scholarly journals An operational approach to forecast the Earth’s radiation belts dynamics

2021 ◽  
Author(s):  
Guillerme Bernoux ◽  
Antoine Brunet ◽  
Éric Buchlin ◽  
Miho Janvier ◽  
Angélica Sicard
Keyword(s):  
Space Weather ◽  
Network Architecture ◽  
Short Term Memory ◽  
Binary Classification ◽  
High Energy ◽  
Radiation Belts ◽  
Point Of View ◽  
Evaluation Methodology ◽  
Time Lags ◽  
System Operator

The Ca  index is a time-integrated geomagnetic index that correlates well with the dynamics of high-energy electron fluxes in the outer radiation belts. Therefore Ca can be used as an indicator for the state of filling of the radiation belts for those electrons. Ca also has the advantage of being a ground-based measurement with extensive historical records. In this work, we propose a data-driven model to forecast Ca up to 24 hours in advance from near-Earth solar wind parameters. Our model relies mainly on a recurrent neural network architecture called Long Short Term Memory that has shown good performances in forecasting other geomagnetic indices in previous papers. Most implementation choices in this study were arbitrated from the point of view of a space system operator, including the data selection and split, the definition of a binary classification threshold, and the evaluation methodology. We evaluate our model (against a linear baseline) using both classical and novel (in the space weather field) measures. In particular, we use the Temporal Distortion Mix (TDM) to assess the propensity of two time series to exhibit time lags. We also evaluate the ability of our model to detect storm onsets during quiet periods. It is shown that our model has high overall accuracy, with evaluation measures deteriorating in a smooth and slow trend over time. However, using the TDM and binary classification forecast evaluation metrics, we show that the forecasts lose some of their usefulness in an operational context even for time horizons shorter than 6 hours. This behaviour was not observable when evaluating the model only with metrics such as the root-mean-square error or the Pearson linear correlation. Considering the physics of the problem, this result is not surprising and suggests that the use of more spatially remote data (such as solar imaging) could improve space weather forecasts.

In situ TEM studies of irradiation effects for materials improvement

1991 ◽  
Vol 49 ◽  
pp. 452-453
Author(s):  
Charles W. Allen
Keyword(s):  
Nuclear Reactor ◽  
Austenitic Stainless Steels ◽  
High Energy ◽  
Point Of View ◽  
In Situ Tem ◽  
Irradiation Effects ◽  
Tem Analysis ◽  
Radiation Induced ◽  
Analytical Tools

Irradiation effects studies employing TEMs as analytical tools have been conducted for almost as many years as materials people have done TEM, motivated largely by materials needs for nuclear reactor development. Such studies have focussed on the behavior both of nuclear fuels and of materials for other reactor components which are subjected to radiation-induced degradation. Especially in the 1950s and 60s, post-irradiation TEM analysis may have been coupled to in situ (in reactor or in pile) experiments (e.g., irradiation-induced creep experiments of austenitic stainless steels). Although necessary from a technological point of view, such experiments are difficult to instrument (measure strain dynamically, e.g.) and control (temperature, e.g.) and require months or even years to perform in a nuclear reactor or in a spallation neutron source. Consequently, methods were sought for simulation of neutroninduced radiation damage of materials, the simulations employing other forms of radiation; in the case of metals and alloys, high energy electrons and high energy ions.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals Lex-Pos Feature-Based Grammar Error Detection System for the English Language

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1686 ◽  
Author(s):  
Nancy Agarwal ◽  
Mudasir Ahmad Wani ◽  
Patrick Bours
Keyword(s):  
Error Detection ◽  
Network Architecture ◽  
English Language ◽  
Short Term Memory ◽  
Detection System ◽  
Structural Characteristics ◽  
Contextual Information ◽  
Systems Model ◽  
Part Of Speech ◽  
Induction Technique

