scholarly journals Modeling the Relationship of ≥2 MeV Electron Fluxes at Different Longitudes in Geostationary Orbit by the Machine Learning Method

2021 ◽  
Vol 13 (17) ◽  
pp. 3347
Author(s):  
Xiaojing Sun ◽  
Ruilin Lin ◽  
Siqing Liu ◽  
Xinran He ◽  
Liqin Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiation Belt ◽  
Weather Prediction ◽  
Energetic Electron ◽  
Geostationary Satellites ◽  
Fully Connected ◽  
Relationship Of ◽  
The Relationship ◽  
Prediction Efficiency ◽  
Earth’S Radiation Belt

The energetic electrons in the Earth’s radiation belt, known as “killer electrons”, are one of the crucial factors for the safety of geostationary satellites. Geostationary satellites at different longitudes encounter different energetic electron environments. However, organizations of space weather prediction usually only display the real-time ≥2 MeV electron fluxes and the predictions of ≥2 MeV electron fluxes or daily fluences within the next 1–3 days by models at one location in GEO orbit. In this study, the relationship of ≥2 MeV electron fluxes at different longitudes is investigated based on observations from GOES satellites, and the relevant models are developed. Based on the observations from GOES-10 and GOES-12 after calibration verification, the ratios of the ≥2 MeV electron daily fluences at 135° W to those at 75° W are mainly in the range from 1.0 to 4.0, with an average of 1.92. The models with various combinations of two or three input parameters are developed by the fully connected neural network for the relationship between ≥2 MeV electron fluxes at 135° W and 75° W in GEO orbit. According to the prediction efficiency (PE), the model only using log10 (fluxes) and MLT from GOES-10 (135° W), whose PE can reach 0.920, has the best performance to predict ≥2 MeV electron fluxes at the locations of GOES-12 (75° W). Its PE is larger than that (0.882) of the linear model using log10 (fluxes four hours ahead) from GOES-10 (135° W). We also develop models for the relationship between ≥2 MeV electron fluxes at 75° W and at variable longitudes between 95.8° W and 114.9° W in GEO orbit by the fully connected neural network. The PE values of these models are larger than 0.90. These models realize the predictions of ≥2 MeV electron fluxes at arbitrary longitude between 95.8° W and 114.9° W in GEO orbit.

An improvement to the ANN-enhanced flow rating method

2021 ◽  
Author(s):  
Bernhard Schmid
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Water Resources ◽  
Flow Rate ◽  
Major Ions ◽  
Rate Of Change ◽  
Data Set ◽  
Input Variables ◽  
Relationship Of ◽  
The Relationship

<p>The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain ‘better’, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author’s experience.</p><p>The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).</p><p>The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the Mödling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the Mödling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only.    </p><p> </p><p>References</p><p>Moatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.</p><p>Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.</p><p>Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.</p><p>Tunqui Neira, J.M., Andréassian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.</p><p>Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.</p>

scholarly journals Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

2020 ◽  
Vol 12 (7) ◽  
pp. 1096
Author(s):  
Zeqiang Chen ◽  
Xin Lin ◽  
Chang Xiong ◽  
Nengcheng Chen
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Machine Learning Algorithms ◽  
Jinsha River ◽  
Precipitation Water ◽  
Relationship Of ◽  
The Relationship

Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

scholarly journals FnnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm

2019 ◽  
Vol 9 (18) ◽  
pp. 3772
Author(s):  
Xiali Li ◽  
Shuai He ◽  
Junzhi Yu ◽  
Licheng Wu ◽  
Zhao Yue
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Classification Performance ◽  
Feature Representation ◽  
Mixed Structure ◽  
Training Time ◽  
Learning Speed ◽  
Learning Machine ◽  
Fully Connected ◽  
The Relationship

