Trade-off analysis using synthetic training data for neural networks in the automotive development process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

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Improving Semi-Supervised Learning for Audio Classification with FixMatch

Electronics ◽

10.3390/electronics10151807 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1807

Author(s):

Sascha Grollmisch ◽

Estefanía Cano

Keyword(s):

Neural Networks ◽

Supervised Learning ◽

Transfer Learning ◽

Data Transfer ◽

State Of The Art ◽

Training Data ◽

Audio Classification ◽

Image Domain ◽

Full Dataset ◽

Audio Data

Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.

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Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

Applied Sciences ◽

10.3390/app11062535 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2535

Author(s):

Bruno E. Silva ◽

Ramiro S. Barbosa

Keyword(s):

Neural Networks ◽

Neural Control ◽

Magnetic Levitation ◽

Low Cost ◽

Training Data ◽

Neural Controllers ◽

Levitation System ◽

Internal Model Controller ◽

Magnetic Levitation System ◽

And Performance

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

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A three-dimensional virtual mouse generates synthetic training data for behavioral analysis

Nature Methods ◽

10.1038/s41592-021-01103-9 ◽

2021 ◽

Vol 18 (4) ◽

pp. 378-381 ◽

Cited By ~ 1

Author(s):

Luis A. Bolaños ◽

Dongsheng Xiao ◽

Nancy L. Ford ◽

Jeff M. LeDue ◽

Pankaj K. Gupta ◽

...

Keyword(s):

Three Dimensional ◽

Training Data ◽

Behavioral Analysis ◽

Virtual Mouse ◽

Synthetic Training Data

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Yolo V4 for advanced Traffic Sign Recognition with synthetic training data generated by various GAN

IEEE Access ◽

10.1109/access.2021.3094201 ◽

2021 ◽

pp. 1-1

Author(s):

Christine Dewi ◽

Rung-Ching Chen ◽

Yan-Ting Liu ◽

Xiaoyi Jiang ◽

Kristoko Dwi Hartomo

Keyword(s):

Training Data ◽

Traffic Sign Recognition ◽

Traffic Sign ◽

Sign Recognition ◽

Synthetic Training Data

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Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

Applied Intelligence ◽

10.1007/s10489-021-02202-y ◽

2021 ◽

Author(s):

Haitham Baomar ◽

Peter J. Bentley

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Weather Conditions ◽

Extreme Weather ◽

Training Data ◽

Aviation Industry ◽

Control Models ◽

Novel Approach ◽

Network Modules ◽

Artificial Neural

AbstractWe describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

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Localization of Scattering Objects Using Neural Networks

Sensors ◽

10.3390/s21010011 ◽

2020 ◽

Vol 21 (1) ◽

pp. 11

Author(s):

Domonkos Haffner ◽

Ferenc Izsák

Keyword(s):

Neural Networks ◽

Plane Waves ◽

Measurement Data ◽

Scattered Wave ◽

Training Data ◽

Data Set ◽

Main Compound ◽

Incident Plane ◽

The Neural Networks ◽

Simulation Package

The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.

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