This work focuses on designing a grammar detection system that understands both structural and contextual information of sentences for validating whether the English sentences are grammatically correct. Most existing systems model a grammar detector by translating the sentences into sequences of either words appearing in the sentences or syntactic tags holding the grammar knowledge of the sentences. In this paper, we show that both these sequencing approaches have limitations. The former model is over specific, whereas the latter model is over generalized, which in turn affects the performance of the grammar classifier. Therefore, the paper proposes a new sequencing approach that contains both information, linguistic as well as syntactic, of a sentence. We call this sequence a Lex-Pos sequence. The main objective of the paper is to demonstrate that the proposed Lex-Pos sequence has the potential to imbibe the specific nature of the linguistic words (i.e., lexicals) and generic structural characteristics of a sentence via Part-Of-Speech (POS) tags, and so, can lead to a significant improvement in detecting grammar errors. Furthermore, the paper proposes a new vector representation technique, Word Embedding One-Hot Encoding (WEOE) to transform this Lex-Pos into mathematical values. The paper also introduces a new error induction technique to artificially generate the POS tag specific incorrect sentences for training. The classifier is trained using two corpora of incorrect sentences, one with general errors and another with POS tag specific errors. Long Short-Term Memory (LSTM) neural network architecture has been employed to build the grammar classifier. The study conducts nine experiments to validate the strength of the Lex-Pos sequences. The Lex-Pos -based models are observed as superior in two ways: (1) they give more accurate predictions; and (2) they are more stable as lesser accuracy drops have been recorded from training to testing. To further prove the potential of the proposed Lex-Pos -based model, we compare it with some well known existing studies.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

A TOPSIS-ELM framework for stock index price movement prediction

2021 ◽  
pp. 1-19
Author(s):  
Sidharth Samal ◽  
Rajashree Dash
Keyword(s):  
Network Architecture ◽  
Binary Classification ◽  
Evaluation Criteria ◽  
Multiple Criteria ◽  
Stock Index ◽  
Activation Functions ◽  
Price Movement ◽  
Movement Prediction ◽  
Learning Machine ◽  
Approximation Capability

In recent years Extreme Learning Machine (ELM) has gained the interest of various researchers due to its superior generalization and approximation capability. The network architecture and type of activation functions are the two important factors that influence the performance of an ELM. Hence in this study, a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) oriented multi-criteria decision making (MCDM) framework is suggested for analyzing various ELM models developed with distinct activation functions with respect to sixteen evaluation criteria. Evaluating the performance of the ELM with respect to multiple criteria instead of single criterion can help in designing a more robust network. The proposed framework is used as a binary classification system for pursuing the problem of stock index price movement prediction. The model is empirically evaluated by using historical data of three stock indices such as BSE SENSEX, S&P 500 and NIFTY 50. The empirical study has disclosed promising results by evaluating ELM with different activation functions as well as multiple criteria.

scholarly journals FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos

2020 ◽  
Vol 15 (5) ◽  
pp. 791-801 ◽  
Author(s):  
Sophia Bano ◽  
Francisco Vasconcelos ◽  
Emmanuel Vander Poorten ◽  
Tom Vercauteren ◽  
Sebastien Ourselin ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Laser Photocoagulation ◽  
Short Term Memory ◽  
Total Duration ◽  
Frame Rate ◽  
Superior Performance ◽  
Computer Assisted ◽  
Convolutional Network ◽  
Spatio Temporal

Abstract Purpose Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a lens/fibre-optic scope, inserted into the amniotic cavity, the abnormal placental vascular anastomoses are identified and ablated to regulate blood flow to both fetuses. Limited field-of-view, occlusions due to fetus presence and low visibility make it difficult to identify all vascular anastomoses. Automatic computer-assisted techniques may provide better understanding of the anatomical structure during surgery for risk-free laser photocoagulation and may facilitate in improving mosaics from fetoscopic videos. Methods We propose FetNet, a combined convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for the spatio-temporal identification of fetoscopic events. We adapt an existing CNN architecture for spatial feature extraction and integrated it with the LSTM network for end-to-end spatio-temporal inference. We introduce differential learning rates during the model training to effectively utilising the pre-trained CNN weights. This may support computer-assisted interventions (CAI) during fetoscopic laser photocoagulation. Results We perform quantitative evaluation of our method using 7 in vivo fetoscopic videos captured from different human TTTS cases. The total duration of these videos was 5551 s (138,780 frames). To test the robustness of the proposed approach, we perform 7-fold cross-validation where each video is treated as a hold-out or test set and training is performed using the remaining videos. Conclusion FetNet achieved superior performance compared to the existing CNN-based methods and provided improved inference because of the spatio-temporal information modelling. Online testing of FetNet, using a Tesla V100-DGXS-32GB GPU, achieved a frame rate of 114 fps. These results show that our method could potentially provide a real-time solution for CAI and automating occlusion and photocoagulation identification during fetoscopic procedures.