The learning speed of online sequential extreme learning machine (OS-ELM) algorithms is much higher than that of convolutional neural networks (CNNs) or recurrent neural network (RNNs) on regression and simple classification datasets. However, the general feature extraction of OS-ELM makes it difficult to conveniently and effectively perform classification on some large and complex datasets, e.g., CIFAR. In this paper, we propose a flexible OS-ELM-mixed neural network, termed as fnnmOS-ELM. In this mixed structure, the OS-ELM can replace a part of fully connected layers in CNNs or RNNs. Our framework not only exploits the strong feature representation of CNNs or RNNs, but also performs at a fast speed in terms of classification. Additionally, it avoids the problem of long training time and large parameter size of CNNs or RNNs to some extent. Further, we propose a method for optimizing network performance by splicing OS-ELM after CNN or RNN structures. Iris, IMDb, CIFAR-10, and CIFAR-100 datasets are employed to verify the performance of the fnnmOS-ELM. The relationship between hyper-parameters and the performance of the fnnmOS-ELM is explored, which sheds light on the optimization of network performance. Finally, the experimental results demonstrate that the fnnmOS-ELM has a stronger feature representation and higher classification performance than contemporary methods.

scholarly journals Hand–Eye Calibration Algorithm Based on an Optimized Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 85
Author(s):  
Jiang Hua ◽  
Liangcai Zeng
Keyword(s):  
Neural Network ◽  
Coordinate System ◽  
Calibration Method ◽  
Calibration Model ◽  
The Neural Network ◽  
Calibration Algorithm ◽  
Robot Grasping ◽  
Relationship Of ◽  
The Relationship ◽  
Camera Coordinate System

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B=fA, which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system.

scholarly journals V. V. Potemkin (on the occasion of his 60th birthday)

1994 ◽  
Vol 40 (1) ◽  
pp. 60-60
Keyword(s):  
Medical Institute ◽  
Medical Studies ◽  
Military Medical Academy ◽  
Medical Academy ◽  
Russian State ◽  
University Professor ◽  
Medical University ◽  
Fully Connected ◽  
Relationship Of ◽  
The Relationship

60 years have passed since the birth of the famous endocrinologist, head of the department of endocrinology of the Russian State Medical University, Professor Vladimir Vasilyevich Potemkin. In 1951, V.V. Potemkin entered the Leningrad Military Medical Academy. S. M. Kirova, at the end of which he worked as a military doctor. In 1960, he was admitted to clinical residency at the Department of Endocrinology of the Institute for Advanced Medical Studies, after which he was enrolled in the graduate school of the same department. During this period, V. V. Potemkin defended his thesis on the topic “The relationship of carbohydrate and lipid metabolism in diabetes mellitus and their clinical significance.” Since 1965, V.V. Potemkin's pedagogical, scientific and medical activities are fully connected with the Russian State Medical University (2nd Pirogov Moscow Medical Institute). V.V. Potemkin is a talented teacher and scientist, one of the leading endocrinologists in our country. 60 years have passed since the birth of the famous endocrinologist, head of the department of endocrinology of the Russian State Medical University, Professor Vladimir Vasilyevich Potemkin. In 1951, V.V. Potemkin entered the Leningrad Military Medical Academy. S. M. Kirova, at the end of which he worked as a military doctor. In 1960, he was admitted to clinical residency at the Department of Endocrinology of the Institute for Advanced Medical Studies, after which he was enrolled in the graduate school of the same department. During this period, V. V. Potemkin defended his thesis on the topic “The relationship of carbohydrate and lipid metabolism in diabetes mellitus and their clinical significance.” Since 1965, V.V. Potemkin's pedagogical, scientific and medical activities are fully connected with the Russian State Medical University (2nd Pirogov Moscow Medical Institute). V.V. Potemkin is a talented teacher and scientist, one of the leading endocrinologists in our country.

