scholarly journals Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks

2022 ◽  
Vol 17 (01) ◽  
pp. P01002
Author(s):  
L. Polson ◽  
L. Kurchaninov ◽  
M. Lefebvre
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hadron Collider ◽  
Liquid Argon ◽  
Significant Loss ◽  
Optimal Filter ◽  
Filter Technique ◽  
Energy Reconstruction ◽  
Processing Techniques ◽  
Signal Processing Techniques

Abstract The liquid argon ionization current in a sampling calorimeter cell can be analyzed to determine the energy of detected particles. In practice, experimental artifacts such as pileup and electronic noise make the inference of energy from current a difficult process. The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut-down of 2025–2027. Signal processing techniques that are used to extract the energy of detected particles in the ATLAS detector will suffer a significant loss in performance under these conditions. This paper compares the presently used optimal filter technique to convolutional neural networks for energy reconstruction in the ATLAS liquid argon hadronic end cap calorimeter. In particular, it is shown that convolutional neural networks trained with an appropriately tuned and novel loss function are able to outperform the optimal filter technique.

scholarly journals Artificial Neural Networks on FPGAs for Real-Time Energy Reconstruction of the ATLAS LAr Calorimeters

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Georges Aad ◽  
Anne-Sophie Berthold ◽  
Thomas Calvet ◽  
Nemer Chiedde ◽  
Etienne Marie Fortin ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Hadron Collider ◽  
Liquid Argon ◽  
Learning Approaches ◽  
High Luminosity ◽  
Proton Proton ◽  
Real Time Processing ◽  
Energy Reconstruction

AbstractThe ATLAS experiment at the Large Hadron Collider (LHC) is operated at CERN and measures proton–proton collisions at multi-TeV energies with a repetition frequency of 40 MHz. Within the phase-II upgrade of the LHC, the readout electronics of the liquid-argon (LAr) calorimeters of ATLAS are being prepared for high luminosity operation expecting a pileup of up to 200 simultaneous proton–proton interactions. Moreover, the calorimeter signals of up to 25 subsequent collisions are overlapping, which increases the difficulty of energy reconstruction by the calorimeter detector. Real-time processing of digitized pulses sampled at 40 MHz is performed using field-programmable gate arrays (FPGAs). To cope with the signal pileup, new machine learning approaches are explored: convolutional and recurrent neural networks outperform the optimal signal filter currently used, both in assignment of the reconstructed energy to the correct proton bunch crossing and in energy resolution. The improvements concern in particular energies derived from overlapping pulses. Since the implementation of the neural networks targets an FPGA, the number of parameters and the mathematical operations need to be well controlled. The trained neural network structures are converted into FPGA firmware using automated implementations in hardware description language and high-level synthesis tools. Very good agreement between neural network implementations in FPGA and software based calculations is observed. The prototype implementations on an Intel Stratix-10 FPGA reach maximum operation frequencies of 344–640 MHz. Applying time-division multiplexing allows the processing of 390–576 calorimeter channels by one FPGA for the most resource-efficient networks. Moreover, the latency achieved is about 200 ns. These performance parameters show that a neural-network based energy reconstruction can be considered for the processing of the ATLAS LAr calorimeter signals during the high-luminosity phase of the LHC.

scholarly journals Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks

2019 ◽  
Vol 11 (21) ◽  
pp. 2585 ◽  
Author(s):  
Michael Fromm ◽  
Matthias Schubert ◽  
Guillermo Castilla ◽  
Julia Linke ◽  
Greg McDermid
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Forest Restoration ◽  
Oil And Gas ◽  
Significant Loss ◽  
Tree Regeneration ◽  
Training Dataset ◽  
Conifer Seedlings ◽  
Oil And Gas Exploration ◽  
Combining Data

Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce large quantities of data that will require specialized processing techniques. In this study, we explored the possibility of using convolutional neural networks (CNNs) on this data for automatically detecting conifer seedlings along recovering seismic lines: a common legacy footprint from oil and gas exploration. We assessed three different CNN architectures, of which faster region-CNN (R-CNN) performed best (mean average precision 81%). Furthermore, we evaluated the effects of training-set size, season, seedling size, and spatial resolution on the detection performance. Our results indicate that drone imagery analyzed by artificial intelligence can be used to detect conifer seedling in regenerating sites with high accuracy, which increases with the size in pixels of the seedlings. By using a pre-trained network, the size of the training dataset can be reduced to a couple hundred seedlings without any significant loss of accuracy. Furthermore, we show that combining data from different seasons yields the best results. The proposed method is a first step towards automated monitoring of forest restoration/regeneration.

scholarly journals Thresholded ConvNet ensembles: neural networks for technical forecasting

2020 ◽  
Vol 32 (18) ◽  
pp. 15249-15262
Author(s):  
Sid Ghoshal ◽  
Stephen Roberts
Keyword(s):  
Neural Networks ◽  
Domain Knowledge ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Financial Time Series ◽  
Visual Representations ◽  
Financial Time ◽  
Subjective Form ◽  
Processing Techniques ◽  
Signal Processing Techniques

Abstract Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiquity in financial media, the reliability of its signals remains a contentious and highly subjective form of ‘domain knowledge’. We investigate the predictive value of patterns in financial time series, applying machine learning and signal processing techniques to 22 years of US equity data. By reframing technical analysis as a poorly specified, arbitrarily preset feature-extractive layer in a deep neural network, we show that better convolutional filters can be learned directly from the data, and provide visual representations of the features being identified. We find that an ensemble of shallow, thresholded convolutional neural networks optimised over different resolutions achieves state-of-the-art performance on this domain, outperforming technical methods while retaining some of their interpretability.

scholarly journals A review of the use of convolutional neural networks in agriculture

2018 ◽  
Vol 156 (3) ◽  
pp. 312-322 ◽  
Author(s):  
A. Kamilaris ◽  
F. X. Prenafeta-Boldú
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Performance Metrics ◽  
Specific Class ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Future Potential ◽  
Processing Techniques

AbstractDeep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.

scholarly journals Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes

2020 ◽  
Vol 10 (22) ◽  
pp. 7999
Author(s):  
Marco Mora ◽  
José Naranjo-Torres ◽  
Verónica Aubin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Learning Transfer ◽  
Structural Complexity ◽  
Intermediate Representation ◽  
Writer Identification ◽  
Identification Performance ◽  
Transfer Mode ◽  
Verification Problem ◽  
Processing Techniques

The writer’s identification/verification problem has traditionally been solved by analyzing complex biometric sources (text pages, paragraphs, words, signatures, etc.). This implies the need for pre-processing techniques, feature computation and construction of also complex classifiers. A group of simple graphemes (“ S ”, “ ∩ ”, “ C ”, “ ∼ ” and “ U ”) has been recently introduced in order to reduce the structural complexity of biometric sources. This paper proposes to analyze the images of simple graphemes by means of Convolutional Neural Networks. In particular, the AlexNet, VGG-16, VGG-19 and ResNet-18 models are considered in the learning transfer mode. The proposed approach has the advantage of directly processing the original images, without using an intermediate representation, and without computing specific descriptors. This allows to dramatically reduce the complexity of the simple grapheme processing chain and having a high hit-rate of writer identification performance.

scholarly journals Why Dilated Convolutional Neural Networks: A Proof of Their Optimality

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 767
Author(s):  
Jonatan Contreras ◽  
Martine Ceberio ◽  
Vladik Kreinovich
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Output Signal ◽  
Optimality Criteria ◽  
Image Processing Techniques ◽  
Dilated Convolution ◽  
Input Signals ◽  
Processing Techniques ◽  
Better Than

One of the most effective image processing techniques is the use of convolutional neural networks that use convolutional layers. In each such layer, the value of the layer’s output signal at each point is a combination of the layer’s input signals corresponding to several neighboring points. To improve the accuracy, researchers have developed a version of this technique, in which only data from some of the neighboring points is processed. It turns out that the most efficient case—called dilated convolution—is when we select the neighboring points whose differences in both coordinates are divisible by some constant ℓ. In this paper, we explain this empirical efficiency by proving that for all reasonable optimality criteria, dilated convolution is indeed better than possible alternatives.

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