Master curves for the response of layered systems to compressional seismic waves

1967 ◽  
Vol 57 (3) ◽  
pp. 515-543 ◽  
Author(s):  
Luis M. Fernandez
Keyword(s):  
Seismic Waves ◽  
Transfer Functions ◽  
Point Of View ◽  
Time Lags ◽  
Master Curves ◽  
Layer System ◽  
Crust And Upper Mantle ◽  
Response Characteristics ◽  
Families Of Curves ◽  
Short Time

abstract The layers of the earth's crust act as a filter with respect to seimic energy arriving at a given station. Consequently the motion recorded at the surface depends not only on the frequency content of the source and on the response characteristics of the recording instrument, but also on the elastic parameters and thicknesses of the transmitting layers. This latter dependence is the basis for a method of investigating the structure of the crust and upper mantle. To facilitate this investigation a set of master curves for the transfer functions of the vertical and horizontal component of longitudinal waves and their ratios is presented. The calculation of these curves is in terms of a dimensionless parameter. This calculation allows one to group the curves corresponding to different crustal models into families of curves. The characteristics of these curves are discussed from the point of view of their “periodicity” in the frequency domain and of their amplitude in order to investigate the influence of the layer parameters. Considerations, either of constructive interference or of Fourier analysis of a pulse multiply reflected within the layer system, reveal that the amplitudes of the transfer curves depend on the velocity contrasts at the interfaces of the system. The “periodicity” or spacing of the peaks depends on the time lags between the first arrivals and the arrivals of the different reverberations. Closely spaced fluctuations correspond to large-time lags, and widely spaced fluctuations to short-time lags.

scholarly journals The effects of the big storm events in the first half of 2015 on the radiation belts observed by EPT/PROBA-V

2016 ◽  
Vol 34 (1) ◽  
pp. 75-84 ◽  
Author(s):  
V. Pierrard ◽  
G. Lopez Rosson
Keyword(s):  
Charged Particle ◽  
High Resolution ◽  
Geomagnetic Storm ◽  
Energetic Particle ◽  
High Energy ◽  
Radiation Belts ◽  
Radiation Environment ◽  
Storm Event ◽  
Storm Events ◽  
Particle Radiation

Abstract. With the energetic particle telescope (EPT) performing with direct electron and proton discrimination on board the ESA satellite PROBA-V, we analyze the high-resolution measurements of the charged particle radiation environment at an altitude of 820 km for the year 2015. On 17 March 2015, a big geomagnetic storm event injected unusual fluxes up to low radial distances in the radiation belts. EPT electron measurements show a deep dropout at L > 4 starting during the main phase of the storm, associated to the penetration of high energy fluxes at L < 2 completely filling the slot region. After 10 days, the formation of a new slot around L = 2.8 for electrons of 500–600 keV separates the outer belt from the belt extending at other longitudes than the South Atlantic Anomaly. Two other major events appeared in January and June 2015, again with injections of electrons in the inner belt, contrary to what was observed in 2013 and 2014. These observations open many perspectives to better understand the source and loss mechanisms, and particularly concerning the formation of three belts.

scholarly journals Theoretical Research on the Gas Phase Density Change in Processes Occurring During Work of the Transport Module Intended for Transport From the Seabed

2018 ◽  
Vol 1 (1) ◽  
pp. 597-604
Author(s):  
Wiktor Filipek ◽  
Krzysztof Broda
Keyword(s):  
Gas Phase ◽  
High Energy ◽  
Point Of View ◽  
Theoretical Research ◽  
Reduced Form ◽  
Phase Density ◽  
Marine Deposits ◽  
Polymetallic Nodules ◽  
Degree Equation ◽  
Seafloor Massive Sulphides

Abstract In recent years, we have observed a great interest in the exploitation of marine deposits by various methods of mining and transport to the surface. However, obtaining natural resources deposited at greater depths such as polymetallic nodules and seafloor massive sulphides – SMS creates a lot of challenges for both scientists and engineers. The solutions developed so far, unfortunately, have so far been characterized by high energy consumption. For several years the authors have been conducting theoretical and experimental research on new concepts of seabed to surface transport. The results of them have been presented in previous publications. This publication presents the results of the continuation of research on the concept of construction and operation of an autonomous transport module (submitted for printing). It focuses on a theoretical analysis of the change in gas phase density in the processes occurring during operation of the transport module intended for transport from the seabed. For this purpose, a reduced form of the van der Waals equation was used in the form of a third-degree equation for parameters interested from the point of view of the transport module.

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