Determining latitudinal extent of energetic electron precipitation  using MEPED on-board NOAA POES

2021 ◽  
Author(s):  
Eldho Midhun Babu ◽  
Hilde Nesse Tyssøy ◽  
Christine Smith-Johnsen ◽  
Ville Maliniemi ◽  
Josephine Alessandra Salice ◽  
...  
Keyword(s):  
Solar Wind ◽  
Radiation Belt ◽  
Climate Models ◽  
Stratospheric Ozone ◽  
Energetic Electron ◽  
Predictor Variable ◽  
Electron Precipitation ◽  
Atmospheric Effects ◽  
Solar Wind Parameters ◽  
Earth’S Radiation Belt

<p>Energetic electron precipitation (EEP) from the plasma sheet and the radiation belts, can collide with gases in the atmosphere and deposit their energy. EEP increase the production of NOx and HOx, which will catalytically destroy stratospheric ozone, an important element of atmospheric dynamics. Therefore, measurement of latitudinal extent of the precipitation boundaries is important in quantifying atmospheric effects of Sun-Earth interaction and threats to spacecrafts and astronauts in the Earth's radiation belt.<br>This study uses measurements by MEPED detectors of six NOAA/POES and EUMETSAT/METOP satellites from 2004 to 2014 to determine the latitudinal boundaries of EEP and its variability with geomagnetic activity and solar wind drivers. Variation of the boundaries with respect to different particle energies and magnetic local time is studied. Regression analyses are applied to determine the best predictor variable based on solar wind parameters and geomagnetic indices. The result will be a key element for constructing a model of EEP variability to be applied in atmosphere climate models.</p>

scholarly journals Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
André Cyr ◽  
Frédéric Thériault
Keyword(s):  
Neural Network ◽  
Operant Conditioning ◽  
Concept Learning ◽  
Specific Pattern ◽  
Spiking Neural Network ◽  
Conditioning Procedure ◽  
Spatial Concept ◽  
Rule Changes ◽  
Relationship Of ◽  
The Relationship

This paper proposes an artificial spiking neural network (SNN) sustaining the cognitive abstract process of spatial concept learning, embedded in virtual and real robots. Based on an operant conditioning procedure, the robots learn the relationship of horizontal/vertical and left/right visual stimuli, regardless of their specific pattern composition or their location on the images. Tests with novel patterns and locations were successfully completed after the acquisition learning phase. Results show that the SNN can adapt its behavior in real time when the rewarding rule changes.

Application of Self-Organizing Neural Network Method in Logging Sedimentary Microfacies Identification

2012 ◽  
Vol 616-618 ◽  
pp. 38-42 ◽  
Author(s):  
Wen Bo Li ◽  
Yun Liang Yu ◽  
Jian Qiang Wang ◽  
Ye Bai ◽  
Xin Wang
Keyword(s):  
Neural Network ◽  
Recognition Rate ◽  
Identification System ◽  
Characteristic Parameters ◽  
Network Method ◽  
Image Characteristic ◽  
Sedimentary Microfacies ◽  
Relationship Of ◽  
The Relationship ◽  
Self Organizing

Studing the identification methods of sedimentary microfacies by the implement of self-organizing neural network model. Picking up the geometrical characteristic parameters and image characteristic parameters of the logging curves. Establishing the relationship of sedimentary microfacies patterns and well logging curves shapes by characteristic parameters. Developing the Sedimentary microfacies patterns identification system and applying it to 1000 wells of the southern area of Daqing Changyuan, the recognition rate can reach 90%, it can prove the validity of the method.

scholarly journals Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 40
Author(s):  
Siti Zulaikha Mohd Jamaludin ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Ahmad Izani Md Ismail ◽  
Mohd. Asyraf Mansor ◽  
Md Faisal Md Basir
Keyword(s):  
Neural Network ◽  
Data Extraction ◽  
Hopfield Neural Network ◽  
Data Set ◽  
Reverse Analysis ◽  
Insurance Agency ◽  
Logical Representation ◽  
Relationship Of ◽  
Extraction Model ◽  
The Relationship

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.

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