Investigation for the Need of Traditional Data-Preprocessing when Applying Artificial Neural Networks to FMCW-Radar Data

Valtl, Jakob; Mèndez, Javier; Mauro, Gianfranco; Cabrera, Antonio G.; Issakov, Vadim

doi:10.1109/iwssip55020.2022.9854472

Cited by 4 publications

(3 citation statements)

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“…As the preprocessing itself does not provide additional information but only consists of filtering and representation shifts, we presume that this can be handled by the ANN itself. This claim was proven in our previous work [17],…”

Section: Methodology and Algorithmssupporting

confidence: 69%

“…There is also an increasing trend to move the previous data preprocessing steps to the ANN as well, [14] as this allows specialized AI accelerators to shorten response times and enable processing on the edge [15], [16]. In previous work it was shown that this approach is in no way inferior to previous, traditional methods if sufficient training data is available [17].…”

Section: B Aimentioning

confidence: 99%

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Universal Adversarial Attacks on the Raw Data From a Frequency Modulated Continuous Wave Radar

Valtl

Issakov

2022

IEEE Access

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This work is a result of a collaboration between the projects "KI-Flex" (project number 16ES1027), funded by the German Federal Ministry of Education and Research (BMBF) within the founding program Microelectronic from Germany innovation driver, the "KoSi" project, (project number 01MM20011C) funded by the German Federal Ministry for Digital and Transport (BMDV) within the funding program "Automatisiertes, Vernetztes Fahren" and the project "TEACHING" (project number 871385) founded by the Horizon 2020 program."ABSTRACT As more and more applications rely on Artificial Intelligence (AI), it is inevitable to explore the associated safety and security risks, especially for sensitive applications where physical integrity is at risk. One of the most interesting challenges that come with AI are adversarial attacks being a wellresearched problem in the visual domain, where a small change in the input data can cause the Neural Network (NN) to make an incorrect prediction. In the radar domain, AI is not that widespread yet but the results that AI applications produce are very promising, which is why more and more applications based on it are being used. This work presents three possible attack methods that are particularly suitable for the radar domain. The developed algorithms generate universal adversarial attack patches for all sorts of radar applications based on NN. The main goal of the algorithms, apart from the computation of universal patches, is the identification of sensitive areas in the raw radar data input which than can be examined more closely. To the best of our knowledge, this is the first work that deals with calculating universal patches on raw radar data, which is of great importance especially for interference analysis. The developed algorithms have been verified on two data sets. One in the field of autonomous driving where the attacks lead to a steering misprediction of up to 0.3 for the steering value which is within [-1,1], with the results also being successfully tested on a demonstrator. The other data set originated from a gesture recognition task, where the attacks decreased the accuracy, originally at 97.0% up to a minimum of 16.5%, which is slightly above 12.5% being the accuracy for a purely random prediction.INDEX TERMS adversarial attacks, artificial neural networks, autonomous vehicles, edge computing, object recognition, radar applications, real-time systems

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Section: Methodology and Algorithmssupporting

confidence: 69%

Section: B Aimentioning

confidence: 99%

Universal Adversarial Attacks on the Raw Data From a Frequency Modulated Continuous Wave Radar

Valtl

Issakov

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

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“…Data pre-processing: Clean and pre-process the collected data. This includes handling missing data, scaling the data, and splitting it into training, validation, and testing sets [46].…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges

Akter,

Islam,

Hasan

2023

IJRCS

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The main objective of this study is to demonstrate the integration of weather forecasts which can lead to a significant reduction in energy costs and carbon emissions while ensuring the reliability of the microgrid operation. By serving a small area or a particular building, the incorporation of weather forecasts can considerably increase the efficiency of microgrid energy management. The planning and operation of microgrids can be greatly improved by using weather predictions, which give useful information about upcoming weather conditions. By forecasting future energy demand and supply based on meteorological conditions, Microgrid Energy Management (MEM) is utilized to optimize the energy management decisions in microgrid systems. Making better choices regarding energy generation, storage, and consumption may be aided by the incorporation of weather forecasts, which can offer a more precise and trustworthy estimate of the energy demand and supply. This strategy can result in increased energy efficiency, decreased energy prices, and decreased carbon emissions, all of which are important goals for contemporary power systems. A promising approach for raising energy effectiveness and lowering greenhouse gas emissions in contemporary power networks is MEM. The incorporation of weather forecasts into MEM can improve decision-making regarding energy management by giving a better insight of future energy demand and supply. This essay examines the advantages and disadvantages of using weather forecasts in MEM through the presentation of a case example. By providing valuable information about future weather conditions, weather forecasts this review explain the Optimized Renewable Energy Integration, Improved Energy Storage Utilization, Load Shifting and Demand Response, Efficient Grid Management for reducing reliance on fossil fuels and lowering energy cost and carbon emissions. In order to address the issues related with MEM employing weather forecasts, this study offers potential fixes for increasing the accuracy of weather forecasts and emphasizes the necessity for more research in this area.

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Study of Assisted Driving System Based on Multi-Sensor Information Fusion

Liu,

Song

2024

2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)

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Investigation for the Need of Traditional Data-Preprocessing when Applying Artificial Neural Networks to FMCW-Radar Data

Cited by 4 publications

References 10 publications

Universal Adversarial Attacks on the Raw Data From a Frequency Modulated Continuous Wave Radar

Universal Adversarial Attacks on the Raw Data From a Frequency Modulated Continuous Wave Radar

Microgrid Energy Management using Weather Forecasts: Case Study, Discussion and Challenges

Study of Assisted Driving System Based on Multi-Sensor Information Fusion

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