scholarly journals Non-volatile artificial synapse based on a vortex nano-oscillator

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leandro Martins ◽  
Alex S. Jenkins ◽  
Lara San Emeterio Alvarez ◽  
Jérôme Borme ◽  
Tim Böhnert ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Current Density ◽  
Output Voltage ◽  
Experimental Results ◽  
Spin Torque ◽  
Input Frequency ◽  
Micromagnetic Simulations ◽  
Artificial Synapse ◽  
Spin Diode

AbstractIn this work, a new mechanism to combine a non-volatile behaviour with the spin diode detection of a vortex-based spin torque nano-oscillator (STVO) is presented. Experimentally, it is observed that the spin diode response of the oscillator depends on the vortex chirality. Consequently, fixing the frequency of the incoming signal and switching the vortex chirality results in a different rectified voltage. In this way, the chirality can be deterministically controlled via the application of electrical signals injected locally in the device, resulting in a non-volatile control of the output voltage for a given input frequency. Micromagnetic simulations corroborate the experimental results and show the main contribution of the Oersted field created by the input RF current density in defining two distinct spin diode detections for different chiralities. By using two non-identical STVOs, we show how these devices can be used as programmable non-volatile synapses in artificial neural networks.

scholarly journals Electrical characterisation of higher order spin wave modes in vortex-based magnetic tunnel junctions

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alex. S. Jenkins ◽  
Lara San Emeterio Alvarez ◽  
Samh Memshawy ◽  
Paolo Bortolotti ◽  
Vincent Cros ◽  
...  
Keyword(s):  
Spin Wave ◽  
Tunnel Junctions ◽  
Magnetic Tunnel Junctions ◽  
Higher Order ◽  
Spin Torque ◽  
Micromagnetic Simulations ◽  
Higher Order Modes ◽  
Spin Diode ◽  
Super High Frequency ◽  
Wave Modes

AbstractNiFe-based vortex spin-torque nano-oscillators (STNO) have been shown to be rich dynamic systems which can operate as efficient frequency generators and detectors, but with a limitation in frequency determined by the gyrotropic frequency, typically sub-GHz. In this report, we present a detailed analysis of the nature of the higher order spin wave modes which exist in the Super High Frequency range (3–30 GHz). This is achieved via micromagnetic simulations and electrical characterisation in magnetic tunnel junctions, both directly via the spin-diode effect and indirectly via the measurement of the coupling with the gyrotropic critical current. The excitation mechanism and spatial profile of the modes are shown to have a complex dependence on the vortex core position. Additionally, the inter-mode coupling between the fundamental gyrotropic mode and the higher order modes is shown to reduce or enhance the effective damping depending upon the sense of propagation of the confined spin wave.

An Estimation of 3-Way Catalyst Performance Using Artificial Neural Networks During Idle Speed

2004 ◽  
Author(s):  
P. N. Botsaris ◽  
D. Bechrakis ◽  
P. D. Sparis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Expert Knowledge ◽  
Operating Conditions ◽  
Experimental Results ◽  
Outlet Temperature ◽  
Practical Applications ◽  
Idle Speed ◽  
Artificial Neural

The intelligent control as fuzzy or artificial is based on either expert knowledge or experimental data and therefore it possesses intrinsic qualities like robustness and ease implementation. Lately, many researchers present studies aim to show that this kind of control can be used in practical applications such as the idle speed control problem in automotive industry. In this study, an estimation of an automobile three-way catalyst performance with artificial neural networks is presented. It may be an alternative approach for an on board diagnostic system (OBD) to predict the catalyst performance. This method was tested using data sets from two kind of catalysts, a brand new and an old one on a laboratory bench at idle speed. The catalyst operation during the “steady state” phase (the phase that the catalyst has reached its operating conditions and works normally) is examined. Further experiments are needed for different catalyst typed before the methods is proposed generally. It consists of 855 elements of catalyst inlet-outlet temperature difference (DT), hydrocarbons (HC), and carbon monoxide (CO) and carbon dioxide (CO2) emissions. The simulation: detects the values of HC, CO, CO2 using the DT as an input to our network forms a neural network. Results showed serious indications that artificial neural networks (or fuzzy logic control laws) could estimate the catalyst performance adequately depending their training process, if certain information about the catalyst system and the inputs and output of such system are known. In this study the “steady state” period experimental results are presented. In this paper the “steady state” period experimental results are presented.

Simulation of Supercritical CO2 Extraction for Peanut Oil Based on Artificial Neural Networks

2010 ◽  
Vol 37-38 ◽  
pp. 1172-1175
Author(s):  
Jin Shan Dong ◽  
Bo Qin Gu
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Processing Parameters ◽  
Extraction Process ◽  
Experimental Results ◽  
Peanut Oil ◽  
Operating Pressure ◽  
Percentage Extraction ◽  
Extraction Experiment

The BP ANN was established based on MATLAB to simulate the supercritical CO2extraction process for extracting peanut oil. The supercritical CO2extraction experiment for peanut oil was carried out and the experimental results were used to train the BP ANN. The operating pressure, temperature and time were regarded as the inputs of the BP ANN and the percentage extraction as the output. By testing the BP ANN with other groups of experimental data, the precision of the BP ANN was verified. This BP ANN can predict the percentage extraction when the processing parameters of supercritical CO2extraction are given, and the optimization of the processing parameters can also be realized.

scholarly journals Power electrical network topology detection using changes of phasor measurements in transient states

2019 ◽  
Vol 139 ◽  
pp. 01063
Author(s):  
Natalja Gotman ◽  
Galina Shumilova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Topology ◽  
Short Circuit ◽  
Transient State ◽  
Test System ◽  
Experimental Results ◽  
Electrical Network ◽  
Phasor Measurements ◽  
Measurement Units

The solution of the problem of a topology detection of an electrical network on changing voltage and current phasors obtained from the phasor measurement units (PMUs) in a transient state using artificial neural networks (ANNs) is considered. Experimental results for the 14-bus test system to detect the failed line after short circuit and the line was turned on by an auto-reclosing device are presented.

INCREMENTAL LEARNING IN BIOLOGICAL AND MACHINE LEARNING SYSTEMS

2002 ◽  
Vol 12 (06) ◽  
pp. 447-465 ◽  
Author(s):  
STEPHAN K. CHALUP
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Side Effects ◽  
Incremental Learning ◽  
Learning Systems ◽  
Experimental Results ◽  
Recurrent Artificial Neural Networks ◽  
Standard Learning

Incremental learning concepts are reviewed in machine learning and neurobiology. They are identified in evolution, neurodevelopment and learning. A timeline of qualitative axon, neuron and synapse development summarizes the review on neurodevelopment. A discussion of experimental results on data incremental learning with recurrent artificial neural networks reveals that incremental learning often seems to be more efficient or powerful than standard learning but can produce unexpected side effects. A characterization of incremental learning is proposed which takes the elaborated biological and machine learning concepts into account.

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