scholarly journals Development of FPGA-based neural network regression models for the ATLAS Phase-II barrel muon trigger upgrade

2021 ◽  
Vol 251 ◽  
pp. 04031
Author(s):  
Rustem Ospanov ◽  
Changqing Feng ◽  
Wenhao Dong ◽  
Wenhao Feng ◽  
Shining Yang
Keyword(s):  
Neural Network ◽  
Phase Ii ◽  
Regression Models ◽  
Trigger System ◽  
Muon Trigger ◽  
Level Trigger ◽  
Lhc Physics ◽  
Effective Selection ◽  
Potential Applications ◽  
Selection Of

Effective selection of muon candidates is the cornerstone of the LHC physics programme. The ATLAS experiment uses a two-level trigger system for real-time selection of interesting collision events. The first-level hardware trigger system uses the Resistive Plate Chamber detector (RPC) for selecting muon candidates in the central (barrel) region of the detector. With the planned upgrades, the entirely new FPGA-based muon trigger system will be installed in 2025-2026. In this paper, neural network regression models are studied for potential applications in the new RPC trigger system. A simple simulation model of the current detector is developed for training and testing neural network regression models. Effects from additional cluster hits and noise hits are evaluated. Efficiency of selecting muon candidates is estimated as a function of the transverse muon momentum. Several models are evaluated and their performance is compared to that of the current detector, showing promising potential to improve on current algorithms for the ATLAS Phase-II barrel muon trigger upgrade.

scholarly journals Fast and resource-efficient Deep Neural Network on FPGA for the Phase-II Level-0 muon barrel trigger of the ATLAS experiment

2020 ◽  
Vol 245 ◽  
pp. 01021
Author(s):  
Stefano Giagu
Keyword(s):  
Neural Network ◽  
Phase Ii ◽  
Deep Neural Network ◽  
Network Architectures ◽  
High Luminosity ◽  
Trigger System ◽  
Atlas Experiment ◽  
Physics Performance ◽  
New Generation ◽  
Logic Resource

The Level-0 muon trigger system of the ATLAS experiment will undergo a full upgrade for the High Luminosity LHC to stand the challenging requirements imposed by the increase in instantaneous luminosity. The upgraded trigger system will send raw hit data to off-detector processors, where trigger algorithms run on a new generation of FPGAs. To exploit the flexibility provided by the FPGA systems, ATLAS is developing novel precision deep neural network architectures based on trained ternary quantisation, optimised to run on FPGAs for efficient reconstruction and identification of muons in the ATLAS “Level-0” trigger. Physics performance in terms of efficiency and fake rates and FPGA logic resource occupancy and timing obtained with the developed algorithms are discussed.

scholarly journals Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting

2019 ◽  
pp. 60-68
Author(s):  
S. V. Sholtanyuk
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Comparative Analysis ◽  
Regression Models ◽  
Time Series Forecasting ◽  
Neural Nets ◽  
Forecasting Model ◽  
The Neural Network ◽  
Accuracy And Stability ◽  
Selection Of

Applicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed. Comparative analysis of received neural networking forecasting model with linear regression has been performed. Conditions, affecting on accuracy and stability of results of the neural network, have been revealed.

scholarly journals Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

2021 ◽  
Vol 11 (11) ◽  
pp. 5235
Author(s):  
Nikita Andriyanov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Inference ◽  
Recognition Accuracy ◽  
Network Architectures ◽  
Reduced Dimensions ◽  
Different Types ◽  
Rectangular Area ◽  
Noise Impulse ◽  
Selection Of

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic

2021 ◽  
Vol 213 ◽  
pp. 106676
Author(s):  
Saeed Mohammadiun ◽  
Guangji Hu ◽  
Abdorreza Alavi Gharahbagh ◽  
Reza Mirshahi ◽  
Jianbing Li ◽  
...  
Keyword(s):  
Decision Tree ◽  
Oil Spill ◽  
Regression Models ◽  
The Arctic ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Oil Spill Response ◽  
Response Method ◽  
Selection Of
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