utilize physical properties of the traffic flow to construct an exact or approximate numerical model. This approach is, however, almost impossible to implement on a larger scale given the difficulty to obtain enough traffic data to describe the starting and boundary conditions of the model. The other option is to use historical traffic data and relate information and patterns they contain to the current traffic state by application of some form of statistical or machine learning approach. We propose to use combination of Ensemble Kalman filter and Cell Transmission Model for this task. These models combine properties of physical model with ability to incorporate uncertainty of the traffic data.

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