scholarly journals Sequential Low-Thrust Orbit-Raising of All-Electric Satellites

Aerospace ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 74
Author(s):  
Pardhasai Chadalavada ◽  
Tanzimul Farabi ◽  
Atri Dutta
Keyword(s):  
Neural Network ◽  
Numerical Simulations ◽  
Power Loss ◽  
Average Power ◽  
Radiation Belts ◽  
Solar Array ◽  
Low Thrust ◽  
Computational Framework ◽  
Transfer Orbits ◽  
The Neural Network

In this paper, we consider a recently developed formulation of the electric orbit-raising problem that utilizes a novel dynamic model and a sequence of optimal control sub-problems to yield fast and robust computations of low-thrust trajectories. This paper proposes two enhancements of the computational framework. First, we use thruster efficiency in order to determine the trajectory segments over which the spacecraft coasts. Second, we propose the use of a neural network to compute the solar array degradation in the Van Allen radiation belts. The neural network is trained on AP-9 data and SPENVIS in order to compute the associated power loss. The proposed methodology is demonstrated by considering transfers from different geosynchronous transfer orbits. Numerical simulations analyzing the effect of thruster efficiency and average power degradation indicate the suitability of starting the maneuver from super-geosynchronous transfer orbits in order to limit fuel expenditure and radiation damage. Furthermore, numerical simulations demonstrate that proposed enhancements are achieved with only marginal increase in computational runtime, thereby still facilitating rapid exploration of all-electric mission scenarios.

scholarly journals Investigation of a Multitasking System for Automatic Ship Berthing in Marine Practice Based on an Integrated Neural Controller

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1167
Author(s):  
Van Suong Nguyen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coordinate System ◽  
Numerical Simulations ◽  
Neural Controller ◽  
The Neural Network ◽  
Artificial Neural ◽  
Ship Berthing

In this article, a multitasking system is investigated for automatic ship berthing in marine practices, based on artificial neural networks (ANNs). First, a neural network with separate structures in hidden layers is developed, based on a head-up coordinate system. This network is trained once with the berthing data of a ship in an original port to conduct berthing tasks in different ports. Then, on the basis of the developed network, an integrated mechanism including three negative signs is linked to achieve an integrated neural controller. This controller can bring the ship to a berth on each side of the ship in different ports. The whole system has the ability to berth for different tasks without retraining the neural network. Finally, to validate the effectiveness of the proposed system for automatic ship berthing, numerical simulations were performed for berthing tasks, such as different ports, and berthing each side of the ship. The results indicate that the proposed system shows a good performance in automatic ship berthing.

scholarly journals Inferring depth-dependent plasma motions from surface observations using the DeepVel neural network

2020 ◽  
Author(s):  
Benoit Tremblay ◽  
Jean-François Cossette ◽  
Maria D. Kazachenko ◽  
Paul Charbonneau ◽  
Alain Patrick Vincent
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Numerical Simulations ◽  
Active Regions ◽  
Transverse Component ◽  
Small Scale ◽  
Solar Photosphere ◽  
Quiet Sun ◽  
The Neural Network ◽  
Spatial Coverage

Coverage of plasma motions is limited to the line-of-sight component at the Sun's surface. Multiple tracking and inversion methods were developed to infer the transverse motions from observational data. Recently, the DeepVel neural network was trained with computations performed by numerical simulations of the solar photosphere to recover the missing transverse component at surface and at two additional optical depths simultaneously from the surface white light intensity in the Quiet Sun. We argue that deep learning could provide additional spatial coverage to existing observations in the form of depth-dependent synthetic observations, i.e. estimates generated through the emulation of numerical simulations. We trained different versions of DeepVel using slices from numerical simulations of both the Quiet Sun and Active Region at various optical and geometrical depths in the solar atmosphere, photosphere and upper convection zone to establish the upper and lower limits at which the neural network can generate reliable synthetic observations of plasma motions from surface intensitygrams. Flow fields inferred in the photosphere and low chromosphere $\tau \in [0.1, 1)$ are comparable to inversions performed at the surface ($\tau \approx 1$) and are deemed to be suitable for use as synthetic observations in data assimilation processes and data-driven simulations. This upper limit extends closer to the transition region ($\tau \approx 0.01$) in the Quiet Sun, but not for Active Regions. Subsurface flows inferred from surface intensitygrams fail to capture the small-scale features of turbulent convective motions as depth crosses a few hundred kilometers. We suggest that these reconstructions could be used as first estimates of a model's velocity vector in data assimilation processes to nowcast and forecast short term solar activity and space weather.

Enhancing Retrieval in Neural Networks

1998 ◽  
Vol 12 (29n30) ◽  
pp. 1255-1262
Author(s):  
M. Andrecut
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
External Field ◽  
Numerical Simulations ◽  
Activity Patterns ◽  
Hopfield Neural Network ◽  
Simple Method ◽  
Saturation Parameter ◽  
The Neural Network ◽  
Low Activity

A simple method for retrieving information in Hopfield neural network is discussed. Numerical simulations for different network sizes were performed in order to test the ability of the model in retrieving patterns. It is shown that an imposed external field (stimulus) enhances the retrieval results of the neural network. For random and unbiased patterns the critical saturation parameter is enhanced from α=0.14 up to α=0.5. Also, important enhancements of the critical saturation parameter are obtained low-activity patterns.

HUXLEY SURROGATE MODEL FOR TWITCH MUSCLE CONTRACTION

2021 ◽  
Author(s):  
Bogdan Milićević ◽  
◽  
Miloš Ivanović ◽  
Boban Stojanović ◽  
Nenad Filipović ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Simulations ◽  
Surrogate Model ◽  
Large Scale ◽  
Original Model ◽  
Muscle Model ◽  
Muscle Twitch ◽  
The Neural Network ◽  
Computationally Intensive ◽  
Muscle Models

Biophysical muscle models, often called Huxley-type models, are based on the underlying physiology of muscles, making them suitable for modeling non-uniform and unsteady contractions. This kind of model can be computationally intensive, which makes the usage of large-scale simulations difficult. To enable more efficient usage of the Huxley muscle model, we created a data-driven surrogate model, which behaves similarly to the original Huxley muscle model, but it requires significantly less computational power. From several numerical simulations, we acquired a lot of data and trained deep neural networks so that the behavior of the neural network resembles the behavior of the Huxley model. Since muscle models are history-dependent we used time series as an input and we trained a recurrent neural network to produce stress and instantaneous stiffness. The real challenge was to get the neural network to predict these values precisely enough for the numerical simulation to work properly and produce accurate results. In our work, we showed results obtained with the original Huxley model and surrogate Huxley model for several muscle twitch contractions. Based on similarities between the surrogate model and the original model we can conclude that the surrogate has the potential to replace the original model within numerical simulations.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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