SVM-Based Dynamic Voltage Prediction for Online Thermally Constrained Task Scheduling in 3-D Multicore Processors

Liao, Chien-Hui; Wen, Charles H.-P.

doi:10.1109/les.2017.2776292

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…With the development of machine learning, a series of artificial intelligence algorithms such as neural networks [37] and support vector machine (SVM) [38] have been widely used in the field of time series prediction. These modern algorithms show superior performance in exploring data with hidden features.…”

Section: Air Traffic Flow Prediction Methodsmentioning

confidence: 99%

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Gui

Zhou

Wang

et al. 2020

IEEE Trans. Veh. Technol.

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Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft aggravate the burden of the ATFM. Thanks to the advanced automatic dependent surveillance-broadcast (ADS-B) technique, the aerial vehicles can be tracked and monitored in a real-time and accurate manner, providing possibility for establishing a more intelligent ATFM architecture. In this paper, we first form an aviation big data platform by using the distributed ADS-B ground stations and the obtained ADS-B messages. By exploring the constructed dataset and mapping the extracted information to the routes, the air traffic flow between different cities can be counted and predicted, where the prediction task is implemented on the basis of two machine learning methods, respectively. The experimental results based on real-world data demonstrate that the proposed traffic flow prediction model adopting long shortterm memory (LSTM) can achieve better performance, especially when abnormal factors in traffic control are considered.

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Section: Air Traffic Flow Prediction Methodsmentioning

confidence: 99%

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Gui

Zhou

Wang

et al. 2020

IEEE Trans. Veh. Technol.

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“…There are other interesting machine learning algorithms which have not been used in this work, such us neural networks [18] and support vector machine (SVM) [19]. These algorithms have been used successfully in the ATM field [20,21].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Study of the Impact of Traffic Flows on the ATC Actions

et al. 2022

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It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be able to use machine learning methods. The aim of this work is to predict the controller’s actions to know his workload. Several machine learning models were tested to try different combinations of features and the selected algorithms and two models were finally chosen. The predictions provided by the models are good enough to be used when a first approximation of the workload in a sector is to be obtained. Finally, explainability techniques were employed to discover the patterns found by the AI in the machine learning models. Thanks to these techniques, we can build a profile of the critical flights that increase the workload the most.

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“…In particular, in the feature selection step, a new feature is selected considering its importance in each iteration and is added to the selected feature set. In the model building step, the failure prediction model is built via the SVM on the basis of the selected feature set updated in each iteration [27][28][29][30]. Finally, one of the failure prediction models is selected considering the prediction accuracy in the model selection step.…”

Section: Introductionmentioning

confidence: 99%

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

Kwon

Kim

2020

Symmetry

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This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

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SVM-Based Dynamic Voltage Prediction for Online Thermally Constrained Task Scheduling in 3-D Multicore Processors

Cited by 5 publications

References 8 publications

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Study of the Impact of Traffic Flows on the ATC Actions

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

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