scholarly journals Noise shielding surrogate models using dynamic artificial neural networks

2021 ◽  
Vol 263 (1) ◽  
pp. 5216-5224
Author(s):  
Francesco Centracchio ◽  
Lorenzo Burghignoli ◽  
Giorgio Palma ◽  
Ilaria Cioffi ◽  
Umberto Iemma
Keyword(s):  
Optimal Design ◽  
Surrogate Models ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Computing Effort ◽  
Noise Shielding ◽  
Self Tuning ◽  
Artificial Neural ◽  
Unconventional Aircraft ◽  
Aircraft Configurations

The optimal design methodologies in aeronautics are known to be constrained by the computational burden required by direct simulations. Due to this reason, the development of efficient metamodelling techniques represents nowadays an imperative need for the designers. In fact, surrogate models has been demonstrated to significantly reduce the number of high-fidelity evaluations, thus alleviating the computing effort. Over the last years, the aeronautical designers community has switched from a design approach predominantly based on direct simulations to an extensive use of metamodels. Recently, to further improve the efficiency, several dynamic approaches based on parameters self-tuning have been developed to support the metamodel construction. This work deals with the use of surrogate models based on Artificial Neural Network for the noise shielding of unconventional aircraft configurations. Here, the insertion loss field of the a Blended Wing Body is reproduced by means of advanced machine learning techniques. The relevant framework is the calculation of the noise emitted by innovative aircraft configurations by means of suitable corrections of existing well-assessed noise prediction tools. The self-tuning algorithm has demonstrated to be accurate and efficient, and the observed performance discloses the possibility to implement numerical strategies for the reliable and robust unconventional aircraft optimal design

International Diversified Portfolio Optimization With Artificial Neural Networks

2019 ◽  
pp. 201-223
Author(s):  
Mehmet Fatih Bayramoglu ◽  
Cagatay Basarir
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Learning Techniques ◽  
Portfolio Performance ◽  
Foreign Companies ◽  
Developed Markets ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Diversified Portfolio

Investing in developed markets offers investors the opportunity to diversify internationally by investing in foreign firms. In other words, it provides the possibility of reducing systematic risk. For this reason, investors are very interested in developed markets. However, developed are more efficient than emerging markets, so the risk and return can be low in these markets. For this reason, developed market investors often use machine learning techniques to increase their gains while reducing their risks. In this chapter, artificial neural networks which is one of the machine learning techniques have been tested to improve internationally diversified portfolio performance. Also, the results of ANNs were compared with the performances of traditional portfolios and the benchmark portfolio. The portfolios are derived from the data of 16 foreign companies quoted on NYSE by ANNs, and they are invested for 30 trading days. According to the results, portfolio derived by ANNs gained 10.30% return, while traditional portfolios gained 5.98% return.

scholarly journals Machine-Learning Techniques for the Optimal Design of Acoustic Metamaterials

2019 ◽  
Vol 187 (3) ◽  
pp. 630-653 ◽  
Author(s):  
Andrea Bacigalupo ◽  
Giorgio Gnecco ◽  
Marco Lepidi ◽  
Luigi Gambarotta
Keyword(s):  
Machine Learning ◽  
Optimal Design ◽  
Machine Learning Techniques ◽  
Acoustic Metamaterials ◽  
Learning Techniques

scholarly journals Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data

2019 ◽  
Author(s):  
James A. Tallman ◽  
Michal Osusky ◽  
Nick Magina ◽  
Evan Sewall
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Surrogate Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Artificial Neural

Abstract This paper provides an assessment of three different machine learning techniques for accurately reproducing a distributed temperature prediction of a high-pressure turbine airfoil. A three-dimensional Finite Element Analysis thermal model of a cooled turbine airfoil was solved repeatedly (200 instances) for various operating point settings of the corresponding gas turbine engine. The response surface created by the repeated solutions was fed into three machine learning algorithms and surrogate model representations of the FEA model’s response were generated. The machine learning algorithms investigated were a Gaussian Process, a Boosted Decision Tree, and an Artificial Neural Network. Additionally, a simple Linear Regression surrogate model was created for comparative purposes. The Artificial Neural Network model proved to be the most successful at reproducing the FEA model over the range of operating points. The mean and standard deviation differences between the FEA and the Neural Network models were 15% and 14% of a desired accuracy threshold, respectively. The Digital Thread for Design (DT4D) was used to expedite all model execution and machine learning training. A description of DT4D is also provided.

scholarly journals Performance Based Machine Learning Model to Enhance Performance of Students

2021 ◽  
Vol 10 (4) ◽  
pp. 1-4
Author(s):  
Bhavesh Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Research Paper ◽  
Machine Learning Techniques ◽  
Precision Error ◽  
Learning Techniques ◽  
Educational Institute ◽  
Artificial Neural

Machine learning techniques are used by many organizations to analyze the data and finding some meaningful hidden pattern from the data, this process is useful by an organization to take the decision making process. Various organizations used like marketing, health care, software organization and education institute etc used it in decision making. We have used machine learning techniques to enhance the performance of students. It will be ultimately used by educational institute to improve the status of educational institute. This research paper includes Naïve Bayes (NB), Logistic Regression (LR), Artificial Neural Network(ANN) and Decision Tree machine learning techniques. Performance of these models have been compared using accuracy measures parameters and ROC index. This research paper has used various parameters like academic performance and demographic information to build the model. In addition to judge the performance also used some additional parameters to measure the performance like F-measure, precision, error rate and recall. The dataset is collected using survey methodology to build the model. As a conclusion found that the Artificial Neural Network model get the best performance among all the models.

scholarly journals Prediction of Motor Failure Time Using An Artificial Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4342 ◽  
Author(s):  
Gustavo Scalabrini Sampaio ◽  
Arnaldo Rabello de Aguiar Vallim Filho ◽  
Leilton Santos da Silva ◽  
Leandro Augusto da Silva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Failure Time ◽  
Machine Learning Techniques ◽  
Network Training ◽  
Model Generalization ